Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China

Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field.

https://rosap.ntl.bts.gov/view/dot/42376/dot_42376_DS1.pdf

Urban traffic signal control with connected and automated vehicles: A survey

The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field because of their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles allows the wireless transmission of the positions, headings, and speeds of vehicles for use by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm, which uses these new, more robust data, was developed. The decentralized, fully adaptive traffic control algorithm uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future. The objective function uses either delay only or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a micro...

Traffic Signal Control with Connected Vehicles

Swedish national road and transport research institute

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 ...

Optimizing traffic control to reduce fuel consumption and vehicular emissions: Integrated approach with VISSIM, CMEM, and VISGAOST

Adaptive Traffic Control Systems (ATCSs), also known as real-time traffic control systems, adjust, in real time, signal timings based on the current traffic conditions, demand, and system capacity Although there are at least 25 ATCS deployments in the United States, these systems may not be well understood by many traffic signal practitioners in the country Their operational benefits are demonstrated, but there are still some reservations among the people in the traffic signal community These systems are considered expensive and complex and they require high maintenance of detectors and communications The study methodology included three sequential efforts The first focused on the selection of ATCSs, which are typically deployed in the United States (and worldwide) and identification of ATCS agencies The next effort undertaken was a literature review that gathered and reported information about ATCS operations and deployments from previous studies Finally, two electronic surveys were conducted: a shorter e-mail survey for ATCS vendors and a longer website-based survey for ATCS users Responses were obtained from 34 of 42 agencies in North America, an 81% response rate Also, 11 responses from agencies in other countries were obtained Municipal and county traffic operations agencies were the major contributors among the 45 agencies that responded to the survey

Adaptive Traffic Control Systems: Domestic and Foreign State of Practice

Genetic algorithms have been shown to be effective tools for optimizations of traffic signal timings. However, only one tool that combines Genetic Algorithms and traffic microsimulation has matured to a commercial deployment: Direct CORSIM optimization, a feature of TRANSYT-7F. This paper presents a genetic algorithm formulation that builds on the best of the recorded methods, by extending their capabilities. It optimizes four basic signal timing parameters and transit priority settings using VISSIM microsimulation as the evaluation environment. The program is the first optimization tool that optimizes traffic control transit priority settings on roads with both private and transit traffic. These settings are optimized either simultaneously with the basic signal timings or separately, thus improving overall traffic operations without changing existing basic signal timings. The optimization has been tested on two VISSIM models: a suburban network of 12 signalized intersections in Park City, UT, and an urban corridor with transit operations in Albany, NY. The results show that timing plans optimized by the genetic algorithm outperformed the timing plans from the field and SYNCHRO. Optimization of the transit priority settings shows that adjustment of these settings has significant impact on travelers delay on the corridors with mixed traffic and transit operations.

Stochastic optimization of traffic control and transit priority settings in VISSIM

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 tim...

VisSim-Based Genetic Algorithm Optimization of Signal Timings

Traffic density estimation is a very important component of an automated traffic monitoring system. Traffic density estimation can be used in a number of traffic applications – from congestion identification to macroscopic traffic control in urban environment. In this paper, we implemented Single Shot Detection (SSD) and MobileNet-SSD to estimate traffic density. SSD is capable of handling different shape, size and view angle of the objects. MobileNet-SSD is a cross-trained model from SSD to MobileNet architecture, which is faster than SSD. In this study, we show a key application area for the SSD and MobileNet-SSD framework. The advantages and shortcomings of the SSD and MobileNet-SSD framework were analyzed using fifty-nine individual traffic cameras. In addition, we compared the two algorithms with manually estimated density. The SSD framework shows significant potential in the field of traffic density estimation. SSD achieved 92.97% average detection accuracy in our experiment. On the other hand, the MobileNet-SSD achieved 79.30% average detection accuracy.

Aleksandar Stevanovic

Papers

Optimizing traffic control to reduce fuel consumption and vehicular emissions: Integrated approach with VISSIM, CMEM, and VISGAOST

Adaptive Traffic Control Systems: Domestic and Foreign State of Practice

Stochastic optimization of traffic control and transit priority settings in VISSIM

VisSim-Based Genetic Algorithm Optimization of Signal Timings

An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD