Modeling Spatial-Temporal Patterns of Bus Delays at and between Stops Using AVL and APC Data and Semi-Markov Techniques

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most transit operators produce automatic vehicle location (AVL) and automatic passenger counter (APC) data to assess and improve transit performance. Conventional analyses usually extract data at stops, and, therefore, underutilize vehicle location data collected between stops. This paper develops a model to study bus stopping patterns using AVL and APC data collected at and between stops. The model contains three major steps: (1) linear-referencing AVL and APC data along transit routes, (2) visually exploring the spatio-temporal patterns of delays, and (3) modeling vehicle movements as continuous-time semi-Markov processes and calibrating them using the revealed patterns. The model can be used to identify locations and times that are more likely to get congested and lead to delays and provide more accurate arrival times to transit users. To demonstrate the model, the paper uses AVL and APC data collected along A-Line rapid route within an eight-day period in Minneapolis, Minnesota.

Original languageEnglish (US)
Title of host publicationCICTP 2018
Subtitle of host publicationIntelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals
EditorsDiange Yang, Xiaokun Wang, Zheng You, Yu Zhang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages666-675
Number of pages10
ISBN (Electronic)9780784481523
StatePublished - Jan 1 2018
Event18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018 - Beijing, China
Duration: Jul 5 2018Jul 8 2018

Publication series

NameCICTP 2018: Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals

Other

Other18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018
CountryChina
CityBeijing
Period7/5/187/8/18

Fingerprint Dive into the research topics of 'Modeling Spatial-Temporal Patterns of Bus Delays at and between Stops Using AVL and APC Data and Semi-Markov Techniques'. Together they form a unique fingerprint.

Cite this