Path-Based Dynamic User Equilibrium Model with Applications to Strategic Transportation Planning

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.

A dynamic traffic assignment model for the assessment of moving bottlenecks

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

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

The evaluation of ITS measures, such as advanced traveler information systems and advanced traffic management systems depend heavily on the use of faster than real time traffic simulation methods. Although many micro-simulation traffic models have already been developed, their execution times are still too slow for large-scale ITS applications. This has lead to the development of mesoscopic simulation and traffic assignment methods. These are simpler traffic models, which trade off some of the fidelity of the results for significant gains in computation time. This paper presents a new dynamic traffic assignment model that is based on the mesoscopic space-time queue network loading method developed by Mahut (2000). This hybrid optimization simulation method was applied to a portion of the Stockholm road network, which consists of 220 zones, 2080 links and 5000 turns.

The traditional trip-based approach to transportation modeling has been used for the past 30 years. Because of limitations of traditional planning for short-term policy analysis, researchers have explored alternative paradigms for incorporating more behavioral realism in planning methodologies. On the demand side, activity-based approaches have evolved as an alternative to traditional trip-based transportation demand forecasting. On the supply side, dynamic traffic assignment models have been developed as an alternative to static assignment procedures. Much of the research effort in activity-based approaches (the demand side) and dynamic traffic assignment techniques (the supply side) has been undertaken relatively independently. To maximize benefits from these advanced methodologies, it is essential to combine them through a unified framework. The objective of this paper is to develop a conceptual framework and explore practical integration issues for combining the two streams of research. Technical, computational, and practical issues involved in this demand–supply integration problem are discussed. The framework is general, but specific technical details related to the integration are explored by using CEMDAP for activity-based modeling and VISTA for dynamic traffic assignment modeling. Solution convergence properties of the integrated system, specifically examining different criteria for convergence, different methods of accommodating time of day, and the influence of step size on convergence are studied. The integrated system developed is empirically applied to two sample networks selected from the Dallas–Fort Worth system in Texas.

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.

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.

Dynamic traffic assignment (DTA) applications require traffic signal control data that are typically difficult to obtain and cumbersome to code in the required format. In addition, the evaluation of future scenarios requires future traffic signal settings consistent with the forecast demand. These signal settings are not known a priori and are costly to estimate. Intuitively, future signal timings need to be reasonably optimized so as to represent what the traffic management agency will do. In the literature, integration between traffic control and DTA models has been formulated as a bi-level or single-level optimization problem with system or user optimal constraints. Most existing solution procedures require certain nested structure with an inner-loop algorithm solving the problem of user-equilibrium or system-optimal assignment and the outer-loop algorithm searching for the optimal signal-timing settings. Most of these solution approaches remain only research tools without practical use because of computational intractability. This research proposes an efficient solution algorithm to the problem. An adaptive traffic signal control model is embedded in a simulation-based DTA model. For each inbound approach at an intersection of interest, the adaptive model uses upstream information and a dynamic rolling-horizon approach to project traffic flow conditions for a dynamic but short (projection) period. The adaptive model provides the signal settings during the entire process of traffic flow simulation and for every iteration of the solution algorithm. Thus, during the entire solution process, the experienced travel times and resulting traffic assignment flows are based on the adaptive (demand-responsive) signal settings, allowing the DTA flows and the adaptive signal settings to be generated simultaneously in a single-loop algorithmic structure. Simulation experiments illustrate the capabilities of the proposed approach.

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

In this paper we analyze measurements gathered from a 2.5 Gbps link in the Finnish university network (Funet) in 2004. The traffic is broken down into origin-destination (OD) pair components based on source and destination IP address. We study the traffic characteristics of these components, and identify four typical representative OD pairs. For these pairs we investigate the validity of a moving IID Gaussian model. We find that the statistical properties of these OD pairs differ significantly from each other, with only some of them close to Gaussian. The OD pairs are also found to have some cross-correlation between each other, contradicting an often made assumption about OD pair independence. Furthermore, the existence of a mean-variance relation between the OD pairs is studied. We find that there is a relation between mean and variance, but for some periods of time it is rather weak.

An important class of models for macroscopic dynamic network loading (DNL) and dynamic traffic assignment (DTA) is based on treating link travel times as a function of link occupancy. However, these models suffer from some problems or deficiencies namely (a) the link outflows can violate first-in-first-out (FIFO), (b) the link outflows can exceed the link outflow capacities, (c) the link inflows can exceed the link inflow capacities, and (d) the link occupancies can exceed the link occupancy capacities. In this paper we introduce methods to overcome each of these problems.To remove problems (a) and (b) we extend the link travel-time model to better reflect behaviour when traffic flow is varying over time. To remove problems (c) and (d) we introduce more substantial changes in the model, to introduce capacities, spillback and queues compatible with the model. These extensions strengthen the realism, behavioural basis and usability of the link travel-time model and the DNL and DTA models that are based on it. They have no obvious adverse implications or side effects and require little additional computational effort. The original model is a special case of the new/extended model: the above extensions are activated if and only if any of the problems (a)–(d) arise, otherwise the new model reduces to the original model.

This article intends to provide an advanced traveller information system based on open geospatial consortium (OGC) standards to perform a fuzzy bi-criteria shortest-path analysis for a multimodal transportation network. To do so, chaining of OGC web services is used to resolve interoperability problems among distributed and heterogeneous resource data. First, a web feature service is assigned to each mode of transportation to gather and convert the data to a standard format, such as geographic markup language. Then, the obtained data are combined to model and construct the multimodal transportation network. Subsequently, a web processing service is designed for performing a fuzzy bi-criteria shortest-path analysis in the multimodal transportation network. Finally, the best routes between the origin–destination pair are presented on a digital map by using a web map service. In the study, among various route-selection criteria, travel time and comfort are considered. To model these criteria, the fuzzy set theory is utilized. By using fuzzy decision tables and a Tsukamoto fuzzy inference system, the desirability of the routes is calculated. The results of comparison among the four travel routes between various origin–destination pairs indicate that the proposed system could be an applicable tool for providing pre-trip information and assisting multimodal travellers under uncertain conditions.

Route swapping in dynamic traffic networks

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.