scholarly journals Application of E-Tag in Pricing Road Tolls and Parking Fees for Traffic Congestion Mitigation

2017 ◽  
Vol 25 ◽  
pp. 2913-2922
Author(s):  
C.P. Chu ◽  
C.Y. Wang ◽  
S.R. Hu
Keyword(s):  
Traffic Congestion ◽  
Congestion Mitigation

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

Driver Back-Tracing Based on Automated Vehicle Identification Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 84-93
Author(s):  
Jian Yuan ◽  
Chunhui Yu ◽  
Ling Wang ◽  
Wanjing Ma
Keyword(s):  
Public Transportation ◽  
Traffic Congestion ◽  
Model Performance ◽  
Economic Loss ◽  
Sensitivity Analyses ◽  
Automated Vehicle ◽  
Congestion Mitigation ◽  
Vehicle Identification ◽  
Traffic Volumes ◽  
Temporal Dimensions

Traffic congestion causes traveler delay, environmental deterioration, and economic loss. Most studies on congestion mitigation focus on attracting travelers to public transportation and expanding road capacity. Few studies have been found to analyze the contribution of different traffic flows to the congestion on roads of interest. This study proposes an approach to driver back-tracing on the basis of automated vehicle identification (AVI) data for congestion mitigation. Driver back-tracing (DBT) aims to estimate the sources of the vehicles on roads of interest in both spatial and temporal dimensions. The spatial DBT model identifies the origins of vehicles on the roads and the temporal DBT model estimates the travel time from the origins to the roads. The difficulty lies in that vehicle trajectories are incomplete because of the low coverage of AVI detectors. Deep neural network classification and regression are applied to the spatial and temporal DBT models, respectively. Simulation data from VISSIM are collected as the dataset because of the lack of field data. Numerical studies validate the promising application and advantages of deep neural networks for the DBT problems. Sensitivity analyses show that the proposed models are robust to traffic volumes. However, turning ratios, and the number and layout of AVI detectors may have noticeable impacts on the model performance.

scholarly journals Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

2021 ◽  
pp. 036119812110357
Author(s):  
Hao Zhou ◽  
Jorge Laval ◽  
Anye Zhou ◽  
Yu Wang ◽  
Wenchao Wu ◽  
...  
Keyword(s):  
Motion Planning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Domain Knowledge ◽  
Imitation Learning ◽  
Longitudinal Motion ◽  
Research Gaps ◽  
Learning Methods ◽  
Congestion Mitigation ◽  
The Impact

Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

Research on Congestion Mitigation Strategy Downstream Off-Ramp for Urban Expressway Based on Fuzzy Logic Method

2011 ◽  
Vol 299-300 ◽  
pp. 1271-1274
Author(s):  
Xue Wen Chen
Keyword(s):  
Traffic Congestion ◽  
Fuzzy Controller ◽  
Green Light ◽  
Light Phase ◽  
Simulation Research ◽  
Congestion Mitigation ◽  
Urban Expressway ◽  
Matlab Program ◽  
Fuzzy Logic Method ◽  
Queue Lengths

As the uncertain characteristics of traffic flow in urban expressway, fuzzy control can be used as an effectual way to solve traffic problem. A fuzzy controller of signal intersection is designed for alleviating traffic congestion downstream off-ramp at the Surface Street. The overtime of green-light and the next time for green-light phase are optimized according to the queue lengths and the numbers of phase delay, and the method corresponds to a man’s decision-making process. Simulation research based on MATLAB program design is carried out and the results show that the methods are promising and the fuzzy controller have a better performance for congestion mitigation downstream off-ramp of urban expressway.

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