Parallel implementations of Dynamic Traffic Assignment models

This study proposes an analytical capacity constrained dynamic traffic assignment (DTA) model along with an efficient path-based algorithm. The model can be applied to analyzing dynamic traffic demand management (TDM) strategies, but its specific feature is allowing for an evaluation of advanced traveler information systems (ATIS) within the strategic transportation planning framework. It is an extension of a former DTA model, where each link is given an infinite capacity and is assumed to be completely traversed inside any time interval it is reach by a path. Thereby, the length of time intervals should be very longer than the link travel times, and the link capacity constraints are overlooked. The paper rests on three key ideas to overcome these restrictions: (1) adding path-link fraction variables to the base model, allowing path flows to spread out over time intervals on long links; (2) uniformly dividing each link into smaller parts (segments), so that each part is more likely to be traversed inside a time interval; (3) imposing a dynamic penalty function on each link, thereby the capacity constraint can be included. The proposed DTA algorithm decomposes the augmented model in terms of origin-destination (OD) pairs and departure time intervals, and utilizes a dynamic column generation technique for generating active paths between the OD pairs. The optimal solution to a one-link network demonstrates that the model is able to approximate the dynamic flow propagation over a link with sensible accuracy. Besides, investigation of the results for a small test network reveals that the algorithm performs very well in computing temporal link flows and queuing delays. Finally, numerical experiments on a real life network indicate that the algorithm converges sufficiently fast and provides path information for each time interval. The network is further used to show the algorithm is capable of assessing a dynamic TDM strategy as well as an ATIS system.

A dynamic traffic assignment model for the assessment of moving bottlenecks

Application of a simulation-based dynamic traffic assignment model

Joint Calibration for DTA Model Using Islands-GA and PC-SPSA

As a subsystem of an Intelligent Transportation System (ITS), an Advanced Traveller Information System (ATIS) disseminates real-time traffic information to travellers. To help travellers better make their route choice decisions, there is a strong need to predict traffic congestion and disseminate the predicted congestion information to travellers. This paper offers some insights and predictions on how ATIS information provision is becoming more pervasive due to recent advances in telecommunication systems. The paper also discusses how ATIS systems will likely evolve based on the experiences of Information Service Providers (ISP) and ATIS modelling specialists. Then, it reviews four types of prediction models: 1) simulation models; 2) dynamic traffic assignment (DTA) models; 3) statistical models; and 4) heuristic models. The functional requirements and capabilities of the four types of prediction models are discussed and summarised. Furthermore, a comprehensive prediction procedure is presented, which combines the four types of prediction models.

Forecasting short-term, nonrecurring traffic dynamics caused by incidents is an essential capability in the Intelligent Transportation Systems. This research proposes a prediction framework in which Conditional Deep Convolutional Generative Adversarial Nets (C-DCGAN) is trained to predict the traffic spillbacks patterns associated with freeway incidents at merging bottleneck. Speed tensors, which depict the spatiotemporal incident-induced impacts for multiple neighboring routes, is a suitable object for the GAN model to understand and predict. Further, we demonstrated how to use the mesoscopic Dynamic Traffic Assignment (DTA) model DynusT to generate a large number of training data, thus speeding up the model training. The developed model achieves both statistical and spatial similarities between predicted speed tensors and actual tensors, to 83.84%. To the best of our knowledge, this line of work is one of the first attempts in the literature to train the Machine Learning model to predict speed tensor representation of multi-location incident-induced spatiotemporal impact at merging bottleneck and speeding up the training via simulation.

A heuristic approach to time-dependent origin-destination traffic demand estimation for the development of large-scale dynamic traffic assignment models

Case‐Based Reasoning for Real‐Time Traffic Flow Management

A traffic incident is one of the major sources for degrading network capacity, inducing traffic congestion, and decreasing network reliability. The impacts of a traffic incident on network reliability have been extensively studied with the use of static network equilibrium or dynamic simulation models. In this paper, an analytical reliability-based dynamic traffic assignment (DTA) model is proposed for assessing the temporal and spatial impacts of a traffic incident on network reliability. The proposed DTA model can be used to estimate the stochastic link flow pattern and route travel time distribution for examining the impacts of a traffic incident on the on-time arrival probability with and without dynamic speed limit (SL) control. It is shown that a traffic incident on a congested road during peak period will greatly decrease the on-time arrival probability, particularly when the incident has greater effects on link capacity degradation with longer duration. Under certain circumstances, SL control can be employed to reduce total network delay during the time intervals with a traffic incident.

Route swapping in dynamic traffic networks

A Dynamic Traffic Assignment Measure of Stability Based on Network Efficiency

Currently, the models and theories for dynamic network traffic assignment have a great deal of limitations and couldn’ t describe and predict the state flow in the dynamic network in real-time online, and couldn’ t support setting the dynamic information platform of ITS. In this paper, we present a new real-time, network, space-time and ahead of time (predictive) model for dynamic network traffic assignment. We derive the splitting parallel algorithms of this model, and could simulate.It can follow, recognize and predict the future state of dynamic traffic flow. We provide theories and methods to the ITS that integrating the function such as dynamic traffic guidance, dynamic traffic system management and control, and dynamic traffic jam easing.

The mathematical models described in the previous chapters are based on the assumptions of intra-period stationariety. This is equivalent to assuming, as stated in Chapter 1, that all significant variables are constant, at least on average, over successive sub-intervals of a reference period long enough to allow the system to reach stationariety condition. This assumption, although acceptable for many applications, does not allow for the satisfactory simulation of some transportation systems such as heavily congested urban road networks or low frequency scheduled services. In the first case, some important phenomena cannot be reproduced by traditional intra-period static models, including demand peaks, temporary capacity variations, temporary over-saturation of supply elements, and formation and dispersion of queues. In the second case, low-frequency services (e.g. two flights per day) may call into question the assumption of intra-period uniform supply and mixed preventive-adaptive users’ choice behavior introduced in the previous chapters. To simulate these aspects, different intra-periodal or within-day dynamic models have recently been developed; these models are usually referred to in the literature as (within-day) Dynamic Traffic Assignment (DTA) models, implying that dynamic assignment models require within-day dynamic demand and supply models.

A Convex Control Model of Dynamic System Optimal Traffic Assignment

The traffic flow patterns in a network and traffic signal optimization are mutually coupled. The conventional traffic signal optimization methods disregard the spatiotemporal variation in network flows. In multi-class disordered traffic, these network flows depend on the vehicle-class-specific characteristics. The study proposes a control strategy combined with dynamic traffic assignment, incorporating the features of multi-class disordered traffic. The Combined Dynamic Traffic Assignment and Control (CDTAC) model is applied to test cases simulated with smaller and larger vehicles, for different levels of congestion, and various proportions of smaller vehicles. Results showed that, in congestion, links with a greater number of smaller vehicles require less green time, and vice versa in the uncongested case. This is because the class-specific speed dominates in congestion over the free-flow speed in determining optimum traffic signal parameters. The proposed CDTAC model yields lower system travel time than fixed control benchmarks that neglect class-specific dynamics.