Abstract
Advanced information and communication technology promote smart city development, especially in urban logistics. Vehicular traffic routing problem is the key factor to influence the logistics chauffeur’s service quality. Different from traditional vehicular ad hoc networks, this study proposes a novel approach using data mining, skyline domination, and multi-criteria decision analysis to develop a context-aware point-of-interest network of vehicular traffic routing for urban logistics. The density-based clustering discovers the logistics destination, referred to as the “points-of-interest (POI),” nearby the logistics chauffeur. The candidate POI filtered by the skyline domination. The multi-criteria decision analysis produces a ranking of candidate POI based on the status of traffic criteria evaluation. We use open data from Google map and Foursquare to construct a context-aware POI network. An experimental system implementation to demonstrate the proposed approach effectiveness. The contribution is to optimize the adaptive vehicular traffic routing solution for the urban logistics in a smart city.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Disc. 5(1–2), 115–153 (2001). https://doi.org/10.1023/A:1009804230409
Mulvenna, M.D., Anand, S.S., Büchner, A.G.: Personalization on the net using web mining: introduction. Commun. ACM 43(8), 122–125 (2000). https://doi.org/10.1145/345124.345165
Abbas, A., Zhang, l., Khan S.U.: A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7), 667–690 (2015). https://doi.org/10.1007/s00607-015-0448-7
Borris, J., Moreno, A., Valls, A.: Intelligent tourism recommender systems: a survey. Expert Syst. Appl. 41(16), 7370–7389 (2014). https://doi.org/10.1016/j.eswa.2014.06.007
Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015). https://doi.org/10.1007/s10707-014-0220-8
Liu, B., Xiong, H., Papadimitriou, S., Fu, Y.J., Yao, Z.J.: A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans. Knowl. Data Eng. 27(5), 1167–1179 (2014). https://doi.org/10.1109/TKDE.2014.2362525
Shenglin, Z., Irwin, K., Lyu, M.R.: A survey of point-of-interest recommendation in location-based social network (2016). arXiv:1607.00647v1 [cs.IR]
Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, pp. 195–203 (2012). https://doi.org/10.1145/2339530.2339562
Zhang, Z., Che, O., Cheang, B., Lim, A., Qin, H.: A memetic algorithm for the multiperiod vehicle routing problem with profit. Eur. J. Oper. Res. 229(3), 573–584 (2013). https://doi.org/10.1016/j.ejor.2012.11.059
Liu, B., Xiong, H.: Point-of-Interest recommendation in location based social networks with topic and location awareness. In: 2013 Siam International Conference on Data Mining, pp. 396–404 (2013). https://doi.org/10.1137/1.9781611972832.44
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. In: 1999 ACM SIGMOD International Conference on Management of Data, vol. 28, no. 2, pp. 49–60 (1999). https://doi.org/10.1145/304181.304187
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering in large spatial database with noise. In: Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 226–231 (1996)
Nassereddine, M., Eskandari, H.: An integrated MCDM approach to evaluate public transportation systems in Tehran. Transp. Res. Part A Policy Pract. 106, 427–439 (2017). https://doi.org/10.1016/j.tra.2017.10.013
Acknowledgements
This research was supported in part by the Ministry of Science and Technology, R.O.C. with a MOST grant 107-2221-E-025-005.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ke, CK., Lai, SC., Huang, LT. (2019). Developing a Context-Aware POI Network of Adaptive Vehicular Traffic Routing for Urban Logistics. In: Chen, JL., Pang, AC., Deng, DJ., Lin, CC. (eds) Wireless Internet. WICON 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-030-06158-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-06158-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06157-9
Online ISBN: 978-3-030-06158-6
eBook Packages: Computer ScienceComputer Science (R0)