Isolating high-priority metro and feeder bus transfers using smart card data

Abstract

Fixed-rail metro (or 'subway') infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service's operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers' transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.

Collaboration as a service (CaaS) to fully integrate public transportation – Lessons from long distance travel to reimagine mobility as a service

“…Emphasis has been largely on physical integration, considering features such as terminal co-location, terminal facilities and other physical attributes that impact consumers when making the transfer from one mode to another (Rietveld, 2000;Givoni & Rietveld, 2007;Halldórsdóttir et al, 2017). Network integration has also been widely considered, largely from a tactical perspective as more and more attention is paid on how networks integrate, and services are provided across operators (Hidalgo & King, 2014;Zhao et al, 2017). Informational integration (Grotenhuis et al, 2007) and fare and ticketing integration are also focus areas for integration studies, particularly given technological advancement and the implementation of smart ticketing systems in many public transportation systems and the raft of data they generate (Pelletier et al, 2011).…”

Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

“…2019, 9,3597 3 of 17 widely applied to estimate the daily complete journeys of transit passengers [21][22][23]. In particular, Zhao et al developed a method with a transfer time threshold of 30 min to extract high-priority transfer pairs within an SC dataset for Nanjing, China [24]. The developed method failed under the scenarios of long bus time headway or a small number of transfers.…”

Assessing the Distribution of Commuting Trips and Jobs-Housing Balance Using Smart Card Data: A Case Study of Nanjing, China

“…Previous researches usually identified metro-to-bus transfers based on the maximum elapsed time between a passenger getting off a metro and then getting on a bus [35]. Most metro-to-bus transfers are made within 30 minutes [36].…”