Dynamic Traffic Assignment (DTA) has evolved substantially since the pioneering work of Merchant and Nemhauser. Numerous formulations and solutions approaches have been introduced ranging from mathematical programming, to variational inequality, optimal control, and simulation-based. The aim of this special issue is to document the main existing DTA approaches for future reference. This opening paper will summarize the current understanding of DTA, review the existing literature, make the connection to the approaches presented in this special issue, and attempt to hypothesize about the future.

Foundations of dynamic traffic assignment : the past, the present and the future

This paper develops a fuzzy-neural model (FNM) to predict the traffic flows in an urban street network, which has long been considered a major element in the responsive urban traffic control systems. The FNM consists of two modules: a gate network (GN) and an expert network (EN). The GN classifies the input data into a number of clusters using a fuzzy approach, and the EN specifies the input–output relationship as in a conventional neural network approach. While the GN groups traffic patterns of similar characteristics into clusters, the EN models the specific relationship within each cluster. An online rolling training procedure is proposed to train the FNM, which enhances its predictive power through adaptive adjustments of the model coefficients in response to the real-time traffic conditions. Both simulation and real observation data are used to demonstrative the effectiveness of the method.

Urban traffic flow prediction using a fuzzy-neural approach

This paper proposes different “dynamic” estimators using time-varying traffic counts to obtain (discrete) time-varying OD flows or average OD flows. All the estimators can combine counts with other available information, e.g., out-dated matrices and surveys, on a general network and can be formulated as optimization problems. In the case of time-varying OD flows, two types of estimators are proposed. Simultaneous estimators produce joint estimates of the whole set of OD matrices, one for each time slice, whereas sequential estimators produce a sequence of OD estimates for successive time slices. A particular type of simultaneous and sequential estimator (namely Generalized Least Squares) was tested on the Italian Brescia–Padua motorway for which “true” OD flows over a whole day were available. Results were generally satisfactory, showing that also in the “no a priori information” case significant estimates could be obtained.

Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts

Estimation and prediction of time-dependent origin-destination flows

With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation industry. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensitive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform and many other factors. Diverse variables and factors in the system are strongly endogenous and interactively dependent. How to design and operate ridesourcing systems is vital—and challenging—for all stakeholders: passengers/users, drivers/service providers, platforms, policy makers, and the general public. In this paper, we propose a general framework to describe ridesourcing systems. This framework can aid understanding of the interactions between endogenous and exogenous variables, their changes in response to platforms’ operational strategies and decisions, multiple system objectives, and market equilibria in a dynamic manner. Under the proposed general framework, we summarize important research problems and the corresponding methodologies that have been and are being developed and implemented to address these problems. We conduct a comprehensive review of the literature on these problems in different areas from diverse perspectives, including (1) demand and pricing, (2) supply and incentives, (3) platform operations, and (4) competition, impacts, and regulations. The proposed framework and the review also suggest many avenues requiring future research.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5517&context=sis_research

Ridesourcing systems: A framework and review

A new systems dynamics approach for the identification of origin-destination (O-D) flows in a traffic system is presented. It is the basic idea of this approach that traffic flow through a facility is treated as a dynamic process in which the sequences of short-time exit flow counts depend by causal relationships upon the time-variable sequences of entrance flow volumes. In that way enough information can be obtained from the counts at the entrances and the exits to obtain unique and bias-free estimates for the unknown O-D flows without further a priori information. Four different methods were developed: an ordinary least squares estimator involving cross-correlation matrices, a constrained optimization method, a simple recursive estimation formula and estimation by Kalman filtering. The methods need only moderate computational effort and are particularly useful for tracking time-variable O-D patterns for on-line identification and control purposes. An analysis of the accuracy of the estimates and a discussion of the convergence properties of the methods are given. Finally, a comparison with some conventional static estimation procedures is carried out using synthetic as well as real data from several intersections. These tests demonstrated that the presented dynamic methods are highly superior to conventional techniques and produce more accurate results.

M. Cremer

Papers

A new class of dynamic methods for the identification of origin-destination flows