TY - JOUR
T1 - TripPlanner
T2 - Personalized trip planning leveraging heterogeneous crowdsourced digital footprints
AU - Chen, Chao
AU - Zhang, Daqing
AU - Guo, Bin
AU - Ma, Xiaojuan
AU - Pan, Gang
AU - Wu, Zhaohui
N1 - Publisher Copyright:
© 2000-2011 IEEE.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms 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. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.
AB - Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms 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. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.
KW - Crowdsourcing
KW - digital footprints
KW - personalization
KW - traffic aware
KW - trip planning
UR - http://www.scopus.com/inward/record.url?scp=84930943035&partnerID=8YFLogxK
U2 - 10.1109/TITS.2014.2357835
DO - 10.1109/TITS.2014.2357835
M3 - 文章
AN - SCOPUS:84930943035
SN - 1524-9050
VL - 16
SP - 1259
EP - 1273
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 3
M1 - 6951432
ER -