Estimating route travel time reliability from simultaneously collected link and route vehicle probe data and roadway sensor data

William L. Eisele, Bhaven Naik, Laurence R Rilett

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The naïve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.

Original languageEnglish (US)
Pages (from-to)286-304
Number of pages19
JournalInternational Journal of Urban Sciences
Volume19
Issue number3
DOIs
StatePublished - Sep 2 2015

Fingerprint

travel time
travel
probe
sensor
vehicle
time
confidence
traffic
performance monitoring
transportation system
statistical method
private sector
confidence interval
loess

Keywords

  • correlation
  • mean
  • probe data
  • reliability
  • travel time
  • variance

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Urban Studies

Cite this

Estimating route travel time reliability from simultaneously collected link and route vehicle probe data and roadway sensor data. / Eisele, William L.; Naik, Bhaven; Rilett, Laurence R.

In: International Journal of Urban Sciences, Vol. 19, No. 3, 02.09.2015, p. 286-304.

Research output: Contribution to journalArticle

@article{83ad5e03fbcc429895d8af64bd4f74d8,
title = "Estimating route travel time reliability from simultaneously collected link and route vehicle probe data and roadway sensor data",
abstract = "Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The na{\"i}ve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.",
keywords = "correlation, mean, probe data, reliability, travel time, variance",
author = "Eisele, {William L.} and Bhaven Naik and Rilett, {Laurence R}",
year = "2015",
month = "9",
day = "2",
doi = "10.1080/12265934.2015.1064778",
language = "English (US)",
volume = "19",
pages = "286--304",
journal = "International Journal of Urban Sciences",
issn = "1226-5934",
publisher = "Taylor and Francis Ltd.",
number = "3",

}

TY - JOUR

T1 - Estimating route travel time reliability from simultaneously collected link and route vehicle probe data and roadway sensor data

AU - Eisele, William L.

AU - Naik, Bhaven

AU - Rilett, Laurence R

PY - 2015/9/2

Y1 - 2015/9/2

N2 - Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The naïve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.

AB - Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The naïve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.

KW - correlation

KW - mean

KW - probe data

KW - reliability

KW - travel time

KW - variance

UR - http://www.scopus.com/inward/record.url?scp=84946500058&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84946500058&partnerID=8YFLogxK

U2 - 10.1080/12265934.2015.1064778

DO - 10.1080/12265934.2015.1064778

M3 - Article

VL - 19

SP - 286

EP - 304

JO - International Journal of Urban Sciences

JF - International Journal of Urban Sciences

SN - 1226-5934

IS - 3

ER -