TY - GEN
T1 - Social-Aware ridesharing
AU - Fu, Xiaoyi
N1 - Funding Information:
Second, in the previous work, riders have specific origins and destinations. We plan to consider a different scenario: the riders only have activities want to finish and there exists a set of candidate destinations which have some functions that enable the riders to accomplish specific activities. For example, a rider wants to go shopping in a mall from his/her home and would like to join ridesharing. In such a scenario, the ridesharing service provider needs to classify riders’ activities as well as mark some places as points of interest (POIs) and assign these POIs functions. The uncertain destinations also incur additional challenges in requests matching. ACKNOWLEDGMENT This paper is under the supervision of Prof. Jianliang Xu. The research presented in this paper is supported by Hong Kong RGC Grants 12201615 and 12200817. REFERENCES [1] Blerim Cici et al. “Assessing the potential of ride-sharing using mobile and social data: a tale of four cities”. In: UbiComp. 2014. [2] Xiaoyi Fu et al. “Efficient Matching of Offers and Requests in Social-Aware Ridesharing”. In: MDM2018 (2018), pp. 197–206. [3] Xiaoyi Fu et al. “Top-k Taxi Recommendation in Re-altime Social-Aware Ridesharing Services”. In: SSTD. 2017. [4] Yafei Li et al. “Towards Social-Aware Ridesharing Group Query Services”. In: IEEE TSC 10 (2017), pp. 646–659. [5] Online news. http://www.cnsoftnews.com/news/201801/ 71658.html. [6] Vijay V. Vazirani. “Approximation Algorithms”. In: Springer Berlin Heidelberg. 2001.
PY - 2019/6
Y1 - 2019/6
N2 - Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. There has been much attention on the optimization of costs/detour in shared rides. However, other important factors, such as social comfort and trust issues, have not been fully explored in the existing work in ridesharing. In this paper, we introduce social-Awareness into ridesharing services and study two social-Aware ridesharing problem: The top-k Social-Aware Taxi Ridesharing (TkSaTR) problem and the Assignment of Requests to Offers (ARO) problem. Furthermore, this paper also presents the directions of future work in social-Aware ridesharing.
AB - Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environmental friendliness. There has been much attention on the optimization of costs/detour in shared rides. However, other important factors, such as social comfort and trust issues, have not been fully explored in the existing work in ridesharing. In this paper, we introduce social-Awareness into ridesharing services and study two social-Aware ridesharing problem: The top-k Social-Aware Taxi Ridesharing (TkSaTR) problem and the Assignment of Requests to Offers (ARO) problem. Furthermore, this paper also presents the directions of future work in social-Aware ridesharing.
KW - LBS
KW - ridesharing
UR - http://www.scopus.com/inward/record.url?scp=85070972236&partnerID=8YFLogxK
U2 - 10.1109/MDM.2019.00-28
DO - 10.1109/MDM.2019.00-28
M3 - Conference proceeding
AN - SCOPUS:85070972236
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 367
EP - 368
BT - Proceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
PB - IEEE
T2 - 20th International Conference on Mobile Data Management, MDM 2019
Y2 - 10 June 2019 through 13 June 2019
ER -