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This paper considers a crowdsourced delivery (CSD) system that effectively utilizes the existing trips to fulfill parcel delivery as a matching problem between CSD drivers and delivery tasks. This matching problem has two major challenges. First, it is a large-scale combinatorial optimization problem that is hard to solve in a reasonable computational time. Second, the evaluation of the objective function for socially optimal matching contains the utility of drivers for performing the tasks, which is generally unobservable private information. To address these challenges, this paper proposes a hierarchical distribution mechanism of CSD tasks that decomposes the matching problem into the task partition (master problem) and individual task-driver matching within smaller groups of drivers (sub-problems). We incorporate an auction mechanism with truth-telling and efficiency into the sub-problems so that the drivers' perceived utilities are revealed through their bids. Furthermore, we formulate the master problem as a fluid model based on continuously approximated decision variables. By exploiting the random utility framework, we analytically represent the objective function of the problem using continuous variables, without explicitly knowing the drivers' utilities. The numerical experiment shows that the proposed approach solved large-scale matching problems at least 100 times faster than a naive LP solver and approximated the original objective value with errors of less than 1%. The code of this work is available at https://github.com/yuki-oyama/fluid-particle-csd. • Propose a mechanism for socially optimal matching in large-scale CSD systems. • Hierarchically decompose the problem into task partition and task-driver matching. • Address unobservable individual drivers' heterogeneity by auction mechanisms. • Cast task partition as perturbed utility maximization and achieve global efficiency. • Drastically reduce computational costs while retaining accuracy of the solution. [ABSTRACT FROM AUTHOR]

Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin–destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel two-stage OD estimation framework based on distributionally robust optimization (DRO) and quasi-sparsity property of large-scale OD demand matrices. The proposed two-stage Distributionally Robust Quasi-Sparsity OD estimation (DR-QSOD) model does not require an accurate or complete distribution assumption of estimates/data. Numerical results demonstrate that DR-QSOD model outperforms stochastic QSOD model in estimating OD demands when the distribution assumption of data is biased. This paper also discusses two different approaches to solve the DR-QSOD model as well as compares their computational efficiency. In addition, DR-QSOD model is shown to keep relatively high quasi-sparsity consistency, which also brings lots of meaningful practical insights. • A Distributionally Robust Quasi-Sparsity OD (DR-QSOD) demand estimation framework is proposed. • DR-QSOD considers data stochasticity and quasi-sparsity of large OD demand matrices. • This method yields to two-stage OD demand estimators. • DR-QSOD outperforms stochastic models when data distribution is biased or incomplete. [ABSTRACT FROM AUTHOR]

• A public transit accessibility under heat exposure model is studied with time-dependent consideration. • A column generation method to urban rail-bus-walk service network integration is devised. • Experiments in Beijing show the impact of heat exposure on public transit accessibility. The evolution of urban transportation systems is increasingly driven by the integration of rail, bus, and walking pathways, forming a cohesive service network. This urban rail-bus-walk service network integration is pivotal in addressing urban challenges and ensuring seamless mobility. Nevertheless, the effectiveness of such integrated systems depends on their accessibility and the willingness of individuals to take them as their primary transportation mode. In the context of climate change, extreme heat events pose an increasing threat to passenger comfort and health, which in turn, affects individuals' decisions to use public transit. Additionally, walking to transit stops or waiting for transportation becomes more challenging during such extreme heat conditions. This paper tackles the challenge of comprehensively addressing the evolving issues related to public transit accessibility in response to rising temperatures, an area of research that has received limited attention until now. This paper introduced an approach aimed at optimizing the urban rail-bus-walk service network with a focus on both accessibility and heat exposure considerations. By calculating the transportation accessibility level under varying temperature conditions and identifying its vulnerability to high temperatures, this paper presents a methodology to enhance the transit service. Such improvement involves not only adjustments to the integration network but also considering of cooling facilities, such as shaded routes and shelters. Column generation is applied to solve this problem and the optimization result demonstrates improvement of accessibility under the extreme heat condition. [ABSTRACT FROM AUTHOR]

• Proposed method to transform BIM models of hub stations into network models. • Improved MDPM by using node embedding, Enhanced applicability of MDPM to simulation of evacuation scenarios. • Based on the simulation results of MDPM, the "congested cluster" is proposed to explain the mechanism of bottleneck cause. • Discussed the impact of bottleneck expansion options on evacuation efficiency. • Simulation results for MDPM were analyzed using DTW to discuss the existence of MFDs during evacuation. In order to predict the evacuation capacity of a hub station in advance during the design and planning period, this paper proposes a network loading model to describe the overall evacuation problem of a hub station. Firstly, this paper introduces two principles: the fundamental diagram and spatial density consistency. The method of abstracting from Building Information Modeling (BIM) to the network model is standardized and objective density consistency is achieved using the social force model. Additionally, the consistency between the results of the microscopic and macroscopic network models is verified through simulation experiments of microscopic scenarios. The Macro-Dynamic Propagation Model (MDPM) has been enhanced in three aspects to better suit evacuation scenarios. Furthermore, the quantization of pedestrians' path selection results is achieved by synchronously propagating node embeddings. Based on the established evacuation case's fundamental diagram of pedestrian flow, the logic of fundamental diagram matching (LFM) is designed to be applicable to evacuation scenarios. Finally, using the real-time density of the nodes and the node embeddings as attribute terms, this paper designs a pedestrian selection model that enables the MDPM to consider the following effect of the pedestrians in the evacuation case. The evacuation process of the hub station is simulated, the distance matrix between the nodes is calculated using the Dynamic Time Warping (DTW) method, the nodes are clustered and analyzed based on this matrix and the macroscopic fundamental diagram (MFD) is statistically calculated from the clustering results. The results suggest that MFD may be an intrinsic feature of a dynamic transportation network system, emerging from node message passing. Additionally, the system formula model bases on the MFD with the optimization idea of the evacuation process is discussed. [ABSTRACT FROM AUTHOR]

Maintaining a fleet of buses to transport students to school is a major expense for school districts. An efficient bus routing system can greatly reduce the time and cost associated with transportation, while also ensuring the safety and comfort of students. The School Bus Routing Problem (SBRP) is a comprehensive real-world problem that has been studied over decades while the neural combinatorial optimization technique is only developed and caught researchers' attention in recent years. Optimization on large-scale SBRP instances is challenging due to the huge number of variables and constraints involved. In this paper, we integrate the school bell time decisions with bus scheduling decisions in a multi-criteria optimization model under single load assumption for both heterogeneous and homogeneous bus fleet scenarios. A Tabu Search heuristic, incorporating a deep neural network that is trained through reinforcement learning, is designed to deal with large-scale bell time and bus scheduling optimization. Numerical experiments show that our solution approach outperformed existing heuristics and a state-of-the-art commercial solver with respect to solution quality for a given execution time. The tests on real-world cases demonstrate that adjusting the school bell time in school districts can result in an improvement in terms of deadhead travel distances from 15% to 55% (average 20%–30%), depending on the original bell time allocation, and the proposed bell time policy. • First paper using deep learning for large scale school bus scheduling optimization. • Deep reinforcement learning is used for training the end-to-end model. • Multi-criteria mixed-integer linear programming models are formulated. • A 2-stage Tabu Search framework is proposed for the sequential decision process. • Substantial potential is shown for integrating deep learning into meta-heuristics. [ABSTRACT FROM AUTHOR]

• Propose an adaptive compression algorithm involving multiple factors. • Develop a GPU parallel computing framework and a supportive compression method. • Design a new index for evaluating compression effectiveness. • Conduct comparative experiments using three substantial and representative datasets. Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm of intelligent transportation systems. The flourishing marine industry has prompted AIS satellites and base stations to generate massive amounts of vessel trajectory data, escalating both data storage and calculation costs. The conventional Douglas-Peucker (DP) algorithm used for trajectory compression sets a uniform threshold, which hampers effective compression. Additionally, compressing and accelerating the computation of large datasets poses a significant challenge in real-world applications. To address these limitations, this paper aims to develop a new Graphics Processing Unit (GPU) parallel computing and compression framework that enables the acceleration of the optimal threshold calculation for each trajectory automatically in maritime big data mining. It achieves this by incorporating a new Adaptive DP with Speed and Course (ADPSC) algorithm, which utilizes the dynamic navigation characteristics of different vessels. It can effectively solve the associated computational time cost concern when using the ADPSC algorithm to compress vast trajectory datasets in the real world. Additionally, this paper proposes a novel evaluation metric for assessing compression efficacy based on the Dynamic Time Warping (DTW) method. Comprehensive experiments encompass vessel trajectory datasets from three representative research areas: Tianjin Port, Chengshan Jiao Promontory, and Caofeidian Port. The experimental results demonstrate that 1) the newly developed ADPSC method outperforms in terms of compression, and 2) the designed GPU parallel computing framework can significantly shorten the compression time for extensive datasets. The GPU-accelerated compression methodology not only minimizes storage and transmission costs for data from both manned and unmanned vessels but also enhances data processing speed, supporting real-time decision-making. From a theoretical perspective, it provides the key to the puzzle of realizing the real-time anti-collision of manned and unmanned ships, particularly in complex waters. It hence makes significant contributions to maritime safety in the autonomous shipping era. [ABSTRACT FROM AUTHOR]

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver's near-future decisions, the so-called aleatoric uncertainty , and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent's future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected. • Propose a deep-ensemble-based non-parametric approach for quantifying aleatoric and epistemic uncertainty in single-agent trajectory prediction. • Induce a causal regularization to enhance the generalizability and give reliable estimates of epistemic uncertainty. • Give insights into the role of speed in epistemic uncertainty estimation and model generalization for trajectory forecasting. [ABSTRACT FROM AUTHOR]

Pedestrian behavior is a complex and important research subject, which has been studied extensively by simulation and experiment. As the basis or premise of other behaviors, rotation behavior is the most common pedestrian behavior in life. However, the current pedestrian simulation method is difficult to depict the rotation, which results in insufficient authenticity of the simulation effect. This paper proposes a systematic work on the modeling and simulation problem of rotation behaviors under multiple scenarios. Firstly, we improve the body model based on elliptical structure to simulate the microscopic behavior of pedestrians in the most realistic way. Then, we modeling the pedestrian rotation behavior in multiple scenarios by proposing two indexes: rotation angle and rotation timing, which include the Case I : Traversing behavior in narrow areas, Case I I : Avoidance behavior in counter flow and Case I I I : Overtaking behavior in medium density flow. After that, we propose a torsional social force model (T-SFM), which significantly improves original model from two aspects: (a) fusing the torsional force; (b) adding the rotation willingness. More than 500 times real experiments are conducted, and according to the analysis results of real experiments, this paper verifies the authenticity of rotation model from both qualitative and quantitative perspectives. Moreover, we calibrate the general parameters to verify the generality of the rotation model. The simulation results show that the T-SFM can not only simulate the rotation behavior with high authenticity, but also has good generality. Our research significantly improves the authenticity of pedestrian simulation and provides support for heterogeneous pedestrians modeling in complex environments. • Rotation behavior was observed in traversing, avoidance and overtaking experiments. • We build a rotation behavior model considering rotation angle and rotation timing. • Torsional social force model (T-SFM) was proposed and validated by experiment. • Males will rotate more to avoid females due to the politeness. [ABSTRACT FROM AUTHOR]

Nowadays, Demand-Responsive Transport companies can help the elderly or disabled citizens in their everyday journeys as well as provide an important link between big cities and their outer suburbs. In practice, many of those trips are scheduled in advance but many others are asked on the fly (online) by the users while the drivers are already on their routes. To answer those online requests, dispatching centers often rely on fast heuristics based on some myopic objectives. However, this deteriorates the flexibility of the dispatch over time. This paper shows the potential of a thorough optimization of the planning of the vehicles at the start of the day under the hypothesis that the transport company is able to forecast the requests that will arrive during the service. Our objective is to design an initial set of routes for the vehicles that maximizes the expected acceptance rate of future requests, since the ones scheduled in-advance are mandatory. In this prospect, we propose a Combinatorial Benders Decomposition framework to solve the so-called Stochastic Dial-a-Ride Problem. A key ingredient for the success of this solver is the use of a clustering-based initialization of the Master Problem. The computational results show the effectiveness of this approach when applied to instances extracted from the literature as well as from actual services provided by Padam Mobility, an international company in Shared Mobility Systems. The proposed method provides a substantial performance gain though traditional heuristics are kept to manage online insertions afterward. • First paper with a complete model for the static Stochastic Dial-a-Ride Problem. • A Combinatorial Benders Decomposition approach is tested on real-world instances. • We show the impact of an efficient initial set of inequalities in the Master Problem. • We introduce a procedure based on hierarchical clustering to build this initial set. • We propose to assign a probability of appearance for each uncertain travel request. • We show how the performance of such an algorithm relies on the prediction quality. [ABSTRACT FROM AUTHOR]

In many densely populated mega-cities around the world, metro systems are becoming overly saturated by the ever-increasing travel demand, which results in overcrowded stations, excessive delay, and unsafe anxieties among riders. The concept of extra-long trains (i.e., trains longer than station platforms) was recently proposed in the literature as a promising way to increase a metro line's capacity without additional infrastructure construction. This paper develops a general modeling framework to optimize train block configuration and operation protocols of a metro line so that extra-long trains can be used under varying demand distribution and infrastructure setting. The design problem is formulated as the integration of two coupled vehicle routing problems which simultaneously optimizes the train block configuration plan, door opening strategy, stop skipping strategy, and train dispatch schedule. This paper proposes two customized solution methods, including an adapted savings heuristic and a destroy-and-repair algorithm. A series of hypothetical examples are tested to demonstrate how the proposed solution approaches outperform an existing commercial solver even for small to moderate problem instances. Moreover, two real-world case studies, with very different demand patterns from two continents, are presented to test the effectiveness of using extra-long trains. Our results show that a metro line with extra-long train operations is capable of serving up to 20%–30% more passenger demand as compared to that with only regular trains, while at the same time producing a similar or lower passenger average travel time. • Design of extra-long trains (i.e., longer than station platforms) in a general setting. • Mixed-integer program to optimize train block configuration and operation protocols. • Customized solution methods (adapted savings and destroy-and-repair). • Extensive experiments with hypothetical and real-world data (from Beijing and Santiago). • Results show significantly increase in metro line capacity without infrastructure construction. [ABSTRACT FROM AUTHOR]

• A Multi-layer Graph Mask Q-Learning algorithm for traffic signals is proposed. • This study constructs a new upper and lower graph structure to extract geometric and spatial features of intersections. • This article introduces an action masking mechanism, enabling the model to be applicable to intersections of arbitrary structures when utilizing any phase as the action. • Model tests were conducted on synthetic road networks with different traffic volumes and two real urban road networks, and the test results showed that the algorithm has strong generalization ability and scalability. Due to its capability in handling complex urban intersection environments, deep reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control (ATSC). However, most existing algorithms are designed for specific road networks or traffic conditions, making it difficult to transfer them to new environments. Moreover, current graph-based algorithms do not fully capture the geometric and spatial features of intersections, leading to incomplete embedding of the agent's environment depiction. Additionally, the actions adopted by these algorithms are inherently based on fixed-cycle phases, limiting the flexibility of traffic signal control. To address the aforementioned issues, this paper proposes a Multi-layer Graph Mask Q-Learning (MGMQ) algorithm for multi-intersection ATSC to optimize traffic and reduce delay. Unlike previous graph-based algorithms, this paper introduces a method for computing multi-layer graphs, dividing the traffic environment into upper-level traffic network-layer graphs and lower-level intersection-layer graphs, and employs the graph attention algorithm and an improved GraphSAGE algorithm for computation. This method not only enables the generation of embedded state for intersections that include geometric and spatial features, but also allows the algorithm to adapt to different traffic conditions and road networks. Additionally, we introduce an action masking mechanism, allowing this algorithm can be adapted to different action spaces. As a result, the algorithm uses arbitrary signal phases as actions to achieve flexible traffic flow control, and can be directly applied to intersections with arbitrary geometry. The final test results demonstrate that a model trained solely on synthetic road networks can be directly transferred to other synthetic network configurations or real-world urban road networks, outperforming current state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the opportunity to plan for a novel on-demand freight delivery service in urban areas with tight service and sustainability requirements. This contribution first attempts to focus on the feasibility of connecting people and freight mobility through the joint usage of transportation options. It builds on the hyperconnectivity principles enabled by the Physical Internet (PI) manifesto for city logistics, referred to as HCL (Hyperconnected City Logistics). To this end, this paper proposes a model-based decision support approach that seeks to produce effective delivery solutions for an on-demand multimodal transshipment problem. The approach considers multiple mobility options such as on-demand urban delivery services, cargo bikes, tramways, and buses to transship goods from one urban logistic hub to another. This novel short-term response planning problem complements the well-known day-before planning problem. The objective function aims to minimize the impact of freight moves in the time and space of urban transport networks. The proposed solution aims to increase the sustainability of cities by reducing congestion levels, the impact of logistics moves, as well as carbon emissions in urban areas. An illustrative case is provided to demonstrate how the novel multi-mode transportation system could operate and to provide an evaluation of the economic and sustainability benefits of such a system in an urban context. For a hyperconnected network, the impact of freight movements on the time and space of urban transport networks can be reduced by 91%. The results demonstrate the potential of urban synchromodality in improving economic efficiency, service capability, and sustainability. [ABSTRACT FROM AUTHOR]

• The effect of "backpressure" on responsive signal control performance in one simple network is studied. • Only a simple network is considered and travellers are supposed to choose their own best routes. • It is shown that on the simple network having backpressure increases equilibrium delays. • It is shown that on the simple network having backpressure reduces network capacity. Many responsive traffic signal control strategies are "pressure-driven". These strategies move green-time from stages with a lower pressure to stages with a higher pressure, at each junction; often aiming to approximately equalise stage pressures at each junction. In some of these pressure-driven strategies each stage pressure at a junction includes a "backpressure" term, which depends on downstream links. Such backpressure terms reduce the pressure on stages and reduce pressure most for those stages leading toward the most highly congested downstream road links. The expectation is that there will then be a reduction in traffic flow exiting the junction toward already highly congested road links, and that this will reduce queue lengths and network delays. This paper shows that this is not always the case by giving two examples where having backpressure within the control policy on a network with two signal controlled junctions (1) increases both equilibrium delays at one of the junctions for certain demands, and (2) never reduces any of the equilibrium delays at either junction. A simple dynamic evolution with a steady reduction in demand is also shown in which two of the considered policies, one with backpressure, give rise to equilibrium delays and queues which tend to infinity, and reveal a gap; in this gap feasible demands have no equilibrium consistent with the two control policies and in a day-to-day dynamical context delays and queues are unbounded. This paper considers only the simple network shown in figure 1 under certain specified conditions. One main avenue for further research might be to consider whether the results shown here also hold in different networks under different conditions. [ABSTRACT FROM AUTHOR]

• This paper proposed a novel deep graph learning framework for airspace complexity prediction that can comprehensively capture the spatio-temporal dependencies between large-scale airspace sectors. • This paper constructed a multimodal adaptive graph convolution module that can effectively explore the multiple spatial dependencies and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven learning manner. • This paper designed a temporal convolution module based on a multiple-time-step self-attention mechanism to extract the mixed short- and long-term temporal patterns from both local and global perspectives. • This paper constructed a comprehensive dataset from real-world air traffic data within Chinese domestic airspace, ensuring its reliability through rigorous calibration with experienced air traffic controllers. Airspace complexity is defined as an essential indicator to comprehensively measure the safety of air traffic operational situations. A reliable prediction of airspace complexity can provide practical guidance for formulating air traffic management strategies and resource allocation. Although extensive efforts have been devoted to computing airspace complexity, previous studies can rarely model the multi-dimensional and combined spatio-temporal features within airspace complexity data. In this paper, we propose a multimodal adaptive spatio-temporal graph neural network to simultaneously explore the spatio-temporal dependencies in the airspace sector network. Specifically, we design a multimodal adaptive graph convolution module to effectively learn the diverse spatial relationships and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven manner. To model dynamic long-short-term temporal patterns, we develop a dilated causal convolution layer with a multiple-time-step self-attention mechanism to accurately predict airspace complexity over a longer time horizon. Extensive experiments on real-world air traffic datasets show that the proposed approach can harness differing spatial modes in achieving higher generalization performance across different temporal patterns, outperforming state-of-the-art methods in all prediction time horizons. [ABSTRACT FROM AUTHOR]

• A timetable synchronization problem of high-speed railway and air transportation systems with passenger routing is proposed. • Based on train unit circulation and aircraft rotation plan, the problem is transformed into a multi-commodity flow model. • Some real-world requirements including train overtaking, train headways, flight spacing requirements at airports and seat capacity are considered. • An ADMM decomposition approach on the problem is designed. • A real-world case study (Shijiazhuang-Zhengding Airport in China) with shuttle buses is conducted. The design of synchronized services is of great significance to the development of intermodal transportation. In the planning of Air and High-Speed Railway (HSR) integration services (AHIS), the operators aim to synchronize flight and train schedules and determine the possible transferring options for intermodal passengers. This paper attempts to design an intermodal timetable for train and flight services considering the time-dependent passenger demand. Different transferring modes are further considered in this paper including both shuttle and rapid walking services. The synchronization problem of "train unit flows", "aircraft flows", "shuttle flows" and "passenger flows" is formulated using a multi-commodity flow model in a time–space network. Based on the Lagrangian relaxation method, we first decompose the original problem into four types of routing subproblems associated with train units, aircrafts, shuttle buses and passengers, respectively. Then the coordination of the four different types of flows is achieved through both the pricing mechanism and Alternating Direction Method of Multipliers (ADMM) method. The proposed model and solution approach are validated using both hypothetical and real-world cases and the results show that the synchronized timetables are able to serve more intermodal passengers and shorten their travelling times. [ABSTRACT FROM AUTHOR]

• Method to validate the alignment of successive camera views for vehicle tracking. • Provides direct feedback from tracking output to ensure the alignment quality. • The method automatically detects and quantifies: gap, overlap and scale factors. • If problems are found the output can be used to recalibrate the alignment. • Found and fixed alignment errors in NGSIM, cleaned NGSIM data will be shared. This paper develops the partial trajectory method to align the views from successive fixed cameras that are used for video-based vehicle tracking across multiple camera views. The method is envisioned to serve as a validation tool of whatever alignment has already been performed between the cameras to ensure high fidelity with the actual vehicle movements as they cross the boundaries between cameras. The strength of the method is that it operates on the output of vehicle tracking in each camera rather than secondary features visible in the camera view that are unrelated to the traffic dynamics (e.g., fixed fiducial points). Thereby providing a direct feedback path from the tracking to ensure the quality of the alignment in the context of the traffic dynamics. The method uses vehicle trajectories within successive camera views along a freeway to deduce the presence of an overlap or a gap between those cameras and quantify how large the overlap or gap is. The partial trajectory method can also detect scale factor errors between successive cameras. If any error is detected, ideally one would redo the original camera alignment, if that is not possible, one could use the calculations from the algorithm to post hoc address the existing alignment. This research manually re-extracted the individual vehicle trajectories within each of the seven camera views from the NGSIM I-80 dataset. These trajectories are simply an input to the algorithm. The resulting method transcends the dataset and should be applicable to most methods that seek to extract vehicle trajectories across successive cameras. That said, the results reveal fundamental errors in the NGSIM dataset, including unaccounted for overlap at the boundaries between successive cameras, which leads to systematic speed and acceleration errors at the six camera interfaces. This method also found scale factor errors in the original NGSIM homographies. In response to these findings, we identified a new aerial photo of the NGSIM site and generated new homographies. To evaluate the impact of the partial trajectory method on the actual trajectory data, the manually re-extracted data were projected into the new coordinate system and smoothed. The re-extracted data shows much greater fidelity to the actual vehicle motion. The re-extracted data also tracks the vehicles over a 14% longer distance and adds 23% more vehicles compared to the original NGSIM dataset. As of publication, the re-extracted data from this paper will be released to the research community. [ABSTRACT FROM AUTHOR]

• This paper sheds light on online and in-person shopping/eat-out activity engagement. • A multivariate ordered probit model is estimated. • Online and in-person activities of multiple purposes are decided as a package bundle. • The online/in-person relationship is different between grocery and other shopping. • There is a need for an integrated passenger and freight demand modeling structure. The virtual (online) and physical (in-person) worlds are increasingly inter-connected. Although there is considerable research into the effects of information and communication technologies (ICT) on activity-travel choices, there is little understanding of the inter-relationships between online and in-person activity participation and the extent to which the two worlds complement one another or substitute for one another. Shopping is one of the activity realms in which the virtual and physical spaces are increasingly interacting. This paper aims to unravel the relationships between online and in-person activity engagement in the shopping domain, while explicitly distinguishing between shopping for non-grocery goods, grocery products, and ready-to-eat meals. Data from the 2017 Puget Sound household travel survey is used to estimate a multivariate ordered probit model of the number of days in a week that a sample of households engages in in-person activity engagement and online activity engagement for each of these shopping activity types – leading to a model of six endogenous outcomes. Model results show that there are intricate complementary and substitution effects between in-person and online shopping activities, that these activities are considered as a single packaged bundle, and that the frequencies of these activities are significantly affected by income, built environment attributes, and household structure. The findings suggest that travel forecasting models should incorporate model components that capture the interplay between in-person and online shopping engagement and explicitly distinguish between non-grocery and grocery shopping activities. Policies that help bridge the digital divide so that households of all socio-economic strata can access goods and services in the virtual world would help improve quality of life for all. Finally, the paper highlights the need to bring passenger and freight demand modeling, at least within urban contexts, into a single integrated structure. [ABSTRACT FROM AUTHOR]

• New optimization problem for last-mile delivery using multiple drones and a truck. • Integrated approach for customer location clustering and truck-drone routing. • Mathematical models for minimizing cost and delivery completion time are provided. • Truck stops for drone dispatch can be a customer or non-customer location. • Efficient machine learning-based heuristic proposed to accelerate solution time. With growing consumer demand and expectations, companies are attempting to achieve cost-efficient and faster delivery operations. The integration of autonomous vehicles, such as drones, in the last-mile network design can curtail many operational challenges and provide a competitive advantage. This paper deals with the problem of delivering orders to a set of customer locations using multiple drones that operate in conjunction with a single truck. To take advantage of the drone fleet, the delivery tasks are parallelized by concurrently dispatching the drones from a truck parked at a focal point (ideal drone launch location) to the nearby customer locations. Hence, the key decisions to be optimized are the partitioning of delivery locations into small clusters, identifying a focal point per cluster, and routing the truck through all focal points such that the customer orders in each cluster are fulfilled either by a drone or truck. In contrast to prior studies that tackle this problem using multi-phase sequential procedures, this paper presents mathematical programming models to jointly optimize all the decisions involved. We also consider two polices for choosing a cluster focal point - (i) restricting it to one of the customer locations, and (ii) allowing it to be anywhere in the delivery area (i.e., a customer or non-customer location). Since the models considering unrestricted focal points are computationally expensive, an unsupervised machine learning-based heuristic algorithm is proposed to accelerate the solution time. Initially, we treat the problem as a single objective by independently minimizing either the total cost or delivery completion time. Subsequently, the two conflicting objectives are considered together for obtaining the set of best trade-off solutions. An extensive computational study is conducted to investigate the impacts of restricting the focal points, and the influence of adopting a joint optimization method instead of a sequential approach. Finally, several key insights are obtained to aid the logistics practitioners in decision making. [ABSTRACT FROM AUTHOR]

• Investigate impacts of heterogeneity in driving behavior on traffic dynamics. • Unifying framework to analyze RV and CAV behavior in mixed traffic. • Mechanism for heterogenous acceleration and CF leading to void creation. • Simulation for compounded effects of heterogeneity in mixed traffic platoons. This paper elucidates the impacts of vehicle heterogeneity on traffic dynamics and throughput of mixed traffic consisting of connected automated vehicles (CAVs) and regular vehicles (RVs). The main premise is that the heterogeneity in preferred acceleration rate, desired speed, and car-following (CF) behavior (e.g., reaction pattern and sensitivity to a traffic disturbance) will change traffic properties in ways that can undermine traffic flow throughput. This paper first decomposes the mechanism into two elements – one driven by acceleration and one by time-varying CF response to disturbances – and then investigates their compounded effect. This paper also provides unifying frameworks to analyze the behavior of RVs and CAVs to facilitate analytical investigations. The results reveal how heterogeneous acceleration and CF behavior may create persistent voids and diminish traffic throughput. Integrating all the elements, throughput reduction is quantified via numerical simulations. [ABSTRACT FROM AUTHOR]

• Analysis of the value of relative flow data in model-based estimation. • Principles to learn link traffic flow model parameters using relative flow data. • Principles to estimate the traffic state using relative flow data. • An explanation on the value of collecting the data with moving observers. • Case study in VISSIM relaxing the theoretical assumptions to test the principles. Traffic flow can be described using three dimensions, i.e., space x , time t and cumulative flow N. This study considers estimating the cumulative flow over space and time, i.e., N (x , t) , using relative flow data collected by stationary and moving observers. Stationary observers, e.g., loop-detectors, can observe flow at fixed position over time. Furthermore, automated or other equipped and connected vehicles can serve as moving observers that observe flow relative to their position over time. To present the value of relative flow data, in this paper, we take the perspective of a model-based estimation approach. In this approach, the data is used in two processes: (1) information assimilation of real-time data and models and (2) learning of the models used in information assimilation based on historical data. This paper focuses on traffic state estimation on links. However, we explain that, in absence of stationary observer that are positioned at the link boundaries, it is valuable to consider the information propagation over nodes. Throughout this study a LWR-model with a triangular fundamental diagram (FD) is used to develop the principles that can be used for the two processes. These principles are tested in a simulation (VISSIM) study. This study shows that we can find the traffic flow model parameters and can partially estimate the link boundary conditions based on relative flow data collected by moving observers alone. It also shows that the traffic flow behavior differs partially from the LWR-model with triangular FD, and therefore, we recommend the option to learn and use other traffic flow models in future research. Overall, relative flow data is considered valuable to obtain model learning datasets and to estimate the traffic state. [ABSTRACT FROM AUTHOR]

• Introduce the spatial-temporal distribution learning method for data cognition. • Design a new type of rebalancing scheme with dynamic and static parts respectively. • Introduce a new flow-type window division which is better than time-type window. • Give the threshold of rebalancing start-up through robustness of complex network. • Divide effective management areas for large-scale static rebalancing. With the growing importance of bike-sharing systems, this paper designs a new framework to solve rebalancing problem. It contains two aspects: dynamic rebalancing within each station and static rebalancing among stations. Firstly, we give a new flow-type task window (F-window) by defining the consistency index of travelers. It is more suitable as a task window for rebalancing than time-type task window (T-window) based on three aspects analysis. Through three assumptions, the temporal-distribution learning model including task window and station storage configuration, are built to realize new dynamic rebalancing. The spatial-distribution learning method is introduced to divide management areas for static rebalancing. The empirical results show that F-window can better match the strong time-sensitive of demand fluctuation. Compared with traditional rebalancing needs hours, each rebalancing within a station can be completed within average 4 min. By setting the station storage configuration, it makes rebalancing in this paper meets the demand of 28.3 times the hourly rebalancing within one week. And the number of vehicles visiting stations has dropped below 20%. [ABSTRACT FROM AUTHOR]

• Deployment of roadside units to close connectivity gap in mixed traffic with CAVs. • Roadside units provide the beyond-line-of-sight motion information to CAVs. • Capturing the benefit of roadside units on network performance for strategic planning. • A deployment model developed to provide a conservative estimate of the benefit. • Cost-and-benefit analysis unveils the potential of such an infrastructure-based solution. In the foreseeable future, the traffic stream will be likely mixed with connected automated vehicles (CAVs) and regular vehicles (RVs). In the mixed traffic environment, when following a RV, due to the lack of vehicle-to-vehicle communications, it may take longer time for a CAV to sense and react than a human driver, which results in longer time headway and the loss of highway throughput. To address such a connectivity gap, this paper investigates an infrastructure-based solution, i.e., the deployment of roadside units to help CAVs in the heterogeneous traffic stream. Specifically, it is envisioned that these roadside units can sense vehicles in their coverage areas and provide the beyond-line-of-sight motion information to CAVs to empower them to react proactively, as they would do when following other CAVs. This paper is devoted to the analysis of the impacts of this type of roadside units at a strategic planning stage. In doing so, we first derive an analytical link performance function to capture their impact on the link capacity and travel time, and then develop a network equilibrium model to gauge their effect on travelers' route choices and thus the flow distribution of both RVs and CAVs across the whole network. This modeling development will allow us to conduct a cost-benefit analysis for a given deployment plan of roadside units. For fair analyses, we further develop an optimization model to determine the optimal deployment plan for a given budget, while focusing on the worst case of its impact, because the flow distribution resulting from our network equilibrium model is not unique. Such a model provides a conservative estimate of the benefit brought by roadside units. Lastly, we offer case studies to demonstrate the models and unveil the potential of such an infrastructure-based solution. [ABSTRACT FROM AUTHOR]

• A generic meshfree SPH framework is proposed for macroscopic crowd simulation. • This framework is applied to solve a specific macroscopic pedestrian flow model. • The SPH method is numerically accurate; a sensitivity analysis elicits its mathematical insight. • A comparison study demonstrates potential advantages of SPH over grid-based methods. • The method can reproduce key phenomena observed in pedestrian flows. Macroscopic pedestrian models are theoretically simpler than microscopic models, and they can potentially be solved faster while producing reasonable predictions of crowd dynamics. Therefore, they can be very useful for applications such as large-scale simulation, real-time state estimation and crowd management. However, the numerical methods presently used to solve macroscopic pedestrian models, which are mostly grid-based, have some shortcomings that limit their applicability. More specifically, they usually include complex procedures for grid generation and remeshing, and they produce simulation results that may not be sufficiently accurate (for example, because of unclear boundaries between flow states). Smoothed Particle Hydrodynamics (SPH) constitutes an alternative numerical method that could potentially overcome these limitations. SPH is a meshfree method where a crowd is represented by a set of particles that possess material properties and move according to macroscopic laws. Relevant state variables at each particle are approximated using information about the material properties of the neighboring particles and a smoothing function. This paper puts forward for the first time a generic SPH framework for solving macroscopic pedestrian models; in addition, it demonstrates that an SPH-based simulation model can produce meaningful and accurate results by means of three case studies. The first case study shows that the proposed numerical method can approximate well the analytical solution of a simple macroscopic model applied to a queue-discharge scenario. The second case study demonstrates that the proposed numerical method can potentially reproduce density dispersion (a phenomenon observed in real crowds) more accurately than grid-based methods, due to its meshfree, Lagrangian, and particle-based nature. The third case study highlights the need to reformulate the acceleration equation of the basic macroscopic model in order to reproduce lane formation in bi-directional flows (also an observed phenomenon) using the proposed SPH framework, and this paper presents a solution to do so. [ABSTRACT FROM AUTHOR]

• Prefer longer route with shorter distance within the ECA; sailing speed within the ECA is lower. • The larger difference the fuel price within and outside the ECA, the more savings the model can achieve. • A set of Pareto optimal solutions is provided for the decision makers. • Provide suggestions for the redesign of ECA. Pollutants such as nitrogen oxides ( NO x ), sulfur dioxide ( SO 2 ), and particulate matters (PM) generated by shipping industry are increasing in recent years. In order to control the ship emission pollution, the International Maritime Organization (IMO) has established the Emission Control Areas (ECAs). In the fierce competition of the shipping market, liner shipping companies are looking for strategies to maintain their core competencies under the emission control policy. To achieve this goal, this paper first proposes a bi-objective mixed integer linear programming model, aiming to optimize sailing routes and speeds within and outside the ECA while minimizing the total fuel cost and SO 2 emissions. Then, a new algorithm is developed to solve the proposed model by combining the two-stage iterative algorithm and fuzzy logic method based on ∊ -constraint. Finally, this paper compares and analyzes the navigation plan of a real sailing route considering and not considering the effects of ECA. Some experiments are conducted to analyze the effects of fuel cost, decision makers, and ECA boundaries on the total fuel cost and SO 2 emissions. The results indicate that the proposed model and algorithm can contribute to save fuel cost and reduce SO 2 emissions under the ECA policy and provide different Pareto optimal solutions. Thus, the effectiveness of the model and the efficiency of the algorithm are validated. [ABSTRACT FROM AUTHOR]

• We propose a hierarchical ramp metering approach that is based on the freeway MFD. • The effect of heterogeneity and capacity drop on freeway MFD are investigated. • The presented control approach is tested using different models as the plant. • The presented control approach is compared with other optimal control approaches. This paper develops a model-based hierarchical control method for coordinated ramp metering on freeway networks with multiple bottlenecks and on- and off-ramps. The controller consists of two levels where at the upper level, a Model Predictive Control (MPC) approach is developed to optimize total network travel time by manipulating total inflow from on-ramps to the freeway network. The lower level controller distributes the optimal total inflows to each on-ramp of the freeway based on local traffic state feedback. The control method is based on a parsimonious aggregated traffic model that relates the freeway total outflow to the number of vehicles on the freeway sections. Studies on aggregated traffic modeling of networks have shown the existence of a well-defined and low-scatter Macroscopic Fundamental Diagram (MFD) for urban networks. The MFD links network aggregated flow and density (accumulation). However, the MFD of freeway networks typically exhibits high scatter and hysteresis loops that challenge the control performance of MFD-based controllers for freeways. This paper addresses these challenges by modelling the effect of density heterogeneity along the freeway and capacity drop on characteristics of freeway MFD using field traffic data. In addition, we introduce a model to predict the evolution of density heterogeneity that is essential to reproduce the dynamics of freeway MFD accurately. The proposed model is integrated as the prediction model of the MPC in the hierarchical control method. The proposed coordinated ramp metering method shows desirable performance to reduce the vehicles total time spent and eliminate congestion. The control approach is compared with other coordinated ramp metering controllers based on the MPC framework with different traffic prediction models (e.g. CTM and METANET). The outcomes of numerical experiments highlight that the MFD-based hierarchical controller (i) is better able to overcome the modeling mismatch between the prediction model and the plant (process model) in the MPC framework and (ii) requires less computation effort than other nonlinear controllers. [ABSTRACT FROM AUTHOR]

• New departure synchronization mechanism in a Multi-Airport system to reduce the probability of separation minima infringement. • Airspace digitalization for efficient potential concurrence event detection and mitigation. • New interdependency metrics for reducing downstream domino effects in ATM. • Rolling horizon methodology for detecting concurrence events, analysing their trajectory interdependencies and applying a strategic mitigation measure that shifts the Estimated Take-Off Time (ETOT) within the assigned Calculated Take-Off Time (CTOT) window. • Use Constraint Programming models supporting optimal mitigation measures. • Experimental work showing the potential benefits of such a STAM procedures by applying the methodology to distribute the demand of a sector located in the London Terminal Manoeuvring Area (LTMA). Pre-tactically managing the imbalances of the network and organizing the resources of the airspace is one of the main objectives of Dynamic Demand & Capacity Balancing (dDCB). Introducing Short-term Air Traffic Flow and Capacity Management (STAM) measures such as minor ground delays or slight speed adjustments applied to a selected number of flights on the day of operations, supports the dDCB process by reducing the traffic complexity and the probability of Air Traffic Controllers interventions. This paper introduces a rolling horizon methodology for detecting concurrence events, analysing their trajectory interdependencies and applying a strategic mitigation measure that shifts the Estimated Take-Off Time (ETOT) within the assigned Calculated Take-Off Time (CTOT) window. The methodology proposes a collaborative decision-making process for a better coordination of departures from airports feeding airspace volumes with high air traffic demand. The experimental part of this paper aims at showing the potential benefits of such a STAM procedure by applying the methodology to distribute the demand of a sector located in the London Terminal Manoeuvring Area (LTMA). Experiments have considered regulated traffic scenarios where ETOT were allocated in order to reduce departure time uncertainties. Lastly, it is analysed the impact of the parameters used by the methodology to calculate the mitigation measures: safe separation criteria, look-ahead time and number of coordinated airports. [ABSTRACT FROM AUTHOR]

• Evaluation of model for collision risk posed by UAS in the airport airspace. • 2D UAS dynamics modeled with kinematic equation for use in Monte-Carlo. • Alert Zone created from collision probability contour with worst-case intruder. • Alert Zone informs tower controller of the potential risk posed by an intruder. • Prediction with sensor input showed better results with more granular data. • Prediction failure (potential drone strike) rate similar to current bird-strike rate. Recent years have seen an increase in reported unmanned aerial systems (UAS) incursion into terminal airspace, likely due to the ease of access to recreational UAS to the general public with minimal understanding of aviation regulations. Such incursions often lead to extensive airport shutdowns due to safety concern and could cause a cascading disruption to airline operations throughout the region. A better assessment tool for the collision risk between the existing air traffic and such non-cooperative intruder could help reduce unnecessary disruption while maintaining safety to air traffic operations. While advancements have been made in the past decade in detection and avoidance of UAS traffics, the systems depend on either having fully cooperative traffic or sensor capability beyond what is currently available. This paper seeks to assess the collision risk posed by an intruding UAS within the airport-restricted "terminal airspace," in the Singapore context, with minimal information on the UAS. This was done through probabilistic conflict prediction with Monte-Carlo simulations under the assumption of a worst-case scenario involving a non-cooperative intruder traveling directly towards the flight corridor. Alert Zones within the existing 5 km terminal airspace could be constructed using the collision prediction models to help air traffic controllers quickly identifying UAS that poses a threat under most circumstances, with the information passed on to the pilot for mitigation actions. Simulations were conducted for a number of conflict-pairs to investigate how the resulting Alert Zones differ. Finally, this paper also investigates how the incorporation of UAS tracking information, under the current reliability level, could be used to augment the collision prediction algorithm and its effect on the management of collision risks in terminal airspace. The result suggested that the method that utilizes the most available information for UAS path modeling could be used with the currently available sensors to produce acceptable collision prediction results. [ABSTRACT FROM AUTHOR]

• Launch a new avenue to capture the effects of covariance between OD flows. • Propose a more generalized model for optimizing traffic count location problem. • Examine and discuss the mathematical properties of the proposed model. • Update stochastic link choice proportions using an adapted traffic flow simulator. Vehicular traffic between different Origin-Destination (OD) pairs for a typical hourly period may statistically correlate with each other. The covariance mainly generated from the daily variation of travel patterns, network topology, and trip chaining activities of household members can be particularly high during the morning peak hour. With the increasing attention on the OD demand variance and covariance in stochastic road networks, a new criterion is proposed in this paper for measuring the estimation accuracy of OD demand covariance. The mathematical properties of this proposed criterion are analyzed to better understand the relationship between the estimation errors of mean and covariance of OD demands. This paper aims to investigate how to optimize the traffic count locations for minimizing the weighted maximum deviation of estimated mean and covariance of OD demands from the "true" values. To consider the effects of stochastic OD demands on the traffic count location problem in the proposed model, link choice proportions are regarded as stochastic variables and updated by an adapted traffic flow simulator in this study. Both the weighted-sum approach and bi-objective approach are examined with the adaption of the firefly algorithm (FA) to solve the single-objective and bi-objective problems. Numerical examples are presented to demonstrate the effects, with and without considering the covariance of the OD demands for the optimization of traffic count locations. [ABSTRACT FROM AUTHOR]

• This paper developed a Bayesian traffic Model to analyze stochastic traffic dynamics. • The Dirichlet-based updating process provided a sound behavior modeling alternative. • The model will converge to statistical User Equilibrium with an infinity long memory. • The model quantifies contribution of different sources to overall traffic variations. The rapid growth of transportation data offers new opportunities to analyze the interaction between travel behavior and transportation system performance, and particularly when issues such as uncertainty and reliability are considered. Many previous studies in this area described the stationary behavior of stochastic transportation systems using user equilibrium (UE) conditions. In contrast, this paper develops a generalized Bayesian model to analyze the dynamic behavior of stochastic transportation systems. In the proposed model, the variability of link volume and travel time stems from the stochasticity in travel demand, transportation supply (e.g. link capacity, free flow travel time, etc.) and route choice. To the best of our knowledge, this is among the first work that considers the three sources of stochasticity simultaneously. In addition, we propose a Bayesian updating approach based on the Dirichlet model to describe the route choice behavior. This approach allows researchers to consider a wide range of route choice behavior of bounded rationality in day-to-day traffic dynamics, including a knowledge updating mechanism based on different memory lengths and weighting factors. This framework is particularly suitable for data-driven studies supported by emerging transportation data due to the computing efficiency of the Dirichlet-based Bayesian updating mechanism and the sound behavioral foundation. This paper shows that the proposed Bayesian model with infinite memory leads to UE conditions under stochastic demand and supply. Subsequently, a numerical case study is conducted to illustrate different day-to-day route choice dynamics with different memory lengths of system performance. This paper also discusses the influence of the three sources of stochasticity towards the aggregated variance of link volumes and travel time. With enough longitudinal travel choice and transportation system performance data, the proposed Bayesian framework could be empirically calibrated and tested, which offers an attractive descriptive alternative to the conventional UE-based transportation system models. [ABSTRACT FROM AUTHOR]

• A framework for modeling the effect of perceived reliability on mode choice. • Cumulative Prospect Theory-based model using observational data. • Estimation of the value and the weighting function parameters from observed data. • Discussion on the extensions of the model to other choice dimensions. This paper utilizes the well-known Cumulative Prospect Theory (CPT) to study how travel mode choice is affected by travel time reliability. Prospect Theory (PT) was originally developed to model decision making under risk, and later extended to CPT, which is also applicable to decision making under uncertainty. Travel time reliability is related to uncertainties in travel time, and its effect on travel behavior can be properly modeled using CPT. PT and CPT applications in transportation are mainly limited to experimental studies. This paper introduces a CPT-based framework for mode choice modeling using observational data. The framework can help travel demand modelers utilize CPT for modeling reliability in their trip-based or activity-based models, which are typically estimated from observational data (household travel surveys). To showcase model applications, the model parameters are estimated using a combination of revealed preference household travel survey data and empirically observed travel time data for a mode choice scenario in the Washington, D.C. area. We estimated the parameters of the utility function, the parameters of the CPT value function, and the parameters of the CPT weighting function and discussed how they imply diminishing sensitivity, risk-aversion in small probability losses, and risk-seeking in high-probability losses. The paper is focused on mode choice, but its extension to other choice dimensions is discussed. [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]

• Users are risk-seeking and willing to participate in lotteries, when stakes are low. • Different from tolls or credits, raffles are based on voluntary participation. • This paper examines the effectiveness of raffles for travel demand management. • Utility-based user equilibrium incorporates users' risks and uncertainties. • Flat Raffles outperform one-step tolls when average users are slightly risk-seeking. Raffles have been widely used in commercial markets to influence consumers' behaviors. Instead of charging users a fee or issuing a credit to users as incentives, the raffle-based scheme incentivizes users to change their choices by giving out credits that are used to enter a lottery. Comparing to tolls or credits, raffles may be less controversial since it is based on voluntary participation. The promises of a raffle-based scheme is that users are more risk-seeking, thus willing to participate in the lottery, when stakes are low. This would particularly fit the traffic management where the cost for a user to change his/her choices may be relatively insignificant comparing to the reward of a raffle. This paper examines the effect of raffle or lotteries for travel demand management, particularly in the morning commute. We establish the utility-based user equilibrium by incorporating risks and uncertainties in users' choices. A class of Flat Raffle schemes is proposed where a constant lottery winning chance is set for a period of time during the morning commute. We show that a marginal user who is indifferent from participating the lottery or not would exist for arbitrary utility functions. Furthermore, we analytically derive and solve for the equilibrium using exponential utility functions, and subsequently the optimal travel profiles under four potential system management objectives. We find that minimizing the total generalized travel cost (TTC) seems a reasonable goal for an optimal Flat Raffle possibly resulting in approximately minimum commuting duration and minimum total queuing delay at the same time. Under the minimum TTC, there is no queuing delay for the user who is indifferent from participating the lottery or not. Most interestingly, a Flat Raffle scheme works the best and outperforms the optimal one-step toll, when the population is overall slightly risk-seeking. This is particularly appealing since the raffle reward is relatively significant comparing with a not-so-significant cost increase associated with users' behavioral change. [ABSTRACT FROM AUTHOR]

• This paper presents a data-driven hybrid control (DDHC) framework. • DDHC framework composed of data-driven control module, performance module, and optimization module. • Control strategies including acceleration strategy and deceleration strategy. • The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. This paper presents a data-driven hybrid control (DDHC) framework that can arrange adaptive control strategies for vehicles to effectively improve the transit performance of the public transport system. The framework depicts a powerful combination of a data-driven control method that is used to imitate the control behaviour of dispatchers and a mathematical optimization method. Three components comprise the DDHC framework: a data-driven control module, a performance module, and an optimization module. The data-driven control module contains a random forest model which is adopted to justify whether to intervene in the operation of a bus line, and if so, which vehicles should be controlled and what type of control strategy should be taken – an acceleration strategy or deceleration strategy. The performance module including vehicle operation state models is used to describe the system evolution. The last component optimizes the specific control actions – which type of acceleration or deceleration strategy should be adopted – by minimizing total passenger travel time. The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. The results show that the DDHC framework with reasonable parameters can suit the needs of real-time control in complex traffic environments. [ABSTRACT FROM AUTHOR]