Crowding cost estimation with large scale smart card and vehicle location data
mode - subway/metro, place - urban, operations - crowding, technology - automatic vehicle monitoring, technology - passenger information, ridership - behaviour, ridership - demand
Public transport, Crowding, Revealed preference, Smart card data, AVL data
Crowding discomfort is an external cost of public transport trips imposed on fellow passengers that has to be measured in order to derive optimal supply-side decisions. This paper presents a comprehensive method to estimate the user cost of crowding in terms of the equivalent travel time loss, in a revealed preference route choice framework. Using automated demand and train location data we control for fluctuations in crowding conditions on the entire length of a metro journey, including variations in the density of standing passengers and the probability of finding a seat. The estimated standing penalty is 26.5% of the uncrowded value of in-vehicle travel time. An additional passenger per square metre on average adds 11.9% to the travel time multiplier. These results are in line with earlier revealed preference values, and suggest that stated choice methods may overestimate the user cost of crowding. As a side-product, and an important input of the route choice analysis, we derive a novel passenger-to-train assignment method to recover the daily crowding and standing probability pattern in the metro network.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Hörcher, D., Graham, D.J., & Anderson, R.J. (2017). Crowding cost estimation with large scale smart card and vehicle location data. Transportation Research Part B: Methodological, Vol. 95, pp. 105–125.