A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data

Table 1

Review of studies on estimating alighting stop in a tap-in transit system.

Author	Data	Assumption and constraints	Analysis/use methodology	Application	Pros	Limitations
Barry et al. (2002) [18]	AFC	Two basic assumptions	Trip chaining	New York	Easy to apply	Lack of one trip estimation
Zhao et al. [19] (2007) and Zhao (2004) [20]	ADC	Walking distance threshold	Database management systems	Chicago	(i) Integrating the AFC and AVL; (ii) examining the spatial connection	The model was just focused on the bus and rail station
Trépanier et al. (2007) [21]	AFC	Walking tolerance is 2 km.	Transportation object-oriented modeling with vanishing route set	Gatineau	The model is quite suitable for regular transit users	Some passenger information such as single ticket user is missing.
Chu and Chapleau (2008) [22]	AFC	5 min temporal leeway for uncertainty	The linear interpolation and extrapolation to infer the vehicle position	Société de transport de l’Outauais	Avoids the overestimation of the transfer.	Improves the results of trip purpose and destination inference.
Nassir et al. (2011) [17]	ADCS; AFC; AVL	Geographical and temporal check Transfer time threshold	OD estimation algorithm	Minneapolis-Saint Paul	Relative relaxation of the search in finding the boarding stops.	The transfer time threshold is fixed
Wang et al. (2011) [23]	ADCS; AVL	Walking tolerance is 1 km or 12 min.	Trip chaining methodology based on next trip is bus or rail	London	Validates the automatic inference results against large-scale survey results	Linking system usage to home addresses; access behavior could be better understood
Munizaga and Palma (2012) [24] and Munizaga et al. (2014) [25]	AFC; GPS; AVL	Generalized time	Position-time alighting estimate model	Santiago	(i) Uses generalized time rather than physical distance; (ii) Replaces larger on-board survey	The one trip per card destination estimation is missing.
Gordon et al. (2013) [26]	AFC; AVL	Walking tolerance is 1 km and max. transfer is 30 min.	Four-step trip chaining algorithm	London	The circuity ratios to decide the potential destination for previous journey.	Not all of the passengers alight from the stops closest to the next journey.
Alsger et al. (2015) [27]	AFC	The dynamic transfer time threshold	OD estimation algorithm	Queensland	Transfer time threshold could be increased.	Extended to compare other estimation methods.