scholarly journals On a Travel Time Estimation Method Based on Aerial Photographs

1985 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
Yasuji Makigami ◽  
Masachika Hayashi
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Aerial Photographs ◽  
Travel Time Estimation

Travel time estimation method for urban road based on traffic stream directions

2016 ◽  
Vol 12 (6) ◽  
pp. 479-503 ◽  
Author(s):  
Dianhai Wang ◽  
Fengjie Fu ◽  
Xiaoqin Luo ◽  
Sheng Jin ◽  
Dongfang Ma
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road ◽  
Traffic Stream

A new travel time estimation method for horizontal strata

2005 ◽  
Author(s):  
M. Turhan Taner ◽  
Sven Treitel ◽  
M. Al‐Chalabi
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation

scholarly journals Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Juan Cheng ◽  
Gen Li ◽  
Xianhua Chen
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Random Forests ◽  
Polynomial Regression ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Time Estimation ◽  
Variable Importance ◽  
Estimation Accuracy ◽  
Travel Time Estimation

Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.

scholarly journals Arterial travel time estimation method using SCATS traffic data based on KNN-LSSVR model

2019 ◽  
Vol 11 (5) ◽  
pp. 168781401984192 ◽  
Author(s):  
Qichun Bing ◽  
Dayi Qu ◽  
Xiufeng Chen ◽  
Fuquan Pan ◽  
Jinli Wei
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Traffic Data

scholarly journals A City Path Travel Time Estimation Method Using ATMS Travel Time and Pattern Data

2015 ◽  
Vol 33 (3) ◽  
pp. 315-321 ◽  
Author(s):  
Sang Bum KIM ◽  
Chil Hyun KIM ◽  
Byung Young YOO ◽  
Yong Seok KWON
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation

scholarly journals Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Jian Gu ◽  
Miaohua Li ◽  
Linghua Yu ◽  
Shun Li ◽  
Kejun Long
Keyword(s):  
Travel Time ◽  
Flow Direction ◽  
Estimation Method ◽  
Time Estimation ◽  
Theoretical Formula ◽  
Travel Time Estimation ◽  
Time Headway ◽  
Estimation Formula ◽  
Link Travel Time ◽  
Vehicle Travel Time

In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.

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