Mesoscopic Dynamic Traffic Assignment Research Articles

Using Conditional Generative Adversarial Nets and Heat Maps with Simulation-Accelerated Training to Predict the Spatiotemporal Impacts of Highway Incidents

An increasingly emphasized research area is the forecast of short-term traffic conditions for nonrecurring traffic dynamics caused by random highway incidents such as crashes or roadway closures. This research proposes a prediction framework which focuses on training a machine learning (ML) model to predict the speed heatmap associated with incidents. Heatmaps contain ideal information that depicts the spatiotemporal characteristics of incident-induced impacts and are suitable objects for ML models to understand and predict. Because of the sparsity of incident data in the real world, we proposed a simulation approach to rapidly expand the training dataset, thus speeding up the model training process. The conditional deep convolutional generative adversarial nets is employed to predict the speed heatmap and the mesoscopic dynamic traffic assignment model DynusT was used to generate many training data. The evaluation shows that the proposed model captures both the tonal and spatial distribution of pixel values at 80.19% similarity between the prediction and actual heatmaps. To the best of our knowledge, this is one of the first attempts in the literature to train ML to predict heatmap representation of incident-induced spatiotemporal impact, and speeding up the training via simulation.

Analyzing Simulation-Based Active Traffic Management Impact on a Large-Scale Regional Network

A vast number of real-time corridor management strategies have been introduced because the dynamics of traffic patterns and increased congestion result in challenging problems on road systems. Although these strategies can offer positive impacts on regional traffic, their evaluation tools are often limited to the scope of one specific corridor. To fill this gap, this study integrates a mesoscopic dynamic traffic assignment simulation model with an existing traffic-responsive ramp metering strategy. This integrated model is suitable for network-wide analysis and large-scale simulation of integrated corridor management strategies. The integrated modeling platform is demonstrated as a practice-ready tool. We present a case study that explores the benefits of metering control under various traffic conditions in a real-world network in Maryland. Both local and network-wide impacts are illustrated in the case study. This is one of the first attempts to simultaneously analyze network-wide traffic impacts and capture minute-by-minute demand–supply interactions under managed corridor strategies. The results indicate that ramp metering is beneficial even under non-recurrent traffic conditions at multiple spatial resolutions.

Analyzing the impact of a planned transit-oriented development on mode share and traffic conditions

ABSTRACTTransit-oriented development (TOD) is a popular planning strategy used to maximize accessibility to transit for various trip purposes. The quantitative effects of TOD on travel mode shift and traffic congestion have not been extensively tested in the current literature. This paper utilizes a seemingly unrelated regressions (SUR) mode share model and a mesoscopic dynamic traffic assignment (DTA) model to analyze the impact of a planned TOD in Maryland. The proposed model aims at improving the understanding of the quantitative impacts of such a TOD on mode share and traffic congestion. The main result of the mode share model indicates that the increase in transit ridership for a transit accessible shopping center is not that significant. Local traffic conditions will deteriorate due to a lack of investment in road infrastructure planned for the TOD area. The proposed method could be a valuable tool for other indicative land development or transportation policy analyses.

Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis

A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.

Investigating the applicability of ADAPTS activity-based model in air quality analysis

The growing recognition of negative impacts of vehicular emissions on global warming and public health is one of the main factors that led transportation policymakers to devise congestion/pollution mitigation strategies. However, in order to attain the best perception of potential results of implementing these policies, it would be essential to understand the response of people in terms of changing their travel behaviors. Activity-based travel demand models provide comprehensive platforms to simulate individuals’ travel behavior at very fine scale and examine the impact of various policies on population. This paper explores the applicability of activity-based models in evaluating the small- and large-scale environmental consequences of transportation policies. To provide a holistic and policy sensitive platform for estimating mobile source emissions, we integrate an activity-based travel demand model with a mesoscopic dynamic traffic assignment (DTA) module and a microscopic emission simulator. Accordingly, the ADAPTS model is extended with an agent-based DTA component, which allows for dynamic interaction between the demand model and network simulator. Then, the US EPA’s mobile source emission model, MOVES, is leveraged to estimate emissions produced by passenger cars. The main challenge in developing such an integrated framework is compatibility of the data among its different components. The performance of this framework is analyzed through two county- and project-level case studies in Chicago metropolitan area. In the county-level analysis, the base-year replication results show a drastic rise in emissions during peak periods, that is, around 14.8% and 27.2% of total daily NOx emission are produced during morning and evening rush hours, respectively. Further, as the project-level analysis, a capacity improvement in a critical ramp in the network is implemented in the DTA model. The simulation results show 24% reduction in VHT and 2.3% increase in VMT in 10-h simulation period in that link.

An optimization method for sustainable traffic control in urban areas

The optimization of traffic signalization in urban areas is formulated as a problem of finding the cycle length, the green times and the offset of traffic signals that minimize an objective function of performance indices. Typical approaches to this optimization problem include the maximization of traffic throughput or the minimization of vehicles’ delays, number of stops, fuel consumption, etc. 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. We propose an optimization method for combining dynamic traffic assignment and network control by minimizing the risk of potential loss induced to travelers by exceeding their budgeted travel time as a result of deployed traffic signal settings, using the Conditional Value-at-Risk model. The proposed methodology can be easily implemented by researchers or practitioners to evaluate their alternative strategies and aid them to choose the alternative with less potential risk. The traffic signal optimization procedure is implemented in TRANSYT-7F and the dynamic propagation and route choice of vehicles is simulated with a mesoscopic dynamic traffic assignment tool (DTALite) with fixed temporal demand and network characteristics. The proposed approach is applied to a reference test network used by many researchers for verification purposes. Numerical experiments provide evidence of the advantages of this optimization method with respect to conventional optimization techniques. The overall benefit to the performance of the network is evaluated with a Conditional Value-at-Risk Analysis where the optimal solution is the one presenting the least risk for ‘guaranteed’ total travel times.

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.