Assessing bike accessibility to metro systems by integrating crowdedness

Abstract

Bike-metro integration is regarded as an effective means of improving the access to metro systems. This study aims to assess the metro accessibility by biking at a finer spatiotemporal scale using a real bike trajectory dataset generated by cyclists. To achieve this goal, we propose a metro accessibility level (MAL) indicator that explicitly integrates metro crowdedness into the accessibility measurement. We then introduce a method to examine the possibility of avoiding metro crowdedness by using the bike as the access mode. The proposed indicator and method are applied to Shanghai, China as a case study. Results show that bike-metro integration increases the accessibility to metro systems in terms of larger population coverage and a higher accessibility level. Omitting the metro crowdedness leads to an overestimation of the accessibility to metro systems, and the overestimation for the morning peak is larger than that of the afternoon peak. Only 19% of the population in walking catchment areas of crowded stations can shift from crowded stations to non-crowded ones. These results provide a good reference for transportation planning, modeling, and policymaking to improve bike-metro integration.