Detection and Estimation of Road Congestion Using Cellular Phones

Hansapalangkul, T.; Keeratiwintakorn, Phongsak; Pattara-Atikom, Wasan

doi:10.1109/itst.2007.4295850

Cited by 6 publications

(1 citation statement)

References 3 publications

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“…Bayir et al, 2009;Berlingerio et al, 2013;Calabrese et al, 2011;Huang et al, 2009), and to the examination of the status and efficiency of current transport systems (e.g. Angelakis et al, 2013;Hansapalangkul et al, 2007;Steenbruggen et al, 2013). Alongside these studies, mobile phone data has also been investigated to explore the possibilities of building model evaluation measures.…”

Section: Problem Statementmentioning

confidence: 99%

Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

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Activity-based micro-simulation transportation models typically predict 24-hour activitytravel 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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