Travel Time Estimation in the Age of Big Data

Published Online:https://doi.org/10.1287/opre.2018.1784

References

  • Chen BY, Yuan H, Li Q, Lam WHK, Shaw SL, Yan K (2014) Map-matching algorithm for large-scale low-frequency floating car data. Internat. J. Geographical Inform. Sci. 28(1):22–38.CrossrefGoogle Scholar
  • Coifman B (2002) Estimating travel times and vehicle trajectories on freeways using dual loop detectors. Transportation Res. Part A: Policy Practice 36(4):351–364.CrossrefGoogle Scholar
  • Dial RB (1971) A probabilistic multipath traffic assignment model which obviates path enumeration. Transportation Res. 5(2):83–111.CrossrefGoogle Scholar
  • Hänseler FS, Molyneaux NA, Bierlaire M (2017) Estimation of pedestrian origin-destination demand in train stations. Transportation Sci. 51(3):981–997.LinkGoogle Scholar
  • Hofleitner A, Herring R, Abbeel P, Bayen A (2012) Learning the dynamics of arterial traffic from probe data using a dynamic bayesian network. IEEE Trans. Intelligent Transportation Systems 13(4):1679–1693.CrossrefGoogle Scholar
  • Hung CH (2003) On the inverse shortest path length problem. PhD thesis, Georgia Tech ISyE, Atlanta.Google Scholar
  • Jaillet P, Qi J, Sim M (2016) Routing optimization under uncertainty. Oper. Res. 64(1):186–200.LinkGoogle Scholar
  • Jenelius E, Koutsopoulos HN (2013) Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Res. Part B: Methodological 53(July):64–81.CrossrefGoogle Scholar
  • Li R, Rose G (2011) Incorporating uncertainty into short-term travel time predictions. Transportation Res. Part C: Emerging Tech. 19(6):1006–1018.CrossrefGoogle Scholar
  • Lubin M, Dunning I (2015) Computing in operations research using julia. INFORMS J. Comput. 27(2):238–248.LinkGoogle Scholar
  • Nikolova E, Stier-Moses NE (2014) A mean-risk model for the traffic assignment problem with stochastic travel times. Oper. Res. 62(2):366–382.LinkGoogle Scholar
  • NYCTLC (2016) Trip record data. Accessed September 7, 2018, http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml.Google Scholar
  • OSM (2015) OpenStreetMap project database. Accessed September 7, 2018, http://planet.openstreetmap.org.Google Scholar
  • Pióro M, Fouquet Y, Nace D, Poss M (2016) Optimizing flow thinning protection in multicommodity networks with variable link capacity. Oper. Res. 64(2):273–289.LinkGoogle Scholar
  • Quddus M, Washington S (2015) Shortest path and vehicle trajectory aided map-matching for low frequency GPS data. Transportation Res. Part C: Emerging Tech. 55(June):328–339.CrossrefGoogle Scholar
  • Santi P, Resta G, Szell M, Sobolevsky S, Strogatz S, Ratti C (2014) Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. USA 111(37):13290–13294.CrossrefGoogle Scholar
  • Wang H, Li Z, Kuo Y, Kifer D (2016) A simple baseline for travel time estimation using large-scale trip data. Proc. 24th ACM SIGSPATIAL Internat. Conf. Adv. Geographic Inform. Systems (ACM, New York), Article No. 61.CrossrefGoogle Scholar
  • Wang Y, Nihan NL (2000) Freeway traffic speed estimation using single loop outputs. Transportation Res. Rec. J. Transportation Res. Board 1727(1):120–126.CrossrefGoogle Scholar
  • Wang Y, Zheng Y, Xue Y (2014) Travel time estimation of a path using sparse trajectories. Proc. 20th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 25–34.CrossrefGoogle Scholar
  • Yang C (2015) Data-driven modeling of taxi trip demand and supply in New York City. PhD thesis, Rutgers University, New Brunswick, NJ.Google Scholar
  • Zhan X, Hasan S, Ukkusuri SV, Kamga C (2013) Urban link travel time estimation using large-scale taxi data with partial information. Transportation Res. Part C: Emerging Tech. 33(August):37–49.CrossrefGoogle Scholar
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