Extracting Spatiotemporal Bus Passenger Trip Typologies from Noisy Mobile Ticketing Boarding Data

We present a framework for extracting spatiotemporal trip typologies using noisy mobile ticketing boarding data sampled from passengers in a bus network. Our case study was the Pioneer Valley Transit Authority in Massachusetts. We first used a greedy approach to infer bus boarding stops. Next, we calculated the multi-dimensional dissimilarity of passenger activation time series using the AWarp alignment algorithm for sparse time series. We then employed hierarchical clustering to discover the spatiotemporal patterns, resulting in four distinct trip pattern typologies. We analyzed the typologies, based on trip length and duration, seasonality and other temporal distributions, spatial distributions, and faretype. Three typologies were linked to regular commuters, distinguished by boarding time or transfer tendency. The fourth typology was primarily associated with leisure or other activities. Our typology method provides valuable passenger behavioral insights and can facilitate demand estimation by planners. Further, we demonstrate a potential for decision-making support for other regional transit authorities with limited passenger data availability.