Development of a Data-Driven Platform for Transit Performance Measures Using Smart Card and GPS Data

Xiaolei Ma · Yinhai Wang

77 citations0/0 files storedJournal of Transportation Engineering

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Transportation Planning and Optimization Human Mobility and Location-Based Analysis Urban Transport Systems Analysis

Abstract

To improve customer satisfaction and reduce operation costs, transit authorities have been striving to monitor transit service quality and identify the key factors to enhance it. The recent advent of passive data collection technologies, e.g., automated fare collection (AFC) and automated vehicle location (AVL), has shifted a data-poor environment to a data-rich environment and offered opportunities for transit agencies to conduct comprehensive transit system performance measures. However, most AFC and AVL systems are not designed for transit performance measures, implying that additional data processing and visualization procedures are needed to improve both data usability and accessibility. This study attempts to develop a data-driven platform for online transit performance monitoring. The primary data sources come from the AFC and AVL systems in Beijing, where a passenger’s boarding stop (origin) and alighting stop (destination) on a flat-rate bus are not recorded. The individual transit rider’s origin and destination can be estimated by utilizing a series of data-mining techniques, which are then incorporated into a regional-map platform for transit performance measures. A multilevel framework is proposed to calculate the network-level speed, route-level travel time reliability, stop-level ridership, and headway variance. These statistics are interactively displayed on a map through a simplified transit GIS data model. This platform not only serves as a data-rich visualization platform to monitor transit network performance for planning and operations, it also intends to take advantage of e-science initiative for data-driven transportation research and applications.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 77 citations exist upstream.

References

2011Smart card data use in public transit: A literature review 2013Mining smart card data for transit riders’ travel patterns 2005Urban Transit : Operations, Planning and Economics 2012Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile 2006Urban Transit: Operations, Planning and Economics 2007Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System 2008Using Travel Time Reliability Measures to Improve Regional Transportation Planning and Operations 2003A Guidebook for Developing a Transit Performance-Measurement System

Cited by

2017Dynamic transit accessibility and transit gap causality analysis 2017Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study 2016Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro 2020Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada 2019

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.