A Hybrid Dispatch Strategy Based on the Demand Prediction of Shared Bicycles
Source abstract
Abstract
With the advent of pile-less shared bicycles, the techniques initially used for public bicycle dispatching were unable to fulfill the routine dispatch tasks, resulting in constant bicycle crowding. In this paper, to alleviate the mess of shared bicycles, we propose a hybrid dispatching algorithm based on bicycle demand data. We take the bicycle stations’ imbalance as an optimization index and use greedy ideas to ensure that after each dispatch all stations get the smallest imbalance. In addition, it is suggested that two assessment metrics evaluate the efficiency of the dispatching technique from the users and operators’ perspectives. It is shown that the proposed dispatching algorithm performs better in terms of user satisfaction and operator revenue, and is less affected by bicycle distribution compared with the traditional manual scheduling algorithm.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Parking Systems Research · Transportation and Mobility Innovations · Urban Transport and Accessibility
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
Tongda College, Nanjing University of Posts & Telecommunications, Yangzhou 225127, China
Funding
National Natural Science Foundation of China · Postdoctoral Research Foundation of China · Postgraduate Research & Practice Innovation Program of Jiangsu Province · NUPTSF · Teaching Reform from Project of Tongda 353 College in NJUPT
References (34)
Show source-provided references
1. Midgley, P. (2018, June 12). Bicycle-Sharing Schemes: Enhancing Sustainable Mobility in Urban Areas. Technical Report, Global Tranport Knowledge Partnership & International Road Federation. Available online: http://10.10.254.10/cache/3/03/www.un.org/6c48ce0251b0cfe3605d275f9709807e/Background-Paper8-P.Midgley-Bicycle.pdf.
2. Bike-sharing: History, Impacts, Models of Provision, and Future10.5038/2375-0901.12.4.3 ↗
3. Will Smart Bikes Succeed as Public Transportation in the United States?10.5038/2375-0901.7.2.1 ↗
4. Midgley, P. (2009). The Role of Smart Bike-sharing Systems in Urban Mobility. Journeys, Red Avocado Film.
5. Shen, S., Wei, Z.Q., Sun, L.J., Su, Y.Q., Wang, R.C., and Jiang, H.M. (2018). The Shared Bicycle and Its Network-Internet of Shared Bicycle (IoSB): A Review and Survey. Sensors, 18.10.3390/s18082581 ↗
6. (2018, June 12). OFO Bicycles. Available online: http://m.ofo.so/.
7. (2018, June 12). Mobike Bicycles. Available online: https://mobike.com/cn/classic/.
8. Sha, C., Qiu, J.M., Li, S.Y., Qiang, M.Y., and Wang, R.C. (2016). A type of low-latency data gathering method with multi-sink for sensor networks. Sensors, 16.10.3390/s16060923 ↗
9. A type of energy-efficient target tracking approach based on grids in sensor networks10.1007/s12083-019-00744-0 ↗
10. Jefri, N.N.N.B., Anuar, K., and Arjunan, S. (2016, January 28–30). Real Time Indoor Measurement of 2G, 3G and LTE Mobile Networks in Malaysia. Proceedings of the 2016 IEEE 3rd International Symposium on Telecommunication Technologies (ISTT), Kuala Lumpur, Malaysia.
11. Low Power Wide Area Networks: An Overview10.1109/COMST.2017.2652320 ↗
12. Ratasuk, R., Mangalvedhe, N., Zhang, Y., Robert, M., and Koskinen, J.P. (November, January 31). Overview of Narrowband IoT in LTE Rel-13. Proceedings of the 2016 IEEE Conference on Standards for Communications and Networking (CSCN), Berlin, Germany.10.1109/CSCN.2016.7785170 ↗
13. Anand, S., and Regi, R. (2018, January 2–3). Remote Monitoring of Water Level in Industrial Storage Tanks Using NB-IoT. Proceedings of the 2018 International Conference on Communication Information and Computing Technology (ICCICT), Mumbai, India.10.1109/ICCICT.2018.8325871 ↗
14. Shi, J., Jin, L., Li, J., and Fang, Z. (2017, January 25–27). A Smart Parking System Based on NB-IoT and Third-party Payment Platform. Proceedings of the 2017 17th International Symposium on Communications and Information Technologies (ISCIT), Cairns, Australia.10.1109/ISCIT.2017.8261235 ↗
15. Connecting Intelligent Things in Smart Hospitals using NB-IoT10.1109/JIOT.2018.2792423 ↗
16. Bike sharing systems: Solving the static rebalancing problem10.1016/j.disopt.2012.11.005 ↗
17. The bike sharing rebalancing problem: Mathematical formulations and benchmark instances10.1016/j.omega.2013.12.001 ↗
18. Solving a static repositioning problem in bike-sharing systems using iterated tabu search10.1016/j.tre.2014.05.017 ↗
19. A multiple type bike repositioning problem10.1016/j.trb.2016.05.010 ↗
20. Fu, S.Y., Han, L.W., Tian, Y., and Yang, G.S. (2012, January 22–24). Path planning for unmanned aerial vehicle based on genetic algorithm. Proceedings of the 2012 IEEE 11th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Kyoto, Japan.10.1109/ICCI-CC.2012.6311139 ↗
21. Pso with Path Relinking for Resource Allocation Using Simulation Optimization10.1016/j.cie.2013.02.004 ↗
22. Horoba, C., and Sudholt, D. (2009). Running Time Analysis of ACO Systems for Shortest Path Problems. International Workshop on Engineering Stochastic Local Search Algorithms, Springer.10.1007/978-3-642-03751-1_6 ↗
23. Ahmed, S.A.A., and Safar, M.J.A. (2018). Shortest Path Planning for Noncircular Omnidirectional Mobile Robot. Intelligent Manufacturing & Mechatronics, Springer.10.1007/978-981-10-8788-2_20 ↗
24. Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning10.1016/j.cie.2018.12.023 ↗
25. Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto10.1007/s11116-015-9669-z ↗
26. Feng, C., Hillston, J., and Reijsbergen, D. (2016). Moment-based probabilistic prediction of bike availability for bike-sharing systems. International Conference on Quantitative Evaluation of Systems, Springer.10.1007/978-3-319-43425-4_9 ↗
27. Statistical study of a concentrated dispersion of deformable particles modeled as an assembly of cyclic lattice chains10.1080/00268978900100601 ↗
28. Optimizing the location of stations in bike-sharing programs: A GIS approach10.1016/j.apgeog.2012.07.002 ↗
29. Public bicycle system station deployment and scale prediction10.4028/www.scientific.net/AMR.790.510 ↗
30. The application research of long-term and short-term prediction in the rental trends of public bicycle stations10.1166/jctn.2016.5858 ↗
31. Bao, J., He, T., Ruan, S., Li, Y., and Zheng, Y. (2017, January 13–17). Planning Bike Lanes based on Sharing-Bikes’ Trajectories. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’17). Association for Computing Machinery, New York, NY, USA.10.1145/3097983.3098056 ↗
32. Evans, M.R., Oliver, D., Shekhar, S., and Harvey, F. (2013, January 11). Fast and exact network trajectory similarity computation: A case-study on bicycle corridor planning. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing (UrbComp ’13). Association for Computing Machinery, New York, NY, USA.10.1145/2505821.2505835 ↗
33. Study of public bicycle system based on metro shuttle
34. (2020, February 10). 2017 Mobike Cup Algorithm Challenge on Biendata Competitions. Available online: https://www.biendata.com/competition/mobike/.