Abstract
Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system.
- Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, and Yu Zheng. 2017. Planning bike lanes based on sharing-bikes’ trajectories. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1377--1386.Google ScholarDigital Library
- Paul Barratt. 2017. Healthy competition: A qualitative study investigating persuasive technologies and the gamification of cycling. Health Place 46 (July 2017), 328--336.Google Scholar
- Stephen Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press.Google ScholarDigital Library
- Jan Brinkmann, Marlin W. Ulmer, and Dirk C. Mattfeld. 2015. Short--term strategies for stochastic inventory routing in bike-sharing systems. Transport. Res. Procedia 10 (Jan. 2015), 364--373.Google Scholar
- Jan Brinkmann, Marlin W. Ulmer, and Dirk C. Mattfeld. 2019. Dynamic lookahead policies for stochastic-dynamic inventory routing in bike-sharing systems. Comput. Operat. Res. 106 (June 2019), 260--279.Google Scholar
- Leonardo Caggiani and Michele Ottomanelli. 2013. A dynamic simulation-based model for optimal fleet repositioning in bike-sharing systems. Procedia Soc. Behav. Sci. 87 (Oct. 2013), 203--210.Google Scholar
- Mengwei Chen, Dianhai Wang, Yilin Sun, E. Owen, D. Waygood, and Wentao Yang. 2018. A comparison of users’ characteristics between station-based bike-sharing system and free-floating bike-sharing system: Case study in Hangzhou, China. Transportation (Aug. 2018), 1--16. https://link.springer.com/article/10.1007/s11116-018-9910-7.Google Scholar
- Federico Chiariotti, Chiara Pielli, Angelo Cenedese, Andrea Zanella, and Michele Zorzi. 2018. Bike sharing as a key smart city service: State of the art and future developments. In Proceedings of the 7th International Conference on Modern Circuits and Systems Technologies (MOCAST’18). IEEE, 1--6.Google ScholarCross Ref
- Federico Chiariotti, Chiara Pielli, Andrea Zanella, and Michele Zorzi. 2018. A dynamic approach to rebalancing bike-sharing systems. Sensors 18, 2 (Feb. 2018), 512.Google ScholarCross Ref
- Hangil Chung, Daniel Freund, and David B. Shmoys. 2018. Bike angels: An analysis of Citi Bike’s incentive program. In Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS). ACM. DOI:https://doi.org/10.1145/3209811.3209866Google Scholar
- Geoff Clarke and John W. Wright. 1964. Scheduling of vehicles from a central depot to a number of delivery points. Operat. Res. 12, 4 (Aug. 1964), 568--581.Google ScholarDigital Library
- Claudio Contardo, Catherine Morency, and Louis-Martin Rousseau. 2012. Balancing a Dynamic Public Bike-sharing System. Vol. 4. Cirrelt, Montreal.Google Scholar
- Forrest W. Crawford and Marc A. Suchard. 2012. Transition probabilities for general birth-death processes with applications in ecology, genetics, and evolution. J. Math. Biol. 65, 3 (Sept. 2012), 553--580.Google ScholarCross Ref
- Mauro Dell’Amico, Manuel Iori, Stefano Novellani, and Thomas Stützle. 2016. A destroy and repair algorithm for the bike-sharing rebalancing problem. Comput. Operat. Res. 71 (July 2016), 149--162.Google Scholar
- Luca Di Gaspero, Andrea Rendl, and Tommaso Urli. 2016. Balancing bike-sharing systems with constraint programming. Constraints 21, 2 (Apr. 2016), 318--348.Google ScholarDigital Library
- Hans Martin Espegren, Johannes Kristianslund, Henrik Andersson, and Kjetil Fagerholt. 2016. The static bicycle repositioning problem--literature survey and new formulation. In Proceedings of the International Conference on Computational Logistics. Springer, 337--351.Google ScholarCross Ref
- Ahmadreza Faghih-Imani and Naveen Eluru. 2016. Incorporating the impact of spatio--temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system. J. Transport Geogr. 54 (June 2016), 218--227.Google Scholar
- Wolfgang Fischer and Kathleen Meier-Hellstern. 1993. The Markov-modulated Poisson process (MMPP) cookbook. Perform. Eval. 18, 2 (Sept. 1993), 149--171.Google ScholarDigital Library
- Elliot Fishman, Simon Washington, and Narelle Haworth. 2014. Bike share’s impact on car use: Evidence from the United States, Great Britain, and Australia. Transport. Res. Part D: Transport Environ. 31 (Aug. 2014), 13--20.Google Scholar
- Iris A Forma, Tal Raviv, and Michal Tzur. 2015. A 3--step math heuristic for the static repositioning problem in bike-sharing systems. Transport. Res. Part B: Methodol. 71 (Jan. 2015), 230--247.Google Scholar
- Christine Fricker and Nicolas Gast. 2016. Incentives and redistribution in homogeneous bike-sharing systems with stations of finite capacity. Euro. J. Transport. Logist. 5, 3 (2016), 261--291.Google ScholarCross Ref
- Felipe González, Carlos Melo-Riquelme, and Louis de Grange. 2016. A combined destination and route choice model for a bicycle sharing system. Transportation 43, 3 (May 2016), 407--423.Google ScholarCross Ref
- Juho Hamari, Jonna Koivisto, and Harri Sarsa. 2014. Does gamification work? A literature review of empirical studies on gamification. In Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS’14). IEEE, 3025--3034.Google ScholarDigital Library
- Sin C. Ho and W. Y. Szeto. 2014. Solving a static repositioning problem in bike-sharing systems using iterated tabu search. Transport. Res. Part E: Logist. Transport. Rev. 69 (Sept. 2014), 180--198.Google ScholarCross Ref
- Raman Kazhamiakin, Annapaola Marconi, Alberto Martinelli, Marco Pistore, and Giuseppe Valetto. 2016. A gamification framework for the long-term engagement of smart citizens. In Proceedings of the International Smart Cities Conference (ISC2’16). IEEE, 1--7.Google ScholarCross Ref
- Raman Kazhamiakin, Annapaola Marconi, Mirko Perillo, Marco Pistore, Giuseppe Valetto, Luca Piras, Francesco Avesani, and Nicola Perri. 2015. Using gamification to incentivize sustainable urban mobility. In Proceedings of the IEEE 1st International Smart Cities Conference (ISC2’15). IEEE, 1--6.Google ScholarCross Ref
- Reza Khoshkangini, Giuseppe Valetto, and Annapaola Marconi. 2017. Generating personalized challenges to enhance the persuasive power of gamification. In Proceedings of the Personalization in Persuasive Technology Workshop (PPT’17). Springer.Google Scholar
- Christian Kloimüllner, Petrina Papazek, Bin Hu, and Günther R. Raidl. 2014. Balancing bicycle sharing systems: An approach for the dynamic case. In Proceedings of the European Conference on Evolutionary Computation in Combinatorial Optimization. Springer, 73--84.Google Scholar
- Alan Krinik and Carrie Mortensen. 2007. Transient probability functions of finite birth-death processes with catastrophes. J. Stat. Plan. Infer. 137, 5 (May 2007), 1530--1543.Google ScholarCross Ref
- Gilbert Laporte, Frédéric Meunier, and Roberto Wolfler Calvo. 2015. Shared mobility systems. 4OR 13, 4 (Dec. 2015), 341--360.Google Scholar
- Linfeng Li and Miyuan Shan. 2016. Bidirectional incentive model for bicycle redistribution of a bicycle sharing system during rush hour. Sustainability 8, 12 (Dec. 2016), 1299.Google ScholarCross Ref
- Hongtao Lv, Fan Wu, Tie Luo, Xiaofeng Gao, and Guihai Chen. 2018. Achieving location truthfulness in rebalancing supply-demand distribution for bike sharing. In Proceedings of the International Conference on Algorithmic Applications in Management. Springer, 256--267.Google ScholarDigital Library
- Eoin O’Mahony and David B. Shmoys. 2015. Data analysis and optimization for (Citi) bike sharing. In Proceedings of the 29th Conference on Artificial Intelligence. AAAI, 687--694.Google Scholar
- Aritra Pal and Yu Zhang. 2017. Free-floating bike sharing: Solving real-life large-scale static rebalancing problems. Transport. Res. Part C: Emerg. Technol. 80 (July 2017), 92--116.Google Scholar
- Julius Pfrommer, Joseph Warrington, Georg Schildbach, and Manfred Morari. 2014. Dynamic vehicle redistribution and online price incentives in shared mobility systems. IEEE Trans. Intell. Transport. Syst. 15, 4 (Mar. 2014), 1567--1578.Google ScholarCross Ref
- Tal Raviv, Michal Tzur, and Iris A. Forma. 2013. Static repositioning in a bike-sharing system: Models and solution approaches. EURO J. Transport. Logist. 2, 3 (Aug. 2013), 187--229.Google ScholarCross Ref
- Svenja Reiss and Klaus Bogenberger. 2016. Optimal bike fleet management by smart relocation methods: Combining an operator-based with an user-based relocation strategy. In Proceedings of the 19th International Conference on Intelligent Transportation Systems (ITSC’16). IEEE, 2613--2618.Google ScholarDigital Library
- David Rojas-Rueda, Audrey de Nazelle, Marko Tainio, and Mark J. Nieuwenhuijsen. 2011. The health risks and benefits of cycling in urban environments compared with car use: Health impact assessment study. Brit. Med. J. 343 (Aug. 2011), d4521.Google Scholar
- Stefan Ropke and David Pisinger. 2006. An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transport. Sci. 40, 4 (Nov. 2006), 455--472.Google ScholarDigital Library
- J. Schuijbroek, Robert C. Hampshire, and W.-J. Van Hoeve. 2017. Inventory rebalancing and vehicle routing in bike-sharing systems. Eur. J. Operation. Res. 257, 3 (Mar. 2017), 992--1004.Google ScholarCross Ref
- Jian-gang Shi, Hongyun Si, Guangdong Wu, Yangyue Su, and Jing Lan. 2018. Critical factors to achieve dockless bike-sharing sustainability in China: A stakeholder-oriented network perspective. Sustainability 10, 6 (June 2018), 2090.Google Scholar
- Adish Singla, Marco Santoni, Gábor Bartók, Pratik Mukerji, Moritz Meenen, and Andreas Krause. 2015. Incentivizing users for balancing bike-sharing systems. In Proceedings of the 29th Conference on Artificial Intelligence. AAAI, 723--729.Google Scholar
- John G. Skellam. 1946. The frequency distribution of the difference between two Poisson variates belonging to different populations. J. Roy. Stat. Soc.: Series A 109 (May 1946), 296.Google ScholarCross Ref
- Paolo Toth and Daniele Vigo. 1998. Exact solution of the vehicle routing problem. In Fleet Management and Logistics. Springer, 1--31.Google Scholar
- Mingshu Wang and Xiaolu Zhou. 2017. Bike-sharing systems and congestion: Evidence from US cities. J. Transport Geogr. 65 (Dec. 2017), 147--154.Google Scholar
- Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, and Michele Zorzi. 2014. Internet of Things for smart cities. IEEE Internet Things J. 1, 1 (Feb. 2014), 22--32.Google ScholarCross Ref
Index Terms
- A Bike-sharing Optimization Framework Combining Dynamic Rebalancing and User Incentives
Recommendations
Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningBike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. A key to success for a bike sharing systems is the effectiveness of rebalancing operations, that is, the efforts of ...
Mobility Modeling and Prediction in Bike-Sharing Systems
MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and ServicesAs an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though providing convenient, low-cost and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems to both users ...
Analyzing Bike Repositioning Strategies Based on Simulations for Public Bike Sharing Systems: Simulating Bike Repositioning Strategies for Bike Sharing Systems
IIAI-AAI '13: Proceedings of the 2013 Second IIAI International Conference on Advanced Applied InformaticsWith the contributions on reducing the traffic congestion and air pollution, bike sharing systems become more popular recently in many metropolitan areas worldwide. Without effective bike redistribution strategies, a bike rental station may easily ...
Comments