Bus Travel Time Predictions Using Additive Models

Many factors can affect the predictability of public bus services such as traffic, weather, day of week, and hour of day. However, the exact nature of such relationships between travel times and predictor variables is, in most situations, not known. In this paper we develop a framework that allows for flexible modeling of bus travel times through the use of Additive Models. The proposed class of models provides a principled statistical framework that is highly flexible in terms of model building. The experimental results demonstrate uniformly superior performance of our best model as compared to previous prediction methods when applied to a very large GPS data set obtained from buses operating in the city of Rio de Janeiro.

[1]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[2]  Liping Fu,et al.  Predicting Bus Arrival Time on the Basis of Global Positioning System Data , 2007 .

[3]  Bin Yu,et al.  Bus arrival time prediction at bus stop with multiple routes , 2011 .

[4]  Ran Hee Jeong.  The prediction of bus arrival time using automatic vehicle location systems data , 2004 .

[5]  Steven I-Jy Chien,et al.  Dynamic Bus Arrival Time Prediction with Artificial Neural Networks , 2002 .

[6]  Amer Shalaby,et al.  PREDICTION MODEL OF BUS ARRIVAL AND DEPARTURE TIMES USING AVL AND APC DATA , 2004 .

[7]  Mehmet Altinkaya,et al.  Urban Bus Arrival Time Prediction : A Review of Computational Models , 2013 .

[8]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[9]  S. Wood.  Thin plate regression splines , 2003 .

[10]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[11]  Wang-Chien Lee,et al.  HTTP: a new framework for bus travel time prediction based on historical trajectories , 2012, SIGSPATIAL/GIS.

[12]  Xiaobo Liu,et al.  A Dynamic Bus‐Arrival Time Prediction Model Based on APC Data , 2004 .

[13]  S. Wood.  Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models , 2004 .

[14]  Bianca Zadrozny,et al.  Bus Travel Time Predictions Using Additive Models , 2014, 2014 IEEE International Conference on Data Mining.

[15]  Martin T. Hagan,et al.  Neural network design , 1995 .

[16]  Bianca Zadrozny,et al.  Vistradas: Visual Analytics for Urban Trajectory Data , 2014, GeoInfo.

[17]  Jan-Ming Ho,et al.  Travel time prediction with support vector regression , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[18]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[19]  Lelitha Vanajakshi,et al.  Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses , 2009 .

[20]  M. Wand,et al.  Semiparametric Regression: Parametric Regression , 2003 .

[21]  Steven I-Jy Chien,et al.  DYNAMIC TRAVEL TIME PREDICTION WITH REAL-TIME AND HISTORICAL DATA , 2003 .

[22]  S. Wood.  Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models , 2011 .

[23]  S. Wood.  Modelling and smoothing parameter estimation with multiple quadratic penalties , 2000 .

[24]  Bianca Zadrozny,et al.  A Data Driven Method For Sweet Spot Identification In Shale Plays Using Well Log Data , 2015 .

[25]  Eric Bouillet,et al.  Predicting arrival times of buses using real-time GPS measurements , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[26]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[27]  Baozhen Yao,et al.  Bus Arrival Time Prediction Using Support Vector Machines , 2006, J. Intell. Transp. Syst..

[28]  Rajat Rajbhandari,et al.  Bus arrival time prediction using stochastic time series and Markov chains , 2005 .

[29]  Christian S. Jensen,et al.  Similarity-based prediction of travel times for vehicles traveling on known routes , 2008, GIS '08.