Exploring the evolution of travel behavior and its relationship with the built environment: a Montreal case study

The built environment has been found to be associated with various travel outcomes such as car usage, mode choice, energy, emissions, etc. Consequently, there has been much effort to improve built environment characteristics to mitigate transport-related issues. This research aims at quantifying the effect of the built environment on travel behavior outcomes, specifically greenhouse gas emissions and active transportation (cycling) over time. First, we estimate the impact of changes in the built environment on emissions under different regional development plans. Second, we explore the evolution and links between utilitarian cycling and neighborhood typologies. In the first part of our research, a regression model is developed in order to estimate census-tract level average household CO2 emissions as a function of urban form and socio-demographic characteristics. Future CO2 emissions are forecasd for year 2031 under three scenarios – business-as-usual, in accordance with the region's sustainable development plan, PMAD, and population forecasts by the provincial transport agency, MTQ. We find that the forecast average household CO2 emissions for 2031 are lower by 9.7 and 5.8% in the PMAD scenario in comparison to the business-as-usual and MTQ scenarios, respectively. Thus, we can expect that a reduction of CO2 emissions can be achieved by 2031 given that the plans detailed in the PMAD are successfully implemented. However, these results also highlight the need for implementing alternative strategies in parallel in order to reduce emissions even further, such as the improvement of the motor-vehicle fuel efficiencies and electrification. Urban form strategies alone would not be sufficient to achieve government objectives on climate change in the short term.The second part of the research aims to further understand the evolution of cycling for commute trips in different neighborhood typologies of Montreal over time (using O-D data from 1998 to 2008). We explore the connections between residential location and cycling through three different methodological approaches; (i) binary logit model; (ii) simultaneous equation model; and (iii) propensity score matching. We find that neighborhood effects have been increasing over the study period. Furthermore, after controlling for residential self-selection, we find that living in urban neighborhoods increases the likelihood of cycling to work and are able to quantify the degree to which preferences towards cycling have been increasing over time. Finally, we observe that commuters living close to the central business district have been increasingly commuting to work by foot, at the expense of cycling.

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