Abstract
This paper deals with a case study where the objective is to identify the optimal subset of routes for real-time traveler information in a highway network. It is assumed that the benefit from providing this information is directly related to the uncertainty of route travel times. The variance of travel times within a time period over consecutive days is employed as the indicator of this uncertainty. New Jersey Turnpike is used as the study network due to the availability of vehicle-by-vehicle network specific data. The data set covers travel times between ∼630 origin-destination (OD) pairs during 2004. The problem of identifying the optimal number of subset of routes is modeled as a nonlinear integer-programming problem. The proposed model is then solved using NEOS server, a common optimization solver available over the Internet. A simple heuristic for the proposed model is also presented.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference |
| Pages | 665-670 |
| Number of pages | 6 |
| State | Published - 2006 |
| Event | ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference - Toronto, ON, Canada Duration: Sep 17 2006 → Sep 20 2006 |
Other
| Other | ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference |
|---|---|
| Country | Canada |
| City | Toronto, ON |
| Period | 9/17/06 → 9/20/06 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Evaluation of travel time variability in new jersey turnpike - A case study. / Bartin, Bekir; Ozbay, Kaan.
Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference. 2006. p. 665-670 1706818.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evaluation of travel time variability in new jersey turnpike - A case study
AU - Bartin, Bekir
AU - Ozbay, Kaan
PY - 2006
Y1 - 2006
N2 - This paper deals with a case study where the objective is to identify the optimal subset of routes for real-time traveler information in a highway network. It is assumed that the benefit from providing this information is directly related to the uncertainty of route travel times. The variance of travel times within a time period over consecutive days is employed as the indicator of this uncertainty. New Jersey Turnpike is used as the study network due to the availability of vehicle-by-vehicle network specific data. The data set covers travel times between ∼630 origin-destination (OD) pairs during 2004. The problem of identifying the optimal number of subset of routes is modeled as a nonlinear integer-programming problem. The proposed model is then solved using NEOS server, a common optimization solver available over the Internet. A simple heuristic for the proposed model is also presented.
AB - This paper deals with a case study where the objective is to identify the optimal subset of routes for real-time traveler information in a highway network. It is assumed that the benefit from providing this information is directly related to the uncertainty of route travel times. The variance of travel times within a time period over consecutive days is employed as the indicator of this uncertainty. New Jersey Turnpike is used as the study network due to the availability of vehicle-by-vehicle network specific data. The data set covers travel times between ∼630 origin-destination (OD) pairs during 2004. The problem of identifying the optimal number of subset of routes is modeled as a nonlinear integer-programming problem. The proposed model is then solved using NEOS server, a common optimization solver available over the Internet. A simple heuristic for the proposed model is also presented.
UR - http://www.scopus.com/inward/record.url?scp=41849149022&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=41849149022&partnerID=8YFLogxK
M3 - Conference contribution
SN - 1424400945
SN - 9781424400942
SP - 665
EP - 670
BT - Proceedings of ITSC 2006: 2006 IEEE Intelligent Transportation Systems Conference
ER -