2,294
Views
98
CrossRef citations to date
0
Altmetric
Original Articles

Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach

, , &
Pages 4-32 | Received 09 Jun 2014, Accepted 24 Nov 2014, Published online: 02 Jan 2015

References

  • Abdulhai, B., Porwal, H., & Recker, W. (1999). Short term freeway traffic flow prediction using genetically-optimized time-delay-based neural networks. California PATH Working Paper: UCB-ITS-PWP-99-1. Berkeley, CA: Institute of Transportation Studies, University of California.
  • Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transportation Research Record, 722, 1–9.
  • Ahmed, S. A. (1983). Stochastic processes in freeway traffic Part I. Robust prediction models. Traffic Engineering & Control, 24(6), 309–310.
  • Bajwa, S., Chung, E., & Kuwahara, M. (2004). An adaptive travel-time prediction model based on pattern matching. Proceedings of the 11th Intelligent Transport System World Congress, Nagoya [CD-ROM].
  • Bajwa, S., Chung, E., & Kuwahara, M. (2005, September). Performance evaluation of an adaptive travel time prediction model. In Proceedings of 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria (pp. 1000–1005).
  • Ben-Akiva, M., Bierlaire, M., Burton, D., Koutsopoulos, H. N., & Mishalani, R. (2001). Network state estimation and prediction for real-time traffic management. Networks and Spatial Economics, 1(3–4), 293–318. doi: 10.1023/A:1012883811652
  • Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2013). Time series analysis: Forecasting and control. John Wiley & Sons.
  • Byon, Y. J., Abdulhai, B., & Shalaby, A. (2009). Real-time transportation mode detection via tracking global positioning system mobile devices. Journal of Intelligent Transportation Systems, 13(4), 161–170. doi: 10.1080/15472450903287781
  • Byon, Y. J., & Liang, S. (2014). Real-time transportation mode detection using smartphones and artificial neural networks: Performance comparisons between smartphones and conventional global positioning system sensors. Journal of Intelligent Transportation Systems, 18(3), 264–272. doi: 10.1080/15472450.2013.824762
  • Casas, J., Torday, A., Perarnau, J., Breen, M., & Ruiz de Villa, A. (2013, October). Present and future methodology for the implementation of decision support systems for traffic management. Australasian Transport Research Forum (ATRF), 36th, Brisbane, Queensland, Australia.
  • Chen, H., & Grant-Muller, S. (2001). Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 9(5), 319–336. doi: 10.1016/S0968-090X(00)00039-5
  • Chen, H., Grant-Muller, S., Mussone, L., & Montgomery, F. (2001). A study of hybrid neural network approaches and the effects of missing data on traffic forecasting. Neural Computing & Applications, 10(3), 277–286. doi: 10.1007/s521-001-8054-3
  • Chen, M., & Chien, S. I. (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(1), 157–161. doi: 10.3141/1768-19
  • Chien, S. I., Liu, X., & Ozbay, K. (2003). Predicting travel times for the South Jersey real-time motorist information system. Transportation Research Record: Journal of the Transportation Research Board, 1855(1), 32–40. doi: 10.3141/1855-04
  • Chien, S. I. J., & Kuchipudi, C. M. (2003). Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering, 129(6), 608–616. doi: 10.1061/(ASCE)0733-947X(2003)129:6(608)
  • Chow, A., Dadok, V., Dervisoglu, G., Gomes, G., Horowitz, R., Kurzhanskiy, A. A., … Varaiya, P. (2008). TOPL: Tools for operational planning of transportation networks. In ASME 2008 Dynamic Systems and Control Conference American Society of Mechanical Engineers (ASME) 2008 Dynamic Systems and Control Conference, Ann Arbor, Michigan, USA (pp. 1035–1042).
  • Chrobok, R. (2005). Theory and Application of Advanced Traffic Forecast Methods (Doctoral dissertation). Universitätsbibliothek Duisburg.
  • Chrobok, R., Hafstein, S. F., & Pottmeier, A. (2004). Olsim: A new generation of traffic information systems. In V. Macho & K. Kremer (Eds.), Forschung und wissenschaftliches Rechnen (pp. 11–25). Göttingen: GWDG (Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen).
  • Chrobok, R., Pottmeier, A., ur Marinosson, S., & Schreckenberg, M. (2002). On-line simulation and traffic forecast: Applications and results. In M. H. Hamza (Ed.), Proceedings of the 6th IASTED International Conference of Internet and Multimedia Systems and Applications (pp. 113–118). Kauai, HI: ACTA Press.
  • Clark, S. (2003). Traffic prediction using multivariate nonparametric regression. Journal of Transportation Engineering, 129(2), 161–168. doi: 10.1061/(ASCE)0733-947X(2003)129:2(161)
  • Clark, S. D., Chen, H. A. I. B. O., & Grant-Muller, S. M. (1999). Artificial neural network and statistical modelling of traffic flows-the best of both worlds (Vol. 2, pp. 215–226). World Transport Research: Selected Proceedings of the 8th World Conference on Transport Research, Antwerp, Belgium.
  • Danech-Pajouh, M., & Aron, M. (1991). ATHENA: A method for short-term inter-urban motorway traffic forecasting. Recherche Transports Sécurité, 6, 11–16.
  • D'Angelo, M. P., Al-Deek, H. M., & Wang, M. C. (1999). Travel-time prediction for freeway corridors. Transportation Research Record: Journal of the Transportation Research Board, 1676(1), 184–191. doi: 10.3141/1676-23
  • Davis, G. A., & Nihan, N. L. (1991). Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering, 117(2), 178–188. doi: 10.1061/(ASCE)0733-947X(1991)117:2(178)
  • Davis, G. A., Nihan, N. L., Hamed, M. M., & Jacobson, L. N. (1990). Adaptive forecasting of freeway traffic congestion. Transportation Research Record, 1287, 29–33.
  • Dia, H. (2001). An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research, 131(2), 253–261. doi: 10.1016/S0377-2217(00)00125-9
  • Dougherty, M. S., & Cobbett, M. R. (1997). Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting, 13(1), 21–31. doi: 10.1016/S0169-2070(96)00697-8
  • Fei, X., Lu, C. 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(6), 1306–1318. doi: 10.1016/j.trc.2010.10.005
  • Gomes, G., Horowitz, R., Kurzhanskiy, A. A., Varaiya, P., & Kwon, J. (2008). Behavior of the cell transmission model and effectiveness of ramp metering. Transportation Research Part C: Emerging Technologies, 16(4), 485–513. doi: 10.1016/j.trc.2007.10.005
  • van Hinsbergen, C. P., Schreiter, T., Zuurbier, F. S., van Lint, J. W. C., & van Zuylen, H. J. (2012). Localized extended kalman filter for scalable real-time traffic state estimation. Intelligent Transportation Systems, IEEE Transactions, 13(1), 385–394. doi: 10.1109/TITS.2011.2175728
  • van Hinsbergen, C. P. I., & van Lint, J. W. (2008). Bayesian combination of travel time prediction models. Transportation Research Record: Journal of the Transportation Research Board, 2064(1), 73–80. doi: 10.3141/2064-10
  • van Hinsbergen, C. P. I., van Lint, J. W. C., & Sanders, F. M. (2007). Short term traffic prediction models. Proceedings of the 14th ITS World Congress, Beijing, China.
  • Huisken, G., & van Berkum, E. C. (2003). A comparative analysis of short-range travel-time prediction methods. In 82nd Transportation Research Board Annual Meeting, Washington, DC.
  • Innamaa, S. (2005). Short-term prediction of travel-time using neural networks on an interurban highway. Transportation, 32(6), 649–669. doi: 10.1007/s11116-005-0219-y
  • Ishak, S., & Al-Deek, H. (2002). Performance evaluation of short-term time-series traffic prediction model. Journal of Transportation Engineering, 128(6), 490–498. doi: 10.1061/(ASCE)0733-947X(2002)128:6(490)
  • Ishak, S., Kotha, P., & Alecsandru, C. (2003). Optimization of dynamic neural network performance for short-term traffic prediction. Transportation Research Record: Journal of the Transportation Research Board, 1836(1), 45–56. doi: 10.3141/1836-07
  • Iwasaki, M., & Saito, K. (1999, November). Short-term prediction of speed fluctuations on a motorway using historical patterns. Proceedings of 6th World Congress on Intelligent Transport Systems (ITS), Toronto, Canada.
  • Iwasaki, M., & Shirao, K. (1996). A short term prediction of traffic fluctuations using pseudo-traffic patterns. In Intelligent Transportation: Realizing the Future. Abstracts of the Third World Congress on Intelligent Transport Systems, Orlando, Florida.
  • Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45. doi: 10.1115/1.3662552
  • Kirby, H. R., Watson, S. M., & Dougherty, M. S. (1997). Should we use neural networks or statistical models for short-term motorway traffic forecasting? International Journal of Forecasting, 13(1), 43–50. doi: 10.1016/S0169-2070(96)00699-1
  • Kotsialos, A., Papageorgiou, M., Diakaki, C., Pavlis, Y., & Middelham, F. (2002). Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET. Intelligent Transportation Systems, IEEE Transactions, 3(4), 282–292. doi: 10.1109/TITS.2002.806804
  • Kuchipudi, C. M., & Chien, S. I. (2003). Development of a hybrid model for dynamic travel-time prediction. Transportation Research Record: Journal of the Transportation Research Board, 1855(1), 22–31. doi: 10.3141/1855-03
  • Kwon, J., Coifman, B., & Bickel, P. (2000). Day-to-day travel-time trends and travel-time prediction from loop-detector data. Transportation Research Record: Journal of the Transportation Research Board, 1717(1), 120–129. doi: 10.3141/1717-15
  • Levin, M., & Tsao, Y. D. (1980). On forecasting freeway occupancies and volumes (abridgment). Transportation Research Record, 773, 47–49.
  • Lingras, P., Sharma, S., & Zhong, M. (2002). Prediction of recreational travel using genetically designed regression and time-delay neural network models. Transportation Research Record: Journal of the Transportation Research Board, 1805(1), 16–24. doi: 10.3141/1805-03
  • van Lint, H. (2004). Reliable travel-time prediction for freeways. Delft: TRAIL Research School.
  • van Lint, H., & Djukic, T. (2012). Applications of Kalman filtering in traffic management and control. In P. Mirchandani (Ed.), Informs tutorials in operations research (Vol. 9, pp. 59–91). Hanover, MD: INFORMS.
  • van Lint, H., Hoogendoorn, S. P., & van Zuylen, H. J. (2002). State space neural networks for freeway travel-time prediction. In J. R. Dorronsoro (Ed.), Artificial Neural Networks—ICANN2002 (pp. 1043–1048). Berlin: Springer.
  • van Lint, H., & Schreuder, M. (2006). Travel time prediction for variable message sign panels: Results and lessons learned from large-scale evaluation study in the Netherlands. In 85th Transportation Research Board Annual Meeting (No. 06–2045), Washington, DC.
  • van Lint, J. W. (2006). Reliable real-time framework for short-term freeway travel-time prediction. Journal of Transportation Engineering, 132(12), 921–932. doi: 10.1061/(ASCE)0733-947X(2006)132:12(921)
  • van Lint, J. W. C. (2008). Online learning solutions for freeway travel time prediction. Intelligent Transportation Systems, IEEE Transactions, 9(1), 38–47. doi: 10.1109/TITS.2008.915649
  • 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(5), 347–369. doi: 10.1016/j.trc.2005.03.001
  • Liu, Y., Lin, P. W., Lai, X., Chang, G. L., & Marquess, A. (2006). Developments and applications of simulation-based online travel time prediction system: Traveling to Ocean City, Maryland. Transportation Research Record: Journal of the Transportation Research Board, 1959(1), 92–104. doi: 10.3141/1959-11
  • Mahmassani, H. S., Fei, X., Eisenman, S., Zhou, X., Qin, X. (2005). DYNASMART-X evaluation for real-time TMC application: CHART test bed. Maryland Transportation Initiative, University of Maryland, College Park, Maryland.
  • Nair, A. S., Liu, J. C., Rilett, L., & Gupta, S. (2001). Non-linear analysis of traffic flow. IEEE Proceedings Intelligent Transportation Systems Conference, Oakland, CA (pp. 681–685).
  • Nanthawichit, C., Nakatsuji, T., & Suzuki, H. (2003). Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway. Transportation Research Record: Journal of the Transportation Research Board, 1855(1), 49–59. doi: 10.3141/1855-06
  • Oda, T. (1990, May). An algorithm for prediction of travel-time using vehicle sensor data. IEEE 3rd Conference on Road Traffic Control, London, UK. IET (pp. 40–44).
  • Ohba, Y., Ueno, H., & Kuwahara, M. (1999). Travel time calculation method for expressway using toll collection system data. Proceedings. 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems. Tokyo, Japan. IEEE (pp. 471–475).
  • Okutani, I., & Stephanedes, Y. J. (1984). Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological, 18(1), 1–11. doi: 10.1016/0191-2615(84)90002-X
  • Papageorgiou, M., Papamichail, I., Messmer, A., & Wang, Y. (2010). Traffic simulation with metanet. In J. Barceló (Ed.), Fundamentals of traffic simulation (pp. 399–430). Springer: New York.
  • 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(1), 163–170. doi: 10.3141/1617-23
  • 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. doi: 10.1111/0885-9507.00154
  • 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. doi: 10.1061/(ASCE)0733-947X(1999)125:6(515)
  • Rice, J., & van Zwet, E. (2004). A simple and effective method for predicting travel-times on freeways. Intelligent Transportation Systems, IEEE Transactions, 5(3), 200–207. doi: 10.1109/TITS.2004.833765
  • Rilett, L. R., & Park, D. (2001). Direct forecasting of freeway corridor travel-times using spectral basis neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1752(1), 140–147. doi: 10.3141/1752-19
  • Saito, M., & Watanabe, T. (1995). Prediction and dissemination system for travel-time utilizing vehicle detectors. Proceedings of the 2nd world congress on Intelligent Transport Systems, Yokohama, Japan.
  • Shen, L. (2008). Freeway travel-time estimation and prediction using dynamic neural networks (Ph. D. Thesis). International University, Florida.
  • Smith, B. L., & Demetsky, M. J. (1997). Traffic flow forecasting: Comparison of modeling approaches. Journal of Transportation Engineering, 123(4), 261–266. doi: 10.1061/(ASCE)0733-947X(1997)123:4(261)
  • Smith, B. L., Williams, B. M., & Oswald, R. K. (2000). Parametric and nonparametric traffic volume forecasting. In 79th Transportation Research Board Annual Meeting, Washington, DC.
  • Smith, B. L., Williams, B. M., & Oswald, R. K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 10(4), 303–321. doi: 10.1016/S0968-090X(02)00009-8
  • Sun, H., Liu, H. X., Xiao, H., He, R. R., & Ran, B. (2003, January). Short term traffic forecasting using the local linear regression model. In 82nd Annual Meeting of the Transportation Research Board, Washington, DC.
  • Tak, S., Kim, S., Jang, K., & Yeo, H. (2014). Real-time travel time prediction using multi-level k-nearest neighbor algorithm and data fusion method, In R. I. Issa & I. Flood (Eds.), Computing in civil and building engineering (pp. 1861–1868). Orlando, FL: ASCE.
  • Torday, A. et al. (2010). Simulation-based decision support system for real time traffic management. In 89th Transportation Research Board Annual Meeting (No. 10–2120), Washington, DC.
  • Vanajakshi, L. D. (2004). Estimation and prediction of travel-time from loop detector data for intelligent transportation systems applications (Doctoral dissertation). Texas A&M University.
  • Vlahogianni, E. I., Golias, J. C., & Karlaftis, M. G. (2004). Short-term traffic forecasting: Overview of objectives and methods. Transport Reviews, 24(5), 533–557. doi: 10.1080/0144164042000195072
  • van der Voort, M., Dougherty, M., & Watson, S. (1996). Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies, 4(5), 307–318. doi: 10.1016/S0968-090X(97)82903-8
  • Vortisch, P. (2001). Use of PTV-software in the traffic management centre (VMZ) Berlin. Presentation at the 11th PTV vision User Group Meeting 2001, Berlin, Germany.
  • Vythoulkas, P. C. (1993). Alternative approaches to short term traffic forecasting for use in driver information systems. In International Symposium on the Theory of Traffic Flow and Transportation. Transportation and traffic theory, Berkeley, CA.
  • Wang, Y., & Papageorgiou, M. (2005). Real-time freeway traffic state estimation based on extended Kalman filter: A general approach. Transportation Research Part B: Methodological, 39(2), 141–167. doi: 10.1016/j.trb.2004.03.003
  • Whittaker, J., Garside, S., & Lindveld, K. (1997). Tracking and predicting a network traffic process. International Journal of Forecasting, 13(1), 51–61. doi: 10.1016/S0169-2070(96)00700-5
  • Williams, B. M. (2001). Multivariate vehicular traffic flow prediction: Evaluation of ARIMAX modeling. Transportation Research Record: Journal of the Transportation Research Board, 1776(1), 194–200. doi: 10.3141/1776-25
  • Williams, B. M., & Hoel, L. A. (2003). Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering, 129(6), 664–672. doi: 10.1061/(ASCE)0733-947X(2003)129:6(664)
  • Wu, C. H., Ho, J. M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. Intelligent Transportation Systems, IEEE Transactions on, 5(4), 276–281. doi: 10.1109/TITS.2004.837813
  • Yang, J. S. (2005). Travel time prediction using the GPS test vehicle and Kalman filtering techniques. In American Control Conference, 2005. Proceedings of the 2005, Portland, OR. IEEE (pp. 2128–2133).
  • Yasui, K., Ikenoue, K., & Takeuchi, H. (1995, November). Use of AVI information linked up with detector output in travel time prediction and OD flow estimation. In Steps Forward. Intelligent Transport Systems World Congress (Vol. 1).
  • Yu, J., Chang, G. L., Ho, H. W., & Liu, Y. (2008). Variation based online travel time prediction using clustered neural networks. In Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on. Beijing, China. IEEE (pp. 85–90).
  • Yun, S. Y., Namkoong, S., Rho, J. H., Shin, S. W., & Choi, J. U. (1998). A performance evaluation of neural network models in traffic volume forecasting. Mathematical and Computer Modelling, 27(9), 293–310. doi: 10.1016/S0895-7177(98)00065-X
  • Zhang, X., & Rice, J. A. (2003). Short-term travel-time prediction. Transportation Research Part C: Emerging Technologies, 11(3), 187–210. doi: 10.1016/S0968-090X(03)00026-3
  • van Zuylen, & Muller (2002). Rgiolab Delft. Proceedings of the 9th World Congress on Intelligent Transport Systems [CD-ROM], Chicago, IL, USA. Retrieved from http://www.regiolab-delft.nl

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.