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Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the M eteorological- T raffic C ausal I nference V ariational A uto- E ncoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies. • Develop causality-aware deep methods between meteorological variations and traffic dynamics. • The designed MT-CIVAE effectively captures complex spatiotemporal correlations. • The designed MT-CIVAE effectively mitigates influence of undetermined confounders. • Experiments have been conducted on real-world traffic flow and speed data from California. [ABSTRACT FROM AUTHOR]

Tracking the source of congestion, namely where the congested traffic flow comes from and goes to, is a key prerequisite to understanding the causes of traffic congestion and facilitates more efficient strategies. In this paper, we track the congestion source by estimating the path flow passing through the congested link. A probabilistic sensor flow assignment model is first developed to infer the whereabouts of each vehicle converging into the congestion. Unlike classical path flow estimation methods, we view path flow as the assigned results of sensor flows rather than OD flows. With this new perspective, an assigned rule, which incorporates route choice preference of drivers and spatial–temporal constraint of vehicular trajectory, is constructed to output more realistic assignments. Moreover, as this model finds most possible destination-path combinations rather than partial paths as assigned results, the complete trip of tracking vehicles, including both driving paths and ODs, can be reconstructed. With the reconstructed trips, disaggregated and hybrid path flow estimation methods are developed to track the source of traffic congestion on the bottleneck link. The open-source pNEUMA dataset is employed to test the proposed and benchmark methods. It demonstrates that our methods can produce a more realistic traffic pattern for congestion tracking. Significant improvements in estimation accuracy have been achieved with the use of sensor flow assignment model. The proposed disaggregated method has also been tested with a city-scale road network. Experiment results demonstrate that our method is more robust to the uncertainty caused by possible destinations than benchmark. • Build a probabilistic Sensor Flow Assignment model to assign detected samples onto the road network. • Develop a trip reconstruction algorithm to identify tracking vehicles' complete driving path and OD. • Propose path flow estimation methods customized for traffic congestion tracking problems. • Demonstrate the advantages of proposed methods with field-test datasets. [ABSTRACT FROM AUTHOR]

Implementing a curbside parking pricing strategy that intentionally leaves certain spaces vacant may seem wasteful, but it can offer advantages for a city. This strategy diminishes the time drivers spend searching for parking, thereby mitigating traffic congestion, air pollution, and other negative externalities. However, there is an additional rationale for keeping some curbside parking spaces unoccupied that is unrelated to negative externalities. In urban areas, parking markets often constitute mixed oligopolies wherein curbside parking is publicly operated, and garage parking is privately operated, each with limited capacity. Multiple pricing equilibria exist in such markets. In the equilibrium yielding the highest welfare, certain curbside parking spots remain unoccupied. The city refrains from reducing the curbside parking fee to occupy all available spots, as this could diminish the utilization of garage parking and potentially lead to increased garage fees, thereby reducing overall parking capacity utilization. In essence, maintaining some unoccupied curbside parking spots can serve as a safeguard against private operators creating contrived scarcity and exploiting their market power. • Novel reason for unoccupied curbside spaces. • Mixed duopoly: public curbside, private garage. • Limited capacity for both parking types. • Some curbside spots vacant in equilibria. • City maintains empty curbside to control garage market power. [ABSTRACT FROM AUTHOR]

With the growing popularity of transportation network companies (TNC), there remains a gap in network equilibrium models that adequately address the emergence of ride-pooling service that pools two orders into one trip. This challenge arises from the need to enumerate all possible combinations of origin–destination (OD) pooling and sequencing. This paper proposes a network equilibrium framework that integrates ride-hailing platforms' decision on vehicle dispatching and driver–passenger matching on congested road networks. To facilitate the representation of vehicle and passenger OD flows and ride-pooling options, a layered OD graph is created encompassing ride-sourcing and ride-pooling services over OD nodes. To capture road congestion, a connection between the layered OD graph and road networks is established where the vehicle flows on the OD graph form source demands on road networks. Numerical examples are performed on a toy example and the Sioux Fall network to demonstrate our model and algorithm. The proposed equilibrium framework can efficiently assist policymakers and urban planners to evaluate the impact of TNCs on traffic congestion. • We propose a layered OD graph to avoid enumeration of routing plans for TNC vehicles. • The OD graph is designed for vehicle dispatching and vehicle-passenger matching. • The equilibrium model couples dispatching, matching and road congestion modules. [ABSTRACT FROM AUTHOR]

Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances. • A hybrid intersection control strategy to accommodate fluctuating traffic demands for CAVs. • DRIC model to integrate current control methods and generate optimal traffic control plans. • An approximation algorithm, i.e. VAC, to provide instant and reliable solutions tailored to practical applications. • Theoretical analysis to validate that VAC is capable of attaining global optimal solutions. • Numerical experiments on both artificial dataset and real-world dataset to evaluate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations. • Using Gaussian process (GP) for Traffic State Estimation (TSE). • Capturing congestion propagation in traffic wave with a rotation kernel. • Leveraging inter-lane correlation to improve TSE accuracy. • Model works well on small datasets and can quantify the TSE uncertainty. • Experiments on real-world and simulated data show GP-TSE accuracy and robustness. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of e-commerce and the need to make last-mile logistics more sustainable have stimulated the development of new distribution paradigms, based on air drones and autonomous delivery robots, which are less sensitive to traffic congestion. In this article we deal with a routing problem in which a fleet of autonomous delivery robots can travel not only on the road network but also on the public transportation system (or part of it) to extend their range of action with a given battery capacity. The problem entails building delivery robot routes synchronized with the rides of the public transportation lines, enabling robots to drop on/off public vehicles, where they use dedicated compartments, to reach customers that would be otherwise out of reach. To this purpose, we develop a tailored destroy-and-repair mechanism that, embedded into a neighborhood search algorithm, allows to effectively explore the feasibility region of large-scale instances. Extensive computational results on instances resembling the distribution of drugs to pharmacies in Rome (Italy) show that the proposed algorithmic mechanism allows to obtain a cost reduction up to about 7.5% with respect to a more traditional approach. Moreover, from a managerial point of view, our experiments show that autonomous delivery robots combined with public transportation can provide huge benefits in terms of costs and emissions reduction, when compared to both traditional and electric vans. • We study last-mile distribution with delivery robots and public transportation lines. • We design a tailored destroy-and-repair mechanism embedded into a neighborhood search. • We test our approach on instances resembling drug distribution to pharmacies in Rome. • Computational results show that the proposed algorithmic mechanism is beneficial. • The proposed delivery framework allows achieving huge costs and emissions benefits. [ABSTRACT FROM AUTHOR]

As the ride-sourcing market expands, ride-sourcing fleets have increased urban traffic congestion, and in turn, road congestion is affecting ride-sourcing operations. It is crucial to incorporate this interaction mechanism into the operational decision of ride-sourcing systems. This mechanism can be described in detail by simulation models. Applying simulation-based optimization methods to the real-time operation of ride-sourcing systems is challenging due to the limitations of simulation efficiency. To tackle this issue, this study first proposes a modular simulation model combining time-driven and event-driven mechanisms. The simulation model comprises two layers: the traffic flow module at the bottom, defined by trip-based and multi-region macroscopic fundamental diagrams and cell transmission models, and the ride-sourcing operation module at the top, which considers the interaction between the traffic congestion and ride-sourcing operation and models the random behavior of passengers and drivers. We verify the efficiency and accuracy of the simulation model. The simulation model is utilized as an evaluator of the objective functions and performance measures. Then, we integrate the Bayesian optimization, parallel sampling, and rolling horizon approaches to develop an efficient and effective simulation-based optimization framework for the dynamic joint decision-making of matching radius, matching intervals, and threshold relocation strategies. Six groups of experiments for the traffic scenario in Berlin reveal some interesting findings. (1) The matching rate first increases and then decreases with the matching radius (a concave function), while the closest study does not think so. (2) As the fleet size grows, passenger waiting time reaches its minimum and then rises because of the increased endogenous congestion generated by ride-sourcing fleets. (3) The optimal operation strategy considering endogenous congestion not only leads to an increase in the system revenue and efficiency but also leads to congestion relief, even though the latter was not our initial objective. This benefit is the most pronounced when the fleet size and background traffic are moderate. • Modeling endogenous and exogenous congestion in ride-sourcing systems with a multi-region MFD and CTM. • Customizing a hybrid of time-driven and event-driven mechanisms to accelerate the simulation model. • Validating the accuracy of the simulation model by the real-world data. • Developing an efficient and effective simulation-based optimization framework for ride-sourcing system operation. • Analyzing the effect of endogenous congestion on ride-sourcing system operational decisions. [ABSTRACT FROM AUTHOR]

Dynamic traffic phenomena, such as hysteresis, stop-and-go traffic, stability, and capacity drop, have significant implications for traffic safety and congestion. Previous studies have suggested that these phenomena are caused by human behavior. However, existing models have not been able to fully reproduce these dynamic traffic phenomena. The focus of our research is on psycho-physical properties and asymmetric behavior, among other human factors, which provide a theoretical explanation for dynamic traffic phenomena. We propose a new car-following model, Asymmetric Repulsive force Model (ARM), based on the common fundamental mechanisms of the two theories. ARM is evaluated from both microscopic and macroscopic perspectives. ARM exhibited similar driving behavior similar to NGSIM data in the time-distance, speed, acceleration, speed-spacing, and spacing-relative speed domains. Additionally, ARM outperforms comparable models in quantitative evaluations of describing trajectory and hysteresis. From the macroscopic perspective, platoon simulation was performed to analyze stop-and-go situations. The results confirmed that ARM can simulate the processes of speed drop, speed recovery and flow non-recovery, and speed and flow recovery. This finding is consistent with previous research on capacity drop using empirical data and suggests that ARM has the potential to simulate various macroscopic traffic phenomena. • We explain the link between the asymmetric behavior and the psycho-physical property. • A novel car-following model ARM (Asymmetric Repulsive force Model) is proposed. • ARM is verified and validated using the NGSIM vehicle trajectory data. • ARM outperformed the comparative model in simulating traffic hysteresis. • ARM described a decrease in flow during the speed recovery process after stop-and-go. [ABSTRACT FROM AUTHOR]

• Vehicle trajectories prior to real crashes are obtained by a long-term drone video recording. • A novel surrogate safety measure (SSM) is proposed to evaluate crash risk for traffic oscillations. • The proposed SSM outperforms other commonly used SSMs. • The performance of the proposed SSM in early crash warning is evaluated. Traffic oscillations, also known as stop-and-go driving conditions, commonly emerge due to the alternative deceleration and acceleration of vehicles in congested traffic conditions. This study proposes a surrogate safety measure (SSM) specifically for estimating crash risks related to this phenomenon. The Crash Risk Indicator for Oscillation (CRIO) is established by integrating driving behavior during oscillations into the Time-to-Collision (TTC) framework. A year-long recording of a busy freeway on sunny weekdays at morning peak hours using unmanned aerial vehicles produced over 200 h of empirical trajectories of all vehicles, which encompassed 20 rear-end crashes that happened in the real world. The performance of the CRIO was assessed by comparing it with five commonly used SSMs. It was found that the oscillation amplitude induced by the leading vehicle and the behavior of the following vehicle during its reaction time significantly affected the likelihood of rear-end crashes. The CRIO diminished the false alarm rate from a range of 25% to 91% (by other SSMs) to 6% with a recall rate of 100%. The CRIO was further employed to predict real-time crash risk once the oscillation was formed. The results show that the CRIO can provide an early crash warning with an average time of 1.72 s in advance. This research contributes to the development of more robust proactive systems for early warning by enabling reliable and effective risk assessments for traffic oscillations. [ABSTRACT FROM AUTHOR]

Accurate traffic prediction is important for efficient traffic operation, management, and user convenience. It enables traffic management authorities to allocate traffic resources efficiently, reducing traffic congestion and minimizing travel time for commuters. With the increase in data sources, traffic prediction methods have shifted from traditional model-based approaches to more data-driven methods. However, accurately predicting traffic under unforeseen events, such as crashes, adverse weather conditions, and road works, remains a challenging task. Hybrid traffic prediction models that combine data-driven and model-based approaches have emerged as promising solutions, considering the advantage of the model-based approach that can replicate unobserved scenarios. This paper proposes a hybrid traffic prediction framework named SMURP (Simulation and Machine-learning Utilization for Real-time Prediction), which overcomes the limitations of the existing methods. The SMURP is a framework that can be applied to any data-driven prediction method. When an event is detected during prediction, the SMURP complements the prediction outcomes with an additional predictor that uses simulated traffic data. The proposed framework is applied to various data-driven prediction models and evaluated in the actual road section. The results show that applying the SMURP to data-driven prediction methods can improve prediction accuracy. • A hybrid framework combining model-based and data-driven prediction methods. • Enhancing real-time traffic prediction by detecting prediction failures. • Previously generated simulation data is used to compensate for prediction failures. • It was verified that the proposed framework can improve prediction performance. [ABSTRACT FROM AUTHOR]

• Microscopic implementation of the Internal Boundary Control in lane-free environments. • Enhancing LQR Performance in Internal Boundary Control by Incorporating Feedforward Extension. • Design of appropriate vehicle movement strategy for lane-free environments. • Internal Boundary Control-induced extensions of the lane-free microscopic simulator TrafficFluid-Sim. The recently introduced TrafficFluid concept proposes that automated vehicles drive lane-free, thus enabling capacity sharing between the two opposite road directions via real-time Internal Boundary Control (IBC). This novel traffic control measure was demonstrated, using macroscopic traffic flow models, to deliver unprecedented improvements of traffic flow efficiency. The present study completes and validates the IBC concept in a much more realistic way via microscopic simulation and active internal boundary moving, using the SUMO-based TrafficFluid-Sim simulation tool. To effectuate IBC, a Linear Quadratic Regulator (LQR), which is a feedback control scheme, is employed. In addition, to enhance the performance of the LQR controller, a feedforward term, accounting for external disturbances, i.e. entering flow and on-ramp flows, is also designed, leading to an augmented LQR-FF control scheme. The LQR and LQR-FF controllers are tested and compared in the created realistic environment, demonstrating how IBC may operate in practice to combat traffic congestion on highways. [ABSTRACT FROM AUTHOR]

• A multi-agent deep reinforcement learning framework has been proposed to alleviate bottleneck congestion. • The experimental scenarios are based on a real-world mandatory lane-changing experiment. • Both the longitudinal and lateral accelerations of CAVs are continuously controlled. • The safety of the lane-changing process is quantified using a newly proposed safety metric. • The introduced 2D collision avoidance strategy outperforms traditional models. Bottleneck areas are prone to severe traffic congestion due to the sudden drop in capacity. To improve traffic efficiency in the bottleneck area, this paper proposes a multi-agent deep reinforcement learning framework integrating collision avoidance strategies to improve traffic efficiency in a mandatory lane change scenario. The proposed method considers distance-keeping and lane-changing coordination in a connected autonomous vehicle (CAV) environment, by controlling vehicles' longitudinal and lateral movement to effectively reduce traffic congestion in a mandatory lane change scenario. This framework was trained and tested in a simulation environment that is the same as the natural driving environment. Compared with real-world data and the benchmark model (a Dueling Double Deep Q-Network-based model), the proposed model shows better performance in terms of average speed, travel time, throughput, and safety in the bottleneck area. The results show that the proposed model can effectively reduce traffic congestion and improve traffic efficiency in a mandatory lane change scenario. [ABSTRACT FROM AUTHOR]

• A real-time jam-absorption driving system proposed to reduce multiple moving jams. • Absorbing end points predicted by estimating moving jam propagation. • Complete and synergetic strategies are embedded in the control model. • Control modes are adaptively and automatically selected in real-time. Jam-absorption driving (JAD) is a novel connected and automated vehicle-based (CAV-based) control strategy that uses either a single or several absorbing cars to clear moving jams. However, a practical control system has not yet been developed. This study presents a real-time control system for operating JAD against multiple moving jams on freeway sections under a partial CAVs and heterogeneous (mixture of cars and trucks) traffic environment. The system dynamically predicts absorbing end points by estimating the propagation of multiple moving jams without using a traffic model. Subsequently, it selects suitable CAVs as absorbing cars in traffic via vehicle-to-infrastructure communication to absorb moving jams. A control model embedded with complete/synergetic JAD strategy decision-making and moving jams–CAVs matching process was developed to guarantee moving jams can be properly absorbed as early as possible. Simulation results demonstrated that the proposed JAD system successfully reduced successive multiple moving jams in the toy model (with two artificial successive moving jams) and hypothetical bottleneck (with series moving jams generated by a speed limit zone) scenarios. Moreover, sensitivity analysis indicated that the JAD system effectively reduced moving jams and enhanced traffic performance (e.g., about 10% fuel consumption reduction) under approximately 1% to 10% CAV market penetration rates (MPRs). This implies that the proposed JAD system can be implemented at low CAV MPRs in the future. [ABSTRACT FROM AUTHOR]

• The time-dependent bus dispatching problem is examined in a doubly dynamic context. • Adding more buses to service does not necessarily increase the service frequency. • An adaptive bus fleet size adjustment approach is proposed with a target loading factor. • The approach reduces bus operating cost while maintaining comparable user costs for passengers. This paper examines the time-dependent bus dispatching problem in a multi-modal context. Traditional studies along this line often optimize the bus frequency or schedule. However, they may fail as the realized bus frequency or schedule is constrained by the time-varying traffic congestion on the road. Adding more buses to service does not necessarily increase the service frequency. Given this, we look into the time-dependent bus dispatching (number of buses in service on road) when taking into account complex multi-modal and multi-directional flow interactions on the road. In particular, the traffic dynamics over clock time is modeled through an aggregate traffic representation with flow interactions between cars and buses, and interactions between traffic in opposite moving directions. Instead of explicitly optimizing the size of dispatched bus fleet, we propose an adaptive fleet size adjustment mechanism where we have a target level of bus loading factor. This adaptive or responsive approach, by taking advantage of the doubly dynamical system proposed in Liu and Geroliminis (2017), adjusts the size of dispatched bus fleet over calendar time and accommodates day-to-day variations of mode choices and traffic patterns. Numerical studies show that the proposed approach can help bus operator to reduce operating cost and improve net benefit while maintaining comparable user costs for passengers. This study offers a new perspective for dynamic bus dispatching strategy and improves our understanding of multi-modal traffic dynamics. [ABSTRACT FROM AUTHOR]

• Propose the CACC-OIFT strategy to dynamically optimize information flow topology (IFT) for CACC. • Under CACC-OIFT, vehicles dynamically deactivate/activate "send" functionality of their V2V communication devices. • CACC-OIFT consists of an IFT optimization model and an adaptive Proportional- Derivative controller. • CACC-OIFT enhances string stability of platoon control in an unreliable V2V communication context. Emergent cooperative adaptive cruise control (CACC) strategies being proposed for platoon formation in the connected autonomous vehicle (CAV) context mostly assume idealized fixed information flow topologies (IFTs) for the platoon, implying guaranteed vehicle-to-vehicle (V2V) communications for the IFT assumed. In reality, V2V communications are unreliable due to failures resulting from communication-related constraints such as interference and information congestion. Since CACC strategies entail continuous information broadcasting, communication failures can occur in congested CAV traffic networks, leading to a platoon's IFT varying dynamically. To explicitly factor IFT dynamics and to leverage it to enhance the performance of CACC strategies, this study proposes the idea of dynamically optimizing the IFT for CACC, labeled the CACC-OIFT strategy. Under CACC-OIFT, the vehicles in the platoon cooperatively determine in real-time which vehicles will dynamically deactivate or activate the "send" functionality of their V2V communication devices to generate IFTs that optimize the platoon performance in terms of string stability under the ambient traffic conditions. The CACC-OIFT consists of an IFT optimization model and an adaptive Proportional-Derivative (PD) controller. Given the adaptive PD controller with a two-predecessor-following scheme, and the ambient traffic conditions and the platoon size just before the start of a time period, the IFT optimization model determines the optimal IFT that maximizes the expected string stability in terms of the energy of speed oscillations. This expectation is because each IFT has specific degeneration scenarios whose probabilities are determined by the communication failure probabilities for that time period based on the ambient traffic conditions. The optimal IFT is deployed for that time period, and the adaptive PD controller continuously determines the car-following behaviors of the vehicles based on the unfolding degeneration scenario for each time instant within that period. The effectiveness of the proposed CACC-OIFT is validated through numerical experiments in NS-3 based on NGSIM field data. The results indicate that the proposed CACC-OIFT can significantly enhance the string stability of platoon control in an unreliable V2V communication context, outperforming CACCs with fixed IFTs or with passive adaptive schemes for IFT dynamics. [ABSTRACT FROM AUTHOR]

• An optimization model is formulated to maximize the automated taxi system's profit. • The effect of traffic congestion is considered in automated taxis' dial-a-ride problem. • A customized Lagrangian relaxation algorithm can solve the model for hard instances. • Ride-sharing contributes more served requests and less delay. • The automated taxis' service quality is sensitive to the delay penalty. In this paper, we study the dial-a-ride problem of ride-sharing automated taxis (ATs) in an urban road network, considering the traffic congestion caused by the ATs. This shared automated mobility system is expected to provide a seamless door-to-door service for urban travellers, much like what the existing transportation network companies (TNC) do, but with decreased labour cost and more flexible relocation operations due to the vehicles' automation. We propose an integer non-linear programming (INLP) model that optimizes the routing of the ATs to maximize the system profit, depending on dynamic travel times, which are a non-linear function of the ATs' flows. It is important to involve traffic congestion in such a routing problem since for a growing number of ATs circulating in the city their number will lead to delays. The model is embedded within a rolling horizon framework, which divides a typical day into several horizons to deal with the real-time travel demand. In each horizon, the routing model is solved with the demand at that interval and assuring the continuity of the trips between horizons. Nevertheless, each horizon model is hard to solve given its number of constraints and decision variables. Therefore, we propose a solution approach based on a customized Lagrangian relaxation algorithm, which allows identifying a near-optimal solution for this difficult problem. Numerical experiments for the city of Delft, The Netherlands, are used to demonstrate the solution quality of the proposed algorithm as well as obtaining insights about the AT system performance. Results show that the solution algorithm can solve the proposed model for hard instances. Ride-sharing makes the AT system more capable to provide better service regarding delay time and the number of requests that can be attended by the system. The delay penalty on the profit objective function is an effective control parameter on guaranteeing the service quality while maintaining system profitability. [ABSTRACT FROM AUTHOR]

• Propose a state transfer risk decision (STRD) method for a large-scale traffic network. • This method is based on the conditional risk decision model. • This method provides an acceptable traffic flow range at the sub-region boundary by risk interval. • Proposed method improves the state stability compared to PI and NSTRD. Network state degradation probabilities increase with traffic density. Specifically, at critical or congested densities, any over-input traffic flow may cause degradation, local traffic jams, and even hysteresis of the road network. Existing macroscopic traffic control methods usually target the maximum capacity or minimum delay to improve the road network efficiency and do not consider the negative effect of perimeter control on the state transfer of the road network. This study provides a method to prevent the transfer of the network state when implementing boundary control. First, according to the data detected for the road network, the concept of a conditional value at risk is used to establish a risk decision model that considers the influence of the boundary input flow rate on sub-region state degradation. Then, based on the risk decision model, we propose a state transfer risk decision (STRD) perimeter control method for a large-scale traffic network with multiple sub-regions. This perimeter control method predicts the traffic demand of every sub-region, selects an acceptable traffic flow range at the sub-region boundary by risk interval, regards the maximum trip completion flow and the minimum total travel time as the decision-making objectives, and controls multiple sub-region boundaries. The simulation results show that compared with proportional integral (PI) control and no STRD (NSTRD) control, the STRD control scheme can effectively prevent the state transfer of all sub-regions, improve the trip completion flow, and decrease the travel delay of the network. [ABSTRACT FROM AUTHOR]

• Optimal parking pricing with parking permits management in a many-to-one park and ride (P + R) network. • A hybrid management with parking pricing and parking permit to reduce system travel cost. • A mixed integer linear program (MILP) to determine system optimum parking permits distribution. • Comprehensive parking management in response to inadequate parking space. This paper investigates optimal parking pricing with parking permit management in a many-to-one park and ride (P + R) network in which each origin is connected to the destination by an auto path, a parallel rail transit path and a P + R path. First, we revisit the parking pricing scheme at P + R parking facility with parking restraint and find that it fails to eliminate extra costs arising from competition for inadequate parking spots. Fortunately, competition for inadequate parking spots can be eliminated by a strategy of parking permits distribution. Then we investigate the optimal number of parking permits distributed to each OD pair in a many-to-one park and ride network. The problem is formulated as a mixed-integer linear program (MILP). However, parking permit management is less efficient than parking pricing at P + R facility when parking provision is sufficient. Therefore, we propose two hybrid management schemes with parking pricing and parking permit to reduce system travel cost. Joint implementation of parking pricing and parking permit not only alleviates the traffic congestion but also eliminates the competition for inadequate parking space. Next, the bi-level programming models are proposed to determine the optimal parking fees at P + R parking facilities in the hybrid management schemes. In the bi-level programming models, the number of parking permits assigned or held by each OD pair is an implicit function with respect to parking fee. A global optimum oriented solution algorithm and simulated annealing algorithm are used to solve the MILP and bi-level programming models respectively. Numerical examples are carried out to show that the hybrid schemes with parking pricing and parking permits are more effective than both pure parking pricing scheme and pure parking permits management scheme. [ABSTRACT FROM AUTHOR]

• Designing one-of-a-kind experiment to monitor urban congestion with a swarm of drones. • Creating the most complete urban multimodal dataset, nicknamed pNEUMA , to study congestion. • Investigating traffic phenomena at different scales of modeling. • Developing an open science initiative with almost half a million trajectories for transportation-oriented research. The new era of sharing information and "big data" has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply "drones" have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA , to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models. [ABSTRACT FROM AUTHOR]

• Designing a macroscopic modeling framework for perimeter gating control and dynamic user equilibrium. • Designing a routing scheme with instantaneous dynamic user equilibrium and predictive travel time. • Application of Proportional-Integral (PI) controller for traffic control. • Investigating the impact of perimeter gating control on routing inside-and-outside the reservoir. • Analysis of emissions in the traffic network. In recent years, several perimeter control strategies have been proposed for traffic management in cities. The common factor found in these works is the use of Macroscopic Fundamental Diagram (MFD) models to describe the dynamics of the network and optimize traffic inside the perimeter by manipulating perimeter inflows. Perimeter gating control strategies are attractive for traffic management inside the inner city. However, it inevitably creates a negative impact on the traffic outside. Most of the works in this research area have neglected vehicle re-routing outside the controlled perimeter, i.e., they do not consider demand elasticity to the central region resulting from gating and the related queues. In this paper, we propose a global modeling framework capable of assessing the effect of perimeter gating control (in terms of queue, emission, and total time spent) on the full network, considering demand elasticity resulting from Dynamic User Equilibrium (DUE). Classical Proportional-Integral (PI) control scheme is used to control traffic congestion inside a central region (reservoir). The modeling framework is comprised of: (i) an accumulation-based MFD model to reproduce traffic dynamics inside the reservoir, (ii) point-queue model to represent queuing vehicles on inbound links to the gating points, and (iii) a time-dependent travel time profile based on a steady-state approximation of MFD dynamics to characterize the alternative road network (bypass). DUE is then implemented, considering instantaneous predicted travel time. This determines how the demand to the inner region is affected by the gating. The functioning of the global system is assessed by total time spent and NOx and CO 2 emissions inside the reservoir and for the full network. The presented simulation results show that the perimeter gating control helps to maintain congestion at the desired level with significant improvements in the total time spent and the mean speed in the network. However, it shows a slight increase in the queues. As expected, deviation to the bypass alternative is significant and should not be neglected when carrying out a global assessment of gating system performance. [ABSTRACT FROM AUTHOR]

• Use ride-sharing vehicle trajectories data to statistically estimate real-world NDL. • Compute zone-to-zone travel time data from trajectory-level RV data to estimate NDL. • Provide a data sharing scheme that does not real individual or business information. • DNL-based user-optimal traffic routing reduces traffic congestion. • Though the NDL pattern varies from day to day, its average is stable and interpretable. Transportation systems are being reshaped by ride sourcing and shared mobility services in recent years. The transportation network companies (TNCs) have been collecting high-granular ride-sourcing vehicle (RV) trajectory data over the past decade, while it is still unclear how the RV data can improve current dynamic network modeling for network traffic management. This paper proposes to statistically estimate network disequilibrium level (NDL), namely to what extent the dynamic user equilibrium (DUE) conditions are deviated in real-world networks. Using the data based on RV trajectories, we present a novel method to estimate the real-world NDL measure. More importantly, we present a method to compute zone-to-zone travel time data from trajectory-level RV data. This would become a data sharing scheme for TNCs such that, while being used to effectively estimate and reduce NDL, the zone-to-zone data reveals neither personally identifiable information nor trip-level business information if shared with the public. In addition, we present an NDL based traffic management method to perform user optimal routing on a small fraction of vehicles in the network. The NDL measures and NDL-based routing are examined on two real-world large-scale networks: the City of Chengdu with trajectory-level RV data and the City of Pittsburgh with zone-to-zone travel time data. We found that, on weekdays in each city, NDLs are likely high when travel demand is high (thus when congestion is mild or heavy). Generally, a weekend midnight exhibits higher NDLs than a weekday midnight. Many NDL patterns are different between Chengdu and Pittsburgh, which are attributed to unique characteristics of both demand and supply in each city. For instance, NDL in Pittsburgh is much more stable from day to day and from hour to hour, comparing to Chengdu. In addition, we observe that origin-destination pairs with high NDLs are spatially and temporally sparse for both cities. For the Pittsburgh network, we evaluate the effectiveness of NDL-based traffic routing, which shows great potential to reduce total travel time with routing a small fraction of vehicles (1% in the experiments), even using dated NDL that is estimated in the prior hour. [ABSTRACT FROM AUTHOR]

• Linear program for integrating shared autonomous vehicles with transit. • Traffic flow modeled through the link transmission model. • Rolling horizon algorithm for application to larger networks. • Integration with transit reduces total person travel time. • Transit is especially useful with low headways or small autonomous vehicle fleets. Use of shared autonomous vehicles (SAVs) for last-mile transportation can improve transit use and reduce road congestion. We investigate the problem of optimal integration of SAVs with transit. By optimal, we mean that transit should be used instead of or to complement SAVs only when it reduces the total system travel time. Although greater use of transit reduces congestion, transit often increases travel time due to frequent stops and transfers. By using a continuous approximation to passenger and vehicle movements, we formulate the problem as a linear program using the link transmission model for traffic flow. One of the main challenges is associating passenger movement with vehicles that can have different destinations (e.g. a transit stop). Although the mathematical program is linear, it nevertheless has a large number of variables. We find a suboptimal solution using a rolling horizon method, which greatly reduces the required computation time. We also demonstrate an example of a possibly unfair passenger-to-vehicle ordering, and propose a first-come-first-served greedy algorithm to match passengers. A suite of experimental results on the Sioux Falls network show that using transit decreases total system travel time, especially when SAV fleet sizes are small. Transit also decreases the time travelers spend waiting, but tends to increase in-vehicle travel time. The methodology could be useful both for future SAV operators and for planners seeking to predict the effects of SAVs on traffic congestion. [ABSTRACT FROM AUTHOR]

• Maneuvers of mainline facilitating vehicles and on-ramp merging vehicles are coordinated. • Facilitating speeds are bounded from below to restrain impact on following traffic for safety. • The Pontryagin Maximum Principle is applied to rigorously derive analytical solutions. • The proposed strategy evaluated for mixed traffic through MATLAB-Aimsun interaction. • Sensitivity test conducted to check effects of different allowable minimum facilitating speeds. This paper presents a trajectory planning strategy for connected automated vehicles (CAVs) to cooperatively carry out mainline facilitating (i.e. gap development) and on-ramp merging maneuvers. The trajectory planning tasks of the mainline facilitating vehicle and the merging vehicle are formulated as two related optimal control problems. The motivation behind the proposed strategy is to restrain a facilitating maneuver's impact on following traffic. To this end, the proposed strategy bounds the speed of the facilitating maneuver from below and meanwhile ensures that the task of gap development can still be fulfilled. Because of the existence of the speed bound, the optimal control problem of the facilitating vehicle becomes constrained in state, in addition to the control constraints. A Pontryagin Maximum Principle (PMP) with state constraints is applied to rigorously derive the analytical solution. The main difficulty of the analytical procedure exists in the fact that the first-order necessary condition on the extremality of the Hamiltonian cannot yield useful information on the property of the optimal control history with regard to making the optimal speed trajectory to satisfy the speed constraint. As a result, additional conditions have to be explored, notably the so-called "jump conditions", among others. Taking advantage of the analytical solution, the proposed strategy is then implemented under a model predictive control framework. Simulation assessments of the proposed strategy are conducted at two levels – individual vehicle level and traffic flow level. At the individual vehicle level, the proposed strategy shows potential to reduce the risk of rear-end collision between the facilitating vehicle and the following vehicle, the most vulnerable pair of vehicles under the influence of gap development. At the traffic flow level, coupled with Aimsun, the proposed strategy is assessed under mixed traffic flow conditions, with various penetration rates of CAVs. The results show that it has potential to generate lower speed variations of traffic flow, a critical factor in traffic flow safety. Meanwhile it does not show negative impact on traffic efficiency in the simulation, and is likely to improve traffic efficiency in the real world. A sensitivity analysis of the effect of the facilitating maneuver's lower speed bound is also conducted. Although there exist several limitations with this study, it sheds some light on future research. [ABSTRACT FROM AUTHOR]

• This paper proposes a network-wide method to identify intersection traffic congestion in a road network. • The proposed method is solely based on low-frequency probe vehicle data, i.e., map-independent. • It is able to rapidly and approximately detect intersection congestion along all turning directions. • It is a computer-aided method in the era of big data can greatly decrease traffic engineers' workload. Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles' turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers' workloads by providing information regarding where and when to investigate and solve traffic congestion problems. [ABSTRACT FROM AUTHOR]

Shared automated vehicles (SAV) are expected to benefit the sustainable development of urban regions and alleviate the negative impacts brought by the increasing number of private cars. In this paper, we envision a future scenario where non-pooled SAVs replace private cars and provide public on-demand mobility services to satisfy the mobility needs of a city's residents. To help service providers make profitable fleet sizing and management decisions, we develop a mixed-integer non-linear programming model that considers the congestion effects and the mode choice of urban travellers in different income classes, between SAVs and bicycles. Our model optimises both strategic decisions (fleet size, initial fleet distribution, and service quality level) and operational decisions (trip assignment, vehicle routing, parking, and relocation). Travellers' preference for both transport modes is described through a binary logit model and congestion effects are described by dynamically varying travel times with respect to traffic flow in a non-linear fashion. In addition, we investigate two types of accept/reject mechanisms (mandatory vs. non-mandatory acceptance) which lead to an endogenously determined acceptance rate that can affect travellers' willingness to use SAV services. The computational challenge posed by the non-linear and non-convex nature of the model is addressed through reformulation and the use of outer-inner approximation methods combined with a breakpoint generation algorithm. We demonstrate the effectiveness of our proposed method in a case study of the city of Delft in The Netherlands, as well as a scaling analysis on three toy networks with various sizes and demand profiles. A sensitivity analysis of key parameters is carried out to assess system performance. Computational results indicate that fleet sizing decisions are influenced not only by the population's geographical distribution and land use patterns but also by the pricing strategy, unit operating costs of the SAV fleet, network congestion level, and traveller behaviour. When the price rate of using SAVs is low, the fleet sizing decisions can also be influenced by the trip accept/reject mechanism and the travellers' sensitivity to the service quality level. In addition, a low price of SAV service will attract more users but may not necessarily bring a higher profit because of the increased traffic congestion. • A mixed integer programming model for SAV fleet sizing and management is proposed. • Endogenous demand is incorporated using a binomial logit model. • An accept/reject mechanism and its impact are explored. • Traffic congestion is included using volume delay functions. • Impacts are estimated and managerial insights are extracted for SAV operators. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicle (CAV) technology is expected to increase road capacity and reduce fuel consumption, but it may take time for all human-driven vehicles (HVs) to be replaced by CAVs. During the transition period, CAVs and HVs will continue to coexist. This paper aims to find an optimal deployment of a CAV platoonable corridor to facilitate CAV platooning and better manage traffic congestion on road networks with a mixed traffic flow of CAVs and HVs. Generally, dedicated infrastructure for CAVs, such as CAV lanes, zones, or corridors, may have distributional welfare effects on travelers. Especially, HV users may be worse off because road space is partially converted to dedicated infrastructure for CAVs. To facilitate equitable infrastructure planning, we develop a bi-level program for a Stackelberg game that incorporates the equity concern, where the planner acts as the leader to determine the optimal deployment of an equitable CAV platoonable corridor at the upper level, while travelers are followers who make user-optimal route choices given the CAV platoonable corridor at the lower level. For the lower-level problem, we model the decision-making of travelers as a platoon-embedded network equilibrium with mixed flow (PNEMF) and derive its equivalent variational inequality (VI) problem. The existence and the uniqueness of the VI solution are proved. To solve the bi-level program efficiently, we propose a simulated-annealing-based corridor search (SACS) algorithm. Our numerical experiments on the Nguyen–Dupuis (ND), Sioux Falls (SF), Anaheim, and Winnipeg networks demonstrate the expected benefits of deploying a CAV platoonable corridor if the CAV market penetration ratio exceeds a certain threshold. In the meantime, both CAV and HV users are better off with the deployed equitable corridor. In addition, we find that the degree of the transportation planner's concern about the inequity issue has a significant impact on the corridor design when the CAV penetration ratio is low. With less concern about the inequity issue, the CAV platoonable corridor is longer and more HVs experience higher generalized travel costs. • Propose a dedicated corridor for connected and autonomous vehicle (CAV) platooning. • Incorporate the equity concern into the infrastructure planning to mitigate potential negative impacts on human-driven vehicles (HVs). • Develop a bi-level program to determine the optimal deployment of an equitable CAV platoonable corridor. • Propose a simulated-annealing-based corridor search algorithm to efficiently find the optimal corridor deployment. • A corridor benefits CAVs and the majority of HVs when the CAV market penetration ratio exceeds a threshold. [ABSTRACT FROM AUTHOR]

The time-dependent pollution routing problem (TDPRP) extends the pollution routing problem (PRP) cause it captures traffic congestion at peak periods in urban transportation. It concerns planning a fleet of homogeneous vehicles to serve all customers, jointly deciding their speed on every arc and the departure time from each node such that the total cost of routes reaches a minimum. To our knowledge, all solution methods for the TDPRP are founded on (meta-)heuristics. In this study, we propose an exact branch-and-price-and-cut (BPC) algorithm for the TDPRP. The master problem is a route selection problem formulated as a set-partitioning model, and the pricing problem is a time-dependent elementary shortest path problem with resource constraints (TDESPPRC), in which the speed and start time at each customer need to be decided. We devise a tailored label-setting algorithm to handle the pricing problem, and the master problem is solved by column generation. Then, two valid inequalities are employed to tighten the lower bound, and several accelerating techniques are used to speed up the label-setting algorithm. Meanwhile, we present an analytical characterization applied to different scenarios. Finally, extensive computational experiments show our algorithm outperform a commercial MIP solver, and the optimal solutions are found for more benchmark instances using less time. • This paper presents an exact solution based on a BPC algorithm for the TDPRP. The corresponding pricing problem is a TDESPPRC, which is tailored to solve by a label-setting algorithm. • This paper introduces an easy-to-check dominance rule by exploiting the structure of the departure time and the total cost function. The extensive computational experiments show the effectiveness and efficiency of our algorithm. • Our new dominance idea could be used to effectively solve the pricing problems of other VRP variants, where the dominance test needs to compare functions, for example, the PRP, the VRPSTW and the TDVRP. [ABSTRACT FROM AUTHOR]

With the constantly rising labor cost and deterioration in traffic congestion, the drone-based service emerges as a game changer for the urban last-mile delivery. Meanwhile, the human courier, as a traditional player in the last-mile delivery, appears as a typical form of gig economy, which is always available but with less revenue to harvest. To help the retailer with the fleet sizing decision-making, a co-sourcing mechanism is introduced, which is by nature a time-varying queue with a finite waiting space. And the orders are subject to cancellation when the waiting exceeds the customers' patience time. Furthermore, three stochastic optimization models are formulated, namely, the pure stability, the pure profit, and the hybrid models, to jointly determine the optimal waiting space threshold and the drone fleet size, which are then approximated with fluid models. Based on the optimal control theory, the optimal decisions are obtained according to the outsourcing probability and time delay. As validated in the simulation, the proposed pure stability model can effectively stabilize the system performance at the control target, while the hybrid model takes into account both the stability and the profit. Finally, the tradeoff between profitability and volatility is analyzed, and the impacts of the parameters on the overall system performance are revealed. • This paper studies an urban delivery setting where drones and human couriers coexist. • A co-sourcing strategy is designed to address the stability and overall profit margin. • A black-box model is constructed to approximate the co-sourcing scenario. • Three fluid control models are built to tackle the mixed urban delivery problem. • The effectiveness of algorithms and models is verified by numerical experiments. [ABSTRACT FROM AUTHOR]

• Investigated the stability of a newly proposed car following model for connected vehicles. • Derived the stability criterion of heterogenous traffic. • Extended CV-IDM by considering time delay and estimation error. • Investigated the new models' stability and oscillation features. Despite the prosperous development of connected vehicle (CV) and its car-following (CF) models, human-driven CV and its CF properties are rarely investigated. This paper studies the stability characteristics of and then extends a recently-developed CF model of human-driven CV which incorporates human factors (CV-CF hereafter) by considering two levels of driver compliance, i.e., low compliance and high compliance. First, we investigate the stability of the CV-CF model, and validate the results with the simulation experiments. We then assess CV 's impact on the mixed traffic flow by deriving a stability criterion of heterogeneous traffic with the Laplace transform based method and analysing the influence of different levels of connectivity and their penetration rates. Furthermore, we extend the CV-CF model by considering two important additional human factors, i.e., time delay and estimation error, and evaluate different human factors' impact on the stability and oscillation characteristics of the CV-CF model. The results reveal that the connected environment indeed promotes the CF stability and alleviates traffic congestion, and that higher compliance to the information provided is generally more beneficial to the stability of traffic flow, except the situation with a large time delay. [ABSTRACT FROM AUTHOR]

• A lane-changing advisory control was proposed using C-ITS. • Hyper-heuristic method was used to optimize lane-changing advisory proportions. • Effects of lane-changing advisory with different connected vehicles were analyzed. Reduction in the number of lanes (lane drop) is common on motorways due to road design, incidents, or road maintenance, and it can be an active bottleneck if the traffic demand is high. If congestion occurs, the lane drop capacity will decrease 10–20%. In order to avoid capacity drop, this study analysed the reason and proposed lane-changing advisory control on the merge lane to distribute lane-changing using Cooperative Intelligent Transport Systems (C-ITS) technology. Further, this study used hyper-heuristic optimisation to obtain the lane-changing advisory proportion of each segment upstream of lane drop. Conditions of different traffic demands were analysed using the microscopic traffic simulation software AIMSUN and its Application Program Interface (API) function. Results indicated that the proposed lane-changing advisory strategy could reduce traffic congestion and obviously improve traffic efficiency. This study also analysed the effects with different proportions of connected vehicles and found that if the connected vehicle ratio is less than 20%, the lane-changing advisory has little impact on the lane drop performance, and that if the penetration rate is more than 50%, the gain in performance is marginal. [ABSTRACT FROM AUTHOR]

• A method to identify Braess's Paradox phenomenon in any given airway network. • Removal of such airway links could reduce the total travel time in an airway network. • Can optimize airway network for better air traffic flow management planning. The ever increasing demand for air travel is likely to induce air traffic congestion which will elicit great economic losses. In the presence of limited airspace capacity as well as the saturated airway network, it is no longer practicable to mitigate air traffic congestion by adding new airways/links. In this paper, we provide a "counter-intuitive" perspective towards air traffic congestion mitigation by removing airways/links from a given airway network. We draw inspiration from Braess's Paradox which suggests that adding extra links to a congested traffic network could make the traffic more congested. The paper explores whether Braess's Paradox occurs in airway networks, or more specifically, whether it is possible to better distribute the flow in an airway network by merely removing some of its airways/links. In this paper, We develop a method for Braess's Paradox detection in any given airway network. To validate the efficacy of the method, a case study is conducted for the South-East Asia airspace covering Singapore airway network, by using 6 months of ADS-B data. The results show that Braess's Paradox does occur in airway networks and the proposed method can successfully identify the airway network links that may cause it. The results also demonstrate that, upon removing such links, the total travel time for a given day traffic at a given flight level, was reduced from 8661.15 min to 8328.64 min, a reduction of 332.5 min. This amounts to a saving of 3.8% in travel time. [ABSTRACT FROM AUTHOR]

• Data-driven automatic incident detection algorithm developed. • Inter-quartile distance based threshold generation perform better than standard normal deviates. • Spatio-temporally denoised thresholds help to improve incident detection performance. • The entire framework can be parallelized using MapReduce to implement on large-scale traffic networks. Automatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper, we propose a data-driven AID framework that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate a robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making it feasible to implement the framework over large networks. Our study shows that such robust thresholds can improve incident detection performance significantly compared to traditional threshold determination. Second, we leverage the knowledge of the topology of the road network to construct threshold heatmaps and perform image denoising to obtain spatio-temporally denoised thresholds. We used two image denoising techniques, bilateral filtering and total variation for this purpose. Our study shows that overall AID performance can be improved significantly using bilateral filter denoising compared to the noisy thresholds or thresholds obtained using total variation denoising. [ABSTRACT FROM AUTHOR]

• We propose an actuated signal control strategy for the CLL design. • The strategy introduces an extra phase between the pre-signal and the main signal. • An optimization procedure is proposed for optimizing the CLL design. • The proposed design concept outperforms the fixed-time CLL design. Contraflow left-turn lane (CLL) design has been increasingly used in China to help relieve traffic congestion associated with left-turning vehicles at signalized intersections. With the CLL design, one or multiple left-turn lanes are setup in the opposing through lanes adjacent to the median. The basic idea of the CLL design is to provide additional capacity to left-turning vehicles by making use of the opposing through lanes. The primary objective of the present study was to propose an actuated signal control strategy to improve the operations of the CLL design at signalized intersections. The proposed control strategy introduced an extra stage between the pre-signal and the main signal at the signalized intersection with the CLL design. Two detectors were installed: one at the upstream median opening and the other at the contraflow lane. A procedure was proposed for optimizing the distance between the upstream median opening and the main signal, such that the discharge rate of the left-turning vehicles and the utilization rate of the contraflow lane can be maximized. Simulation experiments were conducted to compare the performance of the proposed actuated control strategy versus the fixed time control strategy. The results show that the proposed design concept outperforms the fixed time control strategy in increasing capacity and reducing delay for left-turning vehicles. [ABSTRACT FROM AUTHOR]

• 3D CAV trajectories are formulated in the combined temporal-spatial dimensions. • A DFROC algorithm is developed to minimize average control delays at intersections. • Simulation models are developed to examine the proposed algorithm. • The proposed DFROC algorithm is superior to the existing control algorithms. Traffic congestion has become a serious issue worldwide due to the rapid increase in population and traffic demands. Advances in connected automated vehicle (CAV) technology demonstrate the potential to improve traffic mobility and safety performance at intersections. An advanced intersection control system is proposed in this study to coordinate vehicle trajectories and ensure safety and operation efficiency at intersections. A temporal-spatial dimension extension-based trajectory coordination model is developed by formulating all possible trajectories of vehicles at the intersection. Correspondingly, according to the trajectory coordination model, two signal-free control algorithms, including the priority-based algorithm and the Discrete Forward-Rolling Optimal Control (DFROC) algorithm are proposed in this study to manage vehicles at the intersection. These two algorithms, together with the FCFS policy, are compared with the conventional signal control method in a SUMO-based simulation platform. Experimental results indicate that the proposed algorithms outperform the signal control method in terms of reducing total traffic delays at intersections and increasing intersection capacity and operation efficiency. [ABSTRACT FROM AUTHOR]

Highlights • Provide a systematic review of CAV based UTC studies in the last few decades. • Highlight the change of design philosophy for UTC. • Summarize the benefits and drawbacks of current CAV-based UTC methods. • Predict the future study directions. Abstract Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field. [ABSTRACT FROM AUTHOR]

Highlights • Proposed framework to categorize and label congestion patterns automatically using clustering analysis. • Generic and domain-specific features are compared in the clustering application. • Watershed is used to separate different fundamental elements in a pattern. • The domain-specific approach qualitatively and quantitatively performs better than the generic approach. • The generic features perform well in detecting local details in patterns. Abstract Classification of congestion patterns is important in many areas in traffic planning and management, ranging from policy appraisal, database design, to prediction and real-time control. One of the key constraints in applying machine learning techniques for classification is the availability of sufficient data (traffic patterns) with clear and undisputed labels, e.g. traffic pattern X or Y. The challenge is that labelling traffic patterns (e.g. combinations of congested and freely flow areas over time and space) is highly subjective. In our view this means that assessment of how well algorithms label the data should also include a qualitative component that focuses on what the found patterns really mean for traffic flow operations and applications. In this study, we investigate the application of clustering analysis to obtain labels automatically from the data, where we indeed first qualitatively assess how meaningful the found labels are, and subsequently test quantitatively how well the labels separate the resulting feature space. By transforming traffic measurements (speeds) into (colored) images, two different approaches are proposed to extract the features of a large number of traffic patterns for clustering: point-based and area-based. The point-based approach is widely applied in the image processing literature, and explores local interest points in images (i.e. where large changes occur in color intensity); whereas a new area-based approach combines domain knowledge with Watershed segmentation to partition the images into different spatial-temporal segments from which domain specific features, such as wide moving jam patterns, are extracted. The results show that the Watershed segmentation separates the traffic (congestion) patterns into more meaningful and separable classes, comparable to those that have been proposed in the literature. Since there is no ground-truth set of labels, the quantitative assessment tests how well both methods are able to separate the respective feature spaces they construct for the (large) database of traffic patterns. We argue that the more crisp this separation is; the better the labelling has turned out. For this quantitative comparison we train a multinomial classifier that maps unseen patterns to the labels discovered by each of the two labeling approaches. The most important result is that the classifier using the area-based feature vector achieves the highest average levels of confidence in its decisions to classify patterns, implying a highly separable feature vector space. We argue this is good news! Not only does the combination of image processing (Watershed) and domain knowledge (traffic flow characteristics) lead to meaningful labels that can be automatically retrieved from large databases of data; this method also leads to a more efficient separation of the resulting feature space. Our next endeavor is to further refine and use this method to develop a search engine for the (rapidly growing) 200 TB historical database of traffic data hosted by the Dutch National Datawarehouse (NDW). [ABSTRACT FROM AUTHOR]

Highlights • An arch formation based congestion alleviation for crowds gathers at a bottleneck is proposed. • Symmetrically placed obstacles are used to adjust position of an arch formation. • Reasonable arch axis is modified to analyze the arch formation in a crowd. • A model is proposed for calculating proper spacing between symmetrically placed obstacles. Abstract Congestion in pedestrian crowds is a critical issue for evacuation management. This paper presents an approach based on arch formation to alleviate congestion formed by crowds gathering at a bottleneck. In this approach, analyzing a specific self-organized motion pattern, namely, arch formation, the position of the arch formation has a great influence on congestion alleviation. Specifically, an arch formation at an outer position is beneficial for congestion alleviation because it is unstable and collapses easily. The symmetrically placed obstacles, which have a similar role as anti-slide piles, are used to change the position of the arch formation in a crowd. The spacing between the symmetrically placed obstacles is an important parameter for adjusting an arch formation. We present a modified model that originates from the soil mechanics in civil engineering to calculate the proper spacing between the symmetrically placed obstacles. This model analyzes the static attributes of an arch formation and improves the spacing calculation. The proper spacing between symmetrically placed obstacles can alleviate congestion and increase evacuation efficiency. In this paper, the social force model is used to conduct an evacuation simulation, and the simulation results illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights • Image processing algorithm developed for accurate vehicle trajectory tracking. • Development of stop-and-go traffic observed as a result of human driving. • Stronger traffic waves lead to higher fuel consumption. Abstract The traffic experiment conducted by Sugiyama et al. (2007) has been a seminal work in transportation research. In the experiment, a group of vehicles are instructed to drive on a circular track starting with uniform spacing. The isolated experimental environment provides a safe, economic, and controlled environment to study free flow traffic and phantom traffic waves. This article introduces a novel method that automates the data collection process in such an environment. Specifically, the vehicle trajectories are measured using a 360-degree camera, and the fuel rates are recorded via on-board diagnostics (OBD-II) scanners. The video data from the 360-degree camera is then processed by an offline unsupervised computer vision algorithm. To validate the data collection method, the technique is then evaluated on a series of eight experiments. Analysis shows that the collected data are highly accurate, with a mean positional bias of less than 0.002 m and a small standard deviation of 0.11 m. The positional data also yields reliable velocity estimates: the derived velocities are biased by only 0.02 m/s with a small standard deviation of 0.09 m/s. The produced trajectory and fuel rate data can be readily used to study human driving behaviors, to calibrate microsimulation models, to develop fuel consumption models, and to investigate engine emissions. To facilitate future research, the source code and the data are made publicly available online. [ABSTRACT FROM AUTHOR]

Highlights • Propose a macroscopic integrated optimization model of TMA and airport. • Apply the simulated annealing method combined with a time decomposition approach. • Conduct case studies based on Paris Charles De-Gaulle airport. Abstract Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case. [ABSTRACT FROM AUTHOR]

Highlights • A new mobility model combines the advantages of mobile phone and transportation data. • The model can capture ordinary and anomalous human mobility in a real-time manner. • We apply the model to predict crowd gatherings that usually cause traffic jams. Abstract Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams. [ABSTRACT FROM AUTHOR]

Highlights • Linear monocentric cities with heterogeneous travelers. • Linear polycentric cities. • Nonlinear road pricing. • The system optimal distance-based toll function. • The optimal toll function design. • Concave and weakly concave. Abstract Nonlinear road pricing charges each traveler based on his/her trip’s corresponding particular attribute level. In order to help authorities in designing road pricing systems at a strategic level, this paper attempts to address two fundamental questions: (i) what is the value of pricing’s nonlinearity for mitigating traffic congestion? (ii) if a nonlinear toll function is implemented, should it be convex, concave or other shape? Specifically, we consider distance-based pricing in linear cities. For linear monocentric cities with heterogeneous travelers, we show that the system optimal distance-based pricing indeed exhibits nonlinearity. It is proved that: (i) the cost-based system optimal toll function is monotonically increasing and concave with respect to the traveled distance; (ii) the time-based system optimal toll function always exists and is unique. If the initial proportion of each traveler group is invariant along a corridor and the demand function is of exponential type, then the time-based system optimal toll function enables the travelers, living further away from a city center, to face a lower toll level per unit distance. For a linear polycentric city, we demonstrate: (i) there always exists the system optimal differentiated (in terms of city centers) toll functions; (ii) it is highly possible that the system optimal non-differentiated toll function does not exist. Hence, we further propose an optimal toll design model, prove the Lipschitz continuity of its objective and adopt a global-optimization algorithm to solve it. [ABSTRACT FROM AUTHOR]

Highlights • The operation process of a real-world lighterage terminal is analysed. • A coordination strategy is proposed to reduce congestions of vessels and vehicles. • A simulation-based approach identifies the optimal setting for the strategy. • Discrete-event simulation and bi-objective simulation optimization are applied. • The congestion reduction is significant from the numerical experiment. • A mobile application prototype is proposed for real-world implementation. Abstract Lighterage contributes significantly to the maritime industry since thousand tons of necessary supplies have to be delivered to mother vessels anchored near sea every day. A single lighter waits to receive cargoes from multiple suppliers at the berth, while the trucks wait for their turns to enter the terminal. Due to the limited space, the terminal operation becomes vulnerable to the lack of coordination of their arrivals. In this study, despite a traditional industry, we are interested in applying the emerging technologies to improve the operation efficiency. We develop a simulation-based coordination strategy to construct coordinated schedules for both the lighters and suppliers to reduce congestions in the lighterage terminal The discrete event simulation model is developed based on understanding the real-world terminal in Singapore, and the controlled arrival method, determining coordinated arrival schedules of trucks, is introduced and embedded to the simulation model. The simulation model is tuned up with a benchmark setting of 6-month historical data. To find the optimal strategy, an advanced bi-objective simulation optimization algorithm is employed. According to our findings, the proposed strategy could significantly improve the efficiency of both lighters and trucks in various indicators. At the end, a mobile application prototype is proposed to deliver the coordinated schedules to different parties, and improve the communication between parties. [ABSTRACT FROM AUTHOR]

Highlights • We integrate the NFD with a feedback-control-based toll optimization framework. • Various tolling regimes are studied including a new joint distance and delay toll. • We apply the proposed framework on a large-scale DTA model of Melbourne, Australia. • Results show that the joint tolls can reduce the uneven distribution of congestion. Abstract Given the efficiency and equity concerns of a cordon toll, this paper proposes a few alternative distance-dependent area-based pricing models for a large-scale dynamic traffic network. We use the Network Fundamental Diagram (NFD) to monitor the network traffic state over time and consider different trip lengths in the toll calculation. The first model is a distance toll that is linearly related to the distance traveled within the cordon. The second model is an improved joint distance and time toll (JDTT) whereby users are charged jointly in proportion to the distance traveled and time spent within the cordon. The third model is a further improved joint distance and delay toll (JDDT) which replaces the time toll in the JDTT with a delay toll component. To solve the optimal toll level problem, we develop a simulation-based optimization (SBO) framework. Specifically, we propose a simultaneous approach and a sequential approach, respectively, based on the proportional-integral (PI) feedback controller to iteratively adjust the JDTT and JDDT, and use a calibrated large-scale simulation-based dynamic traffic assignment (DTA) model of Melbourne, Australia to evaluate the network performance under different pricing scenarios. While the framework is developed for static pricing, we show that it can be easily extended to solve time-dependent pricing by using multiple PI controllers. Results show that although the distance toll keeps the network from entering the congested regime of the NFD, it naturally drives users into the shortest paths within the cordon resulting in an uneven distribution of congestion. This is reflected by a large clockwise hysteresis loop in the NFD. In contrast, both the JDTT and JDDT reduce the size of the hysteresis loop while achieving the same control objective. We further conduct multiple simulation runs with different random seed numbers to demonstrate the effectiveness of different pricing models against simulation stochasticity. However, we postulate that the feedback control is not applicable with guaranteed convergence if the periphery of the cordon area becomes highly congested or gridlocked. [ABSTRACT FROM AUTHOR]

Highlights • We explore how to periodically convert lanes for bus use on city-wide scales. • Scheduling decisions are found to be impactful in non-steady-state traffic. • Greater benefits and lower costs result from activating conversions late in a rush. • Deploying conversions for short (e.g. 30-min) durations is often effective. • Scheduling times can be fine-tuned to suit rush-period congestion levels. Abstract The paper explores what can occur when select street lanes throughout a city are periodically reserved for buses. Simulations of an idealized city were performed to that end. The city’s time-varying travel demand was studied parametrically. In all cases, queues formed throughout the city during a rush, and dissipated during the off-peak period that followed. Bus lanes were activated all at once across the city, and were eventually deactivated in like fashion. Activation and deactivation schedules varied parametrically as well. Schedules that roughly balanced the trip-time savings to bus riders against the added delays to car travelers were thus identified. Findings reveal why activating conversions near the start of a rush can degrade travel, both by car and by bus. Balance was struck by instead activating lane conversions nearer the end of the rush, when vehicle accumulation in the city was at or near its maximum. Most of the time savings to bus riders accrued after the conversions had been left in place for only 30 min. Leaving them for longer durations often brought modest additional savings to bus travelers. Yet, the added delays to cars often grew large as a result. These findings held even when buses garnered high ridership shares. This was the case when lane conversions gradually induced new bus trips among residents who formerly did not travel. It was also true when high ridership was a pre-existing feature of the city. Activating conversions a bit earlier in a rush was found to make sense only if commuters shifted from cars to buses in very large numbers. Findings also unveiled how to fine-tune activation and deactivation schedules to suit a city’s congestion level. Guidelines for scheduling conversions in real settings are furnished. So is discussion on how these schedules might be adapted to daily variations in city-wide traffic states. Roles for technology are discussed as well. [ABSTRACT FROM AUTHOR]

• A decentralized DRL signal control for multi-modal traffic networks is introduced based on a microscopic environment. • Car traffic and bus performances are improved simultaneously and traded off. • The proposed method is applicable to different bus lane layouts and multiple bus lines. • The scalability and transferability of the model are verified. Managing traffic flow at intersections in a large-scale network remains challenging. Multi-modal signalized intersections integrate various objectives, including minimizing the queue length and maintaining constant bus headway. Inefficient traffic signals and bus headway control strategies may cause severe traffic jams, high delays for bus passengers, and bus bunching that harms bus line operations. To simultaneously improve the level of service for car traffic and the bus system in a multi-modal network, this paper integrates bus priority and holding with traffic signal control via decentralized controllers based on Reinforcement Learning (RL). The controller agents act and learn from a synthetic traffic environment built with the microscopic traffic simulator SUMO. Action information is shared among agents to achieve cooperation, forming a Multi-Agent Reinforcement Learning (MARL) framework. The agents simultaneously aim to minimize vehicles' total stopping time and homogenize the forward and backward space headways for buses approaching intersections at each decision step. The Deep Q-Network (DQN) algorithm is applied to manage the continuity of the state space. The tradeoff between the bus transit and car traffic objectives is discussed using various numerical experiments. The introduced method is tested in scenarios with distinct bus lane layouts and bus line deployments. The proposed controller outperforms model-based adaptive control methods and the centralized RL method regarding global traffic efficiency and bus transit stability. Furthermore, the remarkable scalability and transferability of trained models are demonstrated by applying them to several different test networks without retraining. [ABSTRACT FROM AUTHOR]

In spite of the significant effects of COVID-19, UAM operations are still expected to grow smoothly and healthily in the near future. If such dense UAM traffic relies on tactical planning to resolve conflicts in a decentralized control scheme, urban airspace could soon be heavily congested and airspace complexity could be overwhelming. In this paper, we propose a Quasi-dynamic Air Traffic Assignment (QATA) model, which aims to allocate traffic flows among air routes in the planning horizon in order to organize UAM traffic flows and reduce air traffic congestion and complexity within a centralized strategic planning scheme while meeting the demand and respecting some criteria. Firstly, UAM traffics are modeled as flows that operate on a 3D two-way UAM route network. Next, the QATA problem is formulated as an optimization problem involving network dynamics to minimize the air traffic complexity evaluated by the linear dynamical system and congestion defined by traffic density and energy consumption. A simulation-based rolling horizon framework is introduced to decompose the QATA problem into several modified static air traffic assignment problems in each time interval. In order to overcome the limitations of conventional dynamic traffic assignment algorithms, a simulated annealing algorithm using parallel computing and a novel neighborhood generation strategy is proposed to efficiently optimize the problem. By applying the model to a pre-designed large-scale UAM route network in Singapore's urban airspace, Experimental studies demonstrate the performance of the proposed framework and its applicability. Parallel computing can achieve up to three times faster than the original algorithm. The proposed algorithm significantly reduces the value of the objective function by (32. 20 ± 0. 29) % in 143. 47 ± 3. 74 seconds at the 95% confidence interval of 100 experiments, far better compared to the representative conventional dynamic traffic assignment algorithms. This study could be useful to assist air traffic control authorities and air navigation service providers in addressing various issues in unmanned traffic management. • A macroscopic quasi-dynamic air traffic assignment model for future UAM networks. • Air traffic complexity measures based on intrinsic characteristics of UAM traffic flow. • Simulation-based rolling horizon framework with parallel simulated annealing. • High-density parcel delivery service scenario in Singapore's urban airspace. [ABSTRACT FROM AUTHOR]

• Systematic review on urban traffic control under mixed traffic with CAVs. • Cover signal timing, CAV trajectory/route planning, and their cooperative control. • Summarize mixed traffic control methods at intersections with CAV-dedicated facilities. • Outline promising research directions and challenges. Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve traffic safety. With the development of communication and automation technologies, regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs) will coexist on urban roads in the near future. In this paper, we systematically review the studies on urban traffic control under the mixed traffic environment, which utilize CVs and CAVs to improve the performance of urban traffic control. We first give a glance at the evolution of urban traffic control with the deployment of CVs and CAVs to identify the important and promising research fields in the mixed traffic control. We then review the recent advances in traffic signal timing with CAV/CV trajectory data, CAV trajectory/route planning, and the cooperative control of CAV trajectories and traffic signals from the intersection level to the network level under the mixed traffic environment. Besides, we summarize two types of studies on mixed traffic control at intersections with CAV-dedicated facilities: 1) traffic signal timing and vehicle trajectory planning at intersections with CAV-dedicated lanes, and 2) deployment of CAV-dedicated facilities including CAV-dedicated lanes, links, and zones. Finally, the future research directions and challenges are discussed. We hope that this review could provide researchers with a helpful roadmap for future research on urban traffic control under the mixed traffic environment. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the main concerns in big cities with many adverse socioeconomic effects. A promising solution is to simultaneously regulate the admission of vehicles (i.e., demand management) and redistribute traffic flows within the network (i.e., route guidance). In this work, we integrate demand management with route guidance within a Model Predictive Control framework using regional traffic dynamics with generalized Macroscopic Fundamental Diagram (MFD) shapes. Dealing with generalized MFD shapes is challenging due to the resulting nonlinear and non-convex optimization formulation. To tackle this challenge, we develop two real-time solution approaches: (i) a successive convexification approach that constructs convex bounding sets for all nonlinear terms, and (ii) a linear approximation approach that solves the problem using triangular macroscopic fundamental diagram approximations. The proposed approaches offer a trade-off between execution speed and solution quality, as the linear approximation approach runs faster while the successive convexification approach yields better quality and accuracy solutions. Macroscopic simulation results illustrate the efficiency of the successive convexification and linear approximation approaches yielding an optimality gap of less than 3.5% and 10% in all considered cases, respectively. Furthermore, both approaches outperform a state-of-practice nonlinear solver in terms of solution quality and execution time. Finally, substantial gains are also obtained regarding travel time and traffic flow efficiency in a realistic microsimulation environment. • Regional route guidance and demand management with generalized MFD traffic dynamics. • Custom-made linear approximation and successive convexification solution approaches. • Proposed approaches offer fast and reliable solutions with optimality guarantees. • Developed approaches are superior to a state-of-practice nonlinear solver. [ABSTRACT FROM AUTHOR]

Network-wide perimeter control strategies have been shown promise in recent years. These perimeter control strategies are mostly based on networks with fixed boundaries. However, fixed partitions may not exploit the full potential of control performance when traffic condition dynamically changes. This study proposes a hybrid MPC-based perimeter control method to tackle traffic congestion with uneven traffic demand loading by an MPC-based perimeter control framework in which the control boundary between reservoirs is dynamically altered. The framework integrates dynamic partitions into the prediction model to formulate an optimization, in which optimal traffic performance is achieved by iteratively finding the appropriate partitions as well as the control decision variables. Moreover, a Deep Learning (DL) based estimator is incorporated into the framework to address the hefty computational burden caused by the nesting of optimization loops. In the hybrid scheme, the DL-based prediction replaces the model-based prediction if a confidence condition can be fulfilled. Comparison analysis is conducted to evaluate the necessity of perimeter control with real-time partitioning. Further results show that the hybrid scheme is effective and computationally efficient. Besides, sensitivity analysis of different confidence thresholds pinpoints the robustness of the hybrid scheme. • Hybrid perimeter control for mitigating network-level congestion. • Perimeter control with real-time partitions for heterogeneous urban traffic. • An integration of deep learning and MPC for computational efficiency. • Confidence conditions considered for reliability in the hybrid approach. [ABSTRACT FROM AUTHOR]