Simulation-based Dynamic Traffic Assignment Research Articles

Microsimulation of Demand and Supply of Autonomous Mobility On Demand

Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale people-centric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual’s day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-the-art integrated agent-based demand and supply model—SimMobility—for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobility-sensitive behavioral models in a multiple time-scale structure comprising three simulation levels: ( a) a long-term level that captures land use and economic activity, with special emphasis on accessibility; ( b) a midterm level that handles agents’ activities and travel patterns; and ( c) a short-term level that simulates movement of agents, operational systems, and decisions at a microscopic granularity. In that context, this paper proposes several extensions at the short-term and midterm levels to model and simulate autonomous vehicle systems and their effects on travel behavior. To showcase these features, the first-cut results of a hypothetical on-demand service with autonomous vehicles in a car-restricted zone of Singapore are presented. SimMobility was successfully used in an integrated manner to test and assess the performance of different autonomous vehicle fleet sizes and parking station configurations and to uncover changes in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.

Deployment and Calibration Considerations for Large-Scale Regional Dynamic Traffic Assignment: Case Study for Sydney, Australia

Dynamic traffic assignment (DTA) has received increasing attention in recent years, and there are numerous examples of practical implementations. This work adds to the literature by describing the ongoing experience of building the first large-scale simulation-based DTA model in Australia. The input data for the model are summarized, and an in-depth discussion and an analysis of model output and the calibration process are presented. Current results put 80% of the 322 calibration points spread across the network within an acceptable bound of error, but the project found that alternative metrics of network performance also must be considered so that other aspects of model realism are not neglected. The described DTA model could be used for evaluating important policy decisions and infrastructural development in the context of the macro- and mesoscale network operation. Additionally, this project is a proof of concept for the Australian region and may provide insight to practitioners interested in emerging areas of transport planning and traffic modeling.

Time-of-day vehicle mileage fees for congestion mitigation and revenue generation: A simulation-based optimization method and its real-world application

Congestion pricing of a large-scale network is characterized by expensive-to-evaluate objective functions without closed forms. This paper further enhances a computationally efficient simulation-based optimization (SBO) framework to solve the problem within tight computational budget. This paper applies surrogate models to solve the optimization problem with computationally expensive objective functions based on simulation-based dynamic traffic assignment (DTA). DIRECT (a deterministic search algorithm with modification to Lipschitzian optimization) is used for metamodel parameter tuning. A trade-off of different objectives (i.e. the average travel time minimization, expected network throughput maximization, and toll revenue maximization) are converted into a single desirability function. To demonstrate the SBO framework with an application to the vehicle mileage traveled (VMT) based pricing for a real-world freeway network, this paper utilizes a calibrated simulation-based DTA model to evaluate system performance. A stochastic mesoscopic simulator is applied. We investigate the existence of an invariant macroscopic fundamental diagram (MFD) for the network, and compare simulated MFDs with measurements of fixed detectors and probe data. The proposed SBO framework is generic and can be used to solve other congestion pricing problems.

A New Perspective of Traffic Assignment: A Game Theoretical Approach

Traditional equilibrium models consider transportation networks with well-defined link travel time functions and continuous drivers. Recently, researchers focused on adding the behavioral dimension lacking in traditional equilibrium models by treating drivers as individual decision-makers (atomic drivers). However, there is currently no underpinning theory that supports the shift from macroscopic to microscopic traffic assignment modeling. In this paper, a game theoretical model which provides this link is presented. The authors will show that this model describe drivers' adaptive behaviors as they perform day-to-day route choices. Drivers acquire payoffs with unknown noise of their chosen and alternative routes. This scenario describes a transportation network with the presence of a Traffic Management Center (TMC). Finally, a simulation-based dynamic traffic assignment simulation is carried out to validate the model using the Simulation of Urban MObility (SUMO) open source software. The simulation shows that Nash equilibrium can be achieved almost surely.

A multiclass cell transmission model for shared human and autonomous vehicle roads

Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that the legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high.

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

This paper focuses on modeling agents’ en-route diversion behavior under information provision. The behavior model is estimated based on naïve Bayes rules and re-calibrated using a Bayesian approach. Stated-preference driving simulator data is employed for model estimation. Bluetooth-based field data is employed for re-calibration. Then the behavior model is integrated with a simulation-based dynamic traffic assignment model. A traffic incident scenario along with variable message signs (VMS) is designed and analyzed under the context of a real-world large-scale transportation network to demonstrate the integrated model and the impact of drivers’ dynamic en-route diversion behavior on network performance. Macroscopic Fundamental Diagram (MFD) is employed as a measurement to represent traffic dynamics. This research has quantitatively evaluated the impact of information provision and en-route diversion in a VMS case study. It proposes and demonstrates an original, complete, behaviorally sound, and cost-effective modeling framework for potential analyses and evaluations related to Advanced Traffic Information System (ATIS) and real-time operational applications.

Investigation of Centroid Connector Placement for Advanced Traffic Assignment Models with Added Network Detail

Advanced traffic assignment models, such as simulation-based dynamic traffic assignment, typically incorporate more detailed network representations than do traditional planning models. In this context, the placement of centroid connectors may have a significant effect on model performance, and attention must be paid to their number and location to avoid unrealistic congestion or low utilization of minor roadways by local traffic. Given that the manual inspection of centroid connector placement may be too time-consuming in large regional networks, this paper proposes two simple automatic centroid connector placement strategies for dynamic traffic assignment applications. The first approach radially distributes the connectors to the nearest nodes and is intended to exemplify some limitations of the most common techniques in practice. The second strategy involves dividing the centroid and subsequent demand into two parts, distributing the demand across one sub-centroid linked to nearby nodes and one linked to the periphery, and thus effectively establishing a bilevel distribution. A modification of this strategy involves eliminating nodes at signalized intersections as viable candidates for connection. As part of the evaluation of the methods, a new metric, the locality factor, has been introduced to describe the use of minor streets by local traffic. The numerical experiments, conducted on two real-world networks, exemplify the effects of the incorporation of local streets and the placement of centroid connectors on model results. Sensitivity testing and limited field data comparisons suggest that the bilevel centroid connector placement strategy achieves more realistic results.

Data Mining and Pattern Matching for Dynamic Origin–Destination Demand Estimation

Historical traffic data are widely used in the estimation of origin– destination (O-D) demand patterns in simulation-based dynamic traffic assignment models, in the prediction of traffic states, and as a basis for defining traffic management scenarios. This study investigated the determination of historical traffic patterns by applying a classical clustering algorithm to a very large data set of sensor observations over an extended period and identified appropriate patterns for use in the application of traffic estimation and prediction systems. Systematic identification of similarity and dissimilarity of traffic flow data can lead to a systematic process for defining critical demand scenarios for traffic state prediction. The objective was to explore the impact of various demand scenarios on real-time traffic estimation and prediction. A detailed procedure for clustering traffic flow data that is based on the K-means clustering algorithm is presented. The procedure was applied to a subnetwork in Salt Lake City, Utah. An O-D estimation procedure was applied to each cluster separately; this procedure repeatedly solved a bilevel optimization problem in which the goal was to minimize the deviation from observations and historical demand. For the evaluation of the effect of selecting a best-matching initial matrix on traffic pattern estimation and prediction quality, a comparative application of initial matrix choices was conducted with an online traffic estimation and prediction system. The results indicate that the clustering process results in better starting matrices matched to the day's unfolding traffic and weather conditions.

Simulation-based Optimization of Mixed Road Pricing Policies in a large Real-world Network

The joint optimization of various types of road pricing strategies for a large-scale real-world network is challenging. Dynamic network supply models can overcome this shortcoming, and provide more detailed information regarding the system performance. However, the computational burden of simulation is still a big challenge for the optimization. Utilizing a simulation-based dynamic traffic assignment model, this paper proposes a simulation-based optimization method to solve the mixed road pricing problem, which is characterized by expensive-to-evaluate and non-closed-form multi-objectives. The mixed road pricing problem is formulated to satisfy dynamic user equilibrium conditions. It enables to capture the time-varying network performance via dynamic traffic assignment. The simulation-based optimization method is successfully applied to the joint optimization of a variety of toll facilities in a real-world network of the Montgomery County in Maryland, i.e. an HOT lane, express toll lanes, and a toll road.

Flow-Based and Density-Based Time-Dependent Demand Estimation for Congested Urban Transportation Networks

This paper compares the estimation quality of two time-dependent origin–destination demand estimation methods in congested networks. The first method uses time-dependent link flow rate observations, whereas the second uses time-dependent link density observations. The developed methods adopt a bilevel formulation framework in which the lower-level problem solves for the time-dependent link proportions by using a simulation-based dynamic traffic assignment model. The upper-level problem is in the form of a least squares error minimization program that minimizes the difference between the observations and their corresponding estimated values. The solution algorithm integrates a simulation-based dynamic traffic assignment model and a linear approximation of the least squares error formulation by using a separable programming approach. An iterative solution algorithm is developed to achieve consistency between the estimated demand and the link proportions. A set of experiments conducted with hypothetical and real-world networks examines the performance of the two methods in replicating the observed congestion pattern in these networks. Results show that the density-based demand estimation method is more suitable for demand estimation in congested networks in that the former method captures the flow breakdown and spillback phenomena associated with high congestion.

Surrogate-Based Optimization for Solving a Mixed Integer Network Design Problem

This paper considers the bilevel mixed network design problem (MNDP) used in dynamic traffic assignment (DTA) and the simulation-based optimization solution. The upper level of the bilevel MNDP minimizes the network cost in terms of average travel time by the expansion of existing links and the addition of new candidate links. The lower level is a dynamic user-optimal condition that can be formulated as a variational inequality problem. The MNDP simultaneously finds optimal capacity expansions of existing links and new link additions. A surrogate-based optimization (SBO) framework is proposed for solving the MNDP that is characterized by expensive-to-evaluate objective functions. Because simulation was applied to evaluate those objective functions, additional complexity arose from the fine-grained representation of traffic dynamics in large-scale networks, which were not fully considered by the traditional static user equilibrium. SBO methods enjoy both the advantages of simulation in time-varying network performance evaluation and the efficiency of mathematical optimization. To be more specific, SBO produces computational time savings by exploring the input–output mapping surface in a more systematic and efficient way. For demonstrative purposes, a case study was conducted on the large-scale Montgomery County network in Maryland. In this example, a mesoscopic simulation-based DTA model, DTALite, was used to evaluate the system performance in response to various network design strategies. Results showed that the optimal investment with a moderate budget could reduce 17.73% of the network average travel time in the morning peak. The proposed framework is a general approach, which is ready for application to either continuous or discrete network design problems.

A Variable Time-Discretization Strategies-Based, Time-Dependent Shortest Path Algorithm for Dynamic Traffic Assignment

Within the simulation-based dynamic traffic assignment (SBDTA) model, the time-dependent shortest path (TDSP) algorithm plays a crucial role in the path-set update procedure by solving for the current optimal auxiliary solution (shortest path). Common types of TDSP algorithms require temporal discretization of link/node time/cost data, and the discretization could affect the solution quality of TDSP and of the overall SBDTA as well. This article introduces two variable time-discretization strategies applicable to TDSP algorithms. The strategies are aimed at determining the optimal time discretization for time-dependent links/nodes travel time data. The first proposed strategy produces a specific discretization interval for each link. The second proposed strategy generates time-varying intervals for the same link over the analysis period. The proposed strategies are implemented in a link-based time-dependent A* algorithm in a SBDTA model DynusT and tested with two numerical experiments on two traffic networks. The results show that the proposed discretization methods achieve the research goal—to flexibly and scalably balance the memory usage and run time for SBDTA without degrading the convergence. This property is rather important when dealing with a large real-world network with a long analysis period.

Improving the Convergence of Simulation-based Dynamic Traffic Assignment Methodologies

The ability of simulation-based dynamic traffic assignment (SBDTA) models to produce reliable solutions is crucial for practical applications, particularly for those involving the comparison of modeling results across multiple scenarios. This work reviews, implements and compares novel and existing techniques for finding equilibrium solutions for SBDTA problems, focusing on their convergence pattern and stability of the results. The considered methodologies, ranging from MSA and gradient-based heuristics to column generation frameworks and partial demand loading schemes, have not been previously compared side-to-side in the literature. This research uses a single SBDTA platform to conduct such comparison on three real networks, including one with more than 200,000 trips. Most analyzed approaches were found to require a similar number of simulation runs to reach near-equilibrium solutions. However, results suggest that the quality of the results for a given convergence level may vary across methodologies.

A simulation-based dynamic traffic assignment model with combined modes

This paper presents a dynamic traffic assignment (DTA) model for urban multi-modal transportation network by con­structing a mesoscopic simulation model. Several traffic means such as private car, subway, bus and bicycle are con­sidered in the network. The mesoscopic simulator consists of a mesoscopic supply simulator based on MesoTS model and a time-dependent demand simulator. The mode choice is si­multaneously considered with the route choice based on the improved C-Logit model. The traffic assignment procedure is implemented by a time-dependent shortest path (TDSP) al­gorithm in which travellers choose their modes and routes based on a range of choice criteria. The model is particularly suited for appraising a variety of transportation management measures, especially for the application of Intelligent Trans­port Systems (ITS). Five example cases including OD demand level, bus frequency, parking fee, information supply and car ownership rate are designed to test the proposed simulation model through a medium-scale case study in Beijing Chaoy­ang District in China. Computational results illustrate excel­lent performance and the application of the model to analy­sis of urban multi-modal transportation networks.

Capacity Estimation in Support of Mesoscopic Simulation as Part of Dynamic Traffic Assignment Models

Dynamic traffic assignment (DTA) is increasingly being considered to model advanced strategies. Capacity is a crucial parameter in the calibration of traffic flow models utilized as part of DTA modeling. The Highway Capacity Manual has been used as the authoritative source for defining and estimating capacity in the United States. With increased traffic detector data availability in recent years, it is now feasible to locally measure capacity at bottleneck spots with different methods. This study investigates the benefits and necessity of implementing DTA in the analysis of advanced strategies, such as managed lanes, and the importance of the calibration of the associated traffic model parameters. In this regard, the importance of coding capacity based on detector measurements in DTA tools is illustrated, particularly when there is evidence that the modeled corridor capacity is lower than estimates based on the Highway Capacity Manual. The shortcomings of utilizing the traffic flow model of static assignment tools for assessing managed lanes, even when the measured capacity values are coded, are also demonstrated; this drawback illustrates the need to utilize simulation-based DTA modeling for such assessments.

A Comparative Study of the Cross Entropy Approach with the State–of-the-art Simulation-based Traffic Assignment Algorithms

This paper presents a path-based cross entropy algorithm for solving simulation-based dynamic traffic assignment problem. The performance of the cross entropy algorithm is compared with two state-of-the-art algorithms: method of successive averages and gap function based projection algorithm. The dynamic network loading model is based on a mesoscopic queue model complying with generic first order macroscopic node model. The computational study implemented on four realistic networks shows the cross entropy method provides satisfactory convergence accuracy to user equilibrium.

Flow Equilibrium Under Dynamic Traffic Assignment and Signal Control—An Illustration of Pretimed and Actuated Signal Control Policies

Under intelligent transportation systems, the interaction between signal setting and traffic assignment is an important issue in designing efficient advanced traffic management systems. Most of the studies only describe the interaction under static assumptions, which do not consider possible temporal flow distribution. These assumptions limit the applicability in real-time management and control. This paper focuses on finding equilibrium under the interaction of signal setting and traffic assignment. The problem is constructed and solved through a bi-level framework. The upper level solves for signal setting parameters based on flow distributions, including cycle length and green splits. The lower level solves for user equilibrium dynamic traffic assignment (UEDTA) flows in a traffic network. The signal setting parameters are adjusted through pretimed and actuated signal controls. UEDTA flow patterns are generated through a simulation-based dynamic traffic assignment (DTA) model, DynaTAIWAN. Numerical experiments based on a real network in Kaohsiung City, Taiwan, are conducted to study the dynamic equilibrium. The numerical results indicate the existence of equilibrium flow under signal control and route assignment in a dynamic aspect. The results show that the average travel times under the pretimed signal policy are, in general, better than the times under the actuated signal policy.

A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment

ABSTRACT With emergent interest of Simulation-Based Dynamic Traffic Assignment (SBDTA) in the field of transportation network modeling, deployment of SBDTA models for traffic operations and transportation planning have increased significantly in recent years. In parallel, research and development of innovative approaches of the SBDTA model have enhanced the quality of both the assignment component, i.e, improvement of convergence quality of the Dynamic User Equilibrium (DUE) problem, and the traffic simulation element. However, computational requirement remains to be one of the great challenges for DTA implementations on large-scale networks with a long analysis period. This paper presents a temporal decomposition scheme for large spatial- and temporal-scale dynamic traffic assignment, in which the entire analysis period is divided into Epochs. Vehicle assignment is performed sequentially in each Epoch, thus improving the model scalability and confining the peak run-time memory requirement regardless of the total analysis period. A proposed self-turning scheme adaptively searches for the run-time-optimal Epoch setting during iterations regardless of the characteristics of the modeled network. Extensive numerical experiments confirm the promising performance of the proposed algorithmic schemes.

Gradient Projection Method for Simulation-Based Dynamic Traffic Assignment

A gradient projection method is presented to solve the problem with dynamic traffic assignment. The gradient projection scheme is a feasible direction algorithm that operates directly in the space of path flows. The algorithm was applied to a simulation-based model for dynamic traffic assignment that used microscopic simulation for supply-side modeling. The formulation used link cost functions that were separate for moving and queuing vehicles on each link, so that the formulation could effectively capture the effects of traffic dynamic characteristics on each group, especially in arterial networks. The model and solution algorithm were applied to a small real-world network, and the performance was studied. The gradient projection algorithm avoided the problem of convergence to nonoptimal conditions shown by the prevalent method of successive averages and demonstrated significantly faster convergence.

Dynamic Traffic Assignment Based Evacuation Planning for CBD Areas

Adverse weather conditions and natural phenomena, terrorist attacks or chemical and industrial accidents require the development of area evacuation plans, in order to minimize the risk of potential casualties and injuries. Evacuation Times Estimates (ETE) studies have been considered as an essential tool in the implementation of contingency plans for such situations. In this paper the evacuation of the Central Business District of Thessaloniki, Greece, is examined, assuming an incident occurrence in the city's major road axis. The results are expressed in terms of travel times, delays and the development of traffic queues. The assessment of the evacuation plans is based on a large-scale, simulation-based dynamic traffic assignment model.