TY - GEN
T1 - Collective Travel Planning in Spatial Networks
AU - Shang, Shuo
AU - Chen, Lisi
AU - Wei, Zhewei
AU - Jensen, Christian S.
AU - Wen, Ji-Rong
AU - Kalnis, Panos
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work is partly supported by the National Natural Science Foundation of China (NSFC.61402532), and Beijing Nova Program.
PY - 2017/5/18
Y1 - 2017/5/18
N2 - We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.
AB - We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.
UR - http://hdl.handle.net/10754/624983
UR - http://ieeexplore.ieee.org/document/7929932/
UR - http://www.scopus.com/inward/record.url?scp=85021196794&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2017.36
DO - 10.1109/ICDE.2017.36
M3 - Conference contribution
SN - 9781509065431
SP - 59
EP - 60
BT - 2017 IEEE 33rd International Conference on Data Engineering (ICDE)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -
