Measuring the Added Effectiveness of Using Detailed Spatial and Temporal Data in Generating Accessibility Measures

It is hypothesized that spatially disaggregated and temporally variable data will lead to more accurate determinations of accessibility. This paper examines whether such measures are more effective in predicting public transport mode share and commute duration in Montreal, Canada through regression models. While results show that the model fit to predict mode share is better when accessibility is generated using detailed spatial and temporal data, the improvement is minimal. In predicting commute duration, no improvements are observed. Furthermore, the change in resulting values of accessibility between measures is observable and varies depending on the configuration and frequency of transport supply.