New methodologies for predicting corridor travel time mean and reliability

References

• Abdel-Aty, M. A., Kitamura, R., & Jovanis, P. P. (1995). Investigation effect of travel time variability on route choice using repeated measurement stated preference data. Transportation Research Record, 1493, 39–45.
   Google Scholar
• Carrion, C., & Levinson, D. (2013). Valuation of travel time reliability from a GPS-based experimental design. Transportation Research Part C: Emerging Technologies, 35, 305–323.
   Web of Science ®Google Scholar
• Chen, M., & Chien, S. (2001). Dynamic freeway travel-time prediction with probe vehicle data, link based versus path based. Transportation Research Record: Journal of the Transportation Research Board, 1768, 157–161.
   Web of Science ®Google Scholar
• Chen, Y. (2015). An adaptive corridor-wide signal timing optimization methodology for traffic networks with multiple highway-rail grade crossings (Ph.D. dissertation). Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, NE.
   Google Scholar
• Chen, Y., & Rilett, L. R. (2017). A train data collection and arrival time prediction system for highway-rail grade crossings. Transportation Research Record: Journal of the Transportation Research Board, 2608, 36–45.
   Google Scholar
• Eisele, W. L., Naik, B., & Rilett, L. R. (2015). Estimating route travel time reliability from simultaneously collected link and route vehicle probe data and roadway sensor data. International Journal of Urban Sciences, 19(3), 286–304.
   Web of Science ®Google Scholar
• Fei, X., Lu, C., & Liu, K. (2011). A Bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transportation Research Part C: Emerging Technologies, 19, 1306–1318.
   Web of Science ®Google Scholar
• Fu, L., & Rilett, L. R. (1998). Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516.
   Web of Science ®Google Scholar
• Gan, H., Ye, X., & Wang, Q. (2010). Investigating the effect of travel time variability on drivers’ route choice decisions in shanghai, China. Transportation Planning and Technology, 33(8), 657–669.
   Web of Science ®Google Scholar
• Haykin, S. (1999). Neural Networks: A Comprehensive Foundation (2nd ed). Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Jeffrey, D. J., Russam, K., & Robertson, D. I. (1987). Electronic route guidance by AUTOGUIDE: The research background. Traffic Engineering and Control, 28(10), 525–529.
   Google Scholar
• Jha, K., Wikander, J. P., Eisele, W. L., Burris, M. W., & Schrank, D. L. (2018). Estimating freeway route travel time reliability from data on component links and associated cost implications. International Journal of Urban Sciences, 22(3), 414–430.
   Web of Science ®Google Scholar
• Karlaftis, M. G., & Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, 19(3), 387–399.
   Web of Science ®Google Scholar
• Kuhn, B., Higgins, L., Nelson, A., Finley, M., Ullman, G., Chrysler, S., … Dude, C. (2014). Lexicon for conveying travel time reliability information. Washington, DC: Transportation Research Board. doi:10.17226/22604
   Google Scholar
• Lam, W., & Chan, K. S. (2011). Short-term forecasting of travel time and reliability. International Journal of Urban Sciences, special issue: Seoul Metropolitan Fora 2004 “International Workshop on Behavior in Networks (BiNs)” (pp. 146–155).
   Google Scholar
• Li, R., & Rose, G. (2011). Incorporating uncertainty into short-term travel time predictions. Transportation Research Part C: Emerging Technologies, 19(6), 1006–1018.
   Web of Science ®Google Scholar
• Liu, H., van Zuylen, H., van Lint, H., & Salomons, M. (2006). Predicting urban arterial travel time with state-space neural networks and Kalman filters. Transportation Research Record: Journal of the Transportation Research Board, 1968, 99–108.
   Google Scholar
• Meehan, B. H. (2005). Travel times on dynamic message signs. Institute of Transportation Engineers Journal, 75(9), 23–27.
   Web of Science ®Google Scholar
• Nikovski, D., Nishiuma, N., Goto, Y., & Kumazawa, H. (2005). Univariate short-term prediction of road travel times. In Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems (pp. 1074–1079). Vienna.
   Google Scholar
• Oregon Department of Transportation. (2005, October). Travel time messaging on dynamic message signs. Portland, OR, Publication Number: FHWA-hop-05-048. Retrieved February 15, 2014.
   Google Scholar
• Park, D., & Rilett, L. R. (1998). Forecasting multiple-period freeway link travel times using modular neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1617, 163–170.
   Google Scholar
• Park, D., & Rilett, L. R. (1999). Forecasting freeway link travel times with a multilayer feed-forward neural network. Computer-Aided Civil and Infrastructure Engineering, 14(5), 357–367.
   Google Scholar
• Park, D., Rilett, L. R., & Han, G. (1999). Spectral basis neural networks for real-time travel time forecasting. Journal of Transportation Engineering, 125(6), 515–523.
   Web of Science ®Google Scholar
• Qiao, W., Haghani, A., & Hamedi, M. (2013). A non-parametric model for short-term travel time prediction using bluetooth data. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 17(2), 165–175.
   Web of Science ®Google Scholar
• Shen, L., & Huang, M. (2011). Assessing dynamic neural networks for travel time prediction. Applied Informatics and Communication: Communications in Computer and Information Science, 224, 196–477.
   Google Scholar
• Transportation Research Board. (2013). Incorporating reliability performance measures into the transportation planning and programming processes. doi:10.17226/22596
   Google Scholar
• van Hinsbergen, C. P. I. J., & van Lint, J. W. C. (2008). Bayesian combination of travel time prediction models. Transportation Research Record: Journal of the Transportation Research Board, 2064, 73–80.
   Web of Science ®Google Scholar
• van Hinsbergen, C. P., van Lint, J. W., & Sanders, F. M. (2007). Short term traffic prediction models. Presented at the 14th World Congress on Intelligent Transport system, Beijing, China.
   Google Scholar
• van Hinsbergen, C. P. I. J., van Lint, J. W. C., & van Zuylen, H. J. (2009). Bayesian committee of neural networks to predict travel times with confidence intervals. Transportation Research Part C: Emerging Technologies, 17(5), 498–509.
   Web of Science ®Google Scholar
• van Lint, J. W. C. (2004). Quantifying uncertainty in real-time neural network based freeway travel prediction. Presented at the 83rd Transportation Research Board Annual meeting, Washington, DC.
   Google Scholar
• van Lint, J. W. C., Hoogendoorn, S. P., & van Zuylen, H. J. (2005). Accurate freeway travel time prediction with state-space neural networks under missing data. Transportation Research Part C: Emerging Technologies, 13, 347–369.
   Web of Science ®Google Scholar
• van Lint, J. W. C., Hooqendoorn, S. P., & van Zuvlen, H. J. (2002). Freeway travel time prediction with state-space neural networks modeling state-space dynamics with recurrent neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1811, 30–39.
   Google Scholar
• van Lint, J. W. C., & van Zuylen, H. J. (2015). Monitoring and predicting freeway travel time reliability: Using width and skew of the day-to-day-travel time distribution. Transportation Research Record: Journal of the Transportation Research Board, 1917(1).
   Google Scholar
• Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014). Short-term traffic forecasting: Where we are and where we're going. Transportation Research Part C: Emerging Technologies, 43, 3–19.
   Web of Science ®Google Scholar
• Wu, Z. (2015). Measuring reliability in dynamic and stochastic transportation networks (Ph.D. dissertation). Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, NE.
   Google Scholar
• Wu, Z., & Rilett, L. R. (2014). Predicting corridor travel time reliability in real time using Bluetooth data. Presented at the 21st World Congress on Intelligent Transport systems: Reinventing Transportation in Our connected World, Detroit, Michigan.
   Google Scholar
• Wu, Z., Rilett, L. R., & Chen, Y. (2018). Evaluating the impact of highway-railway grade crossings on travel time reliability on a highway network level. Transportation Research Record: Journal of the Transportation Research Board, 2672(10), 1–11. doi:10.1177/0361198118792756
   Web of Science ®Google Scholar
• Wu, Z., Wu, Z., & Rilett, L. R. (2020). Innovative nonparametric method for data outlier filtering. Transportation Research Record: Journal of Transportation Research Board, doi:10.1177/0361198120945697
   PubMed Web of Science ®Google Scholar
• Yue, H. (2018). Design of HBase and hybrid hadoop ecosystem architecture in transportation data management. Transportation Research Board (TRB) 97th Annual Meeting Compendium.
   Google Scholar
• Yue, H., Rilett, L. R., & Revesz, P. Z. (2016). Spatio-temporal traffic video data archiving and retrieval system. GeoInformatica, 20, 59–94.
   Web of Science ®Google Scholar
• Yue, H., & Yang, R. (2005). Development of intelligent transportation systems and plan of integrated information system. Journal of Wuhan University of Technology, 109, 57–70.
   Google Scholar
• Zeng, X., & Zhang, Y. (2013). Development of recurrent neural network considering temporal-spatial input dynamics for freeway travel time modeling. Computer-Aided Civil and Infrastructure Engineering, 28, 359–371.
   Web of Science ®Google Scholar