Exploiting Network Structure in Multi-criteria Distributed and Competitive Stationary-resource Searching
Article No.: 26, Pages 1 - 33
Abstract
Transportation between satellite cities or inside the city center has always been a crucial factor in contributing to a better quality of life. This article focuses on multi-criteria distributed and competitive route planning for stationary resources in regions where neither real-time nor historical availability of the targeted resource is accessible. We propose an inference-than-planning approach, with an availability inference for stationary resources in areas with no sensor coverage and a distributed routing where no information is shared among agents. We leverage the inferred availability and network structure in the searching space to suggest a two-stage algorithm with three relaxing policies: adjacent cruising, on-orbital annealing, and orbital transitioning. We take two publicly accessible parking-slot datasets from San Francisco and Melbourne for evaluation. Overall results show that the proposed availability inference model can retain decent performance. Furthermore, our proposed routing algorithm maintains the quality of solutions by achieving the Pareto-optimal between searching experience and resource utilization among baseline and state-of-the-art methods under various circumstances.
References
[1]
Mohamed Abdallah, Mohamad Adghim, Munjed Maraqa, and Elkhalifa Aldahab. 2019. Simulation and optimization of dynamic waste collection routes. Waste Manage. Res. 37, 8 (2019), 793–802.
[2]
Md Golam Rabiul Alam, Mohammad Mehedi Hassan, Md ZIa Uddin, Ahmad Almogren, and Giancarlo Fortino. 2019. Autonomic computation offloading in mobile edge for IoT applications. Fut. Gener. Comput. Syst. 90 (2019), 149–157.
[3]
Dou An, Qingyu Yang, Donghe Li, Wei Yu, Wei Zhao, and Chao-Bo Yan. 2020. Where am i parking: Incentive online parking-space sharing mechanism with privacy protection. IEEE Trans. Autom. Sci. Eng. 19, 1 (2020), 143–162.
[4]
Daniel Ayala, Ouri Wolfson, Bhaskar Dasgupta, Jie Lin, and Bo Xu. 2018. Spatio-temporal matching for urban transportation applications. ACM Trans. Spatial Algor. Syst. 3, 4 (2018), 1–39.
[5]
Daniel Ayala, Ouri Wolfson, Bo Xu, Bhaskar Dasgupta, and Jie Lin. 2011. Parking slot assignment games. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 299–308.
[6]
Daniel Ayala, Ouri Wolfson, Bo Xu, Bhaskar DasGupta, and Jie Lin. 2012. Parking in competitive settings: A gravitational approach. In Proceedings of the IEEE 13th International Conference on Mobile Data Management. IEEE, 27–32.
[7]
Daniel Ayala, Ouri Wolfson, Bo Xu, Bhaskar DasGupta, and Jie Lin. 2012. Pricing of parking for congestion reduction. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems. 43–51.
[8]
Claudio Badii, Paolo Nesi, and Irene Paoli. 2018. Predicting available parking slots on critical and regular services by exploiting a range of open data. IEEE Access 6 (2018), 44059–44071.
[9]
Hannah Bast. 2009. Car or public transport–Two worlds. In Efficient Algorithms. Vol. 5760, Springer, 355–367.
[10]
Hannah Bast, Erik Carlsson, Arno Eigenwillig, Robert Geisberger, Chris Harrelson, Veselin Raychev, and Fabien Viger. 2010. Fast routing in very large public transportation networks using transfer patterns. In European Symposium on Algorithms. Springer, 290–301.
[11]
Holger Bast, Stefan Funke, and Domagoj Matijevic. 2006. Transit ultrafast shortest-path queries with linear-time preprocessing. In 9th DIMACS Implementation Challenge [1].
[12]
Mohamed Baza, Mohamed Mahmoud, Gautam Srivastava, Waleed Alasmary, and Mohamed Younis. 2020. A light blockchain-powered privacy-preserving organization scheme for ride sharing services. In Proceedings of the IEEE 91st Vehicular Technology Conference (VTC’20). IEEE, 1–6.
[13]
Asma Belhadi, Youcef Djenouri, Gautam Srivastava, Djamel Djenouri, Alberto Cano, and Jerry Chun-Wei Lin. 2020. A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories. IEEE Trans. Intell. Transport. Syst. 22, 7 (2020), 4496–4506.
[14]
Fabian Bock, Y. Attanasio, and S. Di Martino. 2018. On-street parking data in San Francisco–sfpark sensor data and simulated crowd-sensing data. Harvard Dataverse 10.
[15]
Felix Borutta, Sebastian Schmoll, and Sabrina Friedl. 2019. Optimizing the spatio-temporal resource search problem with reinforcement learning (gis cup). In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 628–631.
[16]
Valentin Buchhold, Peter Sanders, and Dorothea Wagner. 2019. Real-time traffic assignment using engineered customizable contraction hierarchies. J. Exp. Algor. 24 (2019), 1–28.
[17]
Kevin Buchin, Irina Kostitsyna, Bram Custers, and Martijn Struijs. 2019. A sampling-based strategy for distributing taxis in a road network for occupancy maximization (gis cup). In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 616–619.
[18]
Jingwei Chen, Robert C. Holte, Sandra Zilles, and Nathan R. Sturtevant. 2017. Front-to-end bidirectional heuristic search with near-optimal node expansions. arXiv:1703.03868. Retrieved from https://arxiv.org/abs/1703.03868.
[19]
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794.
[20]
Shuo-Yan Chou, Shih-Wei Lin, and Chien-Chang Li. 2008. Dynamic parking negotiation and guidance using an agent-based platform. Expert Syst. Appl. 35, 3 (2008), 805–817.
[21]
Thomas M. Cover, Joy A. Thomas, et al. 1991. Entropy, relative entropy and mutual information. Elements Inf. Theory 2, 1 (1991), 12–13.
[22]
Bhaskar DasGupta, Joao P. Hespanha, James Riehl, and Eduardo Sontag. 2006. Honey-pot constrained searching with local sensory information. Nonlin. Anal.: Theory Methods Appl. 65, 9 (2006), 1773–1793.
[23]
Daniel Delling, Julian Dibbelt, and Thomas Pajor. 2019. Fast and exact public transit routing with restricted pareto sets. In Proceedings of the 21st Workshop on Algorithm Engineering and Experiments (ALENEX’19). SIAM, 54–65.
[24]
Daniel Delling, Thomas Pajor, and Renato F. Werneck. 2015. Round-based public transit routing. Transport. Sci. 49, 3 (2015), 591–604.
[25]
Daniel Delling, Peter Sanders, Dominik Schultes, and Dorothea Wagner. 2009. Engineering route planning algorithms. In Algorithmics of Large and Complex Networks. Vol. 5515, Springer, 117–139.
[26]
Mauro Dell’Orco and Dušan Teodorović. 2005. Multi agent systems approach to parking facilities management. In Applied Research in Uncertainty Modeling and Analysis. Vol. 20, Springer, 321–339.
[27]
Alexandros Efentakis, Dieter Pfoser, and Agnès Voisard. 2011. Efficient data management in support of shortest-path computation. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science. 28–33.
[28]
Matthias Ehrgott and Kathrin Klamroth. 1997. Connectedness of efficient solutions in multiple criteria combinatorial optimization. Eur. J. Operat. Res. 97, 1 (1997), 159–166.
[29]
Jie-Yu Fang, Fandel Lin, and Hsun-Ping Hsieh. 2020. A multi-criteria system for recommending taxi routes with an advance reservation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 308–322.
[30]
Luca M. Gambardella and Marco Dorigo. 1995. Ant-Q: A reinforcement learning approach to the traveling salesman problem. In Machine Learning Proceedings 1995. Elsevier, 252–260.
[31]
Michael R. Garey and David S. Johnson. 1979. Computers and intractability. A Guide to the Theory of NP-completeness (1979).
[32]
Michael R. Garey, David S. Johnson, and Larry Stockmeyer. 1974. Some simplified NP-complete problems. In Proceedings of the 6th Annual ACM Symposium on Theory of Computing. 47–63.
[33]
Cyril Gavoille, David Peleg, Stéphane Pérennes, and Ran Raz. 2004. Distance labeling in graphs. J. Algor. 53, 1 (2004), 85–112.
[34]
Liya Guo, Shan Huang, Jun Zhuang, and Adel W. Sadek. 2013. Modeling parking behavior under uncertainty: A static game theoretic versus a sequential neo-additive capacity modeling approach. Netw. Spatial Econ. 13, 3 (2013), 327–350.
[35]
Peter E. Hart, Nils J. Nilsson, and Bertram Raphael. 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybernet. 4, 2 (1968), 100–107.
[36]
Xinxin Huang, Yingguo Gao, and Xiaohui Duan. 2020. An autonomous parking space planning system based on pattern searching algorithm. In Proceedings of the IEEE 8th International Conference on Smart City and Informatization (iSCI’20). IEEE, 6–12.
[37]
Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, and Yusuke Nojima. 2018. How to specify a reference point in hypervolume calculation for fair performance comparison. Evol. Comput. 26, 3 (2018), 411–440.
[38]
Yanjie Ji, Dounan Tang, Phil Blythe, Weihong Guo, and Wei Wang. 2015. Short-term forecasting of available parking space using wavelet neural network model. IET Intell. Transport Syst. 9, 2 (2015), 202–209.
[39]
Cheng Jin, Lei Wang, Lei Shu, Yuyao Feng, and Xueqing Xu. 2012. A fairness-aware smart parking scheme aided by parking lots. In Proceedings of the IEEE International Conference on Communications (ICC’12). IEEE, 2119–2123.
[40]
Gregor Jossé, Matthias Schubert, and Hans-Peter Kriegel. 2013. Probabilistic parking queries using aging functions. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 452–455.
[41]
Merkourios Karaliopoulos, Konstantinos Katsikopoulos, and Lambros Lambrinos. 2014. Bounded rationality can increase parking search efficiency. In Proceedings of the 15th ACM International Symposium on Mobile ad hoc Networking and Computing. 195–204.
[42]
Joon-Seok Kim, Dieter Pfoser, and Andreas Züfle. 2019. Distance-aware competitive spatiotemporal searching using spatiotemporal resource matrix factorization (gis cup). In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 624–627.
[43]
Oanh Tran Thi Kim, Nguyen H. Tran, Chuan Pham, Tuan LeAnh, My T. Thai, and Choong Seon Hong. 2019. Parking assignment: Minimizing parking expenses and balancing parking demand among multiple parking lots. IEEE Trans. Autom. Sci. Eng. 17, 3 (2019), 1320–1331.
[44]
Yuichi Kobayashi, Noboru Kiyama, Hirokazu Aoshima, and Masamori Kashiyama. 2011. A route search method for electric vehicles in consideration of range and locations of charging stations. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV’11). IEEE, 920–925.
[45]
Amir O. Kotb, Yao-Chun Shen, Xu Zhu, and Yi Huang. 2016. iParker–A new smart car-parking system based on dynamic resource allocation and pricing. IEEE Trans. Intell. Transport. Syst. 17, 9 (2016), 2637–2647.
[46]
Nadav Levy and Itzhak Benenson. 2015. GIS-based method for assessing city parking patterns. J. Transport Geogr. 46 (2015), 220–231.
[47]
Fan Li and Yu Wang. 2007. Routing in vehicular ad hoc networks: A survey. IEEE Vehic. Technol. Mag. 2, 2 (2007), 12–22.
[48]
Peng Li, Demin Li, and Xiaolu Zhang. 2014. CGPS: A collaborative game in parking-lot search. In Proceedings of International Conference on Soft Computing Techniques and Engineering Application. Springer, 105–113.
[49]
Y. H. Li, H. J. Mao, and Y. M. Qin. 2019. Vehicle routing problem with multiple time windows and batch splitting based on inferior first bidirectional search algorithm. In Journal of Physics: Conference Series, Vol. 1314. IOP Publishing, 012116.
[50]
Andy Liaw, Matthew Wiener, et al. 2002. Classification and regression by randomForest. R News 2, 3 (2002), 18–22.
[51]
Fandel Lin and Hsun-Ping Hsieh. 2021. A joint passenger flow inference and path recommender system for deploying new routes and stations of mass transit transportation. ACM Trans. Knowl. Discov. Data 16, 1 (2021), 1–36.
[52]
Fandel Lin and Hsun-Ping Hsieh. 2022. Traveling transporter problem: Arranging a new circular route in a public transportation system based on heterogeneous non-monotonic urban data. ACM Trans. Intell. Syst. Technol. 13, 3 (2022), 1–25.
[53]
Fandel Lin, Hsun-Ping Hsieh, and Jie-Yu Fang. 2020. A route-affecting region based approach for feature extraction in transportation route planning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 275–290.
[54]
Jacek Malczewski. 2006. GIS-based multicriteria decision analysis: A survey of the literature. Int. J. Geogr. Inf. Sci. 20, 7 (2006), 703–726.
[55]
Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, and Evgeny Burnaev. 2021. Reinforcement learning for combinatorial optimization: A survey. Comput. Operat. Res. 134 (2021), 105400.
[56]
Naourez Mejri, Mouna Ayari, Rami Langar, and Leila Saidane. 2016. Reservation-based multi-objective smart parking approach for smart cities. In Proceeedings of the IEEE International Smart Cities Conference (ISC2’16). IEEE, 1–6.
[57]
Lingfeng Ming, Qi Hu, Ming Dong, and Bolong Zheng. 2020. An effective fleet management strategy for collaborative spatio-temporal searching: GIS cup. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems. 651–654.
[58]
Giuseppe Musolino, Antonio Polimeni, Corrado Rindone, and Antonino Vitetta. 2013. Travel time forecasting and dynamic routes design for emergency vehicles. Soc. Behav. Sci. 87 (2013), 193–202.
[59]
John F. Nash Jr. 1950. Equilibrium points in n-person games. Proc. Natl. Acad. Sci. 36, 1 (1950), 48–49.
[60]
Yiwen Nie, Wei Yang, Zhi Chen, Nanxue Lu, Liusheng Huang, and Huan Huang. 2021. Public curb parking demand estimation with poi distribution. IEEE Trans. Intell. Transport. Syst. 23, 5 (2021), 4614–4624.
[61]
City of Melbourne. 2020. On-street Car Parking Sensor Data–2019. Retrieved October 13, 2022 from https://data.melbourne.vic.gov.au/Transport/On-street-Car-Parking-Sensor-Data-2019/7pgd-bdf2.
[62]
Karl Pearson. 1905. The problem of the random walk. Nature 72, 1865 (1905), 294–294.
[63]
Michael Rice and Vassilis Tsotras. 2012. Bidirectional A* search with additive approximation bounds. In International Symposium on Combinatorial Search, Vol. 3.
[64]
Wayne A. Sarasua, Prashant Malisetty, and Mashrur Chowdhury. 2011. Using GIS-based, hitchcock algorithm to optimize parking allocations for special events. Appl. GIS 7, 2 (2011), 1–13.
[65]
Sebastian Schmoll and Matthias Schubert. 2021. Semi-markov reinforcement learning for stochastic resource collection. In Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. 3349–3355.
[66]
Michael Schneider, Andreas Stenger, and Dominik Goeke. 2014. The electric vehicle-routing problem with time windows and recharging stations. Transport. Sci. 48, 4 (2014), 500–520.
[67]
Eshed Shaham, Ariel Felner, Nathan R. Sturtevant, and Jeffrey S. Rosenschein. 2018. Minimizing node expansions in bidirectional search with consistent heuristics. In Proceedings of the 11th Annual Symposium on Combinatorial Search.
[68]
Wei Shao, Flora D. Salim, Tao Gu, Ngoc-Thanh Dinh, and Jeffrey Chan. 2017. Traveling officer problem: Managing car parking violations efficiently using sensor data. IEEE IoT J. 5, 2 (2017), 802–810.
[69]
Dani Simons. 2012. SFpark: San Francisco knows how to park it. Sust. Transport23 (2012), 26–27.
[70]
Qing Song, Meng Li, and Xiaolei Li. 2018. Accurate and fast path computation on large urban road networks: A general approach. PLoS One 13, 2 (2018), e0192274.
[71]
Linda Steg and Robert Gifford. 2005. Sustainable transportation and quality of life. J. Transport Geogr. 13, 1 (2005), 59–69.
[72]
Dušan Teodorović and Panta Lučić. 2006. Intelligent parking systems. Eur. J. Oper. Res. 175, 3 (2006), 1666–1681.
[73]
Barrett W. Thomas, Tobia Calogiuri, and Mike Hewitt. 2019. An exact bidirectional A* approach for solving resource-constrained shortest path problems. Networks 73, 2 (2019), 187–205.
[74]
Vasilis Verroios, Vasilis Efstathiou, and Alex Delis. 2011. Reaching available public parking spaces in urban environments using ad hoc networking. In Proceedings of the IEEE 12th International Conference on Mobile Data Management, Vol. 1. IEEE, 141–151.
[75]
Dorothea Wagner, Thomas Willhalm, and Christos Zaroliagis. 2005. Geometric containers for efficient shortest-path computation. J. Exp. Algor. 10, Article No. 1.3, (2005), 1–30.
[76]
Mingkang Wu, Haobin Jiang, and Chin-An Tan. 2021. Automated parking space allocation during transition with both human-operated and autonomous vehicles. Appl. Sci. 11, 2 (2021), 855.
[77]
Li Xiangdong, Cen Yuefeng, C. E. N. Gang, and Xu Zengwei. 2019. Prediction of short-term available parking space using LSTM model. In Proceedings of the 14th International Conference on Computer Science & Education (ICCSE’19). IEEE, 631–635.
[78]
Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. 2014. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans. Syst. Man Cybernet.: Syst. 45, 1 (2014), 129–142.
[79]
Shuguan Yang, Wei Ma, Xidong Pi, and Sean Qian. 2019. A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources. Transport. Res. Part C: Emerg. Technol. 107 (2019), 248–265.
[80]
Ruonan Zhang, Santosh N. Kabadi, and Abraham P. Punnen. 2011. The minimum spanning tree problem with conflict constraints and its variations. Discr. Optimiz. 8, 2 (2011), 191–205.
[81]
Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Tong Xu, and Hui Xiong. 2020. Semi-supervised city-wide parking availability prediction via hierarchical recurrent graph neural network. IEEE Trans. Knowl. Data Eng. 34, 8 (2020), 3984–3996.
[82]
Jiandong Zhao, Yujie Guo, and Xiaohong Duan. 2017. Dynamic path planning of emergency vehicles based on travel time prediction. J. Adv. Transport. 2017, Article No. 9184891, (2017), 1–14.
[83]
Bolong Zheng, Qi Hu, Lingfeng Ming, Jilin Hu, Lu Chen, Kai Zheng, and Christian S. Jensen. 2021. SOUP: Spatial-temporal demand forecasting and competitive supply. IEEE Trans. Knowl. Data Eng. 35, 2 (2021), 2034–2047.
[84]
Brian D. Ziebart, Anind K. Dey, and J. Andrew Bagnell. 2008. Fast planning for dynamic preferences. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS’08). 412–419.
[85]
Onno Zoeter, Christopher Dance, Stéphane Clinchant, and Jean-Marc Andreoli. 2014. New algorithms for parking demand management and a city-scale deployment. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1819–1828.
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- Exploiting Network Structure in Multi-criteria Distributed and Competitive Stationary-resource Searching
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Publication History
Published: 20 November 2023
Accepted: 19 October 2022
Revised: 17 October 2022
Received: 22 January 2022
Published in TSAS Volume 9, Issue 4
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- National Science and Technology Council of Taiwan
- Ministry of Education (MOE) of Taiwan
- Focused Fields at Top Foreign University
- Information Sciences Institute and Viterbi School of Engineering from University of Southern California
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