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Articles

Recommending taxi routes with an advance reservation – a multi-criteria route planner

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Pages 162-183 | Received 27 Aug 2020, Accepted 16 Feb 2021, Published online: 04 Mar 2021

References

  • Alvarez-Garcia, J. A., Ortega, J. A., Gonzalez-Abril, L., & Velasco, F. (2010). Trip destination prediction based on past GPS log using a hidden Markov model. Expert Systems with Applications, 37(12), 8166–8171.
  • Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., & Werneck, R. F.. (2016). Route planning in transportation networks.Technical Report MSR-TR-2014-4, Microsoft Research, Redmond.
  • Brébisson, A. D., Simon, É, Auvolat, A., Vincent, P., & Bengio, Y. (2015). Artificial neural networks applied to taxi destination prediction. In Proceedings of the 2015th International Conference on ECML PKDD Discovery Challenge, Porto, Portugal.
  • Chen, C., Zhang, D., Zhou, Z. H., Li, N., Atmaca, T., & Li, S. (2013). B-Planner: Night bus route planning using large-scale taxi GPS traces. In Proceedings of the 2013 IEEE international conference on pervasive computing and communications (PerCom) (pp. 225–233), San Diego, USA.
  • Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.
  • Endo, Y., Nishida, K., Toda, H., & Sawada, H. (2017). Predicting destinations from partial trajectories Using Recurrent Neural network. In Proceedings of the pacific-Asia Conference on knowledge Discovery and data mining (PAKDD) (pp. 160–172), Jeju, South Korea.
  • Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.
  • Hyttiä, E., Aalto, S., Penttinen, A., & Sulonen, R. (2010). A stochastic model for a vehicle in a dial-a-ride system. Operations Research Letters, 38(5), 432–435.
  • Hyttiä, E., Häme, L., Penttinen, A., & Sulonen, R. (2010). Simulation of a large scale dynamic pickup and delivery problem. In Proceedings of 3rd International ICST Conference on Simulation tools and techniques. Brussels.
  • Hyttiä, E., Penttinen, A., & Sulonen, R. (2012). Non-myopic vehicle and route selection in dynamic DARP with travel time and workload objectives. Computers & Operations Research, 39(12), 3021–3030.
  • Lassoued, Y., Monteil, J., Gu, Y., Russo, G., Shorten, R., & Mevissen, M. (2017). A hidden Markov model for route and destination prediction. In IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). doi:https://doi.org/10.1109/ITSC.2017.8317888.
  • Li, X., Li, M., Gong, Y.-J., Zhang, X., & Yin, J. (2016). T-DesP: Destination prediction based on Big trajectory data. IEEE Transactions on Intelligent Transportation Systems, 17(8). doi:https://doi.org/10.1109/TITS.2016.2518685
  • Li, Y., Lu, J., Zhang, L., & Zhao, Y. (2017). Taxi booking Mobile App order demand prediction based on short-term traffic forecasting. Transportation Research Record: Journal of the Transportation Research Board, 2634(1), 57–68.
  • Liao, S., Zhou, L., Di, X., Yuan, B., & Xiong, J. (2018). Large-scale short-term urban taxi demand forecasting using deep learning. In 23rd Asia and South Pacific Design Automation Conference. Jeju Island, South Korea. doi:https://doi.org/10.1109/ASPDAC.2018.8297361.
  • Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., & Lin, L. (2019). Contextualized spatial-temporal Network for Taxi origin-destination demand prediction. IEEE Transactions on Intelligent Transportation Systems, 20(10). doi:https://doi.org/10.1109/TITS.2019.2915525
  • Lu, Y., Gu, J., Xie, D., & Li, T. (2019). Integrated route planning algorithm based on spot price and classified travel objectives for EV users. IEEE Access, 7, 122238–122250.
  • Luis, M.-M., João, G., Michel, F., João, M.-M., & Luis, D. (2013). Predicting taxi-passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems, 14(3). doi:https://doi.org/10.1109/TITS.2013.2262376
  • Lv, J., Li, Q., Sun, Q., & Wang, X. (2018). T-CONV: A Convolutional Neural Network for Multi-scale Taxi Trajectory Prediction. In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). doi:https://doi.org/10.1109/BigComp.2018.00021.
  • Manasseh, C., & Sengupta, R. (2013). Predicting driver destination using machine learning techniques. In 16th International IEEE Conference on Intelligent Transportation Systems. doi:https://doi.org/10.1109/ITSC.2013.6728224.
  • Qiu, Y., & Xu, X. (2018). RPSBPT: A route planning scheme with best profit for taxi. In 2018 International Conference on Mobile Ad-Hoc and sensor networks. Shenyang, China. (pp. 121–126).
  • Rodrigues, F., Markou, L., & Pereira, F. C. (2019). Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach. Journal of Information Fusion, 49, 120–129.
  • Rossi, A., Barlacchi, G., Bianchini, M., & Lepri, B. (2019). Modelling taxi drivers’ behaviour for the next destination prediction. IEEE Transactions on Intelligent Transportation Systems, 21(7), 2980–2989.
  • Sanders, P., & Schultes, D. (2007). Engineering fast route planning algorithms. In International workshop on experimental and efficient algorithms, Rome, Italy. (pp. 23–36).
  • Sayarshad, H. R., & Chow, J. Y. J. (2016). Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data. Journal of Advanced Transportation, 50(7), 1275–1294.
  • Sayarshad, H. R., & Gao, H. O. (2018). A scalable non-myopic dynamic dial-a-ride and pricing problem for competitive on-demand mobility systems. Transportation Research Part C: Emerging Technologies, 91, 192–208.
  • Sayarshad, H. R., & Gao, H. O. (2020). Optimizing dynamic switching between fixed and flexible transit services with an idle-vehicle relocation strategy and reductions in emissions. Transportation Research Part A: Policy and Practice, 135, 198–214.
  • Schultes, D. (2008). Route planning in road networks. Dissertation. Universitt Fridericiana zu Karlsruhe.
  • Wang, H., Cheu, R. L., & Lee, D. H. (2014). Intelligent taxi dispatch system for advance reservations. Journal of Public Transportation, 17(3), 115–128.
  • Xingjian, S. H. I., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Twenty-ninth Conference on Neural Information Processing Systems, Montreal. (pp. 802–810).
  • Xu, J., Rahmatizadeh, R., Bölöni, L., & Turgut, D. (2018). Real-Time prediction of taxi Demand Using Recurrent Neural networks. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2572–2581.
  • Yao, H., Tang, X., Wei, H., Zheng, G., & Li, Z. (2019). Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In AAAI Conference on Artificial intelligence. Honolulu, Hawaii, USA.
  • Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., … Li, Z. (2018). Deep multi-view spatial-temporal Network for Taxi demand prediction. In AAAI Conference on Artificial intelligence. New Orleans, Louisiana, USA.
  • Zhang, K., Feng, Z., Chen, S., Huang, K., & Wang, G. (2016). A framework for passengers demand prediction and recommendation. In 2016 IEEE International Conference on Services Computing (SCC). doi:https://doi.org/10.1109/SCC.2016.51.
  • Zhang, H. M., Li, M. L., & Yang, L. (2018). Safe path planning of Mobile robot based on improved A* algorithm in complex terrains. Algorithms, 11(4), 44.
  • Zhang, L., Zhang, G., Liang, Z., & Ozioko, E. F. (2018). Multi-features taxi destination prediction with frequency domain processing. PLoS One, 13(3). doi:https://doi.org/10.1371/journal.pone.0194629
  • Zhao, K., Khryashchev, D., Freire, J., Silva, C., & Vo, H. (2016). Predicting taxi demand at high spatial resolution: Approaching the limit of predictability. In Proceedings of the 2016 IEEE International Conference on Big Data (Big Data). doi:https://doi.org/10.1109/BigData.2016.7840676.
  • Zong, F., Tian, Y., He, Y., Tang, J., & Lv, J. (2019). Trip destination prediction based on multi-day GPS data. Physica A: Statistical Mechanics and its Applications, 515, 258–269.

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