Traffic Monitoring Stations on Interstate 15 in Utah's Salt Lake City metropolitan region are placed at approximately 0.5 mile spacing. It is time to replace and upgrade the detectors that constitute these Traffic Monitoring Stations. The focus of this project is where the new detectors should be deployed and how many of them are required. The research reported here has two parts. The first part evaluated the effectiveness and reliability of the detectors in traffic monitoring stations deployed by the Utah Department of Transportation (UDOT) on Interstate 15 (I-15). The purpose of the second part of the project was to develop an analytical methodology to calculate travel time measures considering the trade-off between spacing and accuracy of the estimates. The project identifies the optimal locations of a finite set of point detectors on the freeway corridor in order to minimize the error in travel time estimation, within the constraints of available capital and state maintenance funding. The analysis showed that the actual location of the detector is important in the estimation of travel time for the freeway section. Depending on which detectors are "selected," one can obtain a rather different picture for the congestion along the freeway section. Further analysis by strategically selecting detectors located near congestion/bottlenecks and other important locations along I-15 showed that UDOT can reduce the number of detectors currently maintained by TMCs and can deploy far fewer than the 0.5 mile spacing guidelines.
[1]
Praveen Edara,et al.
Optimal Placement of Point Detectors on Virginia’s Freeways: Case Studies ofNorthern Virginia and Richmond
,
2008
.
[2]
Robert L. Bertini.
Toward Optimal Sensor Density for Improved Freeway Travel Time Estimation and Traveler Information
,
2007,
2007 IEEE Intelligent Transportation Systems Conference.
[3]
K. Ozbay,et al.
A Clustering Based Methodology for Determining the Optimal Roadway Configuration of Detectors for Travel Time Estimation
,
2006,
2006 IEEE Intelligent Transportation Systems Conference.
[4]
Ruimin Li,et al.
Evaluation of speed-based travel time estimation models
,
2006
.
[5]
Pravin Varaiya,et al.
Probe Vehicle Runs or Loop Detectors?
,
2007
.
[6]
Ying Liu,et al.
Detector Placement Strategies for Freeway Travel Time Estimation
,
2006,
2006 IEEE Intelligent Transportation Systems Conference.
[7]
David E. Goldberg,et al.
Genetic Algorithms in Search Optimization and Machine Learning
,
1988
.