This study provides a methodology to identify travellers’ transportation modes by tracking the mobile phone data, which aims to obtain the accurate mode split rate for providing decision support in urban traffic planning. First, the effective mobile phone singling data and GPS data are collected from the communication operators and a mobile phone app, respectively. Considering the differences in velocity and acceleration of different trip modes, a trip mode characteristic description model is built based on wave characteristics and moving average method. Compared with the wave characteristics, the moving average method shows a better accuracy of 90%. Then training samples are drawn by two data selection methods including probability proportional to size sampling and equal amount sampling. Furthermore, the classifier method for mode choice prediction is developed by support vector machines (SVMs) and back propagation neutral network. Finally, the results of the case study show that using a 30-point moving average training data set can improve the prediction accuracy largely, and the SVM method gets a better accuracy of 82%. The potential of using the mobile phone data to build a new mode choice prediction method in the field of transportation is shown.
Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data
IET Intelligent Transport Systems ; 12 , 10 ; 1220-1226
2018-08-20
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
average training data set , moving average method , mobile handsets , propagation neutral network approaches , transportation , probability , wave characteristics , support vector machine , GPS data , effective mobile phone singling data , support vector machines , mode choice prediction method , neural nets , mobile phone data , travellers , accurate mode split rate , traffic engineering computing , mobile phone app , different trip modes , trip mode characteristic description model , data selection methods , trip mode prediction
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