Surrogate model for real time signal control: theories and applications

Traffic signal controls play a vital role in urban road traffic networks. Compared with fixed-time signal control, which is solely based on historical data, real time signal control is flexible and responsive to varying traffic conditions, and hence promises better performance and robustness in managing traffic congestion. Real time signal control can be divided into model-based and model-free approaches. The former requires a traffic model (analytical or simulation-based) in the generation, optimisation and evaluation of signal control plans, which means that its efficacy in real-world deployment depends on the validity and accuracy of the underlying traffic model. Model-free real time signal control, on the other hand, is constructed based on expert experience and empirical observations. Most of the existing model-free real time signal controls, however, focus on learning-based and rule-based approaches, and either lack interpretability or are non-optimised. This thesis proposes a surrogate-based real time signal control and optimisation framework, that can determine signal decisions in a centralised manner without the use of any traffic model. Surrogate models offer analytical and efficient approximations of complex models or black-box processes by fitting their input-output structures with appropriate mathematical tools. Current research on surrogate-based optimisation is limited to strategic and off-line optimisation, which only approximates the relationship between decisions and outputs under highly specific conditions based on certain traffic simulation models and is still to be attempted for real time optimisation. This thesis proposes a framework for surrogate-based real time signal control, by constructing a response surface that encompasses, (1) traffic states, (2) control parameters, and (3) network performance indicators at the same time. A series of comprehensive evaluations are conducted to assess the effectiveness, robustness and computational efficiency of the surrogate-based real time signal control. In the numerical test, the Kriging model is selected to approximate the traffic dynamics of the test network. The results show that this Kriging-based real time signal control can increase the total throughput by 5.3% and reduce the average delay by 8.1% compared with the fixed-time baseline signal plan. In addition, the optimisation time can be reduced by more than 99% if the simulation model is replaced by a Kriging model. The proposed signal controller is further investigated via multi-scenario analyses involving different levels of information availability, network saturation and traffic uncertainty, which shows the robustness and reliability of the controller. Moreover, the influence of the baseline signal on the Kriging-based signal control can be eliminated by a series of off-line updates. By virtue of the model-free nature and the adaptive learning capability of the surrogate model, the Kriging-based real time signal control can adapt to systematic network changes (such as seasonal variations in traffic demand). The adaptive Kriging-based real time signal control can update the response surface according to the feedback from the actual traffic environment. The test results show that the adaptive Kriging-based real time signal control maintains the signal control performance better in response to systematic network changes than either fixed-time signal control or non-adaptive Kriging-based signal control.Open Acces

From cellular attractor selection to adaptive signal control for traffic networks

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains

APPLICATION OF FUZZY LOGIC TO TRAFFIC SIGNAL CONTROL UNDER MIXED TRAFFIC CONDITIONS

Traffic signal control is commonly used at road intersections to minimise vehicular delay. Fixed time control shows good results in conditions where there is a little fluctuation in traffic demand, however in time-varying traffic fixed time control becomes inflexible and inefficient. This may produce traffic congestion and lead to increased delays and air pollution. Demand responsive traffic signal control must be introduced to overcome these problems. However, all the available demand responsive traffic signal control methods such as Vehicle Actuated Controller (VAC), Traffic Optimisation Logic (TOL), Microprocessor Optimised Vehicle Actuation (MOVA) and Fuzzy Logic Traffic Signal Controllers (FLTSC) have been developed for non-mixed traffic conditions, considering only motor vehicles move in clearly defined lanes, neglecting motorcycles. These demand responsive traffic signal controls are not appropriate for the mixed traffic conditions of developing countries such as Indonesia, where the traffic streams consist of different types of vehicle with a wide variation in their static, dynamic and operating characteristics, and with a particularly high proportion (30% - 70%) of motorcycles. Also there is lack of lane discipline. This thesis describes the design and evaluation of an adaptive traffic signal controller based on fuzzy logic for an isolated four-way intersection with specific reference to mixed traffic in developing countries, including a high proportion of motorcycles. Four proposed controllers have been developed for different schemes. The controllers were designed to be responsive to real time traffic demands. The study identifies two traffic parameters as appropriate as input data for an adaptive traffic signal controller under mixed traffic conditions such as the proposed FLTSC: the average occupancy rate (%) and maximum queue length (metres). The literature study suggest that this data should be collected using advances video image processing. The proposed FLTSC uses maximum queue lengths and average occupancy rates collected during the previous cycle to estimate the number of seconds of green time required by each set of signal groups during the next cycle. The effectiveness of the proposed FLTSC was analysed using the microscopic traffic simulation model VISSIM. Prior to doing so, the VISSIM model was calibrated and validated. From the validation process it was apparent that the VISSIM model could be adapted to simulate mixed traffic conditions by use of the Packet approach. In this approach, motorcycles are modelled as a group of motorcycles. The performance of the proposed FLTSC was contrasted with a Fixed Time Controller (FTC) for different case studies on a simulated four-way intersection. The FTC is represented by the calculation as suggested in the Indonesian Highway Capacity Manual. Separate analysis using TRANSYT show that this is a valid assumption to make. The simulation results show that the proposed FLTSC is generally better than the FTC in terms of the average delay of vehicles at an intersection, especially under time-varying traffic. Further analysis was carried out to compare the performance of the proposed FLTSC against a Vehicle Actuated Controller (VAC) for different traffic conditions on a simulated four- way intersection, East-West and North-South without turning movements. In order to analyse the performance of VAC, a refined VISSIM model was developed. This used the latest version of the VISSIM software and allowed individual vehicles (and particularly motorcycles) to be modelled in mixed traffic. The phase extension time is one of the most critical parameters to affect the overall performance of VAC (Bullen, 1989). To provide a fair comparison of the performance between the proposed FLTSC and the VAC, an investigation was carried out to find the most appropriate extension time for the VAC that was suitable for mixed traffic. The effect of motorcycles to the performance of the VAC was also investigated. Two schemes were carried out to observe it, namely: Scheme 1 where detector detects all vehicle types (DfT, 2006) and Scheme 2 where detector detects all vehicle types, apart from motorcycles. The simulation results show that the VAC System D (DfT, 2006) using an extension time of 1.2 seconds and the VAC Extension Principle (Kell and Fullerton, 1991) with a detector position of 30 metres and extension time of 3.0 seconds produced better performance than the other extension times tested for both schemes in terms of the average delay of vehicles. This is slightly shorter than current practice in developed countries. The simulation results indicate that the performance of the VACs with scheme 1 is generally worse than with scheme 2. The performance of the VACs with scheme 1 against scheme 2 tended to reduce significantly as the percentage of motorcycles in traffic increased. The study compares the effectiveness of FTC, VAC Extension Principle (VAC-EP), VAC System D (VAC-SD) and proposed FLTSC in various traffic conditions. The simulation results indicate that the average delay of the proposed FLTSC is close to the average delay of the FTC when used in cases with constant traffic flows but sometimes worse. However, in cases of time-varying traffic the proposed FLTSC is superior to the FTC. When comparing the simulation results of the proposed FLTSC, VAC-SD and VAC-EP, again the proposed FLTSC does not improve average delay, when traffic flows constant but produces better results in cases of time-varying traffic

Reinforcement Learning Approaches for Traffic Signal Control under Missing Data

The emergence of reinforcement learning (RL) methods in traffic signal control tasks has achieved better performance than conventional rule-based approaches. Most RL approaches require the observation of the environment for the agent to decide which action is optimal for a long-term reward. However, in real-world urban scenarios, missing observation of traffic states may frequently occur due to the lack of sensors, which makes existing RL methods inapplicable on road networks with missing observation. In this work, we aim to control the traffic signals in a real-world setting, where some of the intersections in the road network are not installed with sensors and thus with no direct observations around them. To the best of our knowledge, we are the first to use RL methods to tackle the traffic signal control problem in this real-world setting. Specifically, we propose two solutions: the first one imputes the traffic states to enable adaptive control, and the second one imputes both states and rewards to enable adaptive control and the training of RL agents. Through extensive experiments on both synthetic and real-world road network traffic, we reveal that our method outperforms conventional approaches and performs consistently with different missing rates. We also provide further investigations on how missing data influences the performance of our model.Comment: Published as a conference paper at IJCAI202

PRIORITY BASED TRAFFIC LIGHTS CONTROLLER USING WIRELESS SENSOR NETWORKS

Vehicular traffic is continuously increasing around the world, especially in large urban areas. The resulting congestion has become a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of performance, cost, maintenance, and support. In this paper, the design of a system that utilizes and efficiently manages traffic light controllers is presented. In particular, we present an adaptive traffic control system based on a new traffic infrastructure using Wireless Sensor Network (WSN). These techniques are dynamically adaptive to traffic conditions on both single and multiple intersections. An intelligent traffic light controller system with a new method of vehicle detection and dynamic traffic signal time manipulation is used in the project. The project is also designed to control traffic over multiple intersections and follows international standards for traffic light operations. A central monitoring station is designed to monitor all access nodes.

Utilization-Aware Adaptive Back-Pressure Traffic Signal Control

Back-pressure control of traffic signal, which computes the control phase to apply based on the real-time queue lengths, has been proposed recently. Features of it include (i) provably maximum stability, (ii) low computational complexity, (iii) no requirement of prior knowledge in traffic demand, and (iv) requirement of only local information at each intersection. The latter three points enable it to be completely distributed over intersections. However, one major issue preventing backpressure control from being used in practice is the utilization of the intersection, especially if the control phase period is fixed, as is considered in existing works. In this paper, we propose a utilization-aware adaptive algorithm of back-pressure traffic signal control, which makes the duration of the control phase adaptively dependent on the real-time queue lengths and strives for high utilization of the intersection. While advantages embedded in the back-pressure control are kept, we prove that this algorithm is work-conserving and achieves the maximum utilization. Simulation results on an isolated intersection show that the proposed adaptive algorithm has better control performance than the fixed-period back-pressure control presented in previous works

Evaluation of Automatic Vehicle Specific Identification (AVSI) in a traffic signal control system

Automatic Vehicle Specific Identification (AVSI) is a generic name for advanced vehicle detection systems. By automating the identification of vehicles by sensing the presence of vehicles with roadside detection sites or readers, AVSI is assumed to provide vehicle specific information in traffic signal control systems;In the application of AVSI to traffic signal control systems, as a vehicle passes a reader site, the reader records the arrival time and type of the detected vehicle. The reader would then send the information received to a local microprocessor-based traffic signal controller. The controller\u27s built-in signal control logic would then use the information to adjust traffic signal timing to reflect the present traffic stream\u27s characteristics;The purpose of this research is to evaluate the potential benefits of AVSI at an isolated intersection. The evaluation of the applicability of AVSI at an intersection is accomplished by using a new developed microscopic simulation model. This simulation model is coded in SIMAN simulation language. For the purpose of validating the simulation model, a delay study is conducted at an actual intersection. The validation of the model has established a level of confidence in the obtained simulation results;An important element of this simulation model is the development of a new Vehicle Specific Adaptive (VSA) traffic signal control strategy. VSA control strategy adjusts the signal timing based on AVSI traffic information, that is, it examines individual vehicle performance characteristics before extending a phase green time or implementing a new cycle split;Using the simulation model, the incorporated VSA control strategy is tested against a pretimed control system. The simulation results indicates that through the use of AVSI traffic information, the VSA control logic can improve intersection performance by reducing vehicles stopped delay at an intersection