Examining Travel Time Reliability-Based Performance Indicators for Bus Routes Using GPS-Based Bus Trajectory Data in India

Akhilesh Chepuri · Jairam Ramakrishnan · Shriniwas Shrikant Arkatkar · Gaurang J. Joshi · Srinivas S. Pulugurtha

31 citations0/0 files storedJournal of Transportation Engineering Part A Systems

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 Traffic control and management Traffic Prediction and Management Techniques

Abstract

This paper aims to evaluate travel time variability as well as reliability indexes using global positioning systems (GPS)–based trajectory data of bus trips collected along a selected bus route of the city of Chennai in the southern part of India. Travel time reliability indexes, such as planning time index (PTI), buffer time index (BTI), and buffer time (BT), along with other statistical measures over different time periods are estimated. Generalized extreme value (GEV) distribution is found to be the best-fitted distribution for explaining bus travel time variability reasonably well, using the Kolmogorov-Smirnov (KS) test. Buffer time and 95th percentile travel time are the reliability measures with the most potential, the variation of which reasonably matches the variation in k-value (shape parameter of GEV distribution) over time. The findings from the statistical distribution analysis indicate that travel times during peak hours can be better described using normal distributions. The generic model is developed for predicting volumes based on bus journey speeds. Further, the developed model is validated with the help of travel time data of the same route during a different time period. The study also attempts to demonstrate a methodology for establishing level-of-service (LoS) criteria using reliability indicators. The classification of reliability indicators, considering segment-level travel time data, coefficient of variation (COV) of travel time, and volume-to-capacity ratio (V/C), is finally presented using the cluster technique. Finally, the study concludes that the most effective performance indicators for examining travel time variability on a given bus route are 95th percentile travel time and BT.

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 — 31 citations exist upstream.

References

2004Modelling network travel time reliability under stochastic demand 2003Travel-Time Reliability as a Measure of Service 1979A study of travel time and reliability on arterial routes 2004Uncovering the contribution of travel time reliability to dynamic route choice using real-time loop data 2008Using Travel Time Reliability Measures to Improve Regional Transportation Planning and Operations 2014Public Transport Travel-Time Variability Definitions and Monitoring 2009Empirical Analysis of Travel Time Reliability Measures in Hanshin Expressway Network 2005Estimation of value of travel time reliability

Cited by

2019A Review of Service Reliability Measures for Public Transportation Systems 2020Analysis of Bus Travel Time Variability using Automatic Vehicle Location Data 2019A Data-Driven Analysis for Operational Vehicle Performance of Public Transport Network 2021Probability distributions analysis of travel time variability for the public transit system 2021Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data

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.