Real-Time Rideshare Matching Problem.
(30/05/2013)
This research project presented a Dynamic Rideshare Matching Optimization model that is aimed at identifying suitable matches between passengers requesting rideshare services with appropriate drivers available to carpool for credits and HOV lane privileges. DRMO receives passengers and drivers information and preferences continuously over time and assigns passengers to drivers with respect to proximity in time and space and compatibility of characteristics and preferences among the passengers, drivers and passengers onboard. DRMOP maximizes total number of assignments in a given planning horizon and secures that all the constraint for vehicle occupancy, waiting time to pickup, number of connections, detour distance...
Tác giả: Ghoseiri, K.; Haghani, A.; Hamedi, M. |
Số trang: 73 |
Lĩnh vực: Khác |
Năm XB: 2011 |
Loại tài liệu: Khác
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Tiêu đề | Tải về |
Real-Time Rideshare Matching Problem. | Số trang: 73
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This research project presented a Dynamic Rideshare Matching Optimization model that is aimed at identifying suitable matches between passengers requesting rideshare services with appropriate drivers available to carpool for credits and HOV lane privileges. DRMO receives passengers and drivers information and preferences continuously over time and assigns passengers to drivers with respect to proximity in time and space and compatibility of characteristics and preferences among the passengers, drivers and passengers onboard. DRMOP maximizes total number of assignments in a given planning horizon and secures that all the constraint for vehicle occupancy, waiting time to pickup, number of connections, detour distance for vehicles and relocation distance for passenger are satisfied. The ridesharing preferences and characteristic considered in the model are: age, gender, smoke, and pet restrictions as well as the maximum number of people sharing a ride. To better understand the model, a numerical example with compromise solutions were presented and discussed. The authors currently are working on developing solution algorithms for solving the optimization model proposed in this paper for large scale real-world problems.
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