TY - JOUR
T1 - Towards an AVL-based Demand Estimation Model
AU - Morriea-Matias , Luis
AU - Cats, Oded
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://trrjournalonline.trb.org/doi/pdf/10.3141/2544-16
UR - http://resolver.tudelft.nl/uuid:b6be45b1-6fdb-4fe8-84d7-349d349f5745
M3 - Article
SN - 0361-1981
VL - 2544
SP - 141
EP - 149
JO - Transportation Research Record
JF - Transportation Research Record
ER -