Configuration Methodology for Traffic-Responsive Plan Selection: A Global Perspective

Although several studies have shown the potential great benefits of traffic-responsive plan selection (TRPS) control, time-of-day operation continues to be the primary method used to select patterns for signal control applications. This practice could be largely attributed to the minimal guidelines available on the setup of the TRPS mode. An innovative framework for TRPS system setup is provided, and guidelines for implementing TRPS in a simplified manner are shown. The guidelines, developed at Texas Transportation Institute (TTI), use a comprehensive approach that incorporates a multiobjective evolutionary algorithm and a supervised discriminant analysis. Engineers can directly implement the guidelines presented as an initial design. Hardware-in-the-loop simulation is used to illustrate the performance of TTI’s TRPS configuration methodology

Minimization Problems in Signalized Road Networks

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth's network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization. C1 [Baskan, Ozgur; Ceylan, Huseyin] Pamukkale Univ, Dept Civil Engn, Fac Engn, TR-20160 Denizli, Turkey. [Ozan, Cenk] Adnan Menderes Univ, Dept Civil Engn, Fac Engn, TR-09100 Aydin, Turkey. Document type: Articl

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

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

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review

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

The Effect of One-Way Urban Street on Traffic Operation Performance

Traffic congestion has become a serious problem affecting citizens' daily life. Many adopted strategies are studied and applied to alleviate this problem and reduce traffic pressure on street networks. Some are costly, and others are unsuccessful in solving the problem due to higher demand. The one-way traffic movement with circulation modification is adopted in this work which promotes the capacity of streets to sustain higher demand. The study area is identified with the most congested signalized intersection (Al-Nakhala, Al-Sakhara, and Beirut) along the urban corridor. The obtained results suggest using one-way traffic movement with a circulation network that significantly improves traffic operation and reduces congestion to a great extent. Control delays of selected signalized intersections are reduced from about 95%, and v/c is reduced to 0.69 (Al-Nakhala), 0.59 (Al-Sakhara), and 0.99 (Beirut) intersections. The results of modified traffic movement show excessive delays are not experienced, and the traffic movement modification ensures the sufficiency of the signalized corridor to accommodate the traffic demand. The traffic one-way movement can solve the problem of traffic congestion in the study area and improve the level of service from (F) to (A) and (B)

Dynamic green split optimization in intersection signal design for urban street network

In the past few decades, auto travel demand in the United States has significantly increased, but roadway capacity unfortunately has not expanded as quickly, which has led to severe levels of highway traffic congestion in many areas. In theory, the problem of congestion addressed through demand management and roadway expansion. However, system expansion in urban areas is difficult due to the extremely high cost of land; therefore, maximizing the existing capacity therefore often is considered the most realistic option. In urban areas, most of the traffic congestion and delays typically occur at signalized intersections. This thesis aims to prove the hypothesis that it is possible to increase capacity by establishing traffic signal timing plans that are more effective than existing plans. A new methodology is introduced in this thesis for dynamic green split optimization as a part of intersection signal-timing design to achieve maximized reduction in overall delay at all the intersections within an urban street network. The measurement of effectiveness in this new method is reduction in the average delay per vehicle per signal cycle. This thesis used data from 143 signalized intersections and 334 street segments in the Chicago Loop area street network to demonstrate the proposed methodology. The results suggest that it is possible to reduce delay by approximately 35% through the optimization of signal green splits for the four-hour AM and four-hour PM peak periods of a typical da