Large-Scale Trip Planning for Bike-Sharing Systems

In Bike-Sharing System (BSS), great efforts have been devoted to performing resources prediction, redistribution and trip planning to alleviate the unbalance of resources and inconvenience of bike utilization caused by the explosion of users. However, there is few work in trip planning noticing that the complete trip composes of three segments: from user's start point to a start station, from the start station to a target station and from the target station to user's terminal point. To study the case, this paper addresses a static trip planning problem in BSS by considering system-wide conflicts so as to achieve higher service quality of the system. The problem is formulated as the well-known weighted k-set packing problem. We design two algorithms, a Greedy Trip Planning algorithm (GTP) and a Humble Trip Planning algorithm (HTP), for the problem. For comparison, we design a Random Trip Planning algorithm (RTP) as a benchmark. Extensive simulation results show that GTP and HTP outperform RTP and reveal the impact of different factors on our algorithms.

[1]  Rafael E. Banchs,et al.  Article in Press Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Urban Cycles and Mobility Patterns: Exploring and Predicting Trends in a Bicycle-based Public Transport System , 2022 .

[2]  Dirk C. Mattfeld,et al.  Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns , 2011 .

[3]  Jiming Chen,et al.  Mobility Modeling and Prediction in Bike-Sharing Systems , 2016, MobiSys.

[4]  Adi Botea,et al.  Uncertainty in urban mobility: Predicting waiting times for shared bicycles and parking lots , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[5]  Feng Xia,et al.  LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data , 2017, World Wide Web.

[6]  I-Lin Wang,et al.  Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems , 2013, Oper. Res..

[7]  J. Gutiérrez,et al.  Optimizing the location of stations in bike-sharing programs: A GIS approach , 2012 .

[8]  Andreas Krause,et al.  Incentivizing Users for Balancing Bike Sharing Systems , 2015, AAAI.

[9]  Franca Delmastro,et al.  People-centric computing and communications in smart cities , 2016, IEEE Communications Magazine.

[10]  Piotr Berman,et al.  A d/2 Approximation for Maximum Weight Independent Set in d-Claw Free Graphs , 2000, Nord. J. Comput..

[11]  Min Yang,et al.  A Novel Travel Adviser Based on Improved Back-Propagation Neural Network , 2016, 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS).

[12]  Barun Chandra,et al.  Greedy local improvement and weighted set packing approximation , 2001, SODA '99.

[13]  Hairong Qi,et al.  Personalized Privacy-Preserving Task Allocation for Mobile Crowdsensing , 2019, IEEE Transactions on Mobile Computing.

[14]  Patrick Jaillet,et al.  Dynamic Repositioning to Reduce Lost Demand in Bike Sharing Systems , 2017, J. Artif. Intell. Res..

[15]  Philip S. Yu,et al.  Bicycle-Sharing System Analysis and Trip Prediction , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).

[16]  Naveen Eluru,et al.  Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system , 2015 .

[17]  P. Midgley.  The role of smart bike-sharing systems in urban mobility , 2009 .

[18]  Robert C. Hampshire,et al.  Inventory rebalancing and vehicle routing in bike sharing systems , 2017, Eur. J. Oper. Res..

[19]  Gang Pan,et al.  Bike sharing station placement leveraging heterogeneous urban open data , 2015, UbiComp.

[20]  Francesco Calabrese,et al.  Cityride: A Predictive Bike Sharing Journey Advisor , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[21]  David B. Shmoys,et al.  Data Analysis and Optimization for (Citi)Bike Sharing , 2015, AAAI.

[22]  Zhi Li,et al.  Distributed Trip Selection Game for Public Bike System with Crowdsourcing , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[23]  P. DeMaio.  Bike-sharing: History, Impacts, Models of Provision, and Future , 2009 .

[24]  Carlo G. Prato,et al.  Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior , 2015 .

[25]  Agostino Nuzzolo,et al.  Individual Utility-based Path Suggestions in Transit Trip Planners , 2016 .

[26]  Hairong Qi,et al.  Privacy-Preserving Crowd-Sourced Statistical Data Publishing with An Untrusted Server , 2019, IEEE Transactions on Mobile Computing.

[27]  Tal Raviv,et al.  Static repositioning in a bike-sharing system: models and solution approaches , 2013, EURO J. Transp. Logist..

[28]  Manfred Morari,et al.  Dynamic Vehicle Redistribution and Online Price Incentives in Shared Mobility Systems , 2013, IEEE Transactions on Intelligent Transportation Systems.

[29]  Ta-Hui Yang,et al.  Strategic design of public bicycle sharing systems with service level constraints , 2011 .

[30]  Romain Giot,et al.  Predicting bikeshare system usage up to one day ahead , 2014, 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS).

[31]  R. Kram,et al.  Effects of obesity and sex on the energetic cost and preferred speed of walking. , 2006, Journal of applied physiology.

[32]  Sajal K. Das,et al.  The Internet of People (IoP): A new wave in pervasive mobile computing , 2017, Pervasive Mob. Comput..

[33]  Yu Zheng,et al.  Traffic prediction in a bike-sharing system , 2015, SIGSPATIAL/GIS.