Estimating the route-level passenger demand profile from bus dwell times

Document Type

Journal Article

Publication Date


Subject Area

mode - bus, technology - automatic vehicle monitoring, technology - passenger information, ridership - demand, planning - methods


Passenger demand profile, Bus transit route, Dwell time modelling, Automatic vehicle location data, Bayesian inference, Markov Chain Monte Carlo


This paper explores the feasibility of estimating the demand profile of a bus route consisting of boarding flows, alighting flows, passenger loads, and origin–destination (OD) flows using dwell times as the main data source. Dwell times can be obtained from Automatic Vehicle Location (AVL) data, or door opening and closing records. This data is often more obtainable than the direct demand information based on passenger counts or fare collection information. A flexible Bayesian framework allowing for various dwell time models is employed to infer the passenger demand profile conditional on the observed bus dwell times. We model passenger flows by stop-specific arrival rates and a conditional alighting probability matrix. A modified gravity model is used to further explain the alighting probability matrix and to reduce the number of unknown parameters. Hamiltonian Monte Carlo sampling is implemented to draw the posterior distribution for the parameters of interest. The methodology is validated by three lines of a bus system where alighting times are the main activity time. For these lines dwell time as well as passenger count data are available. The case study shows that accurate estimates of alighting flows and passenger loads can be derived by the proposed framework with dwell times only but that room remains for improving the accuracy of boarding flows and OD flows. Additional boarding counts of a few bus runs, which can be easily integrated into the observation block of the estimation framework, raise the estimation accuracy of boarding and OD flows to an acceptable level. We therefore conclude that the proposed methodology can help “data-poor” bus operators to overcome demand estimation problems for transit planning and operation.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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