This study provides a unique long-term investigation of regional travel demand that addresses several gaps in the existing longitudinal literature. Firstly, it investigates the development of travel demand in terms of both vehicle kilometres travelled (VKT) and passenger kilometres travelled (PKT), based on actual demand, congestion and equilibrium distances, using road and multi-modal transit networks in the Greater Toronto-Hamilton Area (GTHA). Secondly, it identifies influential travel demand determinants after testing an extensive set of variables including longitudinal gravity-based transport accessibility measures. Thirdly, it investigates to what extent the determinants’ influence changes over time and various locations within the study area, providing new insights into the temporal and intra-regional variations of travel demand and its determinants. The findings show that VKT and PKT have grown in absolute and per trip terms, mainly due to substantial population growth, especially in the suburban areas. Whilst average potential travel times by transit have decreased, they are substantially longer than auto travel times. Furthermore, travel demand determinants vary significantly across space by degrees of urbanity, especially for VKT. The findings call for area- and population segment-specific land use and transportation policies across the GTHA.

This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode.