Prediction of Commuter’s Daily Time Allocation

  • Fang Zong Jilin University
  • Jia Hongfei Jilin University
  • Pan Xiang Zhejiang University of Technology
  • Wu Yang Jilin University
Keywords: time allocation, commuting, activity, travel, Support Vector Regression,

Abstract

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

Author Biographies

Fang Zong, Jilin University
College of Transportation
Jia Hongfei, Jilin University
College of Transportation
Wu Yang, Jilin University
College of Transportation

References

Yu, B., Yang, Z.Z., Li S.: Real-Time Partway Deadheading Strategy Based on Transit Service Reliability Assessment, Transportation Research Part A, Vol. 46, No. 8, 2012, pp. 1265–1279

Zong, F., Juan, Z., Jia, H.: Examination of staggered shifts impacts on travel behavior: a case study of Beijing, Transport, 2012, STRA-2011-0129.R1, forthcoming

Ettema, D., Bastin, F., Polak, J., Ashiru, O.: Modelling the joint choice of activity timing and duration, Transportation Research A, Vol. 41, 2007, pp. 827-841

Hamed, M.M., Mannering F.L.: Modeling travelers’ postwork activity involvement: toward a new methodology, Transportation Science, Vol. 27, No. 4, 1993, pp. 381-394

Bowman, J.L., Ben-Akiva, M.E.: Activity-based disaggregate travel demand model system with activity schedules, Transportation Research Part A, Vol. 35, 2000, pp. 1-28

Bhat, C.: Analysis of travel mode and departure time choice for urban shopping trips, Transportation Research B, Vol. 32, 1998, pp. 361-371

Small, K.A.: The scheduling of consumer activities: work trips, American Economic Review, Vol. 72, 1982, pp. 467-479

Small, K.A.: A discrete model for ordered alternatives, Econometrica, Vol. 55, N0. 2, 1987, pp. 409-424

Bhat, C., Steed, J.: A continuous-time model of departure time choice for urban shopping trips, Transportation Research, Vol. 36B, 2002, pp. 207-224

Juan Z., Xianyu J.: Daily Travel Time Analysis with Duration Model, Journal of Transportation System Engineering & Intelligent, Vol. 10, No. 4, 2010, pp. 62-67

Pendyala, R., Bhat, C.R.: An exploration of the relationship between timing and duration of maintenance activities, Transportation, Vol. 31, 2004, pp. 429-456

Habib K.M.N.: Modeling commuting mode choice jointly with work start time and work duration, Transportation Research Part A, Vol. 46, 2012, pp. 33-47

Schwanen, T., Dijst, M.: Travel-time ratios for visits to the workplace: the relationship between commuting time and work duration, Transportation Research Part A, Vol. 36, 2002, pp. 573-592

Kitamura, R., Fujii, S.: Two computational process models of activity-travel behavior. In T. Garling, T. Laitila and K. Westin (eds.) Theoretical Foundations of Travel Choice Modeling, Oxford: Elsevier Science, 1998, pp. 251-279

Guo, J. Y., Bhat, C. R.: Representation and analysis plan and data needs analysis for the activity-travel system, Research Report, Texas department of transportation, 2001

Juan Z., Xianyu J.: Daily Travel Time Analysis with Duration Model, Journal of Transportation System Engineering & Intelligent, Vol. 10, No. 4, 2010, pp. 62-67

Ray P.: Independence of Irrelevant Alternatives, Econometrica, Vol. 41, No. 5, 1973, pp. 987-991

Quddus, M.A., Noland, R.B., Chin, H.C.: An analysis of motorcycle injury and vehicle damage severity using ordered probit models, Journal of Safety Research, Vol. 33, No. 4, 2002, pp. 445-462

Este A., Gringoli F., Salgarelli L.: Support Vector Machines for TCP traffic classification, Computer Networks, Vol. 53, 2009, pp. 2476-2490

Anguita, D., Boni, A., Ridella, S.: Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap, Neural Processing Letters, Vol. 11, 2000, pp. 51-58

Sung, K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, 1998, pp. 39-50

Vapnik, V.: The Nature of Statistical Learning Theory, Springer Verlag, New York, 1995

Li, Y.M., Gong S.G., Liddell, H.M.: Support Vector Regression and Classification Based Multi-view Face Detection and Recognition, IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 300-305

Dong, B., Cao, C., Lee, S.E.: Applying Support Vector Machines to Predict Building Energy Consumption in Tropical Region, Energy and Buildings, Vol. 37, No. 5, 2005, pp. 545-553

Yao, B.Z., Yang, C.Y., Yao, J.B., Sun, J.: Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine, International Journal of Computational Intelligence Systems, Vol. 3, No. 6, 2010, pp. 843-852

Yao, J.B., Yao, B.Z., Li, L., Jiang, Y.L.: Hybrid model for displacement prediction of tunnel surrounding rock, Neural network world, Vol. 22, 2012, pp. 263-275

Yu, B., Yang, Z.Z., Yao, B.Z.: Bus Arrival Time Prediction Using Support Vector Machines, Journal of Intelligent Transportation Systems, Vol. 10, No. 4, 2006, pp. 151-158

Anastasopoulos, P., Islam, M., Perperidou, D., Karlaftis, M.: Hazard-based analysis of travel distance in urban environments: longitudinal data approach, Journal of Urban Planning and Development, Vol. 138, No. 1, 2012, pp. 53-61

Yu, B., William H.K.L., Mei, L.T.: Bus Arrival Time Prediction at Bus Stop with Multiple Routes, Transportation Research Part C, Vol. 19, No. 6, 2011, pp. 1157-1170

Published
2013-10-27
How to Cite
1.
Zong F, Hongfei J, Xiang P, Yang W. Prediction of Commuter’s Daily Time Allocation. PROMET [Internet]. 2013Oct.27 [cited 2019Dec.16];25(5):445-5. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1190
Section
Articles

Most read articles by the same author(s)