TY - JOUR
T1 - TaxiRec : Recommending Road Clusters to Taxi Drivers Using Ranking-Based Extreme Learning Machines
AU - WANG, Ran
AU - CHOW, Chi-Yin
AU - LYU, Yan
AU - LEE, Victor C. S.
AU - KWONG, Sam
AU - LI, Yanhua
AU - ZENG, Jia
PY - 2018/3
Y1 - 2018/3
N2 - Utilizing large-scale GPS data to improve taxi services has become a popular research problem in the areas of data mining, intelligent transportation, geographical information systems, and the Internet of Things. In this paper, we utilize a large-scale GPS data set generated by over 7,000 taxis in a period of one month in Nanjing, China, and propose TaxiRec: a framework for evaluating and discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to seek passengers. In TaxiRec, the underlying road network is first segmented into a number of road clusters, a set of features for each road cluster is extracted from real-life data sets, and then a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster. In addition, TaxiRec can use this model with a training cluster selection algorithm to provide road cluster recommendations when taxi trajectory data is incomplete or unavailable. Experimental results demonstrate the feasibility and effectiveness of TaxiRec.
AB - Utilizing large-scale GPS data to improve taxi services has become a popular research problem in the areas of data mining, intelligent transportation, geographical information systems, and the Internet of Things. In this paper, we utilize a large-scale GPS data set generated by over 7,000 taxis in a period of one month in Nanjing, China, and propose TaxiRec: a framework for evaluating and discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to seek passengers. In TaxiRec, the underlying road network is first segmented into a number of road clusters, a set of features for each road cluster is extracted from real-life data sets, and then a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster. In addition, TaxiRec can use this model with a training cluster selection algorithm to provide road cluster recommendations when taxi trajectory data is incomplete or unavailable. Experimental results demonstrate the feasibility and effectiveness of TaxiRec.
KW - Extreme learning machine
KW - Passenger-finding potential
KW - Recommender system
KW - Taxi trajectory data analytics
UR - http://www.scopus.com/inward/record.url?scp=85034260698&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2017.2772907
DO - 10.1109/TKDE.2017.2772907
M3 - Journal Article (refereed)
SN - 1041-4347
VL - 30
SP - 585
EP - 598
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 3
ER -