A Model for Dynamic Network Traffic Flow Based Virtual Space-Time Network

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

Dynamic traffic management methods constitute the intelligent core of Intelligent Transportation Systems (ITS). In order for these methods to be effective and deployable in real-time, there is a need to develop models that predict future traffic conditions in a computational time much less than real time. In this paper we report on parallel implementations for a class of Dynamic Traffic Assignment (DTA) models, known as macroscopic DTA models. This class of models possess mathematical formulations which are solved using various algorithms. Two parallel decomposition strategies based on network topology and time are investigated and implemented in a distributed memory environment. Numerical results show that for the network topology based decomposition strategy, a speed-up of 5 is observed when the number of processors is 10 and the asymptotic speed-up is about 10. For the time-based decomposition strategy a speed-up of 6.5 is observed when the number of processors is 10 and the asymptotic speed-up is about 25.

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

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.

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

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

Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram

A Dynamic Traffic Assignment Measure of Stability Based on Network Efficiency

Case‐Based Reasoning for Real‐Time Traffic Flow Management

Application of a simulation-based dynamic traffic assignment model

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

Dynamic demands are the basic inputs of some existing dynamic assignment function. However, current data collection technologies do not directly support all the requirements of Dynamic Traffic Assignment (DTA) models. As a new traffic data collection technology, cell probe data could provide link flow, travel time and dynamic traffic demand at the same time. But these parameters are partial for a network.