V2V Mobility Modeling and Simulation Using PBC Messages for Traffic Congestion Mitigation

2018 ◽  
Author(s):  
Afdhal Afdhal ◽  
Ahmadiar Ahmadiar ◽  
Nasaruddin Nasaruddin
Keyword(s):  
Modeling And Simulation ◽  
Traffic Congestion ◽  
Mobility Modeling ◽  
Congestion Mitigation

Urban mobility modeling to reduce traffic congestion in Surabaya: a system dynamics framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Erma Suryani ◽  
Rully Agus Hendrawan ◽  
Philip Faster Eka Adipraja ◽  
Arif Wibisono ◽  
Lily Puspa Dewi
Keyword(s):  
System Dynamics ◽  
Public Transport ◽  
Traffic Congestion ◽  
Supply And Demand ◽  
Urban Mobility ◽  
Rapid Transit ◽  
Content Type ◽  
Mobility Modeling ◽  
Delay Performance ◽  
Mobility Performance

Purpose This paper aims to address the urban mobility and traffic congestion problem under environmental dynamics to improve mobility and reduce traffic congestion using system dynamics (SD) simulation and scenarios. Design/methodology/approach SD simulation was used to analyze urban mobility and traffic congestion. Data were collected from the Transportation Department of Surabaya City. Several scenarios to improve urban mobility and reduce traffic congestion were developed by modifying the structures and parameters of the model. Findings Several factors influence urban mobility, including modal split, trip frequency, delay performance and the ratio of public transport supply and demand. Urban mobility, daily traffic and road capacity are some factors that affect traffic congestion. Scenarios can be designed based on the assumptions of the proposed strategy. Research limitations/implications The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world. Practical implications By implementing several strategies (mass rapid transit and bus rapid transit development and public transport delay reduction), mobility performance is projected to be improved by 70.34-92.96%. With this increased mobility, traffic congestion is projected to decline by 52.5-65.8%. Originality/value The novel contributions of this research are: formulating relationships between several variables; modeling dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion in Surabaya. With the increase in urban mobility and the decrease in average daily traffic, traffic congestion could be reduced by a minimum of 57.6% and a maximum of 69%.

scholarly journals Traffic congestion mitigation: combining engineering and economic perspectives

2011 ◽  
Vol 34 (7) ◽  
pp. 637-645 ◽  
Author(s):  
K. Triantis ◽  
S. Sarangi ◽  
D. Teodorović ◽  
L. Razzolini
Keyword(s):  
Traffic Congestion ◽  
Congestion Mitigation ◽  
Economic Perspectives

scholarly journals Research Paper on Performance Analysis and Recommendations of Traffic Congestion Strategies in Cities of Haryana

2021 ◽  
Vol 9 (VI) ◽  
pp. 5313-5321
Author(s):  
Khushbu Sajid
Keyword(s):  
Traffic Congestion ◽  
Cost Effective ◽  
Transportation Policy ◽  
Decision Makers ◽  
Congestion Mitigation ◽  
Road Congestion ◽  
Transit Capacity ◽  
Cost Efficient ◽  
Cost Factors

This experimental study uses national regulations and survey reports to identify short, medium, and long-term traffic congestion strategies in Haryana's cities. The current study looked into a variety of successful road congestion mitigation techniques, ranging from expanded road capacity to the use of roadways, to see which ones were the most cost-effective. Using an examination of quantitative regression, interviews with transportation policy and decision makers, and alternate matrix criteria, I ranked each traffic congestion mitigation approach from least to most cost efficient based on three cost factors. I discovered that ramp measuring was both the most cost-effective and the most difficult method. Meanwhile, I discovered that expanding transit capacity was the least cost-effective of the solutions I looked into.

scholarly journals Speeding up of the traffic congestion mitigation by stochastic optimization in deep learning

2018 ◽  
Vol 9 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Shinnnosuke Nakamura ◽  
Takumi Uemura ◽  
Gou Koutaki ◽  
Keiichi Uchimura
Keyword(s):  
Deep Learning ◽  
Stochastic Optimization ◽  
Traffic Congestion ◽  
Congestion Mitigation
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