No abstract

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.

No abstract

There is ample evidence of the role of land use and transportation interactions in determining urban spatial structure. The increased digitization of human activity produces a wealth of new data that can support longitudinal studies of changes in land-value distributions and integrated urban microsimulation models. To produce a comprehensive dataset, information from various sources needs to be merged at the land-parcel level to enhance datasets with additional attributes, while maintaining the ease of data storage and retrieval and respecting spatial and temporal relationships. This paper proposes a prototype of a workflow to augment a historical dataset of real estate transactions with data from multiple urban sources and to use machine learning to classify land use of each record based on housing market dynamics. The study finds that engineered parcel-level attributes, capturing housing market dynamics, have stronger predictive power than aggregated socio-economic variables, for classifying property land use.