Building a validation measure for activity-based transportation models based on mobile phone data |
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| Affiliation: | 1. Transportation Research Institute (IMOB), Hasselt University, Wetenschapspark 5, bus 6, B-3590 Diepenbeek, Belgium;2. Department of Transport Engineering, Harbin Institute of Technology (HIT), 1500 Harbin, China;3. School of Transportation Science and Engineering, Beihang University, Beijing 100191, China;4. LEMA, University of Liège, Chemin des Chevreuils 1, Bât B.52/3, 4000 Liège, Belgium;1. Instituto Superior Técnico, Universidade de Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;2. INESC-ID Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;1. Defense Research and Development Organization, Ministry of Defense, Delhi, India;2. Department of Electronics and Communication, Delhi Technological University (formerly DCE), Bawana Road, Delhi, India;1. Department of Energy, Politecnico di Milano, via Ponzio 34/3, 20133 Milan, Italy;2. Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, Ecole Centrale Paris and Supelec, Paris, 92295 Chatenay-Malabry Cedex, France;3. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK;1. SEM, Beijing Jiaotong University, Shangyuancun 3#, Haidian Ward, Beijing 100044, China;2. IPS, Waseda University, 808-0135, Japan;1. Department of Computer Architecture and Technology, University of the Basque Country UPV/EHU, Donostia-San Sebastian 20018, Spain;2. Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Donostia-San Sebastian 20018, Spain |
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| Abstract: | Activity-based micro-simulation transportation models typically predict 24-h activity-travel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure.Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models.By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process. |
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| Keywords: | Activity-travel sequences Activity-based transportation models Travel surveys Mobile phone data |
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