Vehicle re-identification with dynamic time windows for vehicle passage time estimation

Wei Hua Lin, Daoqin Tong

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

A simple method for vehicle re-identification to generate vehicle passage times with loop data is developed. The method departs from other existing methods for vehicle passage time estimation: 1) It handles vehicle signatures one at a time and evaluates each vehicle observed only once. 2) The commonly used prespecified time window is replaced by a dynamic list of vehicles to be matched. 3) Vehicle matching is based on a combined estimation model that integrates spot traffic data with spatial vehicle data. The performance of the algorithm was tested with field data. Furthermore, to examine the effect of some of the assumptions on the performance of the algorithm, we compared the result with that obtained from an offline optimization model based on a spatial constraint that considers as many vehicles as possible for matching. The proposed method is particularly suitable for real-time applications since it can be easily implemented with little calibration effort and is computationally efficient.

Original languageEnglish (US)
Article number5763781
Pages (from-to)1057-1063
Number of pages7
JournalIEEE Transactions on Intelligent Transportation Systems
Volume12
Issue number4
DOIs
StatePublished - Dec 1 2011
Externally publishedYes

Fingerprint

Calibration

Keywords

  • Freeway operations
  • travel time estimation
  • vehicle detection systems

ASJC Scopus subject areas

  • Automotive Engineering
  • Computer Science Applications
  • Mechanical Engineering

Cite this

Vehicle re-identification with dynamic time windows for vehicle passage time estimation. / Lin, Wei Hua; Tong, Daoqin.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 4, 5763781, 01.12.2011, p. 1057-1063.

Research output: Contribution to journalArticle

@article{c33bad233aab48168ffa64790a1c0aba,
title = "Vehicle re-identification with dynamic time windows for vehicle passage time estimation",
abstract = "A simple method for vehicle re-identification to generate vehicle passage times with loop data is developed. The method departs from other existing methods for vehicle passage time estimation: 1) It handles vehicle signatures one at a time and evaluates each vehicle observed only once. 2) The commonly used prespecified time window is replaced by a dynamic list of vehicles to be matched. 3) Vehicle matching is based on a combined estimation model that integrates spot traffic data with spatial vehicle data. The performance of the algorithm was tested with field data. Furthermore, to examine the effect of some of the assumptions on the performance of the algorithm, we compared the result with that obtained from an offline optimization model based on a spatial constraint that considers as many vehicles as possible for matching. The proposed method is particularly suitable for real-time applications since it can be easily implemented with little calibration effort and is computationally efficient.",
keywords = "Freeway operations, travel time estimation, vehicle detection systems",
author = "Lin, {Wei Hua} and Daoqin Tong",
year = "2011",
month = "12",
day = "1",
doi = "10.1109/TITS.2011.2140318",
language = "English (US)",
volume = "12",
pages = "1057--1063",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Vehicle re-identification with dynamic time windows for vehicle passage time estimation

AU - Lin, Wei Hua

AU - Tong, Daoqin

PY - 2011/12/1

Y1 - 2011/12/1

N2 - A simple method for vehicle re-identification to generate vehicle passage times with loop data is developed. The method departs from other existing methods for vehicle passage time estimation: 1) It handles vehicle signatures one at a time and evaluates each vehicle observed only once. 2) The commonly used prespecified time window is replaced by a dynamic list of vehicles to be matched. 3) Vehicle matching is based on a combined estimation model that integrates spot traffic data with spatial vehicle data. The performance of the algorithm was tested with field data. Furthermore, to examine the effect of some of the assumptions on the performance of the algorithm, we compared the result with that obtained from an offline optimization model based on a spatial constraint that considers as many vehicles as possible for matching. The proposed method is particularly suitable for real-time applications since it can be easily implemented with little calibration effort and is computationally efficient.

AB - A simple method for vehicle re-identification to generate vehicle passage times with loop data is developed. The method departs from other existing methods for vehicle passage time estimation: 1) It handles vehicle signatures one at a time and evaluates each vehicle observed only once. 2) The commonly used prespecified time window is replaced by a dynamic list of vehicles to be matched. 3) Vehicle matching is based on a combined estimation model that integrates spot traffic data with spatial vehicle data. The performance of the algorithm was tested with field data. Furthermore, to examine the effect of some of the assumptions on the performance of the algorithm, we compared the result with that obtained from an offline optimization model based on a spatial constraint that considers as many vehicles as possible for matching. The proposed method is particularly suitable for real-time applications since it can be easily implemented with little calibration effort and is computationally efficient.

KW - Freeway operations

KW - travel time estimation

KW - vehicle detection systems

UR - http://www.scopus.com/inward/record.url?scp=82455168017&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=82455168017&partnerID=8YFLogxK

U2 - 10.1109/TITS.2011.2140318

DO - 10.1109/TITS.2011.2140318

M3 - Article

VL - 12

SP - 1057

EP - 1063

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 4

M1 - 5763781

ER -