Dynamic Shared-Taxi Dispatch Algorithm with Hybrid-Simulated Annealing
Taxi is certainly the most popular type of on‐demand transportation service in urban areas because taxi‐dispatching systems offer more and better services in terms of shorter wait times and passenger travel convenience. However, a shortage of taxicabs has always been critical in many urban contexts...
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| Published in | Computer-aided civil and infrastructure engineering Vol. 31; no. 4; pp. 275 - 291 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Blackwell Publishing Ltd
01.04.2016
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| Online Access | Get full text |
| ISSN | 1093-9687 1467-8667 |
| DOI | 10.1111/mice.12157 |
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| Abstract | Taxi is certainly the most popular type of on‐demand transportation service in urban areas because taxi‐dispatching systems offer more and better services in terms of shorter wait times and passenger travel convenience. However, a shortage of taxicabs has always been critical in many urban contexts especially during peak hours, and taxi has great potential to maximize its efficiency by employing the shared‐ride concept. There are recent successes in dynamic ride‐sharing projects that are expected to bring substantial benefits arising from energy consumption and operation efficiency and thus, it is essential to develop advanced shared‐taxi‐dispatch algorithms and investigate the collective benefits of dynamic ride‐sharing by maximizing occupancy and minimizing travel times in real‐time. This article investigates how taxi services can be improved by proposing shared‐taxi algorithms and what type of objective functions and constraints could be employed to prevent excessive passenger detours. Hybrid‐simulated annealing (HSA) is applied to dynamically assign passenger requests efficiently. A series of simulations are conducted with two different taxi operation strategies. The simulation results reveal that allowing ride‐sharing for taxicabs increases productivity over the various demand levels and HSA can be considered as a suitable solution to maximize the system efficiency of dynamic ride‐sharing. |
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| AbstractList | Taxi is certainly the most popular type of on‐demand transportation service in urban areas because taxi‐dispatching systems offer more and better services in terms of shorter wait times and passenger travel convenience. However, a shortage of taxicabs has always been critical in many urban contexts especially during peak hours, and taxi has great potential to maximize its efficiency by employing the shared‐ride concept. There are recent successes in dynamic ride‐sharing projects that are expected to bring substantial benefits arising from energy consumption and operation efficiency and thus, it is essential to develop advanced shared‐taxi‐dispatch algorithms and investigate the collective benefits of dynamic ride‐sharing by maximizing occupancy and minimizing travel times in real‐time. This article investigates how taxi services can be improved by proposing shared‐taxi algorithms and what type of objective functions and constraints could be employed to prevent excessive passenger detours. Hybrid‐simulated annealing (HSA) is applied to dynamically assign passenger requests efficiently. A series of simulations are conducted with two different taxi operation strategies. The simulation results reveal that allowing ride‐sharing for taxicabs increases productivity over the various demand levels and HSA can be considered as a suitable solution to maximize the system efficiency of dynamic ride‐sharing. |
| Author | Jung, Jaeyoung Park, Ji Young Jayakrishnan, R. |
| Author_xml | – sequence: 1 givenname: Jaeyoung surname: Jung fullname: Jung, Jaeyoung email: jyoungjung@gmail.com organization: Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, CA, Irvine, USA – sequence: 2 givenname: R. surname: Jayakrishnan fullname: Jayakrishnan, R. organization: Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, CA, Irvine, USA – sequence: 3 givenname: Ji Young surname: Park fullname: Park, Ji Young organization: Department of Road Transport Research, The Korea Transport Institute, Sejong, Korea |
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| References_xml | – reference: Chiang, W.-C. & Russell, R. A. (1996), Simulated annealing metaheuristics for the vehicle routing problem with time windows, Annals of Operations Research, 63, 3-27. – reference: Fan, W. & Machemehl, R. (2006), Using a simulated annealing algorithm to solve the transit route network design problem, Journal of Transportation Engineering, 132, 122-32. – reference: Black, A. (1995), Urban Mass Transportation Planning, McGraw-Hill Series in Transportation, McGraw-Hill, New York. – reference: Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. (1953), Equations of state calculations by fast computing machines, Journal of Chemical Physics, 21(6), 1087-92. – reference: Tsukada, N. & Takada, K. (2005), Possibilities of the large-taxi dial-a-ride transit system utilizing GPS-AVM, Journal of Eastern Asia Society for Transportation Studies, EASTS, Bangkok, Thailand, 22, 1-10. – reference: Ng, M. W., Park J. & Wallder, S. T. (2010), A hybrid bilevel model for the optimal shelter assignment in emergency evacuations, Computer-Aided Civil and Infrastructure Engineering, 25(8), 547-56. – reference: Jung, J. & Jayakrishnan, R. (2014), Simulation framework for modeling large-scale flexible transit systems, Transportation Research Record, 2466, 31-41. Available at http://trid.trb.org/view.aspx?id=1289577. – reference: Cortés, C. E. & Jayakrishnan, R. (2002), Design and operational concepts of high-coverage point-to-point transit system, Transportation Research Record 1783, 178-87. – reference: Campbell, A. M. & Savelsbergh, M. (2004), Efficient insertion heuristics for vehicle routing and scheduling problems, Transportation Science, 38(3), 369-78. – reference: Sarma, K. C. & Adeli, H. (2001), Bi-level parallel genetic algorithms for optimization of large steel structures, Computer-Aided Civil and Infrastructure Engineering, 16(5), 295-304. – reference: Malek, M., Guruswamy, M., Pandya, M. & Owens, H., (1989), Serial and parallel simulated annealing and tabu search algorithms for the traveling salesman problem, Annals of Operations Research, 21, 59-84. – reference: Dial, R. B. (1995), Autonomous dial-a-ride transit introductory overview, Transportation Research, 3C(5), 261-75. – reference: Seow, K. T., Dang, N. H. & Lee, D. H. (2010), A collaborative multiagent taxi-dispatch system, IEEE Transactions on Automation Science and Engineering, 7(3), 607-16. – reference: Zeferino, J. A., Antunes, A. P. & Cunha, M. C. (2009), An efficient simulated annealing algorithm for regional wastewater system planning, Computer-Aided Civil and Infrastructure Engineering 24, 359-70. – reference: Crama, Y. & Schyns, M. 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