Emergency Traffic-Light Control System Design for Intersections Subject to Accidents

Qi, Liang; Zhou, MengChu; Luan, Wenjing

doi:10.1109/tits.2015.2466073

Cited by 89 publications

(30 citation statements)

References 32 publications

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“…As to the rapid development of ITS, Li Zhu et al [11] proposed a guideline for the utilization of big data in modern ITS, including road traffic accidents analysis, road traffic flow prediction, and so on. Regarding the road traffic accidents analysis, Liang Qi et al presented a system [12] that uses deterministic and stochastic Petri nets to design an emergency traffic-light control system. The system provided emergency responses to the intersections with accidents.…”

Section: Intelligent Transportation Systemmentioning

confidence: 99%

Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

et al. 2019

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In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.

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Section: Intelligent Transportation Systemmentioning

confidence: 99%

Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

et al. 2019

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“…They study the overall system performance taking into account parameters such as vehicular density, message frequency and RSU (RoadSide Unit) radius. Qi et al [25] make use of deterministic and stochastic PNs to design an emergency trafficlight control system for intersections prone to accidents. Reachability analysis techniques are then used to prevent deadlocks and livelocks.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Transportation System to control air pollution and road traffic in cities integrating CEP and Colored Petri Nets

D́ıaz

Macià

Valero

et al. 2018

Neural Comput & Applic

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Air pollution generated by road traffic in large cities is a great concern in today's society since pollution has an important impact on human health, even causing premature deaths. To address the problem, this paper presents an Intelligent Transportation System model based on Complex Event Processing technology and Colored Petri Nets (CPNs). It takes into consideration the levels of environmental pollution and road traffic, according to the air quality levels accepted by the international recommendations as well as the handbook emission factors for road transport methodology. This proposal, therefore, tackles a common problem in today's large cities, where traffic restrictions must be applied due to environmental pollution. CPNs are used in this work as a tool to make decisions about traffic regulations, so as to reduce pollution levels.

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“…As the growth of China's economy, the number of cars is growing obviously. While giving us convenient, comfortable and efficient travel and working methods, this situation has also caused more traffic accidents [1][2][3][4]. According to the current driving style, the judgment of the safety condition of car basically only depends on the driver with eyes.…”

Section: Introductionmentioning

confidence: 99%

A Localization Algorithm of Double Radars Based on Two Moments

et al. 2018

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This paper proposes an algorithm of localization based on FPGA and PC. It calculates the position of the target based on the geometrical relationship between the radars and the target through the speeds of the target and the peak-to-peak values measured by two radars at two moments. Currently, almost all the Doppler radars work with frequency modulated continuous wave(FMCW) and finish the frequency measurement by singlechip machine(SCM). This method only need radars work with continuous wave(CW) and finish the frequency measurement and localization on FPGA and PC. It can achieve the least error localization at the lowest cost. This system can alert the drivers to a vehicle in the blind area and remind the position and the speed of the vehicle relative to their vehicles. It can improve the safety of vehicles when changing lanes and turning, and effectively decrease the incidence of traffic accidents.

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Emergency Traffic-Light Control System Design for Intersections Subject to Accidents

Cited by 89 publications

References 32 publications

Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

An Intelligent Transportation System to control air pollution and road traffic in cities integrating CEP and Colored Petri Nets

A Localization Algorithm of Double Radars Based on Two Moments

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