Jelka Stevanovic

Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions: Integrated Approach with VISSIM, CMEM, and VISGAOST

One way to reduce excessive fuel consumption and vehicular emissions on urban streets is to optimize signal timings. Historically, signal timing optimization tools were used to reduce traffic delay and stops. The concept of optimizing signal timings to reduce fuel consumption and emissions was addressed decades ago with tools that are now considered outdated. This study advocates a fresh approach to integrating existing state-of-the-art tools for reassessing and ultimately minimizing fuel consumption and emissions. VISSIM, CMEM, and VISGAOST were linked to optimize signal timings and minimize fuel consumption and CO2 emissions. As a case study, a 14-intersection network in Park City, Utah, was used. Signal timings were optimized for seven optimization objective functions to find the lowest fuel consumption and CO2 emissions. Findings show that a formula commonly used to estimate fuel consumption in traffic simulation tools inadequately estimates fuel consumption and cannot be used as a reliable objective function in signal timing optimizations. Some of the performance measures used as objective functions in the optimization process were proved to be ineffective. When CMEM-estimated fuel consumption is used as an objective function, estimated fuel savings are around 1.5%, a statistically significant decrease. Further research is needed to find an effective way to minimize fuel consumption and emissions by using the proposed approach.

VisSim-Based Genetic Algorithm Optimization of Signal Timings

Genetic algorithm optimizations of traffic signal timings have been shown to be effective, continually outperforming traditional optimization tools such as Synchro and TRANSYT-7F. However, their application has been limited to scholarly research and evaluations. Only one tool has matured to a commercial deployment: direct CorSim optimization, a feature of TRANSYT-7F. A genetic algorithm formulation, VisSim-based genetic algorithm optimization of signal timings (VISGAOST), is presented; it builds on the best of the recorded methods by extending their capabilities. It optimizes four basic signal timing parameters with VisSim microsimulation as an evaluation environment. The program brings new optimization features not available in the direct CorSim optimization, such as the optimization of phasing sequences, multiple coordinated systems and uncoordinated intersections, fully actuated isolated intersections, and multiple time periods. The formulation has two features that enhance and reduce computational time: optimization resumption and parallel computing. The program has been tested on two VisSim networks: a hypothetical grid network and a real-world arterial of actuated–coordinated intersections in Park City, Utah. The results show that timing plans optimized by the genetic algorithm outperformed the best Synchro plans in both cases, reducing delay and stops by at least 5%.

Traffic control optimization for multi-modal operations in a large-scale urban network

Traditionally, only basic signal timings have been optimized in order to minimize delays and stops of private vehicles. Transit Signal Priority parameters and subsequent beneficiaries, such as transit vehicles and passengers, are usually neglected in the signal timing optimization process. Little research has been done to reveal specific benefits of optimizing Transit Signal Priorities and whether consideration should be given to both private vehicles and others when optimizing signal timings. Research presented here tests optimization of three performance measures (auto delay, transit delay, and person delay) by adjusting signal timings in different ways. A Genetic Algorithm formulation, coupled with a high-fidelity microsimulation model, is used to investigate benefits of each optimization on a large urban traffic corridor. The results show that basic signal timings are the most important measure to optimize when transit and private cars share a corridor. Also, the findings show that personal delay represents a suitable objective function for optimization of signal timings.

Optimizing Signal Timings from the Field: VISGAOST and VISSIM-ASC/3 Software-in-the-Loop Simulation

Traditionally, when traffic signals are retimed, a significant difference is seen between signal timings recommended by optimization software and those implemented in field controllers. Those two sets of signal timings rarely match each other, and often a manual process is involved in transferring the data to and from the field controllers. A method is presented: signal timings are downloaded from field controllers, optimized by a software package, and then uploaded to field controllers. The method is VISGAOST, a stochastic optimization program, working with VISSIM–ASC/3 software-in-the-loop simulation to optimize the signal timings obtained from the field. The method was applied to optimize signal timings for a five-intersection urban arterial segment in West Valley City, Utah. Traffic operations simulated by a high-fidelity VISSIM represented field observations reliably. After thousands of potential signal timings were evaluated, VISGAOST found a better set of signal timings than those used in the field. The final signal timings were tested for robustness under fluctuating traffic in microsimulation. The test results show that these optimized signal timings are more robust than those used in the field. Further applications of the method are needed to test the field performance of the signal timings optimized by VISGAOST.

Long-Term Benefits of Adaptive Traffic Control Under Varying Traffic Flows During Weekday Peak Hours

When adaptive traffic control systems (ATCSs) are evaluated, traffic signal engineers and practitioners often collect data for a few weeks before and after installation. Benefits are then estimated on the basis of this limited data set. The evaluation of an ATCS with microsimulation requires considerable collection of field data. However, once an ATCS is installed, an abundance of data is collected and stored by the ATCS itself. These data (mostly traffic volumes) can be used to recreate field variability of traffic conditions in a model and perform long-term ATCS evaluation studies. This paper reports on the projected long-term benefits of deploying an ATCS. Field traffic data were statistically processed and modeled in microsimulation. The Sydney coordinated adaptive traffic system (SCATS) and two time-of-day (TOD) plans were exposed to variability of field traffic flows modeled in VISSIM. A simple calculation was then performed to extrapolate results to a period of 10 years. Findings showed that SCATS outperformed existing TOD signal-timing plans by about 20% and was better than the best TOD plan that could be theoretically developed on the basis of collection of long-term data. Results from the study revealed that short-term analyses often obscured the true benefits of deploying an ATCS. A computation of the monetary value of achieved benefits showed that limited operational benefits reported in this paper, when projected over the long term, would exceed overall installation costs for SCATS in Park City, Utah. These benefits are expected to increase further with inclusion of the analysis periods that were not part of this study.

Evaluating Robustness of Signal Timings for Varying Traffic Flows

Traffic signal systems are usually retimed on the basis of sampled traffic counts. Yet limited traffic data may not represent typical conditions for optimizing traffic signal systems. When extensive traffic counts are not available, several approaches must be considered: How should signal timings be optimized if multiple counts exist? Should signal timings be based on maximal (or near maximal) traffic counts or should traffic volumes that are more frequently observed in the field be used? This study provides answers to those questions by investigating the performance of signal timing plans developed for various traffic count scenarios, at conditions of varying traffic demand. One key contribution of this study is accurate modeling of varying traffic conditions that were captured from field traffic counts during 155 weekdays in 2009. VISSIMs model of Park City, Utah, calibrated and validated in previous studies, was used to model field traffic conditions and to evaluate the quality of various signal timing plans. Results show that signal timing plans that are based on average traffic flows (mean, mode, and median) perform best (and are most robust) when exposed to day-to-day traffic flow variability. Results also show that although optimizing signal timings for higher traffic demand is suboptimal, this strategy is better than optimizing signals for lower traffic demand and should be used when sufficient traffic data are not available.

Impact of Green Light Optimized Speed Advisory on Unsignalized Side-Street Traffic

In recent years, rapid technological breakthroughs in infrastructure-to-vehicle, vehicle-to-vehicle, and vehicle-to-infrastructure wireless communications have created the possibility of new concepts for traffic signal control. The green light optimized speed advisory (GLOSA) approach uses traffic signal control information and the current position of a vehicle to recommend an appropriate speed for each vehicle, reducing the number of stops at traffic signals. Previous research mainly focused on the impact of GLOSA on the performance of movements on major streets. The purpose of this paper is to examine the effects of GLOSA on delay, capacity, and surrogate safety measures for vehicles that arrive from unsignalized side driveways and access roads. A highly calibrated and validated Vissim simulation model of a five-intersection segment of 3500 South, an urban corridor in Salt Lake City, Utah, was used as a test environment. Scenarios of various traffic loads and GLOSA activations have been defined and simulated. Outputs from Vissim were used to measure the impact of GLOSA. Vehicular trajectory files were postprocessed in the FHWA Surrogate Safety Assessment Model to analyze vehicular conflict data and thereby evaluate the traffic safety effects of GLOSA. The findings show that GLOSA often significantly affects delay of the side-street traffic. However, in general, GLOSA has only a minor impact on the number of conflicts. Regarding the impact of type of traffic control on GLOSA’s operations, the findings show that the fixed-time signals work better than actuated–coordinated signals, and road geometry and proximity of the signalized intersections affect the impact of GLOSA on the side-street traffic.

Backpressure Traffic Control Algorithms in Field-like Signal Operations

Traffic signal control is one of the most common means of traffic management in urban areas. To create an efficient urban transportation network, the optimization of signal control strategy is required. Various methods and tools can be used for that purpose. This study proposes two signal control algorithms that are based on backpressure model, which is originally developed to maximize the throughput in communication networks. Thus, one of the goals was to determine if such control strategies can lead to maximum throughput through an urban traffic network. In addition, the evaluation of the two algorithms included comparison of their performances with the performances of the conventional signal control strategies in microsimulation software. Evaluation results, in terms of various performance measures, demonstrate that backpressure control models are outperformed by conventional (fixed and actuated) signal timings optimized by a genetic algorithm.