J 2017

Analysis of Trends in Data on Transit Bus Dwell Times

ISAAC K., Isukapati, Hana RUDOVÁ, Gregory BARLOW and Stephen SMITH

Basic information

Original name

Analysis of Trends in Data on Transit Bus Dwell Times

Authors

ISAAC K., Isukapati (356 India), Hana RUDOVÁ (203 Czech Republic, guarantor, belonging to the institution), Gregory BARLOW (840 United States of America) and Stephen SMITH (840 United States of America)

Edition

Transportation Research Record: Journal of the Transportation Research Board, 2017, 0361-1981

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 0.695

RIV identification code

RIV/00216224:14330/17:00096436

Organization unit

Faculty of Informatics

DOI

UT WoS

000413464000008

Keywords in English

traffic signal control; bus dwell time; data analysis

Tags

International impact, Reviewed
Změněno: 18/5/2018 04:09, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

Transit vehicles create special challenges for urban traffic signal control. Signal timing plans are typically designed for the flow of passenger vehicles, but transit vehicles, with frequent stops and uncertain dwell times, may have very different flow patterns that fail to match these plans. Transit vehicles stopping on urban streets can also restrict or block other traffic on the road. This results in increased overall wait times and delays throughout the system for transit vehicles and other traffic. Transit signal priority (TSP) systems are often used to mitigate some of these issues, primarily addressing delay to the transit vehicles. However, existing TSP strategies give unconditional priority to transit vehicles, exacerbating quality of service for other modes. In networks where transit vehicles have significant effects on traffic congestion, particularly urban areas, using more realistic models of transit behavior in adaptive traffic signal control could reduce delay for all modes. Estimating the arrival time of a transit vehicle at an intersection requires an accurate model of transit stop dwell times. As a first step toward developing a model for predicting bus arrival times, this paper analyzes trends in automatic vehicle location (AVL) data collected over a two-year period, allowing several inferences to be drawn about the statistical nature of dwell times, particularly for use in real-time control and transit signal priority. Based on this trend analysis, we argue that an effective predictive dwell time distribution model must treat independent variables as random or stochastic regressors.