ReviewA review on computational intelligence methods for controlling traffic signal timing
Introduction
The increasing volume of traffic in cities has a significant effect on the road traffic congestion and therefore the time it takes for road users to reach their destination. Widening roads and increasing their capacity is not sufficient by itself, as the intersection then become a bottleneck. Bottlenecks cannot be prevented, however, the way intersections are controlled has room for improvement.
Since the 1960’s different methods have been presented to manage intersections and for controlling traffic signals’ timing. As one of the first traffic signal controllers, fixed-time or pre-timed controllers applied historical data to determine appropriate time for traffic signals (Cai, 2010). Fixed-time method is not based on current traffic demands and cannot handle unexpected conditions in traffic. Based on the historical traffic volume, the cycle time is divided to several phases. A fixed amount of time is required for clearing the intersection and starting the next phase after each phase, called the safety time. The safety time increases per hour for the case of shorter cycle time. Therefore, there is a lower overall capacity for intersections with shorter cycle times. On the other hand, longer waiting times and longer queues are the consequences of longer cycle times. Webster proposed a formula based on the flow rate of each lane in a link to solve this issue. This formula is useful to find an optimal cycle and appropriate duration for green time in each phase (Webster, 1958). As fixed-time methods cannot predict traffic demand accurately, they are not appropriate for situations such as accidents, and other disturbances that may disrupt traffic conditions.
The next step for improving the control method was actuated or real time controllers. This type of controller emerged in the 1970’s. Traffic-actuated control methods utilize inductive detectors to observe the actual traffic situation. The traffic-actuated controller must have the ability to determine whether the last vehicle of the queue formed at the stop line during the red phase has passed. This detection is useful for having efficient extension or termination of green time, and it is performed by measuring the gap between vehicles. The green time is terminated when the gap between vehicles is larger than the threshold maximum gap. The optimal placement of detectors at an intersection impacts the performance of actuated method. In addition, by increasing the number of detectors the accuracy of the system is improved. In actuated methods, a pre-specified block period time is considered for extending the green time of a phase. Therefore, detection of sparse traffic can have a considerable influence on delay time (Koonce et al., 2008). MOVA (Vincent & Peirce, 1988), LHVORA (Kronborg & Davidsson, 1993), and SOS (Kronborg & Davidsson, 1996) are samples of actuated traffic control system.
Parameters like time, day, season, weather, and some unpredictable situations such as accidents and special events are highly influential on traffic load. Traffic-adaptive control systems was created to take these elements in account in order to more efficiently predict green times. Fixed-time and actuated method do not use a control policy or a parameterized value function. Furthermore, these systems do not utilize accumulative information for improving their performances. In adaptive traffic control systems, the traffic condition is sensed and monitored continuously and the timing of traffic signals is adjusted accordingly. It is useful to note that adaptive controllers and real-time ones are two different concepts, however, it is possible to have a system with both abilities. The controllers with real-time ability in response to sensory inputs are real-time systems, in which the parameters of the controller and internal logic remain unchanged. Alternatively, one of the special features of adaptive systems is their characteristic in adjusting their parameters and internal logic in response to the significant change of the environment (Abdulhai, Pringle, & Karakoulas, 2003).
Both SCATS (Sims & Dobinson, 1980) and SCOOTS (Hunt, Robertson, & Bretherton, 1982) are famous adaptive systems that gather data of the traffic flow in real-time at each intersection to control timing of traffic lights. To obtain traffic information SCATS counts vehicles at each stop line, and SCOOTS applies a set of advanced detectors placed upstream of the stop line. Using these detectors, SCOOTS gives a higher resolution of the traffic condition such as traffic flow and number of cars in the queue before they reach the stop line. SCATS and SCOOTS both use centralized control. OPAC (Gartner, Tarnoff, & Andrews, 1991) and RHODES (Mirchandani & Head, 2001) are two other adaptive traffic controlling systems that use distributed control. These systems are run locally and coordination between intersections are done by communication between neighbors. As an example, when an intersection releases number of vehicles informs the next intersection about the time and number of vehicles to expect.
The use of artificial intelligence methods to control traffic signals started in 1990’s (Malej & Brodnik, 2007). Multiple optimization and estimation methods have been applied for adaptive control. Machine learning techniques are beneficial to create adaptive controllers with the ability to address unpredictable traffic condition issues. More explanation about applying machine learning techniques in controlling traffic signals will be presented in Section 2.
In this paper a review of intelligent methods have been applied for controlling traffic signals are presented. To have a comparison between three key intelligent methods have used in this area, Q-learning, neural network (NN), and fuzzy logic systems (FLS) controllers are implemented for a single intersection. Implementing these methods for an isolated intersection gives the opportunity to focus on the behavior of each method accurately. The reminder of this paper is organized as follows. Section 2 reviews the background machine learning theory and related work in applying machine learning techniques for traffic signal timing. In Section 3, Q-learning, NN, and FLS are used for timing an isolated traffic signal. Related results of experiments are presented in Section 4. Finally, Section 5 concludes the paper.
Section snippets
Background and related work
In this section, the related work and the background useful to understand the material presented in the rest of this paper will be covered.
Traffic signal controllers for an isolated intersection
QLC, NNC, FLC, and fixed-time controller are four controllers designed and implemented in this study for controlling signal times for an isolated intersection with four approaching links Fig. 4.
In most of the previous reviewed studies, the timing of signal is done by extending or terminating the current traffic signal phase. In this case it is not possible to have a estimation of the end of a phase at the start of that phase, therefore traffic signals with timer are not suitable for them.
Experiments results and discussion
For testing different controllers, an intersection with four approaching links and four phases (A, B, C, D) is considered. The cycle times are divided between aforementioned four phases. Zones are the areas that vehicles are released into the intersection, as shown in Fig. 4. Here, four different entrances to the intersection create four zones. The simulation is modeled in PARAMICS version 6.8. and all controllers are implemented in Matlab R2011b.
Two different scenarios are considered for
Conclusion and future work
The main purpose of this paper is to present a review of the previous works that applied computational intelligence methods for traffic signal controlling. There are many different intelligent techniques applied for traffic signal timing, but there is not any study to compare the performance of these techniques in a similar platform. In this paper, we present some of the most valuable previous works applied computational intelligence methods; specially, Q-learning, NN, and FLS, to traffic
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