The dynamic adaptive control of traffic lights can be formulated as a Markov decision problem (MDP). This framework is hardly used, as solving an MDP can be very time-consuming and is only possible for simple infrastructures with a small number of traffic flows. Nevertheless, we show that the MDP framework can be used to construct control policies that approximately minimize the long-run average waiting time at intersections. The MDP-based approach is fast and thus allows real-time use of actual information on traffic conditions, like queue lengths and the arrival times of near-future arrivals. Simulation of an isolated intersection as well as a small network shows that the new policies with arrival information improve pretimed as well as exhaustive control. The new control policies and underlying algorithms scale up well to control networks.

Traffic Responsive Control of Intersections with Predicted Arrival Times: A Markovian Approach

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Solving large structured Markov Decision Problems for perishable inventory management and traffic control

A brief historical overview of empirical and theoretical investigations of vehicular traffic in traffic and transportation networks is presented. This critical overview is based on a consideration of works made by several generations of traffic researchers. It includes more than 1200 references to most well-known empirical results and theoretical approaches. The main focus of this overview is to explain the reason for a paradigm shift that happened in transportation science during last 20 years through the understanding of the empirical nucleation nature of traffic breakdown at highway bottlenecks. We consider also the basic terms and definitions of traffic characteristics of traffic and transportation networks that are used in the book, the objectives of this book as well as the book structure.

Introduction—The Reason for Paradigm Shift in Transportation Science

In this paper, the authors evaluate the impacts of a group-based traffic signal control policy on driver behavior and intersection safety in Japan. Also known as movement-based, this policy refers to a control patten in which the controller is able to separately allocate time to each signal group instead of allocating based on traffic demand. Using data collected at an intersection before and after such a policy was implemented, the authors draw conclusions regarding the potential safety benefits of implementing such a policy. In addition, the authors discuss conclusion regarding the influence of group-based control policy on the occurrence probability and rate of red light running (RLR) and yellow entry time at intersections.

https://www.iatss.or.jp/common/pdf/en/publication/iatss-research/32-2-07.pdf

Impacts of group-based signal control policy on driver behavior and intersection safety

This study intends to investigate operational performance of the group-based signal control approach (GA) in Japan. A cycle-based stochastic approach incorporating the HCM 2000 delay model and Monte Carlo simulation was proposed. To facilitate its application, extensive field data was collected at 11 typical intersections in Germany and Japan to understand the variability of input variables and their correlations. A series of case studies were eventually conducted to investigate the applicability of GA under various conditions, i.e., degree of saturation (DS), split ratio (SR), and the coefficient of variation (COV) of traffic demand. Conclusions supported that the operational performance of GA deteriorated with increasing SR and decreasing DS. GA is apparently advantageous for SRs less than 0.8, COVs less than 0.2, and optimum cycle lengths greater than 90 s.

Investigating Operational Benefits of Group-based Signal Control in Japan Through a Stochastic Approach

The random component of delays and stops can play a significant role in discrete time-adaptive traffic signal control when implemented at isolated intersections. However, the use of Markov chain techniques to calculate expected delay and stops for the latter part of the rolling horizon, where accurate estimates of the undelayed arrival times of individual vehicles at the stopline are not available from detector observations, requires prohibitively large amounts of computer storage and processing. This paper sets out four approximate approaches for the calculation of expected delay and three for the calculation of expected stops, all requiring substantially less storage and processing. These methods are then assessed against results yielded by the Markov chain approach. The assessment suggests that approximations that propagate the first and second moment of the queue-length distribution forward in time give the best results. This approach has the added advantage that it does not presuppose a particular form of vehicle arrival distribution.

David Brookes

Papers

Discrete time-adaptive traffic signal control: the calculation of expected delays and stops