A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

Ren, Chuanxiang; Wang, Jinbo; Qin, Lingqiao; Shen, Li; Cheng, Yang

doi:10.3390/electronics8091058

Cited by 6 publications

(3 citation statements)

References 50 publications

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“…The roadside units (RSU), which were placed in the intersections, can also record the vehicle type, location, trajectory and other information [17][18][19]. In the connected vehicle environment, the status information of each vehicle can also be collected [20]. Two types of data are mutually complemental.…”

Section: Estimated Departure Headway Methodsmentioning

confidence: 99%

Dynamic Estimation of Saturation Flow Rate at Information-Rich Signalized Intersections

et al. 2020

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Intersections are the bottlenecks of the road network. The capacity of signalized intersections restricts the operation of the road network. Dynamic estimation of capacity is necessary for signalized intersections refined management. With the development of technology, more and more detectors were installed near the intersection. It had been the information-rich environment, which provided support for dynamic estimation of capacity. A dynamic estimation method for a saturation flow rate based on a neural network was developed. It would grasp the dynamic change of saturation flow rates and influencing factors. The measure data at three scenarios (through lanes, shared right-turn and through lanes, shared left-turn and through lanes) of signalized intersections in Beijing were taken as examples to validate the proposed method. Firstly, the traffic flow characteristics of the three scenarios and factors affecting the saturation flow rate were analyzed. Secondly, neural network models of the three scenarios were established. Then the hyperparameters of neural network models were determined. After training, the neural network structure and parameters were saved. Lastly, the test set data was validated by the training model. At the same time, the proposed method was compared with the Highway Capacity Manual (HCM) method and the statistical regression method. The results show that both regression models and neural network models have better accuracy than HCM models. In a simple scenario, the neural network models are not much different from the regression models. With the increase of complexity of scenarios, the advantages of neural network models are highlighted. In through-left lane and through-right lane scenarios, the estimated saturation flow rates used by the proposed method were 7.02%, 4.70%, respectively. In the complexity of traffic scenarios, the proposed method can estimate the saturation flow rate accurately and timely. The results could be used for signal control schemes optimizing and operation managing at signalized intersections subtly.

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Section: Estimated Departure Headway Methodsmentioning

confidence: 99%

Dynamic Estimation of Saturation Flow Rate at Information-Rich Signalized Intersections

et al. 2020

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“…The advantages of junctions controlled by an LSD are as follows: Lower fuel consumption and consequently lower emissions and noise levels [31].…”

Section: Junctions Controlled By Light Signaling Devicesmentioning

confidence: 99%

Methodology Designed to Evaluate Accidents at Intersection Crossings with Respect to Forensic Purposes and Transport Sustainability

et al. 2020

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Currently, there are quite a lot of incorrect procedures and mistakes that occur in the forensic area, which lacks analytical approaches toward solving the causes of accidents using s–t diagrams (distance–time diagrams) combined with the software simulation applications. When analyzing accidents, the correct information is of key importance. The aim of this article is to define a new specific technical and analytical approach toward handling expert’s reports on traffic accidents in road transport at intersections, with respect to the traffic lights. A simulation program application is used as a progressive means of accident evaluation. This procedure must become a standard in the methods of modern traffic accident analysis. The application of this methodology with simulation tools for accident reconstruction enables one to perform a very precise analysis of traffic accidents. Mutual space and time relationships of vehicles’ movements have been evaluated here, depending upon the intersection signal plan. To demonstrate the methodology, a real case is used here, reconstructed by means of the complex analytical simulation software PC-Crash. A procedure processed by these means can be beneficial for forensic traffic accident analysis.

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“…Reference [13] proposes a routing scheme for FANET which can adapt to rapid changes in network topology and effectively improve the network performance. In Reference [14], the connected vehicular networks are considered, in which a new intersection signal control model is proposed based on vehicle-to-infrastructure (V2I) communication, and the simulation analysis is made with the real-world data.…”

Section: The Present Issuementioning

confidence: 99%

Mobile Oriented Future Internet (MOFI): Architectural Designs and Experimentations

Koh

2020

Electronics

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A Novel Left-Turn Signal Control Method for Improving Intersection Capacity in a Connected Vehicle Environment

Cited by 6 publications

References 50 publications

Dynamic Estimation of Saturation Flow Rate at Information-Rich Signalized Intersections

Dynamic Estimation of Saturation Flow Rate at Information-Rich Signalized Intersections

Methodology Designed to Evaluate Accidents at Intersection Crossings with Respect to Forensic Purposes and Transport Sustainability

Mobile Oriented Future Internet (MOFI): Architectural Designs and Experimentations

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