Urban Freight OD Estimation Based on Close-End Roads

Author(s): 
H. Xiao, W. Chen, & Y. C. Huang

Affiliation(s): 
Economics and Management College, Chongqing Jiaotong University, Chongqing, 400074, China,‡Information and Technology Department of Library, Chongqing Jiaotong University, Chongqing, 400074, China

Cite this paper
H. Xiao, W. Chen, & Y. C. Huang, “Urban Freight OD Estimation Based on Close-End Roads”, Journal of Mechanical Engineering Research and Developments, vol. 39, no. 2, pp. 347-356, 2016. DOI: 10.7508/jmerd.2016.02.010

ABSTRACT: This paper took the Chengdu-Chongqing expressway as research background, and analyzed the traffic characteristics of Close-end Roads by applying DSRC-OD study method and Gray modeling theory, in the end, this paper got a OD prediction on urban freight of Close-end Roads, that is the Chengdu-Chongqing expressway OD prediction table in vehicle type in next few years and the data also showed that with the improvement of logistics operation the logistics structure is more reasonable in Chengdu-Chongqing region.

Keywords : Freight; OD; Estimation; Close-End roads.

References
[1] Ortuzar, J.D., Willumnsen, L.G., 2011. Modeling Transport, fourth ed. John Wiley and Sons, New York.
[2] Shao, H., Lam, W.H.K., Sumalee, A., Chen, A., Hazelton, M.L., 2014. Estimation of mean and covariance of peak hour origin–destination demands from day-today traffic counts. Transport. Res. Part B 68, 52–75.
[3] Haas, C.N., 1999. On modeling correlated random variables in risk assessment. Risk Anal. 19 (6), 1205–1214.
[4] Waller, S.T., Schofer, J.L., Ziliaskopoulos, A.K., 2001. Evaluation with traffic assignment under demand uncertainty. Transport. Res. Rec. 1771, 69–74.
[5] Zhao, Y., Kockelman, K.M., 2002. The propagation of uncertainty through travel demand models: an exploratory analysis. Ann. Reg. Sci. 36 (1), 145–163.
[6] Duthie, J.C., Unnikrishnan, A., Waller, S.T., 2011. Influence of demand uncertainty and correlations on traffic predictions and decisions. Comput.-Aided Civil Infrastruct. Eng. 26 (1), 16–29.
[7] Shao, H., Lam, W.H.K., Sumalee, A., Chen, A., Hazelton, M.L., 2014. Estimation of mean and covariance of peak hour origin–destination demands from day-today traffic counts. Transport. Res. Part B 68, 52–75.
[8] YIM YB,CAYFORD R. Investigation of Vehicles as Probes Using Global Positioning System and Cellular Phone Tracking: Field Operational Test
[9] Iqbal M S, Choudhury C F, Wang P, et al. Development of origin–destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 2014, 40: 63-74.
[10]Sánchez-Cambronero S, Castillo E, Menéndez J M, et al. Dealing with error recovery in traffic flow prediction using Bayesian networks based on license plate scanning data[J]. Journal of transportation engineering, 2010, 137(9): 615-629.
[11] L.G Willumsen. Estimating time—dependent trip matrices from traffic counts. Transportation and traffic theory, 1984
[12] Maher M. j. Inferences on trip matrices from observations on link volumes: a Bayesian statistical approach. Transportation Res .B Vol17B 435-447, 1983
[13] Cascetta E. Estimation of trip matrices from traffic counts and survey data: A generalized least squares. Transportation Res. B Vol18B 289—299, 1984
[14] H. Spiess. A maximum-1ikelihood model for estimating origin-destination matrices. Transportation Res. B Vol21B 352-365, 1987