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Journal of Intelligent Transportation Systems
Technology, Planning, and Operations
Volume 21, 2017 - Issue 2
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Articles

Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

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Pages 79-93 | Received 09 Nov 2014, Accepted 29 Feb 2016, Published online: 08 Feb 2017

References

  • Bartin, B., & Ozbay, K. (2008). Comparison of tolls with estimated full marginal costs: Theory meets reality. Transportation Research Board 87th Annual Meeting, number 08-2990, Washington, DC.
  • Bates, J., Fearon, J., & Black, I. (2003). Frameworks for modelling the variability of highway journey times. London, UK: Department for Transport.
  • Brilon, W., Geistefeldt, J., & Regler, M. (2005). Reliability of freeway traffic flow: A stochastic concept of capacity. Proceedings of the 16th International Symposium on Transportation and Traffic Theory, pp. 125–144, College Park, MD.
  • Carey, M., Ge, Y., & McCartney, M. (2003). A whole-link travel-time model with desirable properties. Transportation Science, 37(1), 83–96.
  • Chen, B., Lam, W., Sumalee, A., Li, Q., & Tam, M. (2014). Reliable shortest path problems in stochastic time-dependent networks. Journal of Intelligent Transportation Systems, 18(2), 177–189.
  • Chen, X., & Osorio, C. (2014). Analytical formulation of the trip travel time distribution. Transportation Research Procedia, 3, 366–373.
  • Clark, S., & Watling, D. (2005). Modelling network travel time reliability under stochastic demand. Transportation Research Part B, 39(2), 119–140.
  • Daganzo, C. (1995). Properties of link travel time functions under dynamic loads. Transportation Research Part B: Methodological, 29(2), 95–98.
  • Dong, J., & Mahmassani, H. (2009). Flow breakdown, travel reliability and real-time information in route choice behavior. Proceedings of the 18th International Symposium on Transportation and Traffic Theory, Hong Kong, China, July 16–18, 2009.
  • Elefteriadou, L., Roess, R., & Mcshane, W. (1995). Probabilistic nature of breakdown at freeway merge junctions. Transportation Research Record, 1484, 80–89.
  • Emam, E., & Al-Deek, H. (2006). Using real-life dual-loop detector data to develop new methodology for estimating freeway travel time reliability. Transportation Research Record, 1959, 140–150.
  • Federal Highway Administration. (2013). Travel time reliability: Making it there on time, all the time, Retrieved from www.ops.fhwa.dot.gov/publications/tt_reliability/TTR_Report.htm
  • Friesz, T., Bernstein, D., Smith, T., Tobin, R., & Wie, B. (1993). A variational inequality formulation of the dynamic network user equilibrium problem. Operations Research, 41(1), 179–191.
  • Gardner, L., Unnikrishnan, A., & Waller, S. (2010). Solution methods for robust pricing of transportation networks under uncertain demand. Transportation Research Part C: Emerging Technologies, 18(5), 656–667.
  • Garib, A., Radwana, E., & Al-deek, H. (1997). Estimating magnitude and duration of incident delays. Journal of Transportation, 123(6), 459–466.
  • Ghali, M., & Smith, M. (1995). A model for the dynamic system optimum traffic assignment problem. Transportation Research Part B, 29(3), 155–170.
  • Giuliano, G. (1989). Incident characteristics, frequency, and duration on a high volume urban freeway. Transportation Research, 23(5), 387–396.
  • Golob, T., Recker, W., & Leonard, J. (1987). An analysis of the severity and incident duration of truck-involved freeway accidents. Accident Analysis and Prevention, 19(4), 375–395.
  • Guo, F., Rakha, H., & Park, S. (2010). Multistate model for travel time reliability. Transportation Research Record, 2128, 46–54.
  • Highway Capacity Manual. (2000). Washington, DC: TRB, National Research Council.
  • Huntsinger, L., & Rouphail, N. (2011). Calibrating travel demand model volume-delay functions using bottleneck and queuing analysis. Transportation Research Record, 2255(1), 117–124.
  • Jia, A., Williams, B., & Rouphail, N. (2010). Identification and calibration of site specific stochastic freeway breakdown and queue discharge. Transportation Research Record, 2188, 148–155.
  • Jia, A., Zhou, X., Li, M., Rouphail, N., & Williams, B. (2011). Incorporating stochastic road capacity into a day-to-day traffic simulation and traveler learning framework: Model development and case study. Transportation Research Record, 2254(1), 112–121.
  • Jones, B., Janssen, L., & Mannering, F. (1991). Analysis of the frequency and duration of freeway accidents in seattle. Accident, 23(4), 239–255.
  • Khani, A., & Boyles, S. (2015). An exact algorithm for the mean—standard deviation shortest path problem. Transportation Research Part B: Methodological, 81, 252–266.
  • Kuwahara, M. (1990). Equilibrium queueing patterns at a two-tandem bottleneck during the morning peak. Transportation Science, 24(3), 217–229.
  • Lawson, T., Lovell, D., & Daganzo, C. (1997). Using input-output diagram to determine spatial and temporal extents of a queue upstream of a bottleneck. Transportation Research Record, 1572(1), 140–147.
  • Lei, H., Zhou, X., List, G., & Taylor, J. (2015). Characterizing corridor-level travel time distributions based on stochastic flows and segment capacities. Cogent Engineering, 2(1), 990672.
  • Li, J., Fujiwara, O., & Kawakami, S. (2000). A reactive dynamic user equilibrium model in network with queues. Transportation Research Part B: Methodological, 34(8), 605–624.
  • Li, M., & Faghri, A. (2016). Applying problem-oriented and project-based learning in a transportation engineering course. Journal of Professional Issues in Engineering Education and Practice, 142(3), 04016002.
  • Li, M., Rouphail, N. M., Mahmoudi, M., Liu, J., Zhou, X. (2017). Multi-scenario optimization approach for assessing the impacts of advanced traffic information under realistic stochastic capacity distributions. Transportation Research Part C: Emerging Technologies.(Forthcoming)
  • Li, M., Zhou, X., & Rouphail, N. (2011a). Planning-level methodology for evaluating traveler information provision strategies under stochastic capacity conditions. Transportation Research Board 90th Annual Meeting (No. 11-3002).
  • Li, M., Zhou, X., & Rouphail, N. (2011b). Quantifying benefits of traffic information provision under stochastic demand and capacity conditions. Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference (pp. 2118–2123). IEEE.
  • Lo, H. K., & Tung, Y. K. (2003). Network with degradable links: capacity analysis and design. Transportation Research Part B, 37(4), 345–363.
  • Massey, F. J. (1951). The Kolmogorov–Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78.
  • May, A. (1990). Traffic flow fundamentals, Upper Saddle River, NJ: Prentice Hall.
  • Mun, J. (2007). Traffic performance models for dynamic traffic assignment: An assessment of existing models. Transport Reviews, 27(2), 231–249.
  • Nam, D., & Mannering, F. (2000). An exploratory hazard-based analysis of highway incident duration. Transportation Research Part A, 34(2), 85–102.
  • Ng, M., Szeto, W., & Waller, S. (2011). Distribution-free travel time reliability assessment with probability inequalities. Transportation Research Part B: Methodological, 45(6), 852–866.
  • Ng, M., & Waler, S. (2010). A computationally efficient methodology to characterize travel time reliability using the fast Fourier transform. Transportation Research Part B, 44(10), 1202–1219.
  • Oh, J., & Chung, Y. (2006). Calculation of travel time variability from loop detector data. Transportation Research Record, 1945, 12–23.
  • Ozbay, K., & Kachroo, P. (1999). Incident management in intelligent transportation systems. Boston, MA: Artech House.
  • Qiao, W., Haghani, A., Shao, C., & Liu, J. (2016). Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model. Journal of Intelligent Transportation Systems, 20(5), 438–448.
  • Rakha, H., El-Shawarby, I., & Arafeh, M. (2010). Trip travel-time reliability: issues and proposed solutions. Journal of Intelligent Transportation Systems, 14(4), 232–250.
  • Ran, B., Hall, R., & Boyce, D. (1996). A link-based variational inequality model for dynamic departure time/route choice. Transportation Research Part B: Methodological, 30(1), 31–46.
  • Shahabi, M., Unnikrishnan, A., & Boyles, S. (2013). An outer approximation. Transportation Research Part E: Logistics and Transportation Review, 58, 52–66.
  • Shen, W., Nie, Y., & Zhang, H. (2007). On path marginal cost analysis and its relation to dynamic system-optimal traffic assignment. Procedings of 17th International Symposium on Transportation and Traffic Theory, pp. 327–360, London, UK from July 23–25, 2007.
  • Siu, B., & Lo, H. (2008). Doubly uncertain transportation network: degradable capacity and stochastic demand. European Journal of Operational Research, 191(1), 166–181.
  • Smith, B., Qin, L., & Venkatanarayana, R. (2003). Characterization of freeway capacity reduction resulting from traffic accidents. Journal of Transportation Engineering, 129(4), 362–368.
  • Spiess, H. (1990). Technical note—Conical volume-delay functions. Transportation Science, 24(2), 153–158.
  • Srinivasan, K., Prakash, A., & Seshadri, R. (2014). Finding most reliable paths on networks with correlated and shifted log-normal travel times. Transportation Research Part B: Methodological, 66, 110–128.
  • Sullivan, E. (1997). New model for predicting incidents and incident delay. ASCE Journal of Transportation Engineering, 123(4), 267–275.
  • Tanaka, M., Uno, N., Shiomi, Y., & Ahn, Y. (2014). Experimental study of effects of travel time distribution information on dynamic route choice behavior. Journal of Intelligent Transportation Systems, 18(2), 215–226.
  • Thomas, S., & Jacko, R. (2007). Stochastic model for estimating impact of highway incidents on air pollution and traffic delay. Transportation Research Record, 2011(1), 107–115.
  • Vickrey, W. (1969). Congestion theory and transport investment. American Economic Review, 59(2), 251–260.
  • Watling, D., & Balijepalli, N. (2012). A method to assess demand growth vulnerability of travel times on road network links. Transportation Research Part A: Policy and Practice, 46(5), 772–789.
  • Xing, T., & Zhou, X. (2011). Finding the most reliable path with and without link travel time correlation: A Lagrangian substitution based approach. Transportation Research Part B: Methodological, 45(10), 1660–1679.
  • Xing, T., & Zhou, X. (2013). Reformulation and solution algorithms for absolute and percentile robust shortest path problems. Intelligent Transportation Systems, IEEE Transactions on, 14(2), 943–954.
  • Xu, X., Chen, A., Zhou, Z., & Cheng, L. (2012). A multi-class mean-excess traffic equilibrium model with elastic demand. Journal of Advanced Transportation.
  • Yang, L., & Zhou, X. (2014). Constraint reformulation and a Lagrangian relaxation-based solution algorithm for a least expected time path problem. Transportation Research Part B: Methodological, 59, 22–44.
  • Yuan, P., & Juan, Z. (2013). The related congestion failure estimating methodology and model in transportation networks. Physica A: Statistical Mechanics and Its Applications, 392(19), 4330–4344.
  • Zhao, Y., & Kockelman, K. (2002). The propagation of uncertainty through travel demand models: An exploratory analysis. Annals of Regional Science, 36(1), 145–163.
  • Zhou, Z., & Chen, A. (2008). Comparative analysis of three user equilibrium models under stochastic demand. Journal of Advanced Transportation, 42(3), 239–263.

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