Long-distance mode choice model estimation using mobile phone network data

In this paper we develop two methods for the use of mobile phone data to support the estimation of long-distance mode choice models. Both methods are based on logit formulations in which we define likelihood functions and use maximum likelihood estimation. Mobile phone data consists of information about a sequence of antennae that have detected each phone, so the mode choice is not actually observed. In the first trip-based method, the mode of each trip is inferred by a separate procedure, and the estimation process is then straightforward. However, since it is usually not possible to determine the mode choice with certainty, this method might give biased results. In our second antenna-based method we therefore base the likelihood function on the sequences of antennae that have detected the phones. The estimation aims at finding a parameter vector in the mode choice model that would explain the observed sequences best. The main challenge with the antenna-based method is the need for detailed resolution of the available data, i.e., that the mobile phone operator might not be willing or able to provide the modeller with sequences of antennae that have detected the phones. In this paper we show the derivation of the two methods, that they coincide in case of certainty about the chosen mode and discuss the validity of assumptions and their advantages and disadvantages. Furthermore, we apply the first trip-based method to empirical data and compare the results of two different ways of implementing it.