A spatio-temporal approach for identifying the sample size for transport mode detection from GPS-based travel surveys: A case study of London's road network

Compared with conventional household one/two days travel survey, GPS-based travel surveys hold many attractive features for travel behaviour studies. Different machine learning-based techniques have been developed to infer the transportation mode based upon GPS data from such surveys. However, nearly none of these studies calculate the sample size required for validating these techniques. Since different surveys target different study areas for different temporal periods and different travel modes, identifying sample sizes for all transport modes at different spatio-temporal granularities is of imperative urgency given the high time and financial costs of GPS-based travel surveys. Here we use road network journey time data of London to calculate appropriate sample sizes for travel surveys designed either for a specific period-of-the-day, day-of-the-week or month-of-the-year. We also use different transportation analysis zones (central, inner and outer London) to demonstrate the spatial variability of the data over these different survey durations. Then we finally calculate and analyse the range of required sample sizes for different travel modes within these spatio-temporal granularities. This case study provides a good reference of sample size design for GPS-based travel survey in big cities.