Dynamic Traffic Assignment Framework Research Articles

A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network

Numerous contemporary studies have posited that connected and autonomous vehicles (CAVs) hold the potential to enhance traffic safety and augment efficiency substantially. One widely discussed approach to optimize CAV operations within urban traffic networks involves the implementation of dedicated lanes (DLs). This study aims to assist system planners in optimally deploying DLs within heterogeneous urban traffic networks of CAVs and Human-Driven Vehicles (HDVs). In pursuit of this objective, we have introduced a multi-class dynamic traffic assignment framework that enhances network performance and offers insights into traffic dynamics. Additionally, our methodology considers dynamic routing behaviour while devising DLs, formulating and approximating the problem as a mixed-integer linear program (MILP). The resulting strategy delineates the temporal and spatial aspects of the deployment of DLs for CAVs, specifying the quantity and locations of these lanes. Subsequently, we assessed our framework using test-bed networks of varying sizes and demand profiles, evaluating the solution’s quality and the model’s adaptability to diverse traffic conditions. Our findings indicate that implementing DLs for CAVs can bolster vehicular throughput across the network while neglecting dynamic capacity variation in mixed traffic may yield misleading outcomes.

A dynamic traffic assignment framework for policy analysis in cities with significant share of two-wheelers

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.

Development of Dynamic Traffic Assignment Framework for Heterogeneous Traffic Lacking Lane Discipline

Development of Dynamic Traffic Assignment Framework for Heterogeneous Traffic Lacking Lane Discipline

Regional dynamic traffic assignment with bounded rational drivers as a tool for assessing the emissions in large metropolitan areas

Data-driven surveys show that drivers do not always choose the shortest-path for their travels. The ideas of bounded rationality have been used to model this behavior, and relax the main assumption of travel time minimization of the User Equilibrium principle. In this paper, we propose an extension of an existing dynamic traffic assignment framework, for aggregated traffic models based on the Macroscopic Fundamental Diagram and regional networks, that extends the principle of the User Equilibrium to account for bounded rational drivers. We focus on drivers with indifferent preferences, and with preferences for more reliable travel times. The network equilibrium is calculated through Monte Carlo simulations and the classical Method of Successive Averages. We first investigate how the drivers' preferences for reliable travel times influences the traffic dynamics in the regional network. We then discuss a potential application example of the proposed methodological framework for estimating the emissions of Carbon Dioxide CO2 and Monoxide NOx at the network level. The results shed light on the importance of properly accounting for more realistic drivers' behavior for estimating emissions. The main contributions of this study lie on the edge between the disciplines of traffic flow theory and network modeling, with a great potential of application for practitioners to assess traffic emissions on large metropolitan areas.

Regional Dynamic Traffic Assignment Framework for Macroscopic Fundamental Diagram Multi-regions Models

In this paper, we propose a regional dynamic traffic assignment framework for macroscopic fundamental diagram (MFD) models that explicitly accounts for trip length distributions. The proposed framework considers stochasticity on both the trip lengths and the regional mean speed. Consequently, we can define utility functions to assess the cost on alternatives, depending on which terms are considered stochastic. We propose a numerical resolution scheme based on Monte Carlo simulations and use the method of successive averages to solve the network equilibrium. Based on our test scenarios, we show that the variability of trip lengths inside the regions cannot be neglected. Moreover, it is also important to consider the stochasticity on the regional mean speeds to account for correlation between regional paths. We also discuss an implementation of the proposed dynamic traffic assignment framework on the sixth district of the Lyon network, where trip lengths are explicitly calculated. The traffic states are modeled by considering the accumulation-based MFD model. The results highlight the influence of the variability of trip lengths on the predicted traffic states.

Privately Owned Autonomous Vehicle Optimization Model Development and Integration with Activity-Based Modeling and Dynamic Traffic Assignment Framework

This paper presents a first-order approach integrated with activity-based modeling and dynamic traffic assignment framework to model the impact of autonomous vehicles on household travel and activity schedules. By considering shared rides among household members, mode choices, re-planning of departure times, and the rescheduling of activity sequences, two optimization models—basic personal owned autonomous vehicle (POAV) model and enhanced POAV model—are presented. The proposed approach is tested for the different models at the household level with different household sizes. The activity schedules of each household were generated in the Chicago sub-area network. The results show that each POAV can effectively replace multiple conventional vehicles, however, using POAV will lead to more vehicle miles traveled because of detour trips. The proposed enhanced POAV model considers mode choice decision with a household-based approach instead of a trip-based approach to capture the impacts of repositioning trips on mode choice. The results show that, if the generalized travel cost of POAV remains at the same level as conventional vehicles, more passengers will choose to use transit because the repositioning trips increase the total cost.

Submission to the DTA2012 Special Issue: A Case for Higher-Order Traffic Flow Models in DTA

An accurate Dynamic Traffic Assignment (DTA) model should capture real world traffic flow dynamics and predict ‘dynamic’ travel times. Traditional DTA models used simple traffic flow functions such as exit flow functions, delay functions, point queues, and deterministic physical queue models. Recently, simulation based models apply well accepted traffic flow theoretic models to simulate traffic flow. However, a significant number of papers over the last decade have adopted an approximation of LWR traffic flow model, the cell transmission model, for simulating traffic flow in a DTA model. This paper compares three models, namely, LWR, Payne and Aw-Rascle, models, for their suitability to be embedded in a DTA model. Model calibration and flow simulation is performed separately using two different speed–density relationships. Results showed the importance of choice of speed-density relationship in traffic flow simulation. Models were used to simulate traffic state at different discretization levels and it was observed that as discretization becomes finer, the models' accuracy increases. Finally, the models were applied to a two node, two link network to analyze their performance in a DTA framework. The higher-order models captured congestion dissipation better than LWR model which consistently underestimates congestion and travel time.

INTEGRATION Framework for Modeling Eco-routing Strategies: Logic and Preliminary Results

The paper presents the INTEGRATION microscopic traffic assignment and simulation framework for modeling eco-routing strategies. Two eco-routing algorithms are developed: one based on vehicle sub-populations (ECO-Subpopulation Feedback Assignment or ECO-SFA) and another based on individual agents (ECO-Agent Feedback Assignment or ECO-AFA). Both approaches initially assign vehicles based on fuel consumption levels for travel at the facility free-flow speed. Subsequently, fuel consumption estimates are refined based on experiences of other vehicles within the same class. The proposed framework is intended to evaluate the network-wide impacts of eco-routing strategies. This stochastic, multi-class, dynamic traffic assignment framework was demonstrated to work for two scenarios. Savings in fuel consumption levels in the range of 15 percent were observed and potential implementation challenges were identified.

Dynamic marginal cost, access control, and pollution charge: a comparison of bottleneck and whole link models

SUMMARYIn this paper, we investigate theoretical constructions and properties of three interrelated travel demand management measures including marginal cost pricing, access control, and pollution charge under dynamic traffic assignment framework. For congested traffic networks modeled by the two vertical queue models, that is, the whole link model and the deterministic queuing model, on which flows are controlled, we derive dynamic marginal costs for paths and users' external costs for controlled links. As a strategy to implement the access control, the access pricing is formulated as a dynamic system optimal assignment with access (e.g., traffic volume, queue) control problem, wherein the access constraints represent the restrictions on the traffic volumes and/or environmental constraints. For the whole link model case, an optimal control problem formulation is adopted to investigate the dynamic traffic equilibrium. We derive and discuss the necessary condition for operating the transportation system with capacity/environmental constraints optimally. For the deterministic queuing model case, the inflow to a bottleneck is saturated such that no queue would be formed. The dynamic externalities of the two models are compared. It is found that different externality structures of the two models result in different tolling structures to achieve dynamic system optimal assignment. On the basis of this access pricing analysis and an “equivalent” environmental capacity that converts the environmental constraint into traffic volume restriction, we investigate the traffic‐induced air pollution pricing scheme. Copyright © 2012 John Wiley & Sons, Ltd.

Integration of Activity-Based and Agent-Based Models

This study explored the possibility of integrating an operational activity-based model and a dynamic traffic assignment framework. The Tel Aviv, Israel, activity-based model and parts of the functionality of the MATSim agent-based framework were used in an attempt to draw on the best features of both approaches: the disaggregate demand representation from the activity-based model and the disaggregate supply representation of the agent-based framework. The study used the person–activity schedule produced by the activity-based model directly and thus eliminated the need to aggregate origin–destination matrices. This paper compares results produced by this combination with those of a static assignment of the Tel Aviv model, which showed a good fit at the aggregate level. The purpose of the paper is to advance the fully disaggregate implementation of activity-based models. The paper represents a step toward this general goal.

Enhanced Lagged Cell-Transmission Model for Dynamic Traffic Assignment

The lagged cell-transmission model (L-CTM) is an enhanced version of the CTM. Both can be incorporated into a dynamic traffic assignment framework for offline transport planning and policy evaluation and online intelligent transportation system applications. In contrast to the CTM, the L-CTM adopts a nonconcave flow-density relation, which can be used to predict the existence of rather dense traffic in queues coasting toward the end of the queue or to help disprove the existence of this phenomenon. However, this study shows that the L-CTM can yield unrealistic densities, namely, negative densities and densities higher than theoretical jam density, the former of which has not been addressed in the literature. To cope with these unrealistic results, this study improves the L-CTM by introducing one more term in each sending and receiving function of the model. The improved model, the enhanced L-CTM (EL-CTM), is proved to yield nonnegative densities not greater than the jam density but can still allow the use of nonconcave density relations. The EL-CTM yields Lighthill-Whitham-Richards solutions when cell lengths and time intervals tend to zero and includes the CTM and the L-CTM as special cases. The EL-CTM is also shown to give more accurate solutions than the L-CTM (and hence also the CTM) does under a small increase in computation time. Hence the EL-CTM is believed to be more suitable for both online and offline applications in the future.

Incorporating Automated Vehicle Identification Data into Origin-Destination Estimation

A methodology for the incorporation of automated vehicle identification (AVI) data into origin-destination (O-D) estimation and prediction is presented. AVI technologies facilitate the collection of useful data, such as point-to-point travel times and subpath flows. A framework for the incorporation of AVI data into the well-established O-D estimation and prediction process is presented. Improvements are proposed for both the formulation and the inputs to the O-D estimation and prediction model. Furthermore, as the O-D estimation and prediction process is often used in the traffic estimation and prediction context, approaches to the incorporation of AVI data into other areas of the dynamic traffic assignment framework are outlined. Performance and computational issues are also considered, and the results of a case study are presented to demonstrate the approach.