TY - GEN
T1 - Real-Time Distributed Taxi Ride Sharing
AU - Bathla, Kanika
AU - Raychoudhury, Vaskar
AU - Saxena, DIvya
AU - Kshemkalyani, Ajay D.
N1 - Funding Information:
VII. ACKNOWLEDGMENT Research work reported in this paper is partially funded by the Alexander von Humboldt Foundation through the postdoctoral 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 play an important role in urban public transportation. Analyzing taxi traffic of Shanghai, San Francisco, and New York City, we have found that the short trips within city are mostly of commuters during office hours and span a specific city area. Now, if the large number of commuters are ready to share their rides, that will have a huge impact on the 'super-commute' problem faced in various cities of USA and around the world. While ride-sharing can increase taxi occupancy and profit for drivers and savings for passengers, it reduces the overall on-road traffic and thereby the average commute time and carbon foot-print. While centralized ride-sharing services, like car-pooling, can address the problem to some extent, they lack scalability and power to dynamically adapt the taxi schedule for best results. In this paper, we propose a four-way model for the ride-sharing problem and develop a novel distributed taxi ride sharing (TRS) algorithm to address dynamic scheduling of ride sharing requests. Our algorithm shows the overall reduction in total distance travelled by taxis as a result of ride sharing. Empirical results using large scale taxi GPS traces from Shanghai, China show that TRS algorithm can grossly outperform a Taxi Distance Minimization (TDM) algorithm. TRS accommodates 33% higher ride share among passengers while dealing with 44,241 requests handled by 4,000 taxis on a single day in Shanghai.
AB - Taxicabs play an important role in urban public transportation. Analyzing taxi traffic of Shanghai, San Francisco, and New York City, we have found that the short trips within city are mostly of commuters during office hours and span a specific city area. Now, if the large number of commuters are ready to share their rides, that will have a huge impact on the 'super-commute' problem faced in various cities of USA and around the world. While ride-sharing can increase taxi occupancy and profit for drivers and savings for passengers, it reduces the overall on-road traffic and thereby the average commute time and carbon foot-print. While centralized ride-sharing services, like car-pooling, can address the problem to some extent, they lack scalability and power to dynamically adapt the taxi schedule for best results. In this paper, we propose a four-way model for the ride-sharing problem and develop a novel distributed taxi ride sharing (TRS) algorithm to address dynamic scheduling of ride sharing requests. Our algorithm shows the overall reduction in total distance travelled by taxis as a result of ride sharing. Empirical results using large scale taxi GPS traces from Shanghai, China show that TRS algorithm can grossly outperform a Taxi Distance Minimization (TDM) algorithm. TRS accommodates 33% higher ride share among passengers while dealing with 44,241 requests handled by 4,000 taxis on a single day in Shanghai.
KW - Data Analysis
KW - Distributed Coordination
KW - GPS traces
KW - Smart Transportation
KW - Taxi ride sharing
UR - http://www.scopus.com/inward/record.url?scp=85060495353&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2018.8569315
DO - 10.1109/ITSC.2018.8569315
M3 - Conference article published in proceeding or book
AN - SCOPUS:85060495353
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2044
EP - 2051
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 -
