The built environment has long been considered as a potentially influential factor in shaping and changing people’s travel behaviour. However, many gaps still exist in the understanding of the direction, size and mechanism of this influence. This thesis explores the complexities in the influence of the built environment on daily travel using a behaviour-oriented, activity-based modelling approach based on the notion of utility maximisation. The model simulates the full process of decision making in daily activity participation and travel, which involves the decisions on the type and frequency of activity participation, the sequence of activities, the choice of destinations and the time and mode of travel. Moreover, the thesis also addresses the lack of understanding on the influence of the ‘third dimension’ of the built environment — the street facades. A machine learning-based method is proposed to automatically evaluate the qualities of street facades from street view images.

Scenario analyses using the proposed model show that, both commute and non-commute travel are more sensitive to the built environment in proximity to home (in my experiment, 500 metre buffer zone). In the context of Beijing, the total car use and commute car use of a person is significantly affected by the level of land use mix and the continuity of street facades around home, among all built environment features. Non-commute car use is significantly affected by employment density, retail density, accessibility to commercial clusters, bus coverage, road density and the quality and continuity of street facades. Similar effects on the final outcomes of travel behaviour (such as total car use) by different built environment features can happen through diverse processes and have different implications for people’s actual experience and the urban system. Some of the results are consistent with theoretical assumptions and some are not, which provides alternative insights into the relationship between the built environment and travel behaviour.