spatio-temporal effects of bus arrival time information
We analyze temporal and spatial variations in bus ridership that may result from using real-time bus arrival information. Using Random Effects Negative Binomial models of longitudinal average weekday ridership per bus route and controlling for operational, economic and social factors, we assess temporal variations in ridership by means of two types of time-varying coefficients: one that reflects "adjustment interval" effects after information becomes available on a route, during which users learn about information availability and potentially adapt their travel behavior, and the second, a "period" effect, that reflects changes in the underlying information and communications technology over time and ways in which people receive and use information. A k-means cluster analysis of bus stop service areas along routes that accrued the highest ridership allows us to associate the net effects of information to the sociodemographic, built environment, housing, economic, transportation and digital savviness characteristics of service areas. Four clusters of bus stops were identified: two where bus boardings gains were high after Bus Tracker, and two others where boarding gains were either modest or low. This strategy helped to determine the types of spatially-targeted Location-Based Service applications that may be developed to capitalize on basic bus arrival information.
digital divide technology adoption