Dynamic Traffic Assignment Model Research Articles

A Link-Node Nonlinear Complementarity Model for a Multiclass Simultaneous Transportation Dynamic User Equilibria

Static transportation network equilibrium models have evolved from traditional sequential models to simultaneous (combined) models, and then to the multiclass simultaneous models to improve prediction of traffic flow. Most Dynamic Traffic Assignment (DTA) models, however, still deal only with the trip assignment step (traveler route choice) that is one of several steps in the transportation planning process. In this paper, the authors combine a dynamic link-node based discrete-time Nonlinear Complementarity Problem (NCP) DTA model with a static Multiclass Simultaneous Transportation Equilibrium Model (MSTEM) in a unified dynamic link-node based discrete-time NCP Dynamic Multiclass Simultaneous Transportation Equilibrium Model (DMSTEM) model. The new model improves the prediction process and eliminates inconsistencies that arise when the DTA or Dynamic Traffic Assignment with Departure Time (DTA-DT) is embedded in a more comprehensive transportation planning framework. An iterative solution algorithm for the proposed DMSTEM model is proposed by solving several relaxed NCPs in each iteration of the algorithm.

Value-at-Risk Assessment of Dynamic Network Loading

Dynamic Traffic Assignment (DTA) models are widely used for online and offline applications for efficient deployment of traffic control strategies and the evaluation of traffic management schemes and policies. Stable and robust time dependent network states, which are represented by a DTA model, are of great importance for ensuring the existence of network equilibrium through time. Thus, a valid statistical measure is needed for assessing dynamic network loading solutions with regards to their endogenous calculation risk and therefore providing a useful and easily applicable tool for practitioners in selecting the less risky option. In this paper we have considered a common method, Value-at-Risk assessment of financial portfolios, for the assessment of the dynamic network loading solution. The proposed method is implemented on two reference networks and the results are presented here.

A Metaheuristic Dynamic Traffic Assignment Model for O-D Matrix Estimation using Aggregate Data

For a static/dynamic O-D matrix estimation, usually, the basic required information is a starting estimation of O-D matrix and a set of traffic counts. In the era of the Intelligent Transportation Systems, a dynamic estimation of traffic demand has become a crucial issue. Different Dynamic Traffic Assignment (DTA) models have been proposed, used also for O-D matrices estimation. This paper presents a dynamic O-D demand estimator, using a novel simulation-based DTA algorithm. The core of the proposed algorithm is a mesoscopic dynamic network loading model used in conjunction with a Bee Colony Optimization (BCO). The BCO is capable to solve high level combinatorial problems with fast convergence performances, allowing to overcome classical demand-flow relationships drawbacks.

On the holding-back problem in the cell transmission based dynamic traffic assignment models

Traffic holding-back is considered an undesirable issue in dynamic traffic assignment since the vehicles are artificially held back on links in spite of the availability of downstream capacity. Holding-back occurs naturally in some system optimal dynamic traffic assignment models. In this paper, we focus on the holding back issue in the cell transmission based models and review the current methods of solving holding-back to understand their advantages and drawbacks. Then, we propose improvements to overcome the drawbacks of the current models. Finally, we propose a novel model which completely eliminates holding-back in a system optimal dynamic traffic assignment for traffic networks with multiple O–D pairs. Rigorous numerical results illustrate the benefits of using the proposed models.

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 Dynamic Traffic Assignment Measure of Stability Based on Network Efficiency

Over the recent years Dynamic Traffic Assignment (DTA) models are commonly used for the online and offline optimization of traffic control strategies in complex urban environments. The stability of the Dynamic Traffic Assignment is crucial for achieving the best possible representation of traffic phenomena in congested urban networks, where through the progress of time, the increase of traffic congestion reduces the network efficiency. In this paper we investigate the stability analysis of a DTA procedure through the notion of network efficiency. Numerical results of a sensitivity analysis of the interval size for two test networks show the correlation between the stability of the DTA, the time-dependent efficiency of the network and the interval size of the DTA procedure.

Effects of Optimizing Externalities Using Cooperating Dynamic Traffic Management Measures on Network Level

Optimization of traffic network performance using dynamic traffic management (DTM) measures can be viewed as a specific example of solving a network design problem (NDP). Decision variables are the specific settings of DTM measures. DTM measures have been identified as powerful instruments not only to increase network efficiency, but also to improve externalities. As a result, in the optimization the focus is not only on efficiency, but also on climate, air quality, traffic safety, and noise. These assessment criteria are determined using the output of a dynamic traffic assignment model. This results in a dynamic multi-objective NDP, which is solved as a bilevel optimization problem; it results in a Pareto optimal set. This set provides valuable information for the decision-making process, which would not have been available if the compensation principle would have been chosen in advance. Knowledge obtained by optimization of realistic cases can be used to attain knowledge about incorporation of externalities as an objective when optimizing traffic systems using DTM measures. A case study for a realistic network of the city of Almelo shows that the objectives efficiency, climate, and air quality are mainly aligned and mainly opposed to traffic safety and noise. However, there is not a single solution that optimizes all three aligned objectives. Based on the Pareto optimal set, the trade-offs are determined and using cluster analysis the solutions and results are further analyzed for network segments.

A Hybrid Route Choice Model for Dynamic Traffic Assignment

Network user equilibrium or user optimum is an ideal state that can hardly be achieved in real traffic. More often than not, every day traffic tends to be in disequilibrium rather than equilibrium, thanks to uncertainties in demand and supply of the network. In this paper we propose a hybrid route choice model for studying non-equilibrium traffic. It combines pre-trip route choice and en-route route choice to solve dynamic traffic assignment (DTA) in large-scale networks. Travelers are divided into two groups, habitual travelers and adaptive travelers. Habitual travelers strictly follow their pre-trip routes which can be generated in the way that major links, such as freeways or major arterial streets, are favored over minor links, while taking into account historical traffic information. Adaptive travelers are responsive to real-time information and willing to explore new routes from time to time. We apply the hybrid route choice model in a synthetic medium-scale network and a large-scale real network to assess its effect on the flow patterns and network performances, and compare them with those obtained from Predictive User Equilibrium (PUE) DTA. The results show that PUE-DTA usually produces considerably less congestion and less frequent queue spillback than the hybrid route choice model. The ratio between habitual and adaptive travelers is crucial in determining realistic flow and queuing patterns. Consistent with previous studies, we found that, in non-PUE DTA, supplying a medium sized group (usually less than 50%) of travelers real-time information is more beneficial to network performance than supplying the majority of travelers with real-time information. Finally, some suggestions are given on how to calibrate the hybrid route choice model in practice to produce realistic results.

A dynamic traffic assignment model for highly congested urban networks

The management of severe congestion in complex urban networks calls for dynamic traffic assignment (DTA) models that can replicate real traffic situations with long queues and spillbacks. DynaMIT-P, a mesoscopic traffic simulation system, was enhanced and calibrated to capture the traffic characteristics in a sub-area of Beijing, China. The network had 1698 nodes and 3180 directed links in an area of around 18 square miles. There were 2927 non-zero origin–destination (OD) pairs and around 630,000 vehicles were simulated over 4h of the morning peak. All demand and supply parameters were calibrated simultaneously using sensor counts and floating car travel time data. Successful calibration was achieved with the Path-size Logit route choice model, which accounted for overlapping routes. Furthermore, explicit representations of lane groups were required to properly model traffic delays and queues. A modified treatment of acceptance capacity was required to model the large number of short links in the transportation network (close to the length of one vehicle). In addition, even though bicycles and pedestrians were not explicitly modeled, their impacts on auto traffic were captured by dynamic road segment capacities.

Integration of Environmental Objectives in a System Optimal Dynamic Traffic Assignment Model

Abstract: Maintaining air‐quality standards is a major concern for transportation planners and policy makers in the United States. This necessitates considering nontraditional emission objectives in transportation systems modeling. In this research, we integrate emission‐based objective into the traditional travel time based dynamic assignment framework. Carbon monoxide (CO) emissions from vehicles are computed as functions of space mean speed (determined from an embedded mesoscopic traffic flow model). Different performance metrics (CO emission, system wide travel time, and speed profiles) from the integrated model are compared with traditional dynamic assignment model (with travel time minimization objective). In addition, results indicate changes in route choice behavior of the road users when emission objective is integrated to dynamic assignment framework.

Demand and Supply Calibration of Dynamic Traffic Assignment Models

The calibration of dynamic traffic assignment (DTA) models has evolved rapidly over the past decade and has been fueled by the need for applications ranging from long-term planning to real-time traffic operation. Numerous formulations and solution approaches based on either analytical or simulation-based approaches have been introduced. The aim of this paper is the documentation of the existing DTA model calibration approaches for future reference. The literature on the calibration of DTA models can be categorized on the basis of the two major components that need to be calibrated: demand and supply model estimation. Travel behavior modeling and origin–destination demand estimation problems are considered in the determination of demand models. Supply models simulate traffic dynamics, queue formation, dissipation, and spillback in either a microscopic or mesoscopic context. Early DTA model calibration efforts were based on iteration between the two demand and supply components. Recent frameworks have focused on the simultaneous calibration of both components. Therefore, different solution approaches have been addressed with various functional needs and degrees of robustness. This paper summarizes the current understanding of calibration and estimation of all input parameters for a DTA model, reviews the existing literature, and highlights the gaps that need to be addressed in future research.

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 Evaluation of Hurricane Evacuation Strategies for the Houston–Galveston, Texas, Region

The Houston–Galveston, Texas, region has experienced several major hurricanes in recent years. During the evacuation for Hurricane Rita in 2005, the Texas Department of Transportation (DOT) decided to implement contraflow operations on I-45 to relieve massive evacuee congestion departing Houston to the north. The decision to implement contraflow was a difficult one because it involved multiple jurisdictions and required extensive coordination of manpower and resources from various entities. After the Hurricane Rita experience, the Texas DOT implemented a new strategy, referred to as “evaculane,” in which evacuation traffic could use the outside paved shoulder as a traveling lane when an evacuation was under way and evaculane signing beacons were activated. The objective is to increase capacity along key evacuation routes while avoiding the need for full-scale contraflow operation whenever possible. The evaculane on I-10 was successfully put into use during the Hurricane Ike evacuation in 2008. With the widening and completion of evaculanes on I-10 and US-290 as well as a partial contraflow plan for the I-45 corridor, the Texas DOT sponsored a study to develop a decision support tool to help determine whether these strategies would adequately handle the evacuation demand for various Houston–Galveston region evacuation scenarios. This paper describes the quantitative assessment of the performance of alternative evacuation strategies using a dynamic traffic assignment model, DynusT. The evaluation results indicated the evaculanes on I-10 and US-290 can sufficiently handle high evacuation demand on both routes without contraflow operation. In addition, a partial contraflow plan for I-45 was shown to provide sufficient capacity to handle high evacuation demand in lieu of full-scale contraflow operation.

Exploring Link Covering and Node Covering Formulations of Detection Layout Problem

The primary data input used in principal traffic models comes from origin–destination (O-D) trip matrices, which describe patterns of traffic behavior across a network. O-D matrices are a critical requirement in advanced traffic management or information systems that are supported by dynamic traffic assignment models. However, because O-D matrices are not directly observable, current practice adjusts an initial seed matrix from link flow counts that are provided by an existing layout of traffic-counting stations. The adequacy of the detection layout is critical to determining the quality of the adjusted O-D matrix. The usual approaches to the detection layout problem assume that detectors are located at network links. This paper proposes a modified set that formulates the link detection layout problem with side constraints and presents a new metaheuristic tabu search algorithm with high computational efficiency. Emerging information and communication technologies (ICT), especially those based on the detection of the electronic signature of onboard devices (such as Bluetooth devices), allow the location of sensors at intersections. To take into account explicitly how these ICT sensors operate, this paper proposes a new formulation of a node-covering problem with side constraints, which for practical purposes can be efficiently solved with standard professional solvers such as CPLEX.

Unified Framework for Dynamic Traffic Assignment and Signal Control with Cell Transmission Model

This research presents a unified framework for dynamic traffic assignment and signal control optimization. An objective function based on the system-optimal approach with an embedded traffic flow model (the cell transmission model) for dynamic traffic assignment was considered. The optimization model (a mixed-integer program) explicitly considered intersection delay and lost time from phase switches in addition to a traditional travel time objective. Two test networks were used to demonstrate the applicability of the proposed model. Results showed better performance of the models when they were compared with fixed-signal timing plans. The formulation of signal control design also accounted for the variation of cycle length, and results showed the variation of cycle lengths with different objective functions under different levels of congestion.

Integrated Land Use–Transport Model System with Dynamic Time-Dependent Activity–Travel Microsimulation

The development of integrated land use–transport model systems has long been of interest because of the complex interrelationships between land use, transport demand, and network supply. This paper describes the design and prototype implementation of an integrated model system that involves the microsimulation of location choices in the land use domain, activity–travel choices in the travel demand domain, and individual vehicles on networks in the network supply modeling domain. Although many previous applications of integrated transport demand–supply models have relied on a sequential coupling of the models, the system presented in this paper involves a dynamic integration of the activity–travel demand model and the dynamic traffic assignment and simulation model with appropriate feedback to the land use model system. The system has been fully implemented, and initial results of model system runs in a case study test application suggest that the proposed model design provides a robust behavioral framework for simulation of human activity–travel behavior in space, time, and networks. The paper provides a detailed description of the design, together with results from initial test runs.

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.

Modeling Evacuation of a Transport System: Application of a Multimodal Mesoscopic Dynamic Traffic Assignment Model

In this paper, the analysis of a transportation system under emergency conditions due to hazardous events is considered. To assess the effects on the analyzed transport network, an extension to a mesoscopic dynamic traffic assignment (DTA) model was developed to determine quantitative indicators for estimating the exposure component of the total risk incurred by the transport networks in an area. In particular, a new version that is able to allow for multimodal networks and to consider network reliability was introduced. To give a practical example of the proposed model, it has been applied to two real networks, studying evacuation in the hypothesis that in the event of a calamity the population in the area follows the instructions proposed by the municipal civil protection plan. The work shows how adequate quantitative methodologies based on a dynamic approach can be a useful tool to support the process of evacuation planning at several scales.

Implementation of a New Travel Time Estimation Method for Demand Forecasting Models

This paper discusses the implementation of a new travel time estimation method in a regional demand forecasting model. The developed model considers implicitly the influence of signal timing as a function of main street and cross street traffic demands, although signal timing setting is not required as input. The application presented in this paper demonstrates that the developed model is applicable to a large network without the burden of signal timing input requirement. The results indicate that the application of the model can improve the performance of traffic assignment as part of the demand forecasting process. The model is promising to support dynamic traffic assignment (DTA) model applications in the future.

Modelling Externalities using Dynamic Traffic Assignment Models: A Review

Recently, there has been a growing interest in externalities in our society, mainly in the context of climate and air quality, which are of importance when policy decisions are made. For the assessment of externalities in transport, often the output of static traffic assignment models is used in combination with so-called effect models. Due to the rapidly increasing possibilities of using dynamic traffic assignment (DTA) models for large-scale transportation networks and the application of traffic measures, already several models have been developed to assess the externalities using DTA models more precisely. Different research projects have shown that there is a proven relation between the traffic dynamics and externalities, such as emissions of pollutants and traffic safety. This means that the assessment of external effects can be improved by using temporal information about flow, speed and density, which is the output of DTA models. In this paper, the modelling of traffic safety, emissions and noise in conjunction with DTA models is reviewed based on an extensive literature survey. This review shows that there are still gaps in knowledge in assessing traffic safety, much research is available concerning emissions, and although little research has been conducted concerning the assessment of noise using DTA models, the methods available can be used to assess the effects. Most research so far has focused on the use of microscopic models, while mesoscopic or macroscopic models may have a high potential for improving the assessment of these effects for larger networks.