TY - GEN
T1 - Efficient Taxi and Passenger Searching in Smart City using Distributed Coordination
AU - Agrawal, Anmol
AU - Raychoudhury, Vaskar
AU - Saxena, Divya
AU - Kshemkalyani, Ajay D.
N1 - Funding Information:
Research work reported in this paper is partially funded by the Alexander von Humboldt Foundation through the post-doctoral Fellow Dr. Vaskar Raychoudhury. Also, we acknowledge the critical comments and advices provided by Dr. Rajdeep Niyogi, Associate Professor, Department of CSE, IIT Roorkee, India, during the initial stages of this work. We also thank Ms Shrawani Silwal from Miami Univ., Ohio, USA for her help to improve the paper.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/7
Y1 - 2018/12/7
N2 - Taxicabs are an important element of urban public transportation. Taxicabs either cruise through city streets in search of passengers or wait at several hotspots (like airports, rail stations, malls, stadiums, taxi stands, etc). Cruising by empty Taxis increases city traffic and carbon footprint while reducing net profit. Alternatively, there might be places where passengers are waiting long for taxis. In order to improve coordination between taxis and passengers with a view to decrease passenger waiting time and to increase taxi profits, we propose a taxi selection algorithm (TSA) as well as a hotspot recommendation approach (HRA). While the proposed TSA achieves its objective through distributed coordination among the participating taxis and passengers, the HRA uses a clustering approach over a large-scale taxi dataset to pin-point hotspots. The main contribution of this paper lies in extensive experimentation using large-scale taxi dataset of SFO to show that the TSA outperforms existing taxi selection algorithms by finding a taxi which can reach the passenger in minimum time with up to 97.59% accuracy. We also evaluate the HRA using another taxi dataset from NYC which shows that 60% of the times, a taxi will get a passenger following our recommendation scheme.
AB - Taxicabs are an important element of urban public transportation. Taxicabs either cruise through city streets in search of passengers or wait at several hotspots (like airports, rail stations, malls, stadiums, taxi stands, etc). Cruising by empty Taxis increases city traffic and carbon footprint while reducing net profit. Alternatively, there might be places where passengers are waiting long for taxis. In order to improve coordination between taxis and passengers with a view to decrease passenger waiting time and to increase taxi profits, we propose a taxi selection algorithm (TSA) as well as a hotspot recommendation approach (HRA). While the proposed TSA achieves its objective through distributed coordination among the participating taxis and passengers, the HRA uses a clustering approach over a large-scale taxi dataset to pin-point hotspots. The main contribution of this paper lies in extensive experimentation using large-scale taxi dataset of SFO to show that the TSA outperforms existing taxi selection algorithms by finding a taxi which can reach the passenger in minimum time with up to 97.59% accuracy. We also evaluate the HRA using another taxi dataset from NYC which shows that 60% of the times, a taxi will get a passenger following our recommendation scheme.
KW - Data Analysis
KW - Distributed Coordination
KW - Hotspot Recommendation
KW - Smart Transportation
KW - Taxi Selection
UR - http://www.scopus.com/inward/record.url?scp=85060446503&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2018.8569728
DO - 10.1109/ITSC.2018.8569728
M3 - Conference article published in proceeding or book
AN - SCOPUS:85060446503
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1920
EP - 1927
BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Y2 - 4 November 2018 through 7 November 2018
ER -
