scholarly journals Asymptotic problems and numerical schemes for traffic flows with unilateral constraints describing the formation of jams

2017 ◽  
Vol 12 (4) ◽  
pp. 591-617
Author(s):  
Florent Berthelin ◽  
◽  
Thierry Goudon ◽  
Bastien Polizzi ◽  
Magali Ribot ◽  
...  
Author(s):  
Marianne Bessemoulin-Chatard ◽  
Claire Chainais-Hillairet ◽  
Hélène Mathis

2021 ◽  
Author(s):  
Fucheng Wang ◽  
Jiajie Xu ◽  
Chengfei Liu ◽  
Rui Zhou ◽  
Pengpeng Zhao

2021 ◽  
Vol 11 (6) ◽  
pp. 2574
Author(s):  
Filip Vrbanić ◽  
Edouard Ivanjko ◽  
Krešimir Kušić ◽  
Dino Čakija

The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities’ sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions.


Author(s):  
Zhongyang Lu ◽  
Andy H. F. Chow ◽  
Jacky Leung ◽  
Haydn Kwok ◽  
Sammy Cheung

Congestion and traffic-induced air pollution are associated with population growth and economic development. Compared with congestion, there are relatively few studies on modeling and assessment of traffic-induced pollution. This paper presents an empirical assessment and analysis of traffic-induced air pollution with real-world data collected from the Hong Kong Strategic Road Network. The study employed historical data of traffic flows, speeds, and emission of air pollutants collated by the Hong Kong Transport Department and Environmental Protection Department. This paper first reveals the correlation between traffic flows, speeds, and corresponding induced pollutants including nitrogen oxides (NO2, NOX) and carbon monoxide (CO). To gain further statistical insight, a regression analysis was conducted on the flow–speed–emission relationship at three air quality monitoring stations, which revealed the significance of various factors on this relationship. This study contributes to green transport management and urban sustainability.


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