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High maneuverability of motorized two-wheelers amidst vehicles of bigger size and different dynamics invalidates FIFO to traverse through the gaps between other vehicles for faster mobility. The failure of existing dynamic traffic assignment frameworks with multi-class conditions to capture this behaviour results in inaccurate routing. The study proposes a simulation based two-class dynamic traffic assignment framework comprising of two-wheeler specific behaviour. These features when incorporated in the framework will add to the utility of the traditional dynamic traffic assignment framework in travel time prediction and planning level applications and is therefore relevant to regions with significant share of two-wheelers. The study gives a clear view of the effect of two-wheeler specific features on the route choice behaviour based on the dynamic travel time. The results of the study shows that there occurs an unintentional separation of vehicle classes during congestion; this effect can be utilized for a two-wheeler specific policy implication for congestion management in cities. The proposed framework can be employed in identifying the optimal provision of exclusive two-wheeler lanes. It is also observed that the provision of exclusive lanes may sometimes be counterproductive. • Proposed a DTA framework that accounts for two-wheeler specific features in disordered traffic. • Bridges the gap in existing studies that has not considered certain class specific phenomenon in multi-class DTA. • Recognition of influence of class specific behaviour in travel time and routing dynamics. • Obtained optimum number of exclusive two-wheeler lanes and its location based on routing behaviour in network. • A potential strategic planning and policy making tool for urban areas with significant share of two-wheelers. [ABSTRACT FROM AUTHOR]

This report presents a review of the studies incorporating the Macroscopic Fundamental Diagram (MFD) dynamics for emissions estimation using various microscopic estimation frameworks. These studies show the potential of applicability of the MFD-basedtools for emissions estimation. However, the accuracy of existing models in estimating the emissions of large-scale urban networks is questionable due to their inability in capturing the variations in traffic conditions across such networks. As a solution to this problem, we have proposed to develop a multi-reservoir emissions estimation framework by partitioning large-scale networks into smaller regions with homogeneous traffic conditions and low-scatter MFDs like the multi-reservoir Dynamic TrafficAssignment (DTA) models, which can result in more accurate network-wide emissions estimation. The key component of this framework is finding a method to accurately estimate the emissions using aggregated network representation and its corresponding variables. A numerical experiment on an arbitrary network shows that the estimation efficiency can increase significantly by implementing aggregated network representation, albeit the results will be less accurate the more aggregated the representation becomes. The possible reasons and considerations for future applications have been discussed, which would lead to developing calibrated aggregated-level methods, which can estimate the emissions efficiently and accurately. After calibrating the MFD-based emissions estimation method to acceptable levels of accuracy and efficiency, traffic control strategies can be proposed to optimize the energy consumption and emissions of CO, CO2, NOx, PM2.5, CH4, VOC, etc. The proposed control strategies can include perimeter control strategies in the boundaries of the regions, ramp-metering strategies at the connections to the freeways and signal timing strategies, which is known to influence the shape of the MFD. View the NCST Project Webpage

Rapid increases in Saudi Arabia’s vehicular traffic are challenging its urbanized road networks' safe and efficient operation. Facing both physical and economic constraints, transportation planning stakeholders in the country are seeking alternative methods for improving traffic operations and safety systemwide that do not rely upon infrastructure expansion. Using a sector of the Dammam metropolitan area in Saudi Arabia as the study area, we proposed and evaluated a comprehensive, integrated approach to improve both traffic system operations and safety in this urbanized road network. A combined route choice and control modeling framework was used to evaluate the potential systemwide benefits of concurrent driver information regarding the best available route and traffic signal control enhancements to facilitate operational improvements. Benefits were quantified as improvements in total systemwide travel times and reduction in total systemwide delays. To evaluate associated road safety improvements, a comprehensive road safety audit (RSA) was performed in the study area and Highway Safety Manual crash prediction methods were applied to estimate the potential reduction in crashes. Results indicated that concurrent driver information and an upgrade to vehicle-actuated traffic signal controls could improve total systemwide travel time by 22% and reduce the total systemwide delay by over 50%. If appropriate remediation measures are taken to address issues identified during the road safety audit, crashes could be reduced by up to 50 % per year at selected intersections. The study results infer that road safety improvements should be an integral part of any broader traffic systems improvement process and that the concurrent consideration of both traffic system operations and safety could provide the best return on investments. Further research could identify additional variables for consideration in the proposed comprehensive, integrated traffic systems improvement process and evaluate the transferability of this approach using a larger road network than the one considered in this study.

We consider flows over time within the deterministic queueing model of Vickrey and study the solution concept of instantaneous dynamic equilibrium (IDE), in which flow particles select at every decision point a currently shortest path. The length of such a path is measured by the physical travel time plus the time spent in queues. Although IDE has been studied since the eighties, the worst-case efficiency of the solution concept with respect to the travel time is not well understood. We study the price of anarchy for this model under the makespan and total travel time objective and show the first known upper bound for both measures of order O (U · τ) for single-sink instances, where U denotes the total inflow volume and τ denotes the sum of edge travel times. We complement this upper bound with a family of quite complex instances proving a lower bound of order Ω (U · log τ). Funding: The research of the authors was funded by the Deutsche Forschungsgemeinschaft [Grants HA 8041/1-1 and HA 8041/4-1]. [ABSTRACT FROM AUTHOR]

Mathematical approaches for modeling dynamic traffic can be roughly divided into two categories: discrete packet routing models and continuous flow over time models. Despite very vital research activities on models in both categories, their connection was poorly understood so far. We build this connection by specifying a (competitive) packet routing model, which is discrete in terms of flow and time, and proving its convergence to the intensively studied model of flows over time with deterministic queuing. More precisely, we prove that the limit of the convergence process when decreasing the packet size and time step length in the packet routing model constitutes a flow over time with multiple commodities. In addition, we show that the convergence result implies the existence of approximate equilibria in the competitive version of the packet routing model. This is of significant interest as exact pure Nash equilibria cannot be guaranteed in the multicommodity setting. Funding: This work was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany's Excellence Strategy—The Berlin Mathematics Research Center MATH+ [Grant EXC-2046/1, project ID: 390685689] and the Research Training Group [Grant 2236 UnRAVeL]. [ABSTRACT FROM AUTHOR]

We formulate the Multi-Constrained Dynamic Traffic Assignment (DTA) problem as an instance of the nonlinear composite problem. To solve the problem, this paper introduces then the penalized nonlinear alternating direction method of multipliers (ADMM), a numerical algorithm that combines the nonlinear ADMM algorithm with the external penalty method. Numerical results are then presented, analyzed and compared against those obtained by applying the Reformulation-Linearization Technique (RLT)-based convex relaxation method together with piecewise linear approximation of the objective function. [ABSTRACT FROM AUTHOR]

Traffic and transport researchers, policymakers, and vehicle manufacturers are interested in investigating the implications and the influence of autonomous vehicles (AV) because a gradual deployment of such new technologies is expected to take place in the future. This research investigates the impacts of the AVs emergence on traffic performance for the city of Budapest in three future traffic scenarios with different AV market replacement rates for the year 2030. The network was modeled using simulation-based dynamic traffic assignment in PTV Visum software. Four traffic performance parameters were analyzed to explore the impacts of AV's emergence on the network. The results showed noticeable improvements among the four investigated traffic performance parameters. [ABSTRACT FROM AUTHOR]

This dissertation aims to study effective and efficient ways for both travelers and transportation authorities to consider the actions of the other side when they make their corresponding travel or management decisions, such that certain common goals, such as mitigating congestion, reducing cost in travel expenses and improving the overall reliability of the transportation system can be achieved.A novel dynamic traffic routing (DTR) with an adaptive signal control framework is developed to utilize the fast developing wireless communication technologies that makes V2X (Vehicle To Everything) possible. The hyper-path based dynamic traffic routing method takes stochasticity of link travel time into consideration, which ensures robust and reliable routing decisions. In addition, online travel time updating is incorporated into the DTR model. The online updating presented in this dissertation uses both historical information (a priori knowledge) and new information, thanks to the V2X system, to form a posteriori knowledge about the link travel time. Various distributed traffic signal control methods are proposed and tested concurrently with the DTR model to cope with the different levels of the traffic demand. Simulation models are built to test and compare the models developed in this dissertation against the traffic routing methods and traffic signal control models in the literature. In the extensive simulation tests, the author discovers that enabling vehicle re-routing in the network can reduce the average travel time as well as reduce the average queue length at the intersections.

• A specific "cycle-averaged" interpretation of "continuous signal modeling" for time-aggregated of traffic signal boundary conditions and turn movements is presented, which complies with the logic behind it. • Regarding the above-mentioned specific interpretation requirements of a continuous signalized node model are presented and mathematically formulated, which define the continuous signalized (or COS) node model. • A convergent algorithm that solves COS numerically is presented. • Via a series of simple numerical cases it is showed that COS is the only continuous signalized node model that respects all the relevant boundary conditions. In macroscopic dynamic network loading, the role of the node model is to determine transfer volumes between each in- and outgoing link, while respecting in a consistent way all forward and backward-moving traffic waves (or boundary conditions) and desired turning fractions. In addition, capacity constraints on the node itself need to be respected; they result from priority rules on conflict points, or from traffic signal control. The most common way in literature to implement signalized node models is to track second by second which turns have green or are blocked by red. This imposes a fine – and thus computationally demanding – time discretization step in the numerical solution algorithm of the entire dynamic network loading, often unnecessary or even undesired for the DNL application. As an alternative, this paper proposes a continuous approach that finds the average turn (or transfer) volume over a full cycle under the prevailing link boundary conditions and traffic signal timing. This average 'continuous' volume can then be applied to any (fine or coarse) time discretization. To this end, it discusses in depth and proposes mathematical formulations for all boundary and consistency requirements that a traffic signal imposes, and the intricate interactions between stage timing and arrival patterns of vehicles and outflow opportunities (gaps). This COntinuous Signalized or COS node model and its iterative solution algorithm combine all these boundaries and interactions in a theoretically consistent way. In a case study, we show that COS is the only existing, theoretically consistent continuous signalized node model, as alternative models sometimes fail to produce holding-free solutions, and/or violate boundary conditions imposed by the connecting links (especially in more complicated combinations of boundary conditions involving congestion spillback). [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles (AVs) is likely to introduce new mobility experiences for travelers. In particular, AVs would allow travelers to get off at the destinations and then drive themselves elsewhere to park rather than cruise for parking or park at a location with a high parking fee. The self-parking capability is likely to increase the utility of private-owned AVs (PAVs) and make this mobility option more attractive than human-driven vehicles. The present study investigates the dynamics of travelers shifting to PAVs from other transport modes and its negative impact on road traffic congestion. To this end, we propose an agent-based demand model which considers different travel cost components depending on crucial travel attributes such as trip purpose and activity duration. The estimated demand is then fed into a mesoscopic traffic simulation model to examine the resulting road traffic conditions. As charging private vehicles for the congestion they cause is an effective tool for demand management and congestion alleviation, we also integrate a distance-based pricing scheme into the overall modeling framework to investigate its impact on mode choice and transport network performance. A case study is conducted in Melbourne, Australia to demonstrate the proposed methodology. The results indicate that the distance-based pricing scheme can effectively limit the usage of PAVs and reduce traffic congestion, especially in the city center and peripheral suburbs. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field. [ABSTRACT FROM AUTHOR]

We study a dynamic traffic assignment model, where agents base their instantaneous routing decisions on real-time delay predictions. We formulate a mathematically concise model and define dynamic prediction equilibrium (DPE) in which no agent can at any point during their journey improve their predicted travel time by switching to a different route. We demonstrate the versatility of our framework by showing that it subsumes the well-known full information and instantaneous information models, in addition to admitting further realistic predictors as special cases. We then proceed to derive properties of the predictors that ensure a dynamic prediction equilibrium exists. Additionally, we define "-approximate DPE wherein no agent can improve their predicted travel time by more than " and provide further conditions of the predictors under which such an approximate equilibrium can be computed. Finally, we complement our theoretical analysis by an experimental study, in which we systematically compare the induced average travel times of different predictors, including two machine-learning based models trained on data gained from previously computed approximate equilibrium ows, both on synthetic and real world road networks. [ABSTRACT FROM AUTHOR]

Analysing the properties of a network equilibrium can help to have a better view about network state, robustness, and the effect of any variation in the network. This study investigated the impacts of network design history on day-to-day multimodal user equilibrium. In particular, we analyze the long-term evolution of the network, including opening new multimodal options and its impacts on the final network equilibrium. First, the analysis focuses on static network loading with different successive configurations. Then, a more realistic setting is studied by simulation. A large-scale multimodal network with the flexible opening over time of three possible transport facilities shows that the final equilibrium is not unique; more importantly, significant differences can be observed in public transportation occupancy, while user equilibrium is enforced in all situations. Some solutions prove to be better from the collective viewpoint (shorter total travel time), thus giving new insight into public transport planning. [ABSTRACT FROM AUTHOR]

The aim of this work is to increase the precision of the solution of dynamic user equilibrium assuming that the computation takes a reasonable time so that the solution is useful in practice. The proposed method replaces the classical grid-based solution by a near continuous-time solution based on piecewise linear/constant functions that removes a lot of disadvantage of discretization. The testing shows that the precision of the solution can be easily driven by a few approximation parameters that can be changed during computation. Using the proposed method, the near continuous time solution of the dynamic user equilibrium for a real size network can be computed in reasonable computation time. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a severe problem, af-fecting travellers both mentally and economically. To al-leviate traffic congestion, this paper proposes a method using a concept of future time windows to estimate the future state of the road network for navigation. Through our method, we can estimate the travel time not only based on the current traffic state, but the state that ve-hicles will arrive in the future. To test our method, we conduct experiments based on Simulation of Urban MO-bility (SUMO). The experimental results show that the proposed method can significantly reduce the overall travel time of all vehicles, compared to the benchmark Dijkstra algorithm. We also compared our method to the Dynamic User Equilibrium (DUE) provided by SUMO. The experimental results show that the performance of our method is a little better than the DUE. In practice, the proposed method takes less time for computation and is insensitive to low driver compliance: with as low as 40% compliance rate, our method can significantly im-prove the efficiency of the unsignalised road network. We also verify the effectiveness of our method in a signalised road network. It also demonstrates that our method can assign traffic efficiently.

Evacuating the population during crises to safe zones via optimal paths is vital. The evacuation planning process makes two main decisions: which shelter to reach and which path to take towards the chosen shelter. These decisions correspond to shelter allocation and traffic assignment problems, respectively. Many studies tackled these problems with a static formulation in the literature, while only a few considered a dynamic context. We conduct a comprehensive literature review and highlight that most studies independently solve these two problems while both are correlated with traffic conditions. To fill this gap, we propose a new framework to couple the shelter allocation problem (SAP) and the dynamic traffic assignment (DTA) problem and solve them. To capture traffic dynamics, we use a dynamic agent-based simulator. We assume the system determines the evacuees’ shelters to minimize the total evacuation time. However, each evacuee’s concern is reaching a shelter as fast as possible. Therefore, we formulate the DTA problem under stochastic user equilibrium (SUE) principles, i.e., every evacuee aims to minimize his own perceived travel time. We apply the proposed methodology to the network of Luxembourg City and compare its performance with other advanced methods that solve SAP and DTA separately. The comparison shows that solving the dynamic shelter allocation improves the mean evacuation time and significantly decreases the network clearance time compared to other methods with a fixed plan for SAP. The simulation results prove that considering the network state in the SAP can provide a more effective evacuation plan. Moreover, we perform a sensitivity analysis on optimization parameters and evaluate the computation cost of our methodology.

To realize the optimal operation of urban coupled transportation power systems underthe road, charging facilities, and transmission line congestions, a dynamic optimal traffic power flow (DOTPF) model was formulated under congestions. Based on the time space network (TSN) approach, a novel TSN with queues was proposed, considering the moving, parking, charging, and queueing state transitions. A vehicle routing problem was formulated for electric vehicles (EVs) and further incorporated into the dynamic traffic assignment problem (DTAP), reducing the traffic demand losses. With security and reserve constraints, a dynamic security-constrained carbon dioxide-oriented optimal power flow (OPF) problem was formulated to reduce the carbon emission and generation cost, by optimizing the scheduling of thermal units and energy storage systems. A multi-objective DOTPF problem was formulated, and further reformulated into a convex mixed-integer quadratic programming problem. The effectiveness of the proposed DOTPF was verified based on the simulation results on coupled IEEE-30 and Sioux Falls system. [ABSTRACT FROM AUTHOR]

Bridge infrastructures are critical nodes in a transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is essential to identify, retrofit, reconstruct, or, if necessary, demolish the infrastructure systems based on optimal decision-making processes. As one of the crucial components of the transportation network, any bridge failure would impede the post-earthquake rescue operation. Not only the failure of such high-risk critical components during an extreme event can lead to significant direct damages, but it also affects the transportation road network. The consequences of these secondary effects can easily lead to congestion and long queues if the performance of the transportation system before or after an event was not analyzed. These indirect losses can be more prominent compared to the actual damage to bridges. This paper brings about seismic performance assessment for the Cyprus transportation network from which the decision-making platform can be modeled and implemented. This study employs a seismic hazard analysis based on generated USGS ShakeMap scenarios for the risk assessment of the transportation network. Furthermore, identification of the resiliency and vulnerability of the transportation road network is carried out by utilizing the graph theory concept at the network level. Moreover, link performance measures, i.e., traffic modeling of the study region is simulated in a dynamic traffic assignment (DTA) simulation environment. Finally, for earthquake loss analysis of the bridges, the HAZUS loss estimation tool is used. The results of our investigations for three different earthquake scenarios have shown that seismic retrofitting of bridges is a cost-effective measure to reduce the structural and operational losses in the region. [ABSTRACT FROM AUTHOR]

This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model. The algorithm distributes SODTA into local sub-problems, who find optimal values for their decision variables within an intersection. Each sub-problem communicates with its immediate neighbors to reach a consensus on the values of common decision variables. A sub-problem receives proposed values for common decision variables from all adjacent sub-problems and incorporates them into its own offered values by weighted averaging and enforcing a gradient step to minimize its objective function. Then, the updated values are projected onto the feasible region of the sub-problems. The algorithm finds high quality solutions in all tested scenarios with a finite number of iterations. The algorithm is tested on a case study network under different demand levels and finds solutions with at most a 5% optimality gap. [ABSTRACT FROM AUTHOR]

The introduction of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) is projected to enhance network performance and accessibility. The future share distribution of AV and SAV is not yet apparent, nor is which of these two future transport modes will become dominant. Therefore, this research deploys a simulation-based dynamic traffic assignment using Visum software to investigate the impact of varying the share distribution of AVs and SAVs on Budapest's network performance and consumer surplus in three projected future traffic scenarios for the years 2030 and 2050 compared to the Base scenario for 2020. The three future scenarios are presented and characterized by different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of these future cars as follows: Mix-Traffic scenario for 2030, and AV-Focused and SAV-Focused scenarios for 2050. The results revealed that the emergence of AVs and SAVs would improve the overall network performance, and better performance was observed with increasing the share distribution of SAVs. Similarly, the consumer surplus increased in all future scenarios, especially with increasing the share distribution of AVs. Consequently, the advent of AVs and SAVs will improve traffic performance and increase consumer surplus, benefiting road users and authorities. [ABSTRACT FROM AUTHOR]

User equilibrium (UE) and system optimal (SO) are among the essential principles for solving the traffic assignment problem. Many studies have been performed on solving the UE and SO traffic assignment problem; however, the majority of them are either static (which can lead to inaccurate predictions due to long aggregation intervals) or analytical (which is computationally expensive for large-scale networks). Besides, most of the well-known micro/meso traffic simulators, do not provide a SO solution of the traffic assignment problem. To this end, this study proposes a new simulation-based dynamic system optimal (SB-DSO) traffic assignment algorithm for the SUMO simulator, which can be applied on large-scale networks. A new swapping/convergence algorithm, which is based on the logit route choice model, is presented in this study. This swapping algorithm is compared with the Method of Successive Average (MSA) which is very common in the literature. Also, a surrogate model of marginal travel time was implemented in the proposed algorithm, which was tested on real and abstract road networks (both on micro and meso scales). The results indicate that the proposed swapping algorithm has better performance than the classical swapping algorithms (e.g. MSA). Furthermore, a comparison was made between the proposed SB-DSO and the current simulation-based dynamic user equilibrium (SB-DUE) traffic assignment algorithm in SUMO. This proposed algorithm helps researchers to better understand the impacts of vehicles that may follow SO routines in future (e.g., Connected and Autonomous Vehicles (CAVs)).

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network. [ABSTRACT FROM AUTHOR]

It is becoming standard practice for many departments of transportation (DOTs) to use incentive/disincentive clauses (also known as road user costs) with contractors to stay on or ahead of schedule. These road user costs are clauses that DOTs use to calculate a monetary amount to encourage contractors to complete work prior to milestone dates and/or limit the time specified on the contract. The monetary amounts are typically vehicle operating costs and vehicle delay costs encumbered by highway users resulting from construction, maintenance, or rehabilitation activity. In this paper, we propose an innovative way of calculating these costs using varied values of time based on trip purpose and departure time. In addition, we use advanced pre-trip and en route traveler information to determine the influence it has on route choice. Several scenarios are modeled using an advanced, simulation-based dynamic traffic assignment model. The goal of this paper is to identify the governing factors that contribute to road use costs by determining different approaches to derive the value one places on a trip. The approach to this study is twofold: first several research methods were used to derive the value of time. Second, the use of advanced traveler information is introduced to determine if it plays a critical role in route choice. The proposed methodology shows differences in road user cost calculations. Which approach would be more receptive to a contractor while proposing roadway construction? A case study of a roadway construction project in El Paso, Texas, is used to compare different approaches to calculate road user costs.

• Scenario where vehicles adhere to or deviate from the system optimal route guidance. • Reinforcement learning approach to dynamic system optimal assignment problem. • Multi-agent reinforcement learning to assess the impact of non-compliance. • Exploration of the balance between system efficiency and individual user preferences. We consider a scenario where the transportation management center (TMC) guides future autonomous vehicles (AVs) toward optimal routes, aiming to bring the network in line with the system optimal (SO) principle. However, achieving this requires a joint decision-making process, while users may be non-compliant with the TMC's route guidance for personal gain. This paper models a future transportation network with a microscopic simulation, to introduce a novel concept of mixed equilibrium. In this framework, AVs follow the TMC's SO route guidance, while users can dynamically choose to either comply or manually override this autonomy based on their own judgment. We initially model a fully compliant scenario, where the centralized Q-network, analogous to a TMC, is trained using reinforcement learning (RL) to minimize total system travel time (TSTT), providing optimal routes to users. Subsequently, we extend the problem setting to a multi-agent reinforcement learning (MARL) scenario, where users can comply or deviate from the TMC's guidance based on their own decision-making. Through neural fictitious self-play (NFSP), we employ a modulating hyperparameter to investigate the impact of varying degrees of non-compliance on the overall system. Results indicate that our RL approach holds significant potential for addressing the dynamic system optimal assignment problem. Remarkably, the TMC's route guidance retains the essence of SO while integrating some level of non-compliance. However, we also demonstrate that dominant user-centric decision-making may lead to system inefficiencies while creating disparities among users. Our framework serves as an innovative tool in an AV-dominant future, offering a realistic perspective on network performance that aids in formulating effective traffic management strategies. [ABSTRACT FROM AUTHOR]

One of the actions usually conducted to limit exposure to a hazardous event is the evacuation of the area that is subject to the effects of the event itself. This involves modifications both to demand (a large number of users all want to move together) and to supply (the transport network may experience changes in capacity, unusable roads, etc.). In order to forecast the traffic evolution in a network during an evacuation, a natural choice is to adopt an approach based on Dynamic Traffic Assignment (DTA) models. However, such models typically give a deterministic prediction of future conditions, whereas evacuations are subject to considerable uncertainty. The aim of the present paper is to describe an evacuation approach for decision support during emergencies that directly predicts the time-evolution of the probability of evacuating users from an area, formulated within a discrete-time stochastic process modelling framework. The approach is applied to a small artificial case as well as a real-life network, where we estimate users’ probabilities to reach a desired safe destination and analyze time dependent risk factors in an evacuation scenario. [ABSTRACT FROM AUTHOR]

The effectiveness of user-oriented traffic routing applications to mitigate traffic congestion in Intelligent Transportation Systems depends on their degree of adoption, which usually evolves depending on subjective and exogenous factors. This paper proposes a user experience and social dynamics model to analyze and evaluate traffic routing methods, based on fuzzy rules and discrete choice theory. The model has been applied to the optimal Traffic-Weighted Multi-Maps (TWM) routing method to evaluate the adoption dynamics and analyze convergence towards the system optimum. Route unfairness and resistance to change are also considered in the model. Experimental results are obtained simulating the evolution of the drivers’ population behavior. Simulation is carried over synthetic and real networks, using optimized TWM maps. The experimental results show how the TWM system evolves to a stationary System Optimum, improving overall traffic congestion and showing how User Equilibrium variability is bounded as it depends on user routing choices influenced by behavioral patterns.

In this study, we analyse the convergence and stability of dynamic system optimal (DSO) traffic assignment with fixed departure times. We first formulate the DSO traffic assignment problem as a strategic game wherein atomic users select routes that minimise their marginal social costs, called a 'DSO game'. By utilising the fact that the DSO game is a potential game, we prove that a globally optimal state is a stochastically stable state under the logit response dynamics, and the better/best response dynamics converges to a locally optimal state. Furthermore, as an application of DSO assignment, we examine characteristics of the evolutionary implementation scheme of marginal cost pricing. Through theoretical comparison with a fixed pricing scheme, we found the following properties of the evolutionary implementation scheme: (i) the total travel time decreases smoother to an efficient traffic state as congestion externalities are perfectly internalised; (ii) a traffic state would reach a more efficient state as the globally optimal state is stabilised. Numerical experiments also suggest that these properties make the evolutionary scheme robust in the sense that they prevent a traffic state from going to worse traffic states with high total travel times. • Convergence and stability of dynamic system optimal assignment are analysed. • An approach based on the potential game theory is presented. • Rigorous results on the stochastic stability of globally optimal states are obtained. • Characteristics of marginal cost pricing schemes are examined as an application. • An evolutionary pricing scheme is important for ensuring robustness of efficient states. [ABSTRACT FROM AUTHOR]

The Dynamic Traffic Assignment (DTA) is one of the important measures to alleviate urban network traffic congestion. The congestions are usually caused by stochastic traffic demands, which are generally unassignable from time dimension in the real-world but are assumed to be assignable in existing DTA methods (i.e. real-time travel demands). In this paper, a distributed DTA method for preventing urban network traffic congestion caused by stochastic real-time travel demands by improving Multi-Agent Reinforcement Learning (MARL). A team structure, which consists of decision-makers and advisers, is designed to learn parallelly in realistic DTA tasks. To reduce the size of the solution space adaptively, the dynamic critical values advised by adviser agents are adopted as constraints for the strategy space of decision-makers (i.e. main agents). A collaborative heterogeneous-adviser mechanism is designed to avoid deviation of guidance. To enhance the adaptability of DTA to the changeable external environment, the mixed strategy concept is introduced to improve the decision-making process of main agents. The respective mapping mechanisms are designed to define adaptive learning rates to improve the sensitivity of MARL. The Sioux Falls (SF) network is established as a test platform via a Dynamic Network Loading (DNL). The effectiveness of the suggested DTA method is assessed through numerical simulations SF network. Under the influence of the scenario with stochastic real-time travel demands, the results show that the proposed method outperforms in terms of the throughput of the network and the individual average travel time among the overall network. Additionally, the ability of the proposed method in response to the external environment rapidly has also been demonstrated. Adopting the suggested method can improve the state of the art to assign stochastic real-time travel demands dynamically and to avoid potential traffic congestion fundamentally.

In this paper, a new dynamic traffic assignment (DTA) model is proposed for large-scale networks. Existing DTA merely describe traffic flow problems based on the travel time holographic principle, which is easy to distribute the bulk of traffic flows on the shortest path. Among the models examined in this respect, the solution method via estimating maximum correctness and expanding logarithmic correction is suggested. The given method is then validated on the Sioux Falls (SF) sample network, whose results indicate that the proposed solution method is appropriate. The findings also demonstrate a difference of less than 5% from the conventional DTA models. In addition, they show that the maximum log-likelihood (MLL) model has a significantly better performance than the multi-sentence logit function (MLF). After validating the proposed solution method, the large-scale road network in the city of Yazd, as one of the metropolises in Iran, is analyzed, using the method concerned and the DTA is performed at different times of the day and night. [ABSTRACT FROM AUTHOR]

Dynamic user equilibrium (DUE) is a Nash-like solution concept describing an equilibrium in dynamic traffic systems over a fixed planning period. DUE is a challenging class of equilibrium problems, connecting network loading models and notions of system equilibrium in one concise mathematical framework. Recently, Friesz and Han introduced an integrated framework for DUE computation on large-scale networks, featuring a basic fixed-point algorithm for the effective computation of DUE. In the same work, they present an open-source MATLAB toolbox which allows researchers to test and validate new numerical solvers. This paper builds on this seminal contribution, and extends it in several important ways. At a conceptual level, we provide new strongly convergent algorithms designed to compute a DUE directly in the infinite-dimensional space of path flows. An important feature of our algorithms is that they give provable convergence guarantees without knowledge of global parameters. In fact, the algorithms we propose are adaptive, in the sense that they do not need a priori knowledge of global parameters of the delay operator, and which are provable convergent even for delay operators which are non-monotone. We implement our numerical schemes on standard test instances, and compare them with the numerical solution strategy employed by Friesz and Han. [ABSTRACT FROM AUTHOR]

Bridge infrastructures are critical nodes in a transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is essential to identify, retrofit, reconstruct, or, if necessary, demolish the infrastructure systems based on optimal decision-making processes. As one of the crucial components of the transportation network, any bridge failure would impede the post-earthquake rescue operation. Not only the failure of such high-risk critical components during an extreme event can lead to significant direct damages, but it also affects the transportation road network. The consequences of these secondary effects can easily lead to congestion and long queues if the performance of the transportation system before or after an event was not analyzed. These indirect losses can be more prominent compared to the actual damage to bridges. This paper brings about seismic performance assessment for the Cyprus transportation network from which the decision-making platform can be modeled and implemented. This study employs a seismic hazard analysis based on generated USGS ShakeMap scenarios for the risk assessment of the transportation network. Furthermore, identification of the resiliency and vulnerability of the transportation road network is carried out by utilizing the graph theory concept at the network level. Moreover, link performance measures, i.e., traffic modeling of the study region is simulated in a dynamic traffic assignment (DTA) simulation environment. Finally, for earthquake loss analysis of the bridges, the HAZUS loss estimation tool is used. The results of our investigations for three different earthquake scenarios have shown that seismic retrofitting of bridges is a cost-effective measure to reduce the structural and operational losses in the region.

• Dynamic user equilibrium (DUE) path delays may be realistically described as weakly monotone increasing (WMI), and inverse travel demand functions may be realistically described as strongly monotone decreasing (SMD). • We establish convergence of fixed-point DUE algorithms under the conditions of Lipschitz continuity, WMI path delays, and SMD inverse travel demand functions. • We provide a detailed numerical example showing that the assumptions of Lipschitz continuity, WMI path delays, and SMD inverse travel demand functions hold along the feasible trajectory of fixed-point iterates en route to a DUE solution. In this paper we present sufficient conditions for convergence of projection and fixed-point algorithms used to compute dynamic user equilibrium with elastic travel demand (E-DUE). The assumption of strongly monotone increasing path delay operators is not needed. In its place, we assume path delay operators are merely weakly monotone increasing, a property assured by Lipschitz continuity, while inverse demand functions are strongly monotone decreasing. Lipschitz continuity of path delay is a very mild regularity condition. As such, nonmonotone delay operators may be weakly monotone increasing and satisfy our convergence criteria, provided inverse demand functions are strongly monotone decreasing. We illustrate convergence for nonmonotone path delays via a numerical example. [ABSTRACT FROM AUTHOR]

This paper aims to contribute to the challenging issue of one microscopic simulation round for dynamic traffic assignment. It relays on the selfish behavior of the vehicle agent that benefits from a more accurate estimation of its travel time. The main novelty is that, rather than considering the average travel times in the network links according to the present vehicles, the vehicle must first know at what time it is able to cross the nodes located at both extremities of the road. This is achieved through a negotiation between the vehicle agent and the node agents to book the crossing time. In order to assess this new paradigm, this paper compares it with well-known approaches in an elementary network. The result invites to extend the approach to more general cases. A discussion of the opportunities and limitations of the approach extension is provided in this paper. One of the notable opportunities, is that the proposed approach has a great potential to improve the energy efficiency by exploring mobile navigation applications and connected autonomous vehicles. [ABSTRACT FROM AUTHOR]

In this study, we aim to solve the shared autonomous vehicle (SAV) routing problem under the effects of congestion in the road network. The SAV routing problem is the problem of finding an optimal SAV-traveler assignment as well as the SAV route choice. Since widespread use of SAVs would cause significant congestion of road networks, it is essential to consider the effects of traffic congestion on SAV route choice. We develop a tabu search (TS) heuristic to solve for SAV routing problem. The heuristic aims to minimize the total person travel time experienced by travelers by exploring the solution space using a swap procedure. The total person travel time is defined as the total time spent by all travelers entering the network in an SAV trip. A Nearest Traveler Neighborhood is defined to choose candidate travelers to consider for the swap procedure. An agent based simulation of the traffic network is used to determine the experienced travel times for each solution from the TS heuristic. The Sioux Falls network is used to test the performance of TS for various demand and fleet sizes. A series of experiments are performed to understand the sensitivity of the heuristic to its parameters and the congestion in the road network. The heuristic is found to produce encouraging results in reducing the total person travel time for differing fleet sizes and demand levels. [ABSTRACT FROM AUTHOR]

Incentive-based travel demand management (IBTDM) programs endow monetary incentives to encourage travel demand redistribution across space and time. They are more appealing than alternatives such as congestion charging because commuters do not need to pay out of pocket. However, such congestion-alleviation solutions are usually managed by small private companies with constrained incentive budgets. Thus, the incentive should be wisely determined so that a limited incentive budget can be effectively used to fulfill maximum social welfare while maintaining the financial health of the IBTDM program. It is essential to know whether IBTDM is financially sound—that is, whether financial investment in IBTDM will lead to more than the equivalent value in total system travel time reduction. However, optimizing the link-based endowment scheme in a large-scale network is challenging because 1) the objective function and the budget constraint are both characterized by expensive-to-evaluate functions without closed form, and 2) it is a large-scale optimization problem that contains massive amount of decision variables. In this study, a computationally efficient surrogate-based optimization framework that is suitable for high-dimensional problems is proposed. A simulation-based dynamic traffic assignment model is used to evaluate the performance of transportation systems, and a Kriging model with partial least squares acts as the surrogate to approximate the simulation model. The results show that the optimal network-wide link-based incentive scheme improves the performance of the system. The higher the incentive budget, the more effective the incentive and the lower the marginal utility of the incentive. Furthermore, in a well-designed incentive scheme, a $1M investment in IBTDM would lead to much more than the equivalent of $1M in total system travel time reduction, which proves the economic viability of IBTDM and provides support for its promotion. IBTDM implemented within smaller regions and tighter incentive budgets produces higher utility ratios. [ABSTRACT FROM AUTHOR]

In dynamic traffic assignment problems, travelers choose routes that minimize their cost of traveling to their destination. In traditional continuum models, travelers have a global perception of traffic; in this study, two novel reactive route-choice strategies are devised in which travelers have a local perception of traffic. In Strategy A, travelers identify a set of feasible temporary destinations that are equidistant from their final destination, and then choose an optimal path within the local perception region that minimizes their cost of traveling to the temporary destination set. In Strategy B, travelers identify a direct path that they can follow to move closer to their final destination within a short, fixed time. These route-choice strategies are used to formulate complete continuum dynamic models for traffic flows in an urban city that consist of a conservation law and a set of eikonal equations or optimization problems involving ordinary differential equations. High-order numerical schemes and suitable solution algorithms are applied to solve these equations on unstructured triangular meshes. Numerical examples are also presented to compare the novel local route-choice strategies with traditional global strategies. • Two novel local route-choice strategies are devised to study travelers' behavior. • Complete continuum dynamic models are formulated for traffic flows in a city. • High-order numerical schemes on unstructured triangular meshes are designed. • Some individuals under local strategies may perform better. • Increasing the distance horizon enhances overall traffic efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage model for the estimation of origin-destination (OD) demands by the time of day over the year with the use of offline traffic data from the real-time travel information system. In the first stage, a travel time recursive function is proposed to use the offline travel speed data for the investigation of the spatial and temporal relationships between time-dependent OD demands and traffic counts. As such, it is not required to carry out the time-consuming dynamic traffic assignment (DTA) process which is frequently used in the conventional time-dependent OD estimation models. Using the results in the first stage together with the available traffic count data, a least-squares method is adopted to formulate the time-dependent OD demand estimation problem as a quadratic programming model in the second stage. A solution algorithm is adapted for solving the proposed model. Then, the proposed method is easy for implementation in practice. Particularly, when the traffic accident occurs in the network, the estimated time-dependent OD demands can be helpful for understanding the complex travel behavior (e.g., departure time choice) under uncertainty condition. The numerical examples are presented to illustrate the applications of the proposed model.

Urban traffic routing has to deal with individual mobility and collective wellness considering citizens, multi-modal transport, and fleet traffic with conflicting interests such as electric vehicles, local distribution, public transport, and private vehicles. Different interests, goals, and regulations, suggest the development of new multi-objective routing mechanisms which may improve traffic flow. In this work, Traffic Weighted Multi-Maps (TWM) is presented as a novel traffic routing mechanism based on the strategical generation and distribution of complementary cost maps for traffic fleets, oriented towards the application of differentiated traffic planning and control policies. TWM is built upon a centralized control architecture, where a Traffic Management Center generates and distributes customized cost maps of the road network. These maps are used individually to calculate routes. In this research, we present the TWM theoretical model and experimental results based on microscopic simulations over a real city traffic network under multiple scenarios, including traffic incidents management. Experimental evaluation takes into account driver's adherence to the system and considers a multi-objective analysis both for the global network parameters (congestion, travel time, and route length) and for the subjective driving experience. Experimental results deliver performance improvements from 20% to 50%. TWM is fully compatible with existing traffic routing systems and has promising future evolution applying new algorithms, policies and network profiles.

This paper proposed a dynamic traffic assignment method based on the connected transportation system to express the time-varied traffic flows caused by uncertain traffic demand and supply in the real traffic network accurately. In dynamic traffic assignment, because of the cumulative volumes of successive time intervals, a route-based traffic assignment algorithm is used for dynamic traffic assignment in this paper. Besides, a novel shortest path algorithm based actual and artificial link is proposed for the time-expanded network that consists of two kinds of arcs: the actual arc and the artificial one. Then, a simple example is used to illustrate the performance of the proposed method. The computational results show that the novel shortest path algorithm based actual and artificial link could be applied in the time-expanded networks with two types of arcs, and the route-based assignment method could achieve optimal balance solutions under different demand modes.

Abstract Recently, an open-source light-weight dynamic traffic assignment (DTA) package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. Aiming to bridge the modeling gaps between multi-agent simulation and optimization in a multimodal environment, we further design and develop DTALite-S to simplify the traffic flow dynamic representation details in DTALite for future extensions. We hope to offer a unified modeling framework with inherently consistent space–time network representations for both optimization formulation and simulation process. This paper includes three major modeling components: (1) mathematic formulations to describe traffic and public transportation simulation problem on a space–time network; (2) transportation transition dynamics involving multiple agents in the optimization process; (3) an alternating direction method of multipliers (ADMM)-based modeling structure to link different features between multi-agent simulation and optimization used in transportation. This unified framework can be embedded in a Lagrangian relaxation method and a time-oriented sequential simulation procedure to handle many general applications. We carried out a case study by using this unified framework to simulate the passenger traveling process in Beijing subway network which contains 18 urban rail transit lines, 343 stations, and 52 transfer stations. Via the ADMM-based solution approach, queue lengths at platforms, in-vehicle congestion levels and absolute deviation of travel times are obtained within 1560 seconds.The case study indicate that the open-source DTALite-S integrates simulation and optimization procedure for complex dynamic transportation systems and can efficiently generate comprehensive space-time traveling status.

Emergency evacuation is to transfer people from dangerous places to safe areas, so as to reduce or even avoid the potential harm to people. It is inherently a comprehensive system composed of evacuation managers, evacuees, road networks, shelters, etc. Security is one of the important indicators of such system. Moreover, in order to ensure the normal and efficient operation of evacuation system, each component should cooperate well with each other, thus making stability another important index of the evacuation system. In order to optimize evacuation safety, some residential areas may be arranged to stay much longer which is hard to be accepted, namely, the stability of evacuation system is low. In this paper, a system-based evacuation CSO model at residential level is proposed which compromises the security and stability of evacuation systems. The CSO model is a bi-level network optimization model, the upper level aims at minimizing the total risk of evacuation subject to the residential tolerance level and the lower level conveys a cell transmission-based dynamic traffic assignment problem. Using our model, we also study the impact of the number of shelters, the organizational form of road intersections, the uncertainty of evacuation demand and risk distribution on evacuation system.