The Effects of Detector Spacing on Travel Time Prediction on Freeways
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The Effects of Detector Spacing on Travel Time Prediction on Freeways

Authors: Piyali Chaudhuri, Peter T. Martin, Aleksandar Z. Stevanovic, Chongkai Zhu

Abstract:

Loop detectors report traffic characteristics in real time. They are at the core of traffic control process. Intuitively, one would expect that as density of detection increases, so would the quality of estimates derived from detector data. However, as detector deployment increases, the associated operating and maintenance cost increases. Thus, traffic agencies often need to decide where to add new detectors and which detectors should continue receiving maintenance, given their resource constraints. This paper evaluates the effect of detector spacing on freeway travel time estimation. A freeway section (Interstate-15) in Salt Lake City metropolitan region is examined. The research reveals that travel time accuracy does not necessarily deteriorate with increased detector spacing. Rather, the actual location of detectors has far greater influence on the quality of travel time estimates. The study presents an innovative computational approach that delivers optimal detector locations through a process that relies on Genetic Algorithm formulation.

Keywords: Detector, Freeway, Genetic algorithm, Travel timeestimate.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062224

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[1] D. Bremmer, K. Cotton, D. Cotey, and C. Prestrud, "Measuring congestion: learning from operational data," Transportation Research Record, Transportation Research Board of the National Academies, No. 1895, 2004, pp. 186-196.
[2] A. Massey, G. W. Saylor, H. W. Wood, B. Baur, and E. Hauser, "Summary of ITS best management practices and technologies for the state of ohio," in ASCE Conference Proceedings, October 2001, pp.127- 134.
[3] J. Kwon, K. Petty, and P. Varaiya, "Probe vehicle runs or loop detectors? Effect of detector spacing and sample size on the accuracy of freeway congestion monitoring," Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., January 2006.
[4] K. Ozbay, B. Bartin, and S. Chien, "South Jersey real-time motorist information system: technology and practice," Transportation Research Record, Transportation Research Board of the National Academies, No. 1886, 2004, pp. 68-75.
[5] B. Bartin, K. Ozbay, and C. Iyigun, "A clustering based methodology for determining the optimal roadway configuration of detectors for travel time estimation," Transportation Research Record, Transportation Research Board National Research Council, No. 2000, 2007, pp. 98-105.
[6] I. Fujito, R. Margiolta, W. Huang, and W. A. Perez, "Effect of detector spacing on performance measure calculations," Transportation Research Record, No. 1945, Transportation Research Board of the National Academies, 2006, pp. 1-11.
[7] K. S. Chan, and W. H. K. Lam, " Optimal speed detector density for the network with travel time information," Transportation Research Record, Transportation Research Board of the National Academies, No 36, 2002, pp. 203-223.
[8] A. Sen, P. Thankuriah, X. Zhu, and A. Karr, "Frequency of probe reports and variance of travel time estimates," Journal of Transportation Engineering, ASCE, Vol. 123, No. 4, 1997, pp. 290-297.
[9] X. Ban, L. Chu, and H. Benouar, "Bottleneck identification and calibration for corridor management planning," Transportation Research Record, Transportation Research Board of the National Academies, No. 1999, 2007, pp. 40-53.
[10] H. Chen, M. S. Dougherty, and H. R. Kirby, "The effects of detector spacing on traffic forecasting," Computer-Aided Civil and Infrastructure Engineering, Volume 16, No. 6, 2001, pp. 422- 430.
[11] R. Cheu, and S. Ritchie, "Automated detection of lane-blocking freeway incidents using artificial neural networks," Transportation Research Record, Part C, Volume 3, Issue 6, December 1995, pp. 371-388.
[12] Goldberg, D. E, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989, pp. 1-32.
[13] K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, 2001, pp. 13-45.
[14] VISSIM 4.30 Manual. Planung Transport Verkehr (PTV) AG., 2007.
[15] P.T. Martin, A. Stevanovic, I. Vladisavljevic, and D. Jovanovic. "VISUM-Online (OTACHAT)," University of Utah Traffic Laboratory, Salt Lake City, Utah, UTL-1106-90, January 2007.
[16] Travel Time Data Collection Handbook, FHWA-PL-98-035, TTI, Texas A&M University, Texas, 1998.
[17] P.T. Martin, I. Vladisavljevic, and D. Yusufzyanova, "The I-15 Express lanes evaluation", University of Utah Traffic Laboratory, Salt Lake City, Utah, UTL-1106- 89, November 2007, vol 9.
[18] P.T. Martin, I. Vladisavljevic, J. Ries, and B. Nadimpalli, "Express lane genetic algorithm microsimulation evaluation part 2", University of Utah Traffic Laboratory, Salt Lake City, Utah, UTL- 02- 08- 96, November 2008.
[19] KMZ file provided by UDOT.
[20] P. Edara, J. Guo, B. L. Smith, and C. McGhee, "Optimal placement of point detectors on Virginia-s freeways: case studies of northern Virginia and Richmond," Virginia Transportation Research Council, Virginia Department of Transportation, VTRC 08-CR3, January 2008.
[21] C.S. ReVelle, and Eiselt, H.A, "Location analysis: a synthesis and survey," European Journal of Operational Research, Vol. 165, 2005, pp. 1-19.
[22] S. Chan, and H.K. Lam, "Optimal speed detector density for the network with travel time information," Transportation Research A, Vol. 36, 2002, pp. 203-223.
[23] A. Ehlert, M. Bell, and S. Grosso, "The optimization of traffic count locations in road networks," Transportation Research Record, Part B, Vol. 40, 2006, pp. 460-479.
[24] M. Gen, and R. Cheng, Genetic Algorithms & Engineering Optimization, New York: John Wiley and Sons, 2000, ch.2.
[25] K. Deb, A. Pratap, S. Argawal, and T. Meyarivan, "A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II," Int conference on parallel problem solving from nature No.6, Paris, France, 2000, vol. 1917, pp. 849-858.
[26] K. Deb, A. Pratap, S. Argawal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, Vol. 6, 2002, pp.182-197.
[27] P. Ngatchou, Z. Anahita and M.A. El-Sharkawi, "Pareto multi objective optimization," in Proc. of the 13th International Conference on Intelligent Systems Application to Power Systems, Nov. 2005, pp. 84-91.