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Article

PREDICTION OF BUS TRAVEL TIME USING ARTIFICIAL NEURAL NETWORK

DOI: 10.7708/ijtte.2015.5(4).06


5 / 4 / 410-424 Pages

Author(s)

Johar Amita - Centre for Transportation System, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India -

Jain Sukhvir Singh - Centre for Transportation System, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India -

Garg Pradeep Kumar - Centre for Transportation System, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India -


Abstract

The objective of this study is to apply artificial neural network (ANN) for development of bus travel time prediction model. The bus travel time prediction model was developed to give real time bus arrival information to the passenger and transit agencies for applying proactive strategies. For development of ANN model, dwell time, delays and distance between the bus stops was taken as input data. Arrivals/departure times, delays, average speed between the bus stop and distance between the bus stops were collected for two urban routes in Delhi. Model was developed, validated and tested using GPS (Global Positioning System) data collected from field study. Comparative study reveals that ANN model outperformed the regression model in terms of accuracy and robustness.


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References:

Abdelfattah, A.M.; Khan, A.M. 1998. Models for predicting bus delays, Transportation Research Record. DOI: http://dx.doi.org/10.3141/1623-02, 1623(1): 8-15.

 

Cathey, F.W.; Dailey, D.J. 2003. A prescription for Transit Arrival/Departure Prediction using Automatic Vehicle Location Data, Transportation Research Part C: Emerging Technologies. DOI: http://dx.doi.org/10.1016/S0968-090X(03)00023-8, 11: 241-264.

 

Chang, H.; Park, D.; Lee, S.; Lee, H.; Baek, S. 2010. Dynamic Multi-Interval Bus Travel Time Prediction Using Bus Transit Data, Transportmetrica, 6(1): 19-38.

 

Chen, M.; Chien, S. 2002. Dynamic Freeway Travel Time Prediction Using Probe Vehicle Data: Link-based vs. Path-based, Journal of the Transportation Research Board. DOI: http://dx.doi.org/10.3141/1768-19, 1768: 157-161.

 

Chien, S.I-J.; Kuchipudi, C.M. 2003. Dynamic Travel Time Prediction with Real-time and Historic Data, Journal of Transportation Engineering, ASCE. DOI: http://dx.doi.org/10.1061/(ASCE)0733-947X(2003)129:6(608), 129(6): 608-616.

 

Chien, S.I-J.; Ding, Y.; Wei, C. 2002. Dynamic Bus Arrival Time Prediction with Artificial Neural Networks, Journal of Transportation Engineering. DOI: http://dx.doi.org/10.1061/(ASCE)0733-947X(2002)128:5(429), 128(5): 429-438.

 

Demuth, H.; Beal, M.; Hagan, M. 2007. Neural Network Toolbox 5 user’s Guide of the MATLAB Software. Natick, Mass.

 

Dia, H. 2001. An object-oriented Neural Network Approach to Short-term Traffic Forecasting, European Journal of Operational Research. DOI: http://dx.doi.org/10.1016/S0377-2217(00)00125-9, 131(2): 253-261.

 

European Conference on Ministers of Transport (ECMT). 2002. Implementing Sustainable Urban Transport Policies. Final Report, ECMT/OECD Publication Service, Paris, France.

 

Gurmu, K.Z.; Fan, W. 2014. Artificial Neural Network Travel Time Prediction Model for Buses using only GPS Data, Journal of Public Transportation, 17(2): 45-65.

 

Hecht-Nielsen, R. 1987. Kolmogorov’s Mapping Neural Network Existence Theorem. In Proceedings of the First IEEE International Conference on Neural Networks, San Diego, 4-11.

 

Jang, J. 2013. Short-Term Travel Time Prediction using Kalman Filter Combined with a variable Aggregation Interval Scheme. In Proceeding of the Eastern Asian Society for Transportation Studies, 9.

 

Jeong, R.; Rilett, L.R. 2004. Bus Arrival Time Prediction Using Artificial Neural Network Model. In Proceedings of the IEEE Intelligent Transportation Systems Conference, Washington, D.C., USA, 988-993.

 

Li, C-S.; Chen, M-C. 2013. Identifying Important Variables for Predicting Travel Time of Freeway with Non-recurrent Congestion with Neural Network, Neural Computing and Applications. DOI: http://dx.doi.org/10.1007/s00521-012-1114-z, 23(6): 1611-1629.

 

Lin, W.H.; Zeng, J. 1999. Experimental Study of Real-Time Bus Arrival Time Prediction with GPS Data, Transportation Research Record, 1666: 101-109.

 

Liu, X.; Chien, S.I.; Chen, M. 2012. An Adaptive Model for Highway Travel Time Prediction, Journal of Advanced Transportation. DOI: http://dx.doi.org/10.1002/atr.1216, 48(6): 642-654.

 

Mahmoudabadi, A. 2010. Using Artificial Neural Network to Estimate Average Speed of Vehicles in Rural Roads. In Proceedings of the International Conference on Intelligent Networking and Computing.

 

Mazloumi, E.; Moridpour, S.; Currie, G.; Rose, G. 2012. Exploring the Value of Traffic Flow Data in Bus Travel Time Prediction, Journal of Transportation Engineering. DOI: http://dx.doi.org/10.1061/(ASCE)TE.1943-5436.0000329, 138(4): 436-446.

 

Neural Ware Inc. 1993. Neural Computing. Technical Publications Group, Pittsburgh, PA.

 

Patnaik, J.; Chien, S.; Bladikas, A. 2004. Estimation of Bus Arrival Times Using APC Data, Journal of Public Transportation, 7(1): 1-20.

 

Ramakrishna, Y.; Ramakrishna, P.; Lakshmanan, V.; Sivanandan, R. 2006. Bus Travel Time Prediction using GPS Data. In Proceedings of the Map India. Available from Internet: http://www.gisdevelopment.net/proceedings/mapindia/2006/.

 

Rice, J.; Zwet, V.E. 2004. A Simple and Effective Method for Predicting Travel Times on Freeways, IEEE Transactions on Intelligent Transportation Systems. DOI: http://dx.doi.org/10.1109/TITS.2004.833765, 5(3): 200-207.

 

Shalaby, A.; Farhan, A. 2004. Prediction Model of Bus Arrival and Departure Times Using AVL and APC Data, Journal of Public Transportation, 7(1): 41-61.

 

Sinha, K.C. 2003. Sustainability and Urban Public Transportation, Journal of Transportation Engineering, 1294: 331-341.

 

Sivanandam, S.N.; Sumathi, S.; Deepa, S.N. 2010. Introduction to Neural Networks using Matlab 6.0, Tata McGraw Hill, New Delhi.

               

Smith, B.L.; Demetsky, M.J. 1995. Short-term Traffic Flow Prediction: Neural Network Approach, Transportation Research Record, 1453: 98-104.

 

Tong, H.Y.; Hung, W.T. 2002. Neural Network Modelling of Vehicle Discharge Headway at Signalized Intersection: Model Description and Results, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/S0965-8564(00)00035-5, 36(1): 17-40.

 

Transport Research Board (TRB). 2001. Making Transit Work. Special Report, 257, National Academy, Washington, D.C.

 

Vanajakshi, L.; Subramanian, S.C.; Sivanandan, R. 2008. Travel Time Prediction under Heterogeneous Traffic condition using Global Positioning System Data from Buses, IET Intelligent Transport System. DOI: http://dx.doi.org/10.1049/iet-its:20080013, 3(1): 1-9.

 

Williams, B.; Hoel, L. 2003. Modelling and Forecasting Vehicle Traffic Flow as a Seasonal Arima Process: Theoretical Basis and Empirical Results, Journal of Transportation Engineering, 129(6): 664-672.

 

You, J.; Kim, T.J. 2000. Development and Evaluation of Hybrid Travel Time Forecasting Model, Transportation Research Part C: Emerging Technologies. DOI: http://dx.doi.org/10.1016/S0968-090X(00)00012-7, 8(1-6): 231-256.

 

Yu, B.; Lam, W.H.K.; Tam, M.L. 2011. Bus Travel Time Prediction at Bus Stop with Multiple Routes, Transportation Research Part C: Emerging Technologies. DOI: http://dx.doi.org/10.1016/j.trc.2011.01.003, 19(6): 1157-1170.

 

Yu, B.; Yang, Z.-Z.; Chen, K.; Yu, B. 2010. Hybrid Model for Prediction of Bus Arrival Time at Next Station, Journal of Advanced Transportation. DOI: http://dx.doi.org/10.1002/atr.136, 44(3): 193-204.

 

Zhang, X.; Rice, J.A. 2003. Short-Term Travel Time Prediction Using A Time-Varying Coefficient Linear Model, Transportation Research Part C: Emerging Technologies, 11: 187-210.

 

Zheng, F.; Zuylen, H.V. 2013. Urban Link Travel Time Estimation Based on Sparse Probe Vehicle Data, Transportation Research Part C: Emerging Technologies. DOI: http://dx.doi.org/10.1016/j.trc.2012.04.007, 31: 145-147.