Modelling departure time choice using mobile phone data.

The rapid growth in passive mobility tracking technologies has led to departure time choice studies based on GPS data in recent years (e.g. Peer et al., 2013). GPS data however typically has limited sample sizes and is affected by technical issues like signal losses and battery depletion leading to gaps in the data. On the other hand, the rapid growth in mobile phone penetration rates has led to the emergence of alternative passive mobility datasets such as Global System for Mobile communication (GSM) data. GSM data covers much wider proportions of the population and can be used to infer departure time information. This motivates this research where we investigate the potential use of GSM data for modelling departure time choice. We describe practical approaches to extract relevant information from GSM data and propose a modelling framework that accounts for the fact that the desired departure times are unobserved. We assume that the preferred departure times vary randomly across the users and apply the mixed logit framework to jointly estimate the distribution parameters of the preferred departure times and the sensitivities to schedule delay. Comparison of the model results and time valuation metrics derived from the GSM data with similar metrics derived from the GPS data of a subset of the users reveals that the fewer time gaps in the GSM data lead to reliable model outputs. The proposed framework can be used for mobile phone and other passive data sources with unobserved preferred departure times. [ABSTRACT FROM AUTHOR]