TY - GEN
T1 - Forecasting car rental demand based temporal and spatial travel patterns
AU - Lei, Shuo
AU - Wang, Haiquan
AU - Yang, Chen
AU - Du, Bowen
AU - Zhong, Runxing
AU - Huang, Runhe
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Recent years, shared mobility services have gained momentum across the world. Meanwhile, rental car industry has seen great developments in China and has reached a scale of economy. Knowing the rental behavior pattern and forecasting the demand become more important for rental businesses. To this end, in this paper, we aim to analyze the rental mobility pattern by examining multiple factors in a holistic manner. A special goal is to predict the demand of a given region. Specifically, we first analyze regular mobility based on real trips of rental cars. Then, we extract key features from multiple types of rental-related data, such as rental behavior profiles and geo-social information of regions. Next, based on these features, we develop a multi-Task learning based regression approach for predicting rental cars' demand. This approach can effectively learn not only fundamental features but also relationships between regions by considering multiple factors. Finally, we conduct extensive experiments on real-world rental trip data collected in Beijing. The experimental results validate the effectiveness of the proposed approach for forecasting rental demand in the real world.
AB - Recent years, shared mobility services have gained momentum across the world. Meanwhile, rental car industry has seen great developments in China and has reached a scale of economy. Knowing the rental behavior pattern and forecasting the demand become more important for rental businesses. To this end, in this paper, we aim to analyze the rental mobility pattern by examining multiple factors in a holistic manner. A special goal is to predict the demand of a given region. Specifically, we first analyze regular mobility based on real trips of rental cars. Then, we extract key features from multiple types of rental-related data, such as rental behavior profiles and geo-social information of regions. Next, based on these features, we develop a multi-Task learning based regression approach for predicting rental cars' demand. This approach can effectively learn not only fundamental features but also relationships between regions by considering multiple factors. Finally, we conduct extensive experiments on real-world rental trip data collected in Beijing. The experimental results validate the effectiveness of the proposed approach for forecasting rental demand in the real world.
UR - https://www.scopus.com/pages/publications/85050213614
U2 - 10.1109/UIC-ATC.2017.8397484
DO - 10.1109/UIC-ATC.2017.8397484
M3 - 会议稿件
AN - SCOPUS:85050213614
T3 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
SP - 1
EP - 8
BT - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017
Y2 - 4 April 2017 through 8 April 2017
ER -