A Dynamic Latent Class Model to Estimate the Crowding Cost of Subway Riders

摘要: Quantitative measurement of crowding disutilities in public transport is important in investment appraisals, demand modeling, and supply-side decisions such as fare optimisation. To this end, most of the studies use a stated preference (SP) survey and estimate the traveller’s perceived value of crowding in terms of a crowding multiplier–the ratio of value-of-travel-time under crowded and uncrowded conditions (Wardman and Whelan, 2011). SP studies generally elicit preferences of riders in a hypothetical route choice experiment and estimate discrete choice models (DCMs) to obtain the crowding multiplier (See Bansal et al., 2019, for the review). Whereas the hypothetical bias is a major limitation of the SP data, the required information to estimate DCMs (riders’ route preferences and attributes of all available routes) is difficult to obtain using conventional revealed preference (RP) surveys (Tirachini et al., 2016). Due to these challenges, early crowding valuation studies relying on the RP data either deviated from DCMs (Kroes et al., 2014) or complemented the RP data with the SP data (Batarce et al., 2015). However, the emerging use of smart cards for fare collection provides an alternative way to collect the required RP data. Tirachini et al.(2016) first illustrated how smart card data can be used for the crowding valuation of the Mass Rapid Transit users in Singapore. Along the same lines, Hörcher et al.(2017) integrated the smart card data with the vehicle location data to estimate the crowding multiplier of Hong Kong Mass Transit Railway (MTR) users. We identify three research gaps in the crowding valuation literature. First, whereas dynamic route …