Abstract
Modern cars are pervasively equipped with multiple sensors meant to improve in-vehicle quality of life, efficiency and safety. The aggregation on a remote back-end of the information collected from these sensors may give rise to one of the biggest and most pervasive sensor networks around the world, making possible to extract new knowledge, or contextual awareness, in a detail never experienced before. Anyhow, an open issue with probe vehicles is the achievable spatio-temporal sensing coverage, since vehicles are not uniformly distributed over the road network, because drivers mostly select a shortest time path to destination. In this paper we present an evolution of the standard A\(\varvec{^{*}}\) algorithm, where the route is chosen in a probabilistic way, with the goal to maximize the spatio-temporal coverage of probe vehicles. The proposed algorithm has been empirically evaluated by means of a public dataset of more than 320.000 real taxi trajectories, showing promising performances in terms of achievable sensing coverage.
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References
Di Martino, S.: Discovering information from spatial big data for ITS. Intelligent Transport Systems (ITS): Past, Present and Future Directions, pp. 109–130 (2017)
Robert Bosch GmbH: Bosch Community-based parking (2016). http://www.bosch-mobility-solutions.com/en/connected-mobility/community-based-parking/. Accessed 27 June 2016
Petty, K., Mahoney III, W.: Enhancing road weather information through vehicle infrastructure integration. Transp. Res. Rec. J. Transp. Res. Board 2015, 132–140 (2007)
Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49(11), 32–39 (2011)
Wang, X., Zheng, X., Zhang, Q., Wang, T., Shen, D.: Crowdsourcing in ITS: the state of the work and the networking. IEEE Trans. Intell. Transp. Syst. 17(6), 1596–1605 (2016)
Di Martino, S., Rossi, S.: An architecture for a mobility recommender system in smart cities. Procedia Comput. Sci. 98, 425–430 (2016)
Kwoczek, S., Di Martino, S., Nejdl, W.: Stuck around the stadium? An approach to identify road segments affected by planned special events. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp. 1255–1260 (2015)
Kwoczek, S., Di Martino, S., Nejdl, W.: Predicting and visualizing traffic congestion in the presence of planned special events. J. Vis. Lang. Comput. 25(6), 973–980 (2014)
Bock, F., Martino, S.D., Sester, M.: What are the potentialities of crowdsourcing for dynamic maps of on-street parking spaces? In: Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science. ACM, pp. 19–24 (2016)
Bock, F., Attanasio, Y., Di Martino, S.: Spatio-temporal road coverage of probe vehicles: a case study on crowd-sensing of parking availability with taxis. In: Bregt, A., Sarjakoski, T., van Lammeren, R., Rip, F. (eds.) GIScience 2017. LNGC, pp. 165–184. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56759-4_10
Mathur, S., Jin, T., Kasturirangan, N., Chandrasekaran, J., Xue, W., Gruteser, M., Trappe, W.: ParkNet: drive-by sensing of road-side parking statistics. In: Proceedings of 8th International Conference on Mobile Systems, Applications, and Services, pp. 123–136. ACM, New York (2010)
Bock, F., Di Martino, S.: How many probe vehicles do we need to collect on-street parking information? In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 538–543. IEEE (2017)
Masutani, O.: A sensing coverage analysis of a route control method for vehicular crowd sensing. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 396–401. IEEE (2015)
Boriboonsomsin, K., Barth, M.J., Zhu, W., Vu, A.: Eco-routing navigation system based on multisource historical and real-time traffic information. IEEE Trans. Intell. Transp. Syst. 13(4), 1694–1704 (2012)
Di Martino, S., Giorio, C., Galiero, R.: A rich cloud application to improve sustainable mobility. In: Tanaka, K., Fröhlich, P., Kim, K.-S. (eds.) W2GIS 2011. LNCS, vol. 6574, pp. 109–123. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19173-2_10
Lugar, G.F., Stubblefield, W.A.: Artificial Intelligence Structures and Strategies for Complex Problem Solving. Addison Wesley Longman, Harlow (1998)
Pearl, J., Kim, J.H.: Studies in semi-admissible heuristics. IEEE Trans. Pattern Anal. Mach. Intell. 4, 392–399 (1982)
Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: CRAWDAD dataset EPFL/mobility, 24 February 2009. http://crawdad.org/epfl/mobility/20090224. Accessed Feb 2009
Chen, C., Zhang, D., Ma, X., Guo, B., Wang, L., Wang, Y., Sha, E.: CrowdDeliver: planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Trans. Intell. Transp. Syst. 18(6), 1478–1496 (2017)
Axer, S., Pascucci, F., Friedrich, B.: Estimation of traffic signal timing data and total delay for urban intersections based on low-frequency floating car data. In: Proceedings of the 6th Mobility, TUM 2015 (2015)
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: ACM SIGSPATIAL GIS 2009. ACM, November 2009. https://www.microsoft.com/en-us/research/publication/map-matching-for-low-sampling-rate-gps-trajectories/
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Asprone, D., Di Martino, S., Festa, P. (2018). Improving Sensing Coverage of Probe Vehicles with Probabilistic Routing. In: R. Luaces, M., Karimipour, F. (eds) Web and Wireless Geographical Information Systems. W2GIS 2018. Lecture Notes in Computer Science(), vol 10819. Springer, Cham. https://doi.org/10.1007/978-3-319-90053-7_1
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DOI: https://doi.org/10.1007/978-3-319-90053-7_1
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