Towards an AVL-based Demand Estimation Model
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Abstract
The rapid increase in automated data collection in the public transport industry facilitates the adjustment of operational planning and real-time operations based on the prevailing traffic and demand conditions. In contrast to automated passenger counts systems, automated vehicle location (AVL) data are often available for the entire public transport fleet for monitoring purposes. However, the potential value of AVL data in estimating passenger volumes has been overlooked. This study examined whether AVL data could be used as a stand-alone source for estimating onboard bus loads. The modeling approach infers maximum passenger load stop from the timetable and then constructs the load profile by reverse engineering through a local constrained regression of dwell times as a function of passengers flows. To test and demonstrate the potential value of the proposed method, a proof of concept was performed by conducting unsupervised experiments on 1 month of AVL data collected from two bus lines in Dublin, Ireland. The results suggest that this method can potentially estimate passenger loads in real time in the absence of their direct measurement and can easily be introduced by public transport operators.