Management of resource allocation on vehicle-sharing schemes: the case of Thessaloniki’s bike-sharing system (original) (raw)

Abstract

The purpose of this paper is to propose a holistic optimization-based framework for addressing the re-balancing problem of vehicle-sharing schemes. In order to address this issue, the problem is decomposed into three (3) sub-problems that include: (1) the dynamic prediction of vehicle demand in stations or zones (in the case of free-floating systems); (2) the optimization of the assignment of vehicles from stations/zones using the predicted demand of the first step; and (3) the optimization of the route that the re-balancing vehicle should follow, under transverse distance minimization objectives, and given the optimized assignment of the second step. As the route optimization of the rebalancing vehicle is computationally intensive, a heuristic algorithm is developed that transforms the route optimization problem into the one (1)-Commodity Pickup and Delivery Capacitated Traveling Salesman Problem. The applicability of the proposed methodology is illustrated through its application on the real case of the bike-sharing system in the city of Thessaloniki in Greece.

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References

• Abadi M et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems, s.l.: Software available from tensorflow.org
• Alvarez-Valdes R et al (2016) Optimizing the level of service quality of a bike-sharing system. Omega 62:163–175
  Article Google Scholar
• Blondiau T, Van Zeebroeck B, Haubold H (2016) Economic benefits of increased cycling. Transp Res Procedia 14:2306–2313
  Article Google Scholar
• Chemla D, Meunier F, Calvo R (2013) Bike sharing systems: solving the static rebalancing problem. Discrete Optim 10(2):120–146
  Article Google Scholar
• Chollet F (2015) Keras, s.l.: Github
• Commission European (2011) Roadmap to a single European transport area—towards a competitive and resource efficient transport system. European Commission, Brussels
  Google Scholar
• Corcoran J et al (2014) Spatio-temporal patterns of a public bicycle sharing program: the effect of weather and calendar events. J Transp Geogr 41:292–305
  Article Google Scholar
• Erdoğan G, Laporte G, Calvo RW (2014) The static bicycle relocation problem with demand intervals. Eur J Oper Res 238(2):451–457
  Article Google Scholar
• Faghih-Imani A, Hampshire R, Marla L, Eluru N (2017) An empirical analysis of bike sharing usage and rebalancing: evidence from Barcelona and Seville. Transp Res Part A Policy Pract 97:177–191
  Article Google Scholar
• Fernández A et al (2018) Balancing strategies for bike sharing systems. s.l. Springer, Berlin, pp 208–222
  Google Scholar
• Friman M, Gärling T, Ettema D, Olsson L (2017) How does travel affect emotional well-being and life satisfaction? Transp Res Part A 106:170–180
  Google Scholar
• Froehlich J, Neumann J, Oliver N (2009) Sensing and predicting the pulse of the city through shared bicycling. s.l., s.n. pp 1420–1426
• Hernández-Pérez H, Salazar-González JJ (2004) A branch-and-cut algorithm for a traveling salesman problem with pickup and delivery. Discrete Appl Optim 145(1):126–139
  Article Google Scholar
• Hulot P, Aloise D, Jena S (2018) Towards station-level demand prediction for effective rebalancing in bike-sharing systems. London, s.n
• Hyland M, Hong Z, de Farias Pinto H, Chen Y (2018) Hybrid cluster-regression approach to model bikeshare station usage. Transp Res Part A Policy Pract 115:71–89
  Article Google Scholar
• Kaltenbrunner A et al (2010) Urban cycles and mobility patterns: exploring and predicting trends in a bicycle-based public transport system. Pervasive Mobile Comput 6(4):455–466
  Article Google Scholar
• Kim K (2018) Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations. J Transp Geogr 66:309–320
  Article Google Scholar
• Lazarus J et al (2019) Bikesharing evolution and expansion: understanding how docked and dockless models complement and compete—a case study of San Francisco. Transportation Research Board of the National Academies, Washington
  Google Scholar
• Lin L, He Z, Peeta S (2018) Predicting station-level hourly demand in a large-scale bike-sharing network: a graph convolutional neural network approach. Transp Res Part C Emerg Technol 97:258–276
  Article Google Scholar
• Lozano Á et al (2018) Multi-agent system for demand prediction and trip visualization in bike sharing systems. Appl Sci 8(1):67–88
  Article Google Scholar
• Mattern D (2017) An integer-programming formulatin to solve lumosity’s “pet detective”, s.l. Sampled Systems LLC, Indianapolis
  Google Scholar
• McKinney W (2010) Data structures for statistical computing in Python. s.l. In: Proceedings of the 9th Python in science, pp 51–56
• Mitchell S, OSullivan M, Dunning I (2011) PuLP: a linear programming toolkit for python. The University of Auckland, Auckland
  Google Scholar
• Mulley C et al (2013) Valuing active travel: including the health benefits of sustainable transport in transport appraisal frameworks. Res Transp Bus Manag 7:27–34
  Article Google Scholar
• Noland R, Smart M, Guo Z (2016) Bikeshare trip generation in New York City. Transp Res Part A Policy Pract 94:164–181
  Article Google Scholar
• Oja P et al (2011) Health benefits of cycling: a systematic review. Scand J Med Sci Sports 21(4):496–509
  Article Google Scholar
• Pal A, Zhang Y (2017) Free-floating bike sharing: solving real-life large-scale static rebalancing problems. Transp Res Part C Emerg Technol 80:92–116
  Article Google Scholar
• Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
  Google Scholar
• Reiss S, Bogenberger K (2016) Optimal bike fleet management by smart relocation methods: combining an operator-based with an user-based relocation strategy. s.l. In: IEEE, pp 2613–2618
• Shaheen S, Guzman S, Zhang H (2010) Bikesharing in Europe, the Americas and Asia: past, present and future. Transp Res Rec 2143(1):159–167
  Article Google Scholar
• Si H et al (2019) Mapping the bike sharing research published from 2010 to 2018: a scientometric review. J Clean Prod 213(10):415–427
  Article Google Scholar
• Singleton P (2018) Walking (and cycling) to well-being: modal and other determinants of subjective well-being during the commute. Travel Behav Soc 16:249–261
  Article Google Scholar
• United States Department of Transportation (2012) Bike Sharing in the United States: State of Practice and Guide to Implementation, s.l. Prepared by: Toole Design Group and the Pedestrian and Bicycle Information Center, for: USDOT Federal Highway Administratio
• Vaitsis P, Basbas S, Nikiforiadis A (2019) How eudaimonic aspect of subjective well-being affect transport mode choice? The case of Thessaloniki, Greece. Soc Sci 8(1):9
  Article Google Scholar
• Vogel P, Mattfeld D (2011) Strategic and operational planning of bike-sharing systems by data mining—a case study. Springer, Heidelberg, pp 127–141
  Google Scholar
• Wang B, Kim I (2018) Short-term prediction for bike-sharing service using machine learning. Transp Res Procedia 34:171–178
  Article Google Scholar
• Woods R, Masthoff J (2017) A comparison of car driving, public transport and cycling experiences in three European cities. Transp Res Part A Policy Pract 103:211–222
  Article Google Scholar
• Zhao J, Wang J, Deng W (2015) Exploring bikesharing travel time and trip chain by gender and day of the week. Transp Res Part C Emerg Technol 58:251–264
  Article Google Scholar

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Funding

The funding was provided by Competitiveness of Enterprises and Small and Medium-sized Enterprises (Grant No. T1EDK-02831).

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Authors and Affiliations

1. Centre for Research and Technology Hellas - Hellenic Institute of Transport, 6th km Charilaou-Thermi Rd., 57001, Thermi, Thessaloniki, Greece
   Georgia Aifadopoulou, Georgios Tsaples, Josep Maria Salanova Grau & Ioannis Mallidis
2. Department of Accounting and Finance, School of Economics, University of Western Macedonia, Kozani, Greece
   Nikolaos Sariannidis

Authors

1. Georgia Aifadopoulou
2. Georgios Tsaples
3. Josep Maria Salanova Grau
4. Ioannis Mallidis
5. Nikolaos Sariannidis

Corresponding author

Correspondence toIoannis Mallidis.

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Aifadopoulou, G., Tsaples, G., Salanova Grau, J.M. et al. Management of resource allocation on vehicle-sharing schemes: the case of Thessaloniki’s bike-sharing system.Oper Res Int J 22, 1001–1016 (2022). https://doi.org/10.1007/s12351-020-00569-3

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• Received: 04 March 2020
• Revised: 27 April 2020
• Accepted: 07 May 2020
• Published: 13 May 2020
• Version of record: 13 May 2020
• Issue date: April 2022
• DOI: https://doi.org/10.1007/s12351-020-00569-3

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