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Frontiers of Earth Science

Front. Earth Sci.    2018, Vol. 12 Issue (2) : 253-263
Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories
Tao XU1,2, Xiang LI1,2(), Christophe CLARAMUNT3
1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
3. The French Naval Academy Research Institute, Lanveoc-Poulmic, BP 600, 29240 Brest Naval, France
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Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage. With large scale historical vehicle trajectory data and meteorology data, this research develops a BPNN-based approach through integrating multiple factors affecting trip travel time into a BPNN model to predict trip-oriented travel time for OD pairs in urban network. Results of experiments demonstrate that it helps discover the dominate trends of travel time changes daily and weekly, and the impact of weather conditions is non-trivial.

Keywords trip-oriented travel time prediction (TOTTP)      urban network      Back Propagation Neural Networks (BPNN)      historical vehicle trajectories     
Corresponding Author(s): Xiang LI   
Just Accepted Date: 07 December 2016   Online First Date: 17 March 2017    Issue Date: 09 May 2018
 Cite this article:   
Tao XU,Xiang LI,Christophe CLARAMUNT. Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories[J]. Front. Earth Sci., 2018, 12(2): 253-263.
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Tao XU
Xiang LI
Christophe CLARAMUNT
Fig.1  Overview of the methodology.
Weather condition Value
Light Snow
Moderate Rain 3
Heavy Rain
Moderate Snow
Heavy Snow
Tab.1  Quantified weather conditions
AQI levels Value
Excellent 1
Good 2
Light pollution 3
Moderate pollution 4
Severe pollution 5
Serious pollution 6
Tab.2  Quantified AQI
Fig.2  Structure of BPNN model.
Fig.3  Road network and 3 selected traffic zones.
Origin and destination Route No. Number of trips Route length/km
From Zone Bto Zone A Route 1 5206 14.36
Route 2 2751 11.91
Route 3 273 18.87
From Zone C to Zone A Route 4 205 26.95
Route 5 169 23.78
Route 6 45 29.08
Tab.3  Trip and route information
Fig.4  Selected routes (a) from Zone B to Zone A and (b) from Zone C to Zone A.
Fig.5  The relationship between training error E and different values of h or a when a or h is fixed during a training process for Route 4.
Fig.6  Predicted travel time for trips in June 2014 from Zone B to Zone A.
Fig.7  Predicted travel time for trips in June 2014 from Zone C to Zone A.
Route n l/km The proposed method Moving average method
E^/min R E^/min R
1 218 14.36 3.64 0.253 5.28 0.368
2 420 11.91 3.28 0.275 4.03 0.338
3 27 18.87 9.87 0.523 14.31 0.758
4 9 26.95 3.03 0.112 9.59 0.356
5 16 23.78 11.47 0.482 13.21 0.556
6 4 29.08 8.63 0.297 19.98 0.687
Tab.4  Error comparison between moving average method and the proposed method
Fig.8  Error analysis by hour for trips along Route 2 on the first day of 5 continuous workdays.
Input factors* E^
DTWAC 3.28
DTAC 4.08
DTWA 4.11
DTWC 4.32
DT 6.23
Tab.5  Meteorological factor analysis
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