Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis

Abstract Big Data sources offer new possibilities for urban mobility and accessibility studies. As people carry out their activities in a city, they leave behind a digital fingerprint that can be used to analyze the population’s daily mobility patterns and determine the exact times of travel between points of origin and destination at different times of the day. These data present high spatial and temporal resolution, and enable accurate and dynamic analysis of accessibility. The objective of this study was to conduct a dynamic analysis of urban accessibility considering its two main components: travel times and the attractiveness of destinations. To this end, we calculated travel times between transport zones using the Google Maps API and constructed origin and destination (OD) travel matrices from mobile phone records. Several scenarios were generated to analyze dynamic accessibility and the separate influence of its two components. We also conducted a cluster analysis to characterize transport zones according to their accessibility in each of the scenarios and times of day considered. Our results indicate that these new sources of geolocated data show considerable potential for use in time-sensitive accessibility studies, since they yield more accurate and realistic information than static or partially dynamic analyses. Such information could help politicians take better decisions concerning transport and land use.