Abstract
To offer personalized, interactive, and traffic-aware trip planning to travellers, in this chapter, we propose a novel framework called TripPlanner leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints. First, based on the information in crowdsourced LBSN and taxi GPS traces, we construct a dynamic point-of-interest network model. Then, a two-phase approach is proposed for personalized trip planning. In the route search phase, candidate routes are generated by interactively working with users. In the route augmentation phase, heuristic algorithms are applied to add user’s preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. We validate the efficiency and effectiveness of TripPlanner by extensive evaluations using large-scale real-world data sets.
Part of this chapter is based on a previous work: C. Chen, D. Zhang, B. Guo, X. Ma, G. Pan and Z. Wu, “TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints,” in IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 3, pp. 1259-1273, June 2015, doi: https://doi.org/10.1109/TITS.2014.2357835.
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Chen, C., Zhang, D., Wang, Y., Huang, H. (2021). TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Trajectory Data. In: Enabling Smart Urban Services with GPS Trajectory Data. Springer, Singapore. https://doi.org/10.1007/978-981-16-0178-1_10
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DOI: https://doi.org/10.1007/978-981-16-0178-1_10
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