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Freeway Travel Time Estimation Using Limited Loop Data

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2008, Master of Science, University of Akron, Civil Engineering.
Providing drivers with real-time, high-quality traveler information is becoming increasingly important as congestion continues to grow in cities across the United States. Studies have shown that not only is congestion increasing, but travel time reliability also is a growing problem. Travelers would like to have information about traffic condition or the extent of delay due to construction or incident. Since congestion is treated as a major factor influencing travel decisions, most of the large metropolitan areas are providing travel time information to motorists through dynamic message signs (DMS), 511 programs, the Internet, highway advisory radio, and other sources. Traffic condition is affected by current events and historical travel pattern. Today, real time data can be gathered from microwave radar, automatic vehicle tag matching, video detection, license plate matching, and most commonly, inductive loops. Loop detectors can be placed in each lane to provide volume, occupancy and local speed information. Although closely spaced loop detectors are helpful to system operation, operation of such a system imposes a significant economic burden and enormous pressure on system maintenance. In addition, with the proliferation of cell phone usage, loop detector data is no longer critical to incident detection. On the other hand, the effectiveness of using loop detector data to reliably estimate travel time has not been proved. In recent years, researchers discussed if the spacing of detectors could be extended and wondered about the effect of detector spacing. This topic is necessary and timely because of the widespread use of the loop detection system today. The focal point of the discussion is how to determine the appropriate detector spacing needed for different applications while maintaining the same level of data quality. This thesis aimed at studying different freeway travel time estimation methods and exploring impact of loop detector spacing on travel time estimation. We performed the analysis on a sixteen-mile stretch of I-75 in Cincinnati, Ohio and used both simulation and field test to evaluate the testing results. First, the commonly used midpoint method for travel time estimation was examined under various traffic and roadway conditions. We started with the existing 1/3 mile spacing, increased it by using fewer detectors to obtain data for analysis. Then, enhancements were introduced over the midpoint method using different data processing methods reported by other researchers to improve its performance. Preliminary results showed that using the midpoint method, different detector spacings result in different levels of accuracy and generally the estimation error increases with the detector spacing. Moreover, as traffic congestion sets in, the estimation errors from the existing methods increase significantly. After a congestion based error correction term is introduced, the improved midpoint method is able to make more accurate travel time estimations at larger spacings under work zone and incident conditions. The work has also been tested by filed data collected through probe vehicles. Based on field data, the estimated travel times from the improved method matches closely with those measured by the floating cars; the differences are within 10%. Results from this study showed that a larger detector spacing than the commonly used 1/3 mile may not necessarily worsen the estimation results. Overall, the one-mile spacing scheme has outperformed the other tested alternatives in the testbed area. Another part of the thesis is to study the reliability of probe vehicle technique. License Plate Matching Survey was conducted to carry out the analysis. The results showed that the accuracy of probe vehicle travel time will be affected by the standard deviation of travel time and also different analysis period will make the result different. Minimum required sample size was examined as the last part of the thesis.
Ping Yi, Ph.D. (Advisor)
Ala Abbas, Ph.D. (Committee Member)
William Schneider, Ph.D. (Committee Member)

Recommended Citations

Citations

  • Ding, S. (2008). Freeway Travel Time Estimation Using Limited Loop Data [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1205288596

    APA Style (7th edition)

  • Ding, Silin. Freeway Travel Time Estimation Using Limited Loop Data. 2008. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1205288596.

    MLA Style (8th edition)

  • Ding, Silin. "Freeway Travel Time Estimation Using Limited Loop Data." Master's thesis, University of Akron, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1205288596

    Chicago Manual of Style (17th edition)