Impact of unplanned service disruptions on urban public transit systems

Abstract

This paper proposes a general unplanned incident analysis framework for public transit systems from the supply and demand sides using automated fare collection (AFC) and automated vehicle location (AVL) data. Specifically, on the supply side, we propose an incident-based network redundancy index to analyze the network's ability to provide alternative services under a specific rail disruption. The impacts on operations are analyzed through the headway changes. On the demand side, the analysis takes place at two levels: aggregate flows and individual response. We calculate the demand changes of different rail lines, rail stations, bus routes, and bus stops to better understand the passenger flow redistribution under incidents.Individual behavior is analyzed using a binary logit model based on inferred passengers' mode choices and socio-demographics using AFC data. The public transit system of the Chicago Transit Authority is used as a case study. Two rail disruption cases are analyzed, one with high network redundancy around the impacted stations and the other with low. Results show that the service frequency of the incident line was largely reduced (by around 30%∼70%) during the incident time. Nearby rail lines with substitutional functions were also slightly affected. Passengers showed different behavioral responses in the two incident scenarios. In the low redundancy case, most of the passengers chose to use nearby buses to move, either to their destinations or to the nearby rail lines. In the high redundancy case, most of the passengers transferred directly to nearby lines. Corresponding policy implications and operating suggestions are discussed.

Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty

“…During a disruption, affected passengers need to find an alternative path or use other travel modes (such as transfer to another bus route). However, due to a lack of knowledge of the system (especially during incident time), the new routes chosen by passengers may not be optimal or even cause more congestion (Mo et al, 2022). For example, during a rail disruption, most of the passengers may choose some bus routes that are parallel to the interrupted rail line as an alternative.…”