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A Spatio-temporal Network for Demand Prediction of Electric Vehicle Sharing Systems

Published: 29 March 2020 Publication History

Abstract

With the improvement of environmental awareness, one-way electric vehicle sharing systems with stations are gradually known. Vehicle rebalance and station expansion are the two things that system operators care most about. In this paper, we study the problem of forecasting travel demand, which can be used to infer the place to deploy new station and provide suggestions for vehicles scheduling. As an attempt to make use of both spatial and temporal features, we propose a spatio-temporal network based on Convolutional Long Short-Term Memory (ConvLSTM) to predict traveling demand in an area without historical travel records. Convolution networks make sure that when demand in an area is predicted, geographical features of its neighborhoods will also be considered. With LSTM, demand will be treated as time series. Therefore, temporal associations are also considered. Our network improves the prediction accuracy which has been corroborated through experiments on real-life data conducted with other regression methods. In addition, it can be observed from prediction curves, that trend of curve predicted by our method is closer to the real curve. Our work provides a travel demand predicting solution with commercial potential that helps to make business decisions.

References

[1]
Abughali, I. K. A. and Minz, S. 2015. Binarizing Change for Fast Trend Similarity Based Clustering of Time Series Data. In Proceedings of the Pattern Recognition and Machine Intelligence. 6th International Conference, PReMI 2015. Springer International Publishing, Cham, Switzerland, 257--267. DOI= http://dx.doi.org/10.1007/978-3-319-19941-2_25.
[2]
Ait-Ouahmed, A., Josselin, D. and Zhou, F. 2018. Relocation optimization of electric cars in one-way car-sharing systems: modeling, exact solving and heuristics algorithms. Int J Geogr Inf Sci, 32, 2 (2018), 367--398. DOI= https://doi.org/10.1080/13658816.2017.1372762.
[3]
Alfian, G., Rhee, J., Ijaz, M. F., Syafrudin, M. and Fitriyani, N. L. 2017. Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing. Applied Sciences, 7, 6 (06/ 2017), 598 (522 pp.). DOI= http://dx.doi.org/10.3390/app7060598.
[4]
Becker, H., Ciari, F. and Axhausen, K. W. 2017. Modeling free-floating car-sharing use in Switzerland: A spatial regression and conditional logit approach. Transport Res C-Emer, 81 (Aug 2017), 286--299. DOI= http://dx.doi.org/10.1016/j.trc.2017.06.008.
[5]
Boldrini, C., Incaini, R. and Bruno, R. 2017. Relocation in Car Sharing Systems with Shared Stackable Vehicles: Modelling Challenges and Outlook. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (Yokohama, Japan, 2017). IEEE, Piscataway, NJ, USA, 2017. DOI= http://dx.doi.org/10.1109/ITSC.2017.8317752.
[6]
Boyaci, B., Zografos, K. G. and Geroliminis, N. 2015. An optimization framework for the development of efficient one-way car-sharing systems. Eur J Oper Res, 240, 3 (Feb 1-2015), 718--733. DOI= http://dx.doi.org/10.1016/j.ejor.2014.07.020.
[7]
Boyaci, B., Zografos, K. G. and Geroliminis, N. 2017. An integrated optimization-simulation framework for vehicle and personnel relocations of electric carsharing systems with reservations. Transportation Research Part B: Methodological, 95 (01/ 2017), 214--237. DOI= http://dx.doi.org/10.1016/j.trb.2016.10.007.
[8]
Choi, J. and Yoon, J. 2017. Utilizing Spatial Big Data platform in evaluating correlations between rental housing car sharing and public transportation. Spat Inf Res, 25, 4 (Aug 2017), 555--564. DOI= http://dx.doi.org/10.1007/s41324-017-0122-6.
[9]
Gambella, C., Malaguti, E., Masini, F. and Vigo, D. 2018. Optimizing relocation operations in electric car-sharing. Omega-Int J Manage S, 81 (Dec 2018), 234--245. DOI= http://dx.doi.org/10.1016/j.omega.2017.11.007.
[10]
Hochreiter, S. and Schmidhuber, J. 1997. Long Short-Term Memory. Neural Computation, 9, 8 (1997), 1735--1780. DOI= http://doi.acm.org/10.1162/neco.1997.9.8.1735.
[11]
Hui, Y., Ding, M. T., Qian, C., Wang, W. and Xu, Q. 2017. Research on the operational characteristics of car sharing service stations: A case study of a car sharing program in Hangzhou. Transp Res Proc, 25 (2017). DOI= http://doi.acm.org/10.1016/j.trpro.2017.05.352.
[12]
Li, Y., Zheng, Y., Zhang, H. and Chen, L. 2015. Traffic prediction in a bike-sharing system. In Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015. Association for Computing Machinery, New York, NY, DOI= http://dx.doi.org/10.1145/2820783.2820837.
[13]
Liu, J., Sun, L., Li, Q., Ming, J., Liu, Y. and Xiong, H. 2017. Functional zone based hierarchical demand prediction for bike system expansion. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017. Association for Computing Machinery, 957--966. DOI= http://dx.doi.org/10.1145/3097983.3098180.
[14]
Liu, Z., Shen, Y. and Zhu, Y. 2018. Inferring dockless shared bike distribution in new cities. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining, WSDM 2018. Association for Computing Machinery, New York, NY, 378--386. DOI= http://dx.doi.org/10.1145/3159652.3159708.
[15]
Mounce, R. and Nelson, J. D. 2019. On the potential for one-way electric vehicle car-sharing in future mobility systems. Transport Res a-Pol, 120 (Feb 2019), 17--30. DOI= http://dx.doi.org/10.1016/j.tra.2018.12.003.
[16]
Niu, S. Y. and Xu, F. C. 2016. Study on the Time-sharing Lease Mode of New-energy Cars in China. In Proceedings of the The 2016-5th International Conference on Sustainable Energy and Environment Engineering (Icseee 2016). Atlantis Press, 574--578. DOI= https://doi.org/10.2991/icseee-16.2016.104.
[17]
Pagani, A., Bruschi, F. and Rana, V. 2017. Knowledge Discovery from Car Sharing Data for Traffic Flows Estimation. In Proceedings of the 2017 Smart City Symposium Prague (Scsp). IEEE, Piscataway, NJ, USA, 6 pp. DOI= http://doi.org/10.1109/SCSP.2017.7973845.
[18]
Pei-Chann, C., Jheng-Long, W., Yahui, X., Min, Z. and Xiao-Yong, L. 2019. Bike sharing demand prediction using artificial immune system and artificial neural network. Soft Computing, 23, 2 (01/30-2019), 613--626. DOI= http://dx.doi.org/10.1007/s00500-017-2909-8.
[19]
Po-Chuan, C., He-Yen, H., Sigalingging, X. K., Yan-Ru, C. and Jenq-Shiou, L. 2017. Prediction of Station Level Demand in a Bike Sharing System Using Recurrent Neural Networks. In Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring). IEEE, 5 pp. DOI= http://dx.doi.org/10.1109/VTCSpring.2017.8108575.
[20]
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K. and Woo, W.-C. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Proceedings of the 29th Annual Conference on Neural Information Processing Systems, NIPS 2015. Neural information processing systems foundation, 802--810.
[21]
Shuo, L., Haiquan, W., Chen, Y., Bowen, D., Runxing, Z. and Runhe, H. 2017. Forecasting car rental demand based temporal and spatial travel patterns. In Proceedings of the 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, Piscataway, NJ, USA, 8 pp. DOI= http://dx.doi.org/10.1109/UIC-ATC.2017.8397484.
[22]
Tomaras, D., Boutsis, I. and Kalogeraki, V. 2018. Modeling and Predicting Bike Demand in Large City Situations. In Proceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), 19--23 March 2018. IEEE, Piscataway, NJ, USA, 10 pp. DOI= http://dx.doi.org/10.1109/PERCOM.2018.8444588.
[23]
Vosooghi, R., Puchinger, J., Jankovic, M. and Sirin, G. 2017. A critical analysis of travel demand estimation for new one-way carsharing systems. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16-19 Oct. 2017. IEEE, Piscataway, NJ, USA, 7 pp. DOI= http://dx.doi.org/10.1109/ITSC.2017.8317917.
[24]
Wagle, S., Uttamani, S., Dsouza, S. and Devadkar, K. 2019. Predicting Surface Air Temperature Using Convolutional Long Short-Term Memory Networks. In Proceedings of the 2nd International Conference on Communications and Cyber-Physical Engineering, ICCCE 2019. Springer Verlag, 183--188. DOI= http://dx.doi.org/10.1007/978-981-13-8715-9_23.
[25]
Yuan, Z., Zhou, X. and Yang, T. 2018. Hetero-ConvLSTM: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018. Association for Computing Machinery, New York, NY, 984--992. DOI= http://dx.doi.org/10.1145/3219819.3219922.
[26]
Zhou, X., Shen, Y., Zhu, Y. and Huang, L. 2018. Predicting multi-step citywide passenger demands using attention-based neural networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining, WSDM 2018. Association for Computing Machinery, 736--744. DOI= http://dx.doi.org/10.1145/3159652.3159682.

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cover image ACM Other conferences
APIT '20: Proceedings of the 2020 2nd Asia Pacific Information Technology Conference
January 2020
185 pages
ISBN:9781450376853
DOI:10.1145/3379310
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Published: 29 March 2020

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Author Tags

  1. Convolutional LSTM
  2. Demand Prediction
  3. Electric Vehicle Sharing System
  4. Regression
  5. Spatio-temporal

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