The efficiency of a transport system depends on several elements, such as available technology, governmental policies, the planning process, and control strategies. Indeed, the interaction between these elements is quite complex, leading to intractable decision making problems. The planning process and real-time control strategies have been widely studied in recent years, and there are several practical implementations with promising results. In this paper, we review the literature on Transit Network Planning problems and real-time control strategies suitable to bus transport systems. Our goal is to present a comprehensive review, emphasizing recent studies as well as works not addressed in previous reviews.

Planning, operation, and control of bus transport systems: A literature review

A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cross-blocking. A five-intersection traffic network has been studied in which each intersection is governed by an autonomous intelligent agent. Two types of agents, a central agent and an outbound agent, were employed. The outbound agents schedule traffic signals by following the longest-queue-first (LQF) algorithm, which has been proved to guarantee stability and fairness, and collaborate with the central agent by providing it local traffic statistics. The central agent learns a value function driven by its local and neighbours' traffic conditions. The novel methodology proposed here utilises the Q-Learning algorithm with a feedforward neural network for value function approximation. Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings.

Reinforcement learning-based multi-agent system for network traffic signal control

In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.

Traffic signal timing via deep reinforcement learning

http://userweb.eng.gla.ac.uk/konstantinos.ampountolas/files/pubs/CEP2002.pdf

A multivariable regulator approach to traffic-responsive network-wide signal control

The control of a network of signalized intersections is considered. Vehicles arrive in iid (independent, identically distributed) streams at entry links, independently make turns at intersections with fixed probabilities or turn ratios, and leave the network upon reaching an exit link. There is a separate queue for each turn movement at each intersection. These are point queues with no limit on storage capacity. At each time the control selects a ‘stage’, which actuates a set of simultaneous vehicle movements at given iid saturation flow rates. Network evolution is modeled as a controlled store-and-forward (SF) queuing network. The control can be a function of the state, which is the vector of all the queue lengths. A set of demands is said to be feasible if there is a control that stabilizes the queues, that is the time-average of every mean queue length is bounded. The set of feasible demands D is a convex set defined by a collection of linear inequalities involving only the mean values of the demands, turn ratios and saturation rates. If the demands are in the interior Do of D, there is a fixed-time control that stabilizes the queues. The max pressure (MP) control is introduced. At each intersection, MP selects a stage that depends only on the queues adjacent to the intersection. The MP control does not require knowledge of the mean demands. MP stabilizes the network if the demand is in Do. Thus MP maximizes network throughput. MP does require knowledge of mean turn ratios and saturation rates, but an adaptive version of MP will have the same performance, if turn movements and saturation rates can be measured. The advantage of MP over other SF network control formulations is that it (1) only requires local information at each intersection and (2) provably maximizes throughput. Examples show that other local controllers, including priority service and fully actuated control, may not be stabilizing. Several modifications of MP are offered including one that guarantees minimum green for each approach and another that considers weighted queues; also discussed is the effect of finite storage capacity.

Max pressure control of a network of signalized intersections

Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. After illustrating the main reasons for infrastructure deterioration due to traffic congestion, a comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway networks, and route guidance. Selected application results, obtained from either simulation studies or field implementations, are briefly outlined to illustrate the impact of various control actions and strategies. The paper concludes with a brief discussion of future needs in this important technical area.

/pdf/review-of-road-traffic-control-strategies-2pzcx3izzi.pdf

Review of road traffic control strategies

The objectives, approach, advantages, and some application results of recent extensions of the traffic-responsive urban control (TUC) strategy are presented. Based on well-known methods of the automatic control theory, TUC allows for traffic-responsive coordinated signal control of large-scale urban networks that is particularly efficient under saturated traffic conditions. The first version of the TUC strategy controlled only the green splits. After initial development and the first field implementations and evaluations, TUC was expanded to perform real-time cycle and offset control, and to allow for public transport priority. Simulation investigations of the extended TUC application in parts of the urban networks of Tel Aviv and Jerusalem, Israel, by use of the AIMSUN microscopic simulator demonstrate the high efficiency of the new signal control strategy.

Extensions and New Applications of the Traffic-Responsive Urban Control Strategy: Coordinated Signal Control for Urban Networks:

http://www.iasi.cnr.it/ewgt/13conference/67_dinopoulou.pdf

Applications of the urban traffic control strategy TUC

The recently developed network-wide real-time signal control strategy TUC has been implemented in three traffic networks with quite different traffic and control infrastructure characteristics: Chania, Greece (23 junctions); Southampton, UK (53 junctions); and Munich, Germany (25 junctions), where it has been compared to the respective resident real-time signal control strategies TASS, SCOOT and BALANCE. After a short outline of TUC, the paper describes the three application networks; the application, demonstration and evaluation conditions; as well as the comparative evaluation results. The main conclusions drawn from this high-effort inter-European undertaking is that TUC is an easy-to-implement, inter-operable, low-cost real-time signal control strategy whose performance, after very limited fine-tuning, proved to be better or, at least, similar to the ones achieved by long-standing strategies that were in most cases very well fine-tuned over the years in the specific networks.

International comparative field evaluation of a traffic-responsive signal control strategy in three cities.

The paper presents the objectives, the approach, the advantages, and some application results of the recent extensions of the traffic signal control strategy TUC (Traffic-responsive Urban Control). Based on well-known methods of the Automatic Control Theory, TUC allows for the traffic-responsive coordinated signal control of large-scale urban networks that is particularly efficient under saturated traffic conditions. The first version of the TUC strategy was controlling only the green splits. After the initial development and the first field implementations and evaluations, TUC was expanded so as to perform real-time cycle and offset control, and to allow for public transport priority. Simulation investigations of the extended TUC application in parts of the urban networks of Tel Aviv and Jerusalem, Israel, by use of the AIMSUN microscopic simulator demonstrate the high efficiency of the new signal control strategy.

Vaya Dinopoulou

Papers

Review of road traffic control strategies

Extensions and New Applications of the Traffic-Responsive Urban Control Strategy: Coordinated Signal Control for Urban Networks:

Applications of the urban traffic control strategy TUC

International comparative field evaluation of a traffic-responsive signal control strategy in three cities.

Extensions and new applications of the traffic signal control strategy TUC