Matching Models and Solution Algorithms for Urban Taxipool.

• Main Authors: Chuan-che Wu, 吳權哲
• Other Authors: 顏上堯
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/24110451253078197740

碩士 === 國立中央大學 === 土木工程研究所 === 95 === Traffic volume has significantly grown and taxi becomes more popular than before in Taiwan. Therefore, taxipool that enhances the taxi utilization can not only relieve traffic congestion, but can also save energy. However, in Taiwan taxipool matching is manually performed by planning personnel with experience in current practice, without a systematic analysis. Such a manual approach is considered to be less efficient, and can possibly result in an inferior feasible solution. Although single origin or single destination matching problems have been researched in literature, they are different from the multiple origin-destination (OD) pairing matching problems that mostly occur in real word. As a result, the proposed models or methods cannot be directly applied to the practical multiple origin/destination matching problems. Therefore, in this research, based on the system planner perspective and focusing on advanced-order passenger trips, we develop a system optimization matching framework that contains several matching models in two stages: 1. fleet scheduling with passenger matching and 2. single taxi scheduling with passenger matching. The matching models are expected to be an effective tool for the planner to help simultaneously solve passenger matching and fleet scheduling. We construct a multiple OD pair matching framework that is divided into two stages. In the first stage, we construct a fleet scheduling with passenger matching model which matches the daily advanced-order passenger trips with taxis. In the second phase, we construct two single taxi scheduling with passenger matching models, in order to decompose the fleet-flow solution from the first stage and to get each taxi schedule with matched passengers. We employ network flow techniques to develop these three models, each including multiple fleet-flow networks and multiple passenger-flow networks to formulate the daily flows of taxis and passengers in the dimensions of time and space. Some side constraints between the fleet- and passenger-flow networks are set to comply with real operating requirements. The three models are formulated as integer multiple commodity network flow problems, which are characterized as NP-hard and cannot be optimally solved in a reasonable time for large-scale problems. Therefore, to efficiently solve large-scale problems occurring in real world, we develop a solution algorithm for each model, based on Lagrangian relaxation with subgradient methods. To evaluate the matching framework and solution algorithm in practice, we perform a case study. A computerized random generator is designed to generate different problem instances used for testing. Finally, conclusions and suggestions are given.