Temporal Variations in Activity Network Using Smart Card Data

Abstract

This study explores temporal variations in activity networks for four million passengers, differentiated as workers and non-workers, using public transport based on a large-scale smart card dataset generated over 105 days in Beijing. We aim to capture their day-to-day transition and cumulative temporal expansion in activity network using transit over days, weeks, and months. Particularly, workers and non-workers are automatically identified based on their different daily routines, whose activity networks are characterized by six features concerning space coverage, distance coverage, and frequency coverage in two ways, namely, on a per-day transition and with an accumulation of days. The transition features of the networks are statistically analyzed and compared by time, while how the expansion features evolve with time are modeled. Results show that, on weekdays, workers are more likely to travel longer (have larger distance coverage), but cover less area (have smaller space coverage) than non- workers. While opposite patterns occur on weekends. Traveling in the ‘North-South’ direction is weakly correlated with traveling in the ‘East-West’ direction. Workers on weekdays, as well as non-workers on weekends, make longer ‘North-South’ trips. Manhattan distance, trip count, and perimeter present a ∩ shape in their probability density functions, while the remaining features decline dramatically, with probability density functions fit by the exponential distribution. The distance coverage expands faster than that of space coverage. Most passengers increase coverage of space and distance when time expands (obviously no one decreases coverage over time, but some don’t change). The research enables findings on temporal load-balancing, long-term cumulative expansion in travel demands of workers and non-workers, re-balancing the distribution of existing workplace and residential location opportunities, and constructing transit-oriented developments with mixed functions over time.