Multi-objective Optimization in Traffic Signal Control

Traffic Signal Control systems are one of the most popular Intelligent Transport Systems and they are widely used around the world to regulate traffic flow. Recently, complex optimization techniques have been applied to traffic signal control systems to improve their performance. Traffic simulators are one of the most popular tools to evaluate the performance of a potential solution in traffic signal optimization. For that reason, researchers commonly optimize traffic signal timing by using simulation-based approaches. Although evaluating solutions using microscopic traffic simulators has several advantages, the simulation is very time-consuming. Multi-objective Evolutionary Algorithms (MOEAs) are in many ways superior to traditional search methods. They have been widely utilized in traffic signal optimization problems. However, running MOEAs on traffic optimization problems using microscopic traffic simulators to estimate the effectiveness of solutions is time-consuming. Thus, MOEAs which can produce good solutions at a reasonable processing time, especially at an early stage, is required. Anytime behaviour of an algorithm indicates its ability to provide as good a solution as possible at any time during its execution. Therefore, optimization approaches which have good anytime behaviour are desirable in evaluation traffic signal optimization. Moreover, small population sizes are inevitable for scenarios where processing capabilities are limited but require quick response times. In this work, two novel optimization algorithms are introduced that improve anytime behaviour and can work effectively with various population sizes. NS-LS is a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a local search which has the ability to predict a potential search direction. NS-LS is able to produce good solutions at any running time, therefore having good anytime behaviour. Utilizing a local search can help to accelerate the convergence rate, however, computational cost is not considered in NS-LS. A surrogate-assisted approach based on local search (SA-LS) which is an enhancement of NS-LS is also introduced. SA-LS uses a surrogate model constructed using solutions which already have been evaluated by a traffic simulator in previous generations. NS-LS and SA-LS are evaluated on the well-known Benchmark test functions: ZDT1 and ZDT2, and two real-world traffic scenarios: Andrea Costa and Pasubio. The proposed algorithms are also compared to NSGA-II and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D). The results show that NS-LS and SA-LS can effectively optimize traffic signal timings of the studied scenarios. The results also confirm that NS-LS and SA-LS have good anytime behaviour and can work well with different population sizes. Furthermore, SA-LS also showed to produce mostly superior results as compared to NS-LS, NSGA-II, and MOEA/D.Ministry of Education and Training - Vietna

Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and need to be called repeatedly in iterations of MOEAs. As a result, traffic signal timing optimisation process is time-consuming. Anytime behaviour of an algorithm indicates its ability to return as good solutions as possible at any time during its implementation. Therefore, anytime behavior is desirable in traffic signal timing optimisation algorithms. In this study, we propose an optimisation strategy (NSGA-II-LS) to improve anytime behaviour based on NSGAII and local search. To evaluate the validity of the proposed algorithm, the NSGA-II-LS, NSGA-II and MODEA are used to optimize signal durations of an intersection in Andrea Costa scenario. Results of the experiment show that the optimization method proposed in this study has good anytime behaviour in the traffic signal timings optimization problem

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

Evolving dynamic multiple-objective optimization problems with objective replacement

This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span

Multi-objective optimization coordination for urban arterial roadway based on operational-features

In this paper, a new coordinated control model is proposed based on the vehicular operational features, and a multi-objective optimization algorithm NSGA-II is employed to the model for the operation of the vehicle traveling on an urban arterial road taking three evaluation indexes into consideration as the average vehicle delay, the queue length, and the vehicle exhaust emission. A numerical experiment was made in an urban arterial road with three intersections on VISSIM for the proposed strategy, and the simulation results were compared with two commonly used pre-timed methods: Webster’s method and MAXBAND coordinated control method to verify the effectiveness of the proposed strategy in dealing with the unbalanced traffic volume condition, and it proved its advantages in designing and managing traffic systems more efficiently

Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition

Data Analytic Approach to Support the Activation of Special Signal Timing Plans in Response to Congestion

Improving arterial network performance has become a major challenge that is significantly influenced by signal timing control. In recent years, transportation agencies have begun focusing on Active Arterial Management Program (AAM) strategies to manage the performance of arterial streets under the flagship of Transportation Systems Management & Operations (TSM&O) initiatives. The activation of special traffic signal plans during non-recurrent events is an essential component of AAM and can provide significant benefits in managing congestion. Events such as surges in demands or lane blockages can create queue spillbacks, even during off-peak periods resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change the signal timing in real time based on traffic signal engineer/expert observations of incident and traffic conditions at the intersections upstream and downstream of congested locations. This dissertation develops methods to automate and enhance such decisions made at traffic management centers. First, a method is developed to learn from experts’ decisions by utilizing a combination of Recursive Partitioning and Regression Decision Tree (RPART) and Fuzzy Rule-Based System (FRBS) to deal with the vagueness and uncertainty of human decisions. This study demonstrates the effectiveness of this method in selecting plans to reduce congestion during non-recurrent events. However, the method can only recommend the changes in green time to the movement affected by the incident and does not give an optimized solution that considers all movements. Thus, there was a need to extend the method to decide how the reduction of green times should be distributed to other movements at the intersection. Considering the above, this dissertation further develops a method to derive optimized signal timing plans during non-recurrent congestion that considers the operations of the critical direction impacted by the incident, the overall corridor, as well as the critical intersection movement performance. The prerequisite of optimizing the signal plans is the accurate measurements of traffic flow conditions and turning movement counts. It is also important to calibrate any utilized simulation and optimization models to replicate the field traffic states according to field traffic conditions and local driver behaviors. This study evaluates the identified special signal-timing plan based on both the optimization and the DT and FRBS approaches. Although the DT and FRBS model outputs are able to reduce the existing queue and improve all other performance measures, the evaluation results show that the special signal timing plan obtained from the optimization method produced better performance compared to the DT and FRBS approaches for all of the evaluated non-recurrent conditions. However, there are opportunities to combine both approaches for the best selection of signal plans

Green Wave Traffic Optimization - A Survey

The objective of this survey is to cover the research in the area of adaptive traffic control with emphasis on the applied optimization methods. The problem of optimizing traffic signals can be viewed in various ways, depending on political, economic and ecological goals. The survey highlights some important conflicts, which support the notion that traffic signal optimization is a multi-objective problem, and relates this to the most common measures of effectiveness. A distinction can be made between classical systems, which operate with a common cycle time, and the more flexible, phase-based, approach, which is shown to be more suitable for adaptive traffic control. To support this claim three adaptive systems, which use alternatives to the classical optimization procedures, are described in detail.

Application of Metaheuristics in Signal Optimisation of Transportation Networks: A Comprehensive Survey

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.With rapid population growth, there is an urgent need for intelligent traffic control techniques in urban transportation networks to improve the network performance. In an urban transportation network, traffic signals have a significant effect on reducing congestion, improving safety, and improving environmental pollution. In recent years, researchers have been applied metaheuristic techniques for signal timing optimisation as one of the practical solution to enhance the performance of the transportation networks. Current study presents a comprehensive survey of such techniques and tools used in signal optimisation of transportation networks, providing a categorisation of approaches, discussion, and suggestions for future research