ABSTRACT
Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization.
In this paper, we propose a data-driven approach to develop bike lane construction plans based on large-scale real world bike trajectory data. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of the number of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. Finally, we deploy our system on Microsoft Azure, providing extensive experiments and case studies to demonstrate the effectiveness of our approach.
Supplemental Material
- Jie Bao, Chi-Yin Chow, Mohamed F Mokbel, and Wei-Shinn Ku. 2010. Efficient evaluation of k-range nearest neighbor queries in road networks MDM. IEEE, 115--124.Google Scholar
- Jie Bao, Ruiyuan Li, Xiuwen Yi, and Yu Zheng. 2016. Managing massive trajectories on the cloud. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 41. Google ScholarDigital Library
- Sanjay Chawla, Yu Zheng, and Jiafeng Hu 2012. Inferring the root cause in road traffic anomalies Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 141--150.Google Scholar
- Zaiben Chen, Heng Tao Shen, and Xiaofang Zhou. 2011. Discovering popular routes from trajectories. In ICDE. IEEE, 900--911. Google ScholarDigital Library
- Paul DeMaio. 2009. Bike-sharing: History, impacts, models of provision, and future. Journal of Public Transportation Vol. 12, 4 (2009), 3. Google ScholarCross Ref
- Jennifer Dill and Kim Voros 2007. Factors affecting bicycling demand: initial survey findings from the Portland, Oregon, region. Transportation Research Record: Journal of the Transportation Research Board 2031 (2007), 9--17. Google ScholarCross Ref
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, and others 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD, Vol. Vol. 96. 226--231.Google ScholarDigital Library
- Michael R Evans, Dev Oliver, Shashi Shekhar, and Francis Harvey 2012. Summarizing trajectories into k-primary corridors: a summary of results SIGSPATIAL GIS. ACM, 454--457.Google Scholar
- Michael R Evans, Dev Oliver, Shashi Shekhar, and Francis Harvey 2013. Fast and exact network trajectory similarity computation: a case-study on bicycle corridor planning. In UrbComp. ACM, 9.Google Scholar
- Geoff French, Jim Steer, and Nick Richardson 2014. Handbook for cycle-friendly design. https://goo.gl/m3DwoY. (2014).Google Scholar
- Binh Han, Ling Liu, and Edward Omiecinski 2012. Neat: Road network aware trajectory clustering. In ICDCS. IEEE, 142--151. Google ScholarDigital Library
- John A Hartigan and Manchek A Wong 1979. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 28, 1 (1979), 100--108.Google ScholarCross Ref
- Abdeltawab M Hendawi, Jie Bao, Mohamed F Mokbel, and Mohamed Ali 2015. Predictive tree: An efficient index for predictive queries on road networks Data Engineering (ICDE), 2015 IEEE 31st International Conference on. IEEE, 1215--1226.Google Scholar
- Liang Hong, Yu Zheng, Duncan Yung, Jingbo Shang, and Lei Zou 2015. Detecting urban black holes based on human mobility data Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 35.Google Scholar
- Tetsuro Hyodo, Norikazu Suzuki, and Katsumi Takahashi. 2000. Modeling of bicycle route and destination choice behavior for bicycle road network plan. Transportation Research Record: Journal of the Transportation Research Board 1705 (2000), 70--76. Google ScholarCross Ref
- Zhe Jiang, Michael Evans, Dev Oliver, and Shashi Shekhar. 2016. Identifying K Primary Corridors from urban bicycle GPS trajectories on a road network. Information Systems Vol. 57 (2016), 142--159. Google ScholarDigital Library
- Ahmed Kharrat, Iulian Sandu Popa, Karine Zeitouni, and Sami Faiz 2008. Clustering algorithm for network constraint trajectories. Headway in Spatial Data Handling. Springer, 631--647. Google ScholarCross Ref
- Xiaolei Li, Jiawei Han, Jae-Gil Lee, and Hector Gonzalez. 2007. Traffic density-based discovery of hot routes in road networks International Symposium on Spatial and Temporal Databases. Springer, 441--459.Google Scholar
- Yuhong Li, Jie Bao, Yanhua Li, Yingcai Wu, Zhiguo Gong, and Yu Zheng. 2016. Mining the most influential k-location set from massive trajectories SIGSPATIAL GIS. ACM, 51.Google Scholar
- Yexin Li, Yu Zheng, Huichu Zhang, and Lei Chen. 2015. Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 33. Google ScholarDigital Library
- Dongyu Liu, Di Weng, Yuhong Li, Jie Bao, Yu Zheng, Huamin Qu, and Yingcai Wu. 2017. SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations. IEEE Transactions on Visualization and Computer Graphics, Vol. 23, 1 (2017), 1--10. Google ScholarDigital Library
- Junming Liu, Leilei Sun, Weiwei Chen, and Hui Xiong. 2016. Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1005--1014.Google ScholarDigital Library
- Wuman Luo, Haoyu Tan, Lei Chen, and Lionel M Ni. 2013. Finding time period-based most frequent path in big trajectory data SIGMOD. ACM, 713--724.Google Scholar
- Dev Oliver, Shashi Shekhar, Xun Zhou, Emre Eftelioglu, Michael R Evans, Qiaodi Zhuang, James M Kang, Renee Laubscher, and Christopher Farah 2014. Significant route discovery: A summary of results. International Conference on Geographic Information Science. Springer, 284--300. Google ScholarCross Ref
- Dimitris Papadias, Jun Zhang, Nikos Mamoulis, and Yufei Tao. 2003. Query processing in spatial network databases. In VLDB. VLDB Endowment, 802--813. Google ScholarCross Ref
- Kathryn M Parker, Janet Rice, Jeanette Gustat, Jennifer Ruley, Aubrey Spriggs, and Carolyn Johnson. 2013. Effect of bike lane infrastructure improvements on ridership in one New Orleans neighborhood. Annals of behavioral medicine Vol. 45, 1 (2013), 101--107.Google ScholarCross Ref
- J Pucker 2001. Cycling safety on bikeways vs. roads. Transportation Quarterly Vol. 55, 4 (2001), 9--11.Google Scholar
- David Rojas-Rueda, A De Nazelle, O Teixidó, and MJ Nieuwenhuijsen 2012. Replacing car trips by increasing bike and public transport in the greater Barcelona metropolitan area: a health impact assessment study. Environment international Vol. 49 (2012), 100--109. Google ScholarCross Ref
- Greg Rybarczyk and Changshan Wu 2010. Bicycle facility planning using GIS and multi-criteria decision analysis. Applied Geography, Vol. 30, 2 (2010), 282--293. Google ScholarCross Ref
- Joe H Ward Jr. 1963. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, Vol. 58, 301 (1963), 236--244. Google ScholarCross Ref
- Guojun Wu, Yichen Ding, Yanhua Li, Jie Bao, Yu Zheng, and Jun Luo 2017. Mining Spatio-Temporal Reachable Regions over Massive Trajectory Data ICDE. 1--12.Google Scholar
- Jing Yuan, Yu Zheng, and Xing Xie 2012. Discovering regions of different functions in a city using human mobility and POIs Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 186--194.Google Scholar
- Jing Yuan, Yu Zheng, Chengyang Zhang, Xing Xie, and Guang-Zhong Sun 2010. An interactive-voting based map matching algorithm MDM. IEEE Computer Society, 43--52.Google Scholar
- Yu Zheng 2015. Trajectory data mining: an overview. TIST, Vol. 6, 3 (2015), 29. Google ScholarDigital Library
- Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 5, 3 (2014), 38. Google ScholarDigital Library
- Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. 2011. Urban computing with taxicabs. In Proceedings of the 13th international conference on Ubiquitous computing. ACM, 89--98. Google ScholarDigital Library
- Xingxin Zhu. 2016. Bike sharing schemes promote green transport. http://www.telegraph.co.uk/news/world/china-watch/technology/sharing-bikes-to-promote-green-transport/. (2016).Google Scholar
Index Terms
Planning Bike Lanes based on Sharing-Bikes' Trajectories
Recommendations
Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories
KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIllegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. Traditional approaches to detect illegal vehicle parking events rely highly on active ...
Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study
Problem definition: We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which are made available by smart city infrastructure, such as bike-sharing systems. The key decision is where to build bike lanes in the ...
Understanding bike trip patterns leveraging bike sharing system open data
Bike sharing systems are booming globally as a green and flexible transportationmode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in ...
Comments