Estimation of Dynamic Origin-Destination Matrices in a Railway Transportation Network integrating Ticket Sales and Passenger Count Data
arxiv(2023)
摘要
Accurately estimating Origin-Destination (OD) matrices is a topic of
increasing interest for efficient transportation network management and
sustainable urban planning. Traditionally, travel surveys have supported this
process; however, their availability and comprehensiveness can be limited.
Moreover, the recent COVID-19 pandemic has triggered unprecedented shifts in
mobility patterns, underscoring the urgency of accurate and dynamic mobility
data supporting policies and decisions with data-driven evidence. In this
study, we tackle these challenges by introducing an innovative pipeline for
estimating dynamic OD matrices. The real motivating problem behind this is
based on the Trenord railway transportation network in Lombardy, Italy. We
apply a novel approach that integrates ticket and subscription sales data with
passenger counts obtained from Automated Passenger Counting (APC) systems,
making use of the Iterative Proportional Fitting (IPF) algorithm. Our work
effectively addresses the complexities posed by incomplete and diverse data
sources, showcasing the adaptability of our pipeline across various
transportation contexts. Ultimately, this research bridges the gap between
available data sources and the escalating need for precise OD matrices. The
proposed pipeline fosters a comprehensive grasp of transportation network
dynamics, providing a valuable tool for transportation operators, policymakers,
and researchers. Indeed, to highlight the potentiality of dynamic OD matrices,
we showcase some methods to perform anomaly detection of mobility trends in the
network through such matrices and interpret them in light of events that
happened in the last months of 2022.
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