TY - GEN
T1 - Ride sharing
T2 - 8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015
AU - Shmueli, Erez
AU - Mazeh, Itzik
AU - Radaelli, Laura
AU - Pentland, Alex Sandy
AU - Altshuler, Yaniv
N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Ride sharing’s potential to improve traffic congestion as well as assist in reducing CO2 emission and fuel consumption was recently demonstrated by works such as [1]. Furthermore, it was shown that ride sharing can be implemented within a sound economic regime, providing values for all participants (e.g., Uber). Better understanding the utilization of ride sharing can help policy makers and urban planners in modifying existing urban transportation systems to increase their “ride sharing friendliness” as well as in designing new ride sharing oriented ones. In this paper, we study systematically the relationship between properties of the dynamic transportation network (implied by the aggregated rides) and the potential benefit of ride sharing. By analyzing a dataset of over 14 Million taxi trips taken in New York City during January 2013, we predict the potential benefit of ride sharing using topological properties of the rides network only. Such prediction can ease the analysis of urban areas, with respect to the potential efficiency of ride sharing for their inhabitants, without the need to carry out expensive and time consuming surveys, data collection and analysis operations.
AB - Ride sharing’s potential to improve traffic congestion as well as assist in reducing CO2 emission and fuel consumption was recently demonstrated by works such as [1]. Furthermore, it was shown that ride sharing can be implemented within a sound economic regime, providing values for all participants (e.g., Uber). Better understanding the utilization of ride sharing can help policy makers and urban planners in modifying existing urban transportation systems to increase their “ride sharing friendliness” as well as in designing new ride sharing oriented ones. In this paper, we study systematically the relationship between properties of the dynamic transportation network (implied by the aggregated rides) and the potential benefit of ride sharing. By analyzing a dataset of over 14 Million taxi trips taken in New York City during January 2013, we predict the potential benefit of ride sharing using topological properties of the rides network only. Such prediction can ease the analysis of urban areas, with respect to the potential efficiency of ride sharing for their inhabitants, without the need to carry out expensive and time consuming surveys, data collection and analysis operations.
UR - http://www.scopus.com/inward/record.url?scp=84925310848&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-16268-3_55
DO - https://doi.org/10.1007/978-3-319-16268-3_55
M3 - منشور من مؤتمر
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 434
EP - 439
BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings
A2 - Xu, Kevin
A2 - Agarwal, Nitin
A2 - Osgood, Nathaniel
Y2 - 31 March 2015 through 3 April 2015
ER -
