Transportation Modes Identification from Mobile Phone Data Using Probabilistic Models
Transportation modes identification is an important transportation research problem with wide applications. Traditional methods are mainly done based on GPS, WiFi and some other electronic devices, which are actually not in adequately widespread use. The popularity of mobile phones makes the work of identification by mobile phone data valuable. In this paper, based on mobile phone data without other equipment for assistance, we design a probabilistic method to identify transportation modes. The method consists of a Hidden Markov Model with two sub-models for different traffic conditions. The Speed Distribution Law (SDL) based approach is used under the normal condition; to improve the performance of our method under the congested condition, the Cumulative Prospect Theory (CPT) based approach is adopted as a supplementary way to do identification. Experiments on real data show that our method can reach high accuracy in the normal and congested condition alike.
KeywordsMobile Phone Hide Markov Model Time Cost Average Speed Route Choice
Unable to display preview. Download preview PDF.
- 1.Anderson, I., Muller, H.: Practical Activity Recognition using GSM Data. In Technical Report CSTR-06-016, Department of Computer Science, University of Bristol (2006)Google Scholar
- 2.Foddy, M., Smithson, M., Schneider, S., Hogg, M.A.: Resolving Social Dilemmas: Dynamic, Structural, and Intergroup Aspects. Psychology Press (1999)Google Scholar
- 3.Froehlich, J., Dillahunt, T., Klasnja, P., Mankoff, J., Consolvo, S., Harrison, B., Landay, J.: UbiGreen: Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits. In: Computer Human Interaction (CHI). ACM, New York (2009)Google Scholar
- 6.Ko, J., Guensler, R.L.: Characterization of Congestion Based on Speed Distribution: A Statistical Approach Using Gaussian Mixture Model. In: TRB Committee on Highway Capacity and Quality of Service (AHB40) in Division A (2004)Google Scholar
- 7.Mun, M., Estrin, D., Burke, J., Hansen, M.: Parsimonious Mobility Classification using GSM and WiFi Traces. In: Proceedings of the 5th Workshop on Embedded Networked Sensors, HotEmNets (2008)Google Scholar
- 9.Reddy, S., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Determining Transportation Mode On Mobile Phones. In: ISWC (2008)Google Scholar
- 12.van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworth (1979)Google Scholar
- 14.Zheng, Y., Xie, X.: Learning Transportation Mode from Raw GPS Data for Geographic Application on the Web. In: WWW 2008 (2008)Google Scholar
- 15.Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.: Understanding mobility based on GPS data. In: Ubiquitous Computing. ACM, New York (2008)Google Scholar