| 摘要(英) |
Mobile phone data have been extensively explored for travelers’ behavior in the area of transportation planning, especially for extracting input data to trip generation, O-D estimation, mode choice and traffic assignment. In this research, we particularly address the inference of transportation modes using mobile phone data, because, as compared with the other three steps in the traditional travel demand forecasting procedure, research on extracting input data for mode choice using mobile phone data is relatively inadequate and thus the research gap need to be fulfilled. The obstacle that prevents the usage of mobile phone data from inferring the transportation mode is mainly due the two types of drawbacks, temporal and spatial uncertainties, and oscillation phenomenon. As a result, inaccurate locations of data points such as missing data, drifting data points, jump-back data points, and off-road data points appear and hence prevent the mobile phone data from being practically used. Among all the undesired phenomenon, this research will stress on the problem of jump-back and off-road data points, along with other influential factors such as level of urbanization (divided by an artificial separating line) which makes the layout and density of cell towers as well as traffic flow pattern totally different. A novel method was proposed to deal with jump-back data points whereas a map-matching method using hidden Markov model was adopted to treat off-road data points. For comparison purpose we also adopt two classification methods, i.e., support vector machine and deep neural network, to investigate how (1) the four scenarios formed based on the five modal features (including travel time, starting time of trace, traversal speed between traces, maximum speed, and average speed), (2) four combinations formed by the two directional O-D pairs and two subareas separated by longitude 121.206, (3) three proportion settings of training/testing data, and (4) map-matching techniques (with or without) would affect the accuracy rates associated with two training methods, i.e., SVM and DNN methods, in inferring transportation modes (i.e., either bus or vehicle).
The results show that the refined mobile phone data with (1) the first scenario consisting of five modal features, (2) the directional O-D data beginning from National Central University, (3) the proportion setting of (60, 40), (4) SVM, and (5) whether or not considering map-matching, resulted in the best performance with accuracy index of 99.89%. However, the feature ‘‘travel time’’ in the first scenario can only be collected by the investigator in the field survey. Therefore, the approach takes instead the third scenario (without considering travel time) for later analysis and can still achieve accuracy rate of, respectively, 80.38% for incorporating map-matching treatment, and 81.36% for without incorporating map-matching treatment. The performance of the former is a little inferior to the latter, however, it is still acceptable because the quality of the refined data points is still not justified which may affect the performance of later analysis. Theoretically the refined data after map-matching would improve the accuracy of data location, however, the speed distributions between the two types of transportation modes will thus become more similar, due to part of shorter distance paths corresponding to the statistical mode of speed distribution before map-matching were projected onto longer ones. Consequently, the extraction of useful modal features for inferring transportation modes becomes more difficult. Nevertheless, with the expected advances in mobile phone technology, the accuracy of locations obtained from and the frequencies associated with mobile phone data will improve and can of course get better performance in terms of accuracy rates. |
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