Volume List  / Volume 10 (1)

Article

COMPARATIVE ASSESSMENT OF DYNAMIC TRAVEL TIME PREDICTION MODELS IN THE DEVELOPING COUNTRIES CITIES

DOI: 10.7708/ijtte.2020.10(1).09


10 / 1 / 96-110 Pages

Author(s)

Prosper Sebastiani Nyaki - Department of Logistics and Transport Studies, National Institute of Transport, Dar es Salaam Tanzania -

Hannibal Bwire - Department of Transportation and Geotechnical Engineering, College of Engineering and Technology, University of Dar es Salaam, Tanzania -

Nurdin Kassim Mushule - Department of Transportation and Geotechnical Engineering, College of Engineering and Technology, University of Dar es Salaam, Tanzania -


Abstract

Providing real and accurate travel time information usually, assists road users to plan their trips and choose the appropriate mode of transport. However, accurate prediction of travel time is a challenging problem, especially in developing countries where heterogeneous flow conditions exist and there are no records of information about the travel time for travelers. Most of the dynamic travel-time prediction models developed emphasize on link travel time without taking into account delay time at the intersections and waiting time at the bus stops. The objective of this study was to compare Multi - Linear Regression and Artificial Neural Network models to obtain a suitable model for developing a dynamic travel-time prediction model using waiting time at the bus stop, intersection delay time, link distance, traffic volume, link travel time, peak hours and off-peak hours as model inputs. Link travel time was modeled by a well - trained Neural Network and Kalman filtering dynamic algorithm using field survey data collected by employing public buses in Dar es Salaam city. The model was validated by using data collected in five main routes in Dar es Salaam City. The Root Mean Square Error and Mean Absolute Percent Error were used to evaluate the performance of the model by comparing it with other prediction models. Findings indicate that the integration of the Artificial Neural Network and Kalman Filter algorithm model (ANN-KF) promised to be a reasonable model for predicting dynamic travel time in Dar es Salaam city.


Download Article

Number of downloads: 114


Acknowledgements:

The authors would like to acknowledge the support given by the National Institute of Transport and JICA for providing data used in this study


References:

Amita, J.; Singh, J.S.; Kumar, G.P. 2015. Prediction of Bus Travel Time Using Artificial Neural Network, International Journal for Traffic and Transport Engineering 5(4): 410-424.

 

Amita, J.; Jain, S.S.; Garg, P.K. 2016. Prediction of Bus Travel Time using ANN: A Case Study in Delhi, Transportation Research Procedia 17: 263-272.

 

Arhin, S.; Noel, E.; Anderson, M.F.; Williams, L.; Ribisso, A.; Stinson, R. 2016. Optimization of transit total bus stop time models, Journal of Traffic and transportation Engineering 3(2): 146-153.

 

Bai, C.; Peng, Z.R.; Lu, Q.C.; Sun, J. 2015. Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes, Computational Intelligence and Neuroscience 2015(432389): 1-9.

 

Chen, M.; Liu, X.; Xia, J.; Chien, S.I. 2004. A Dynamic Bus‐Arrival Time Prediction Model Based on APC Data, Computer‐Aided Civil and Infrastructure Engineering 19(5): 364-376.

 

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.

 

Chien, S.I.J.; Ding, Y.; Wei, C. 2002. Dynamic Bus Arrival Time Prediction with Artificial Neural Networks, Journal of Transportation Engineering 128(5): 429-438.

 

Čelan, M.; Lep, M. 2017. Bus Arrival Time Prediction Based on Network Model, Procedia Computer Science 113: 138-145.

 

Elhenawy, M.; Chen, H.; Rakha, H.A. 2014. Dynamic Travel Time Prediction Using Data Clustering and Genetic Programming, Transportation Research Part C: Emerging Technologies 42: 82-98.

 

Fan, W.; Gurmu, Z. 2015. Dynamic Travel Time Prediction Models for Buses Using Only GPS Data, International Journal of Transportation Science and Technology 4(4): 353-366.

 

Jeong, R.; Rilett, R. 2004. Bus Arrival Time Prediction Using Artificial Neural Network Model. In Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, 988-993.

 

Jiang, Z.; Zhang, C.; Xia, Y. 2014. Travel Time Prediction Model for Urban Road Network Based on Multi-Source Data, Procedia-Social and Behavioral Sciences 138: 811-818.

 

Jindal, I.; Qin, T.; Chen, X.; Nokleby, M.; Ye, J. 2017. A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip. Available from internet: http:// http://arxiv.org/abs/1710.04350.

 

Kumar, B.A.; Vanajakshi, L.; Subramanian, C. 2014. Pattern-Based Bus Travel Time Prediction under Heterogeneous Traffic Conditions. Transportation Research Record, Transportation Research Board, National Research Council, Washington, DC. 16 p.

 

Kumar, B.A.; Vanajakshi, L.; Subramanian, S.C. 2018. A hybrid model based method for bus travel time estimation, Journal of Intelligent Transportation Systems 22(5): 390-406.

 

Kumar, B.A.; Vanajakshi, L.; Subramanian, S.C. 2017. Bus Travel Time Prediction Using a Time-Space Discretization Approach, Transportation Research Part C: Emerging Technologies 79: 308-332.

 

Li, M.; Wu, H.; Wang, Y.; Handroos, H.; Carbone, G. 2017. Modified Levenberg–Marquardt Algorithm for Backpropagation Neural Network Training in Dynamic Model Identification of Mechanical Systems, Journal of Dynamic Systems, Measurement, and Control 139(3): 031012. 14 p.

 

Shi, C.; Chen, B.; Li, Q. 2017. Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data, ISPRS International Journal of Geo-Information 6(8): 253.

 

Wu, C.H.; Ho, J.M.; Lee, D.T. 2004. Travel-Time Prediction with Support Vector Regression, IEEE transactions on intelligent transportation systems 5(4): 276-281.

 

Xiong, G., Kang, W., Wang, F., Riekki, J., & Pirttikangas, S. (2015). Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network. IEEE 18th International Conference on Intelligent Transportation Systems Continuous 92: 523-528.

 

Yu, Z.; Wood, J.S.; Gayah, V.V. 2017. Using Survival Models to Estimate Bus Travel Times and Associated Uncertainties, Transportation Research Part C: Emerging Technologies 74: 366-382.

 

Zaki, M.; Ashour, I.; Zorkany, M.; Hesham, B. 2013. Online Bus Arrival Time Prediction Using Hybrid Neural Network and Kalman Filter Techniques, International Journal of Modern Engineering Research 3(4): 2035-2041.

 

Zheng, F. 2011. Modeling Urban Travel Times. Thesis Series T2011/9, The Netherlands TRAIL Research School.

 

Zheng, F.; Van Zuylen, H. 2013. Urban Link Travel Time Estimation Based on Sparse Probe Vehicle Data, Transportation Research Part C: Emerging Technologies 31: 145-157.

 

Zhu, X.; Fan, Y.; Zhang, F.; Ye, X.; Chen, C.; Yue, H. 2018. Multiple-Factor Based Sparse Urban Travel Time Prediction, Applied Sciences 8(2): 279. 18 p.