Multi-Stakeholders Comparative Assessment of Freeway Traffic Conditions by Data Envelopment Analysis

Article Preview

Abstract:

Effective assessment of traffic conditions is a key issue involved in alleviating freeway congestion, evaluating capital improvements and estimating travel time. Since the goals and objectives of assessment are inherently an expression of the various stakeholders affected by the traffic conditions, the assessment process and result must address the interests of all stakeholders. In this paper, a methodology and its application to assess traffic conditions on urban freeways are described. The methodology, which synthesizes Data Envelopment Analysis (DEA) and Analytical Hierarchy Process (AHP), can devise an overall traffic conditions assessment regarding various stakeholders’ preferences. Application of the methodology to six real-life freeway corridors in Jilin Province indicated that the stakeholders can gain new insight into the overall traffic conditions behind multiple performance measures with our method, and the assessment results is helpful in identifying transportation investment priorities for specific regions and improving resource utilization among competing sectors.

You have full access to the following eBook

Info:

[1] T. Choe, A. Skabardonis, and P. Varaiya. Freeway Performance Measurement System: Operational Analysis Tool. In Transportation Research Record: Journal of the Transportation Research Board, No. 1811, Transportation Research Board, Washington, D.C., (2002) 67–75.

DOI: 10.3141/1811-08

Google Scholar

[2] R. L. Bertini, and A. M. Myton. Use of Performance Measurement System Data to Diagnose Freeway Bottleneck Locations Empirically in Orange County, California. In Transportation Research Record: Journal of the Transportation Research Board, No. 1925, Transportation Research Board, Washington, D.C., (2005) 48–57.

DOI: 10.1177/0361198105192500106

Google Scholar

[3] Z. Zuduo, A. Soyoung, C. Danjue and L. Jorge. Applications of wavelet transform for analysis of freeway traffic: Bottlenecks, transient traffic, and traffic oscillations. Transportation Research Part B, 45, (2011) 372–384.

DOI: 10.1016/j.trb.2010.08.002

Google Scholar

[4] Y. Kamarianakis, and P. Prastacos. Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches. In Transportation Research Record: Journal of the Transportation Research Board, No. 1857, Transportation Research Board, Washington, D.C., (2005) 74–84.

DOI: 10.3141/1857-09

Google Scholar

[5] D. Hussein and T. Kim. Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data. Information Fusion, 12, (2011) 20–27.

DOI: 10.1016/j.inffus.2010.01.001

Google Scholar

[6] M.G. Karlaftis and E.I. Vlahogianni. Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C, 19, (2011) 387–399.

DOI: 10.1016/j.trc.2010.10.004

Google Scholar

[7] R. E. Turochy, and B. L. Smith. Measuring Variability in Traffic Conditions by Using Archived Traffic Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1804, Transportation Research Board, Washington, D.C., (2002) 168–172.

DOI: 10.3141/1804-22

Google Scholar

[8] J. L. Catbagan, and H. Nakamura. Evaluation of Performance Measures for Two-Lane Expressways in Japan. In Transportation Research Record: Journal of the Transportation Research Board, No. 1988, Transportation Research Board, Washington, D.C., (2006) 111–118.

DOI: 10.1177/0361198106198800114

Google Scholar