TY - GEN
T1 - A scalable approach for data-driven taxi ride-sharing simulation
AU - Ota, Masayo
AU - Vo, Huy
AU - Silva, Claudio
AU - Freire, Juliana
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - As urban population grows, cities face many challenges related to transportation, resource consumption, and the environment. Ride sharing has been proposed as an effective approach to reduce traffic congestion, gasoline consumption, and pollution. Despite great promise, researchers and policy makers lack adequate tools to assess tradeoffs and benefits of various ride-sharing strategies. Existing approaches either make unrealistic modeling assumptions or do not scale to the sizes of existing data sets. In this paper, we propose a real-time, data-driven simulation framework that supports the efficient analysis of taxi ride sharing. By modeling taxis and trips as distinct entities, our framework is able to simulate a rich set of realistic scenarios. At the same time, by providing a comprehensive set of parameters, we are able to study the taxi ride-sharing problem from different angles, considering different stakeholders' interests and constraints. To address the computational complexity of the model, we describe a new optimization algorithm that is linear in the number of trips and makes use of an efficient indexing scheme, which combined with parallelization, makes our approach scalable. We evaluate our framework and algorithm using real data - 360 million trips taken by 13,000 taxis in New York City during 2011 and 2012. The results demonstrate that our framework is effective and can provide insights into strategies for implementing city-wide ride-sharing solutions. We describe the findings of the study as well as a performance analysis of the model.
AB - As urban population grows, cities face many challenges related to transportation, resource consumption, and the environment. Ride sharing has been proposed as an effective approach to reduce traffic congestion, gasoline consumption, and pollution. Despite great promise, researchers and policy makers lack adequate tools to assess tradeoffs and benefits of various ride-sharing strategies. Existing approaches either make unrealistic modeling assumptions or do not scale to the sizes of existing data sets. In this paper, we propose a real-time, data-driven simulation framework that supports the efficient analysis of taxi ride sharing. By modeling taxis and trips as distinct entities, our framework is able to simulate a rich set of realistic scenarios. At the same time, by providing a comprehensive set of parameters, we are able to study the taxi ride-sharing problem from different angles, considering different stakeholders' interests and constraints. To address the computational complexity of the model, we describe a new optimization algorithm that is linear in the number of trips and makes use of an efficient indexing scheme, which combined with parallelization, makes our approach scalable. We evaluate our framework and algorithm using real data - 360 million trips taken by 13,000 taxis in New York City during 2011 and 2012. The results demonstrate that our framework is effective and can provide insights into strategies for implementing city-wide ride-sharing solutions. We describe the findings of the study as well as a performance analysis of the model.
KW - scalability
KW - shortest-path index
KW - simulation
KW - taxi ride sharing
UR - http://www.scopus.com/inward/record.url?scp=84963768111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963768111&partnerID=8YFLogxK
U2 - 10.1109/BigData.2015.7363837
DO - 10.1109/BigData.2015.7363837
M3 - Conference contribution
AN - SCOPUS:84963768111
T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
SP - 888
EP - 897
BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
A2 - Luo, Feng
A2 - Ogan, Kemafor
A2 - Zaki, Mohammed J.
A2 - Haas, Laura
A2 - Ooi, Beng Chin
A2 - Kumar, Vipin
A2 - Rachuri, Sudarsan
A2 - Pyne, Saumyadipta
A2 - Ho, Howard
A2 - Hu, Xiaohua
A2 - Yu, Shipeng
A2 - Hsiao, Morris Hui-I
A2 - Li, Jian
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Y2 - 29 October 2015 through 1 November 2015
ER -