Studies on the Technology and Application of Transit Signal Priority on Exclusive Line

2015 ◽  
Vol 743 ◽  
pp. 774-779
Author(s):  
Q.L. Wang
Keyword(s):  
Stopping Times ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Transit Signal Priority ◽  
Bus Priority ◽  
Average Delay ◽  
Average Speed ◽  
Traffic Jam ◽  
Bus Signal Priority ◽  
Key Features

Bus priority is the effective methods of reducing traffic jam in large and medium-sized cities. Application and assessment of bus signal priority is studied, bus signal priority whole scheme is put forward based on GPS pointing and intelligent dispatch by investigating the situation of No.36 bus waiting time at stops and intersections. Based on Zigbee active request bus signal priority, dataflow process under local request and central request is analyzed, the principle of bus signal priority on balanced distance headway is put forward, and adjustment of key features parameters realized combining with SCATS traffic signal control system. The application assessment shows that, there are average 651 priority requests and 286 priority buses every day, priority efficiency is 43.9%.The average speed of No.36 bus increased 15.8%, the delay time reduced 13.2%, the stopping times reduced 27%, the twice stop situation at intersections basically disappeared, average delay at each intersection increased 3%.

Stochastic Model of Traffic Jam and Traffic Signal Control

2011 ◽  
Vol 131 (2) ◽  
pp. 303-310
Author(s):  
Ji-Sun Shin ◽  
Cheng-You Cui ◽  
Tae-Hong Lee ◽  
Hee-hyol Lee
Keyword(s):  
Stochastic Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam

Traffic Flow Data Collection and Signal Control System Based on Internet of Things and Cloud Computing

2013 ◽  
Vol 846-847 ◽  
pp. 1608-1611 ◽  
Author(s):  
Hui Jie Ding
Keyword(s):  
Cloud Computing ◽  
Control System ◽  
Data Collection ◽  
Internet Of Things ◽  
Traffic Flow ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Flow Data ◽  
Traffic Jam ◽  
Sensing Data

As more and more cars are in service, the traffic jam becomes a serious problem in our society. At the same time, more and more sensors make the cars more and more intelligent, and this promotes the development of Internet of things. Real time monitoring the cars will produce massive sensing data, the Cloud computing gives us a good manner to solve this problem. In this paper, we propose a traffic flow data collection and traffic signal control system based on Internet of things and the Cloud computing. The proposed system contains two main parts, sensing data collection and traffic status control subsystem.

scholarly journals A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

2021 ◽  
Vol 5 (4) ◽  
pp. 80-84
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Traffic Flow ◽  
Density Estimation ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Modern Life ◽  
Traffic Jam ◽  
Novel Approach

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

Traffic signal control based on a predicted traffic jam distribution

2009 ◽  
Vol 14 (2) ◽  
pp. 134-137 ◽  
Author(s):  
Cheng-You Cui ◽  
Ji-Sun Shin ◽  
Fumihiro Shoji ◽  
Hee-Hyol Lee
Keyword(s):  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam

scholarly journals Urban Traffic Signal Control Based on Multiobjective Joint Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yongrong Wu ◽  
Yijie Zhou ◽  
Yanming Feng ◽  
Yutian Xiao ◽  
Shaojie He ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Urban Traffic ◽  
Joint Optimization ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Timing Optimization ◽  
Average Delay ◽  
Annealing Algorithm

This paper proposes two algorithms for signal timing optimization of single intersections, namely, microbial genetic algorithm and simulated annealing algorithm. The basis of the optimization of these two algorithms is the original timing scheme of the SCATS, and the optimized parameters are the average delay of vehicles and the capacity. Experiments verify that these two algorithms are, respectively, improved by 67.47% and 46.88%, based on the original timing scheme.

An Intelligent Traffic Signal Control System Based on ARM Cortex-M3

2014 ◽  
Vol 602-605 ◽  
pp. 1378-1382 ◽  
Author(s):  
Shan Ying Cheng ◽  
Xue Mei Zhou ◽  
Qin Jiang
Keyword(s):  
Control System ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Jam ◽  
Task Control ◽  
Embedded Rtos ◽  
Stable Performance ◽  
Population Genetic Algorithm ◽  
Result Analysis

In order to alleviate traffic jam, an intelligent traffic signal control system base on ARM Cortex-M3 is implemented. In the system, STM32F207 is processor. Embedded RTOS CoOS is transplanted to achieve multi-task control of traffic signal in software design. A new multi-population genetic algorithm is developed to optimize green ratio. The result analysis shows that the system has stable performance and it makes the optimization of green ratio convenient and swift.

Real-Time Coordinated Signal Control Through Use of Agents with Online Reinforcement Learning

2003 ◽  
Vol 1836 (1) ◽  
pp. 64-75 ◽  
Author(s):  
Min Chee Choy ◽  
Ruey Long Cheu ◽  
Dipti Srinivasan ◽  
Filippo Logi
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Multiagent System ◽  
Fuzzy Rule ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Rule Base ◽  
Traffic Network ◽  
Average Delay

A multiagent architecture for real-time coordinated signal control in an urban traffic network is introduced. The multiagent architecture consists of three hierarchical layers of controller agents: intersection, zone, and regional controllers. Each controller agent is implemented by applying artificial intelligence concepts, namely, fuzzy logic, neural network, and evolutionary algorithm. From the fuzzy rule base, each individual controller agent recommends an appropriate signal policy at the end of each signal phase. These policies are later processed in a policy repository before being selected and implemented into the traffic network. To handle the changing dynamics of the complex traffic processes within the network, an online reinforcement learning module is used to update the knowledge base and inference rules of the agents. This concept of a multiagent system with online reinforcement learning was implemented in a network consisting of 25 signalized intersections in a microscopic traffic simulator. Initial test results showed that the multiagent system improved average delay and total vehicle stoppage time, compared with the effects of fixed-time traffic signal control.

scholarly journals An Improved Transit Signal Priority Strategy for Real-World Signal Controllers that Considers the Number of Bus Arrivals

2019 ◽  
Vol 12 (1) ◽  
pp. 287 ◽  
Author(s):  
Peikun Lian ◽  
Yiyuan Wu ◽  
Zhenlong Li ◽  
Jack Keel ◽  
Jiangang Guo ◽  
...  
Keyword(s):  
Real World ◽  
Field Application ◽  
The Other ◽  
Adverse Impact ◽  
Cumulative Number ◽  
Transit Signal Priority ◽  
Bus Priority ◽  
Average Delay ◽  
The Real ◽  
Green Strategy

Active transit signal priority (TSP) is used more conveniently and widely than the other strategies for real-world signal controllers. However, the active TSP strategies of real-world signal controllers use the first-come-first-served rule to respond to any active TSP request and are not effective at responding to the number of bus arrivals. With or without the green extension strategy, the active TSP has little impact on the final green time of priority phase, even in the case where more buses arrive during the priority phase. The reduced green time of early green strategy is relatively large when a bus arrives, and it would be worse when more buses arrive, the active TSP has a big adverse impact on the final green time of the non-priority phase. Therefore, the active TSP strategies of real-world signal controllers cannot handle the downtown intersection where many bus lines converge or where many buses arrive in a signal cycle during the evening rush hour. Traffic engineers need to do much work to optimize the TSP parameters before field application. Consequently, it is necessary to improve the TSP strategy of the real-world signal controllers for the intersections with a lot of bus arrivals. In order to achieve that objective, the authors present the CNOB (cumulative number of buses) TSP strategy based on the Siemens 2070 signal controller. The TSP strategy extends the max call time according to the number of buses in the arrival section when priority phases are active. The TSP strategy truncates the green time according to the number of buses in the storage section when non-priority phases are active. The experiment’s result shows that the CNOB TSP strategy can not only significantly reduce the average delay per person without using TSP optimization but can also reduce the adverse impact on the general vehicles of non-bus-priority approaches for the intersections with a lot of bus arrivals. Additionally, because the system dynamically adjusts, traffic engineers do not need to do much optimization work before the TSP implementation.

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