應用兩階段基因演算法於考量乘客屬性及營運成本之公車路網排班問題之研究

Application of Two-phase Genetic Algorithm for Bus Route Network Scheduling Problem with Passenger Properties and Operating Expenses

楊為翔(Wei-Shiang Yang)

指導教授 ： 楊康宏

中原大學/電機資訊學院/工業與系統工程研究所/碩士(2019年)

https://doi.org/10.6840/cycu201900470

摘要

隨著城鄉差距逐日擴大，城市人口不斷提高，市區交通負載量也隨之逐年攀升。因此政府致力於推行大眾運輸系統，鼓勵民眾能多搭乘大眾交通運輸系統，希望能藉此舒緩市區嚴重塞車的窘境，減少整體交通時間，並響應節能減碳的環保政策。公車是大多數城市所仰賴的大眾運輸交通工具，交通規劃如路網設計、時刻表、班次的密集度及旅途時間等之設計，以及公車的發車時間與準時性，會影響乘客選擇搭乘大眾交通運輸工具的意願。如何盡可能地規劃一完善的公車路網排班，以符合乘客需求成為一大課題。
本研究以兩階段基因演算法分別對公車停站模式以及公車發車時間進行迭代求解，目標為最小化流失乘客人數以及公司營運成本。根據調整班次數量、平均候車人數以及其變異係數，分為6個情境進行分析，其實驗結果顯示，本研究所建構之公車路網排班基因演算法，在不同的情境之下皆具有穩定的求解能力。

關鍵字

公車排班問題 ； 路網排班問題 ； 乘客屬性 ； 多目標 ； 柏拉圖前緣 ； 基因演算法

並列摘要

As the urban-rural gap widens and the urban population continues to increase, the municipal traffic load will also increase year by year. Therefore, the government is committed to promoting the public transportation system and encouraging people to take it. It hopes to ease the dilemma of severe traffic jams in the city, reduce overall traffic time, and respond to energy conservation and carbon reduction policies. Most cities rely on public transport is the bus system. The design of traffic planning, such as road network design, timetable, shift intensity, and travel time, as well as the bus departure time and punctuality, will affect passengers' choice of public transport. How to plan a perfect bus network scheduling as much as possible to meet the needs of passengers has become a significant issue.
In this study, the two-layer genetic algorithm is used to solve the problem with different bus stop mode and the bus departure time. The problem tries to minimize the number of passengers who have not boarded and the bus company's operating costs. According to the adjustment of the number of shifts, the average number of waiting people and its coefficient of variation, this study analyzes six scenarios. The experimental results show that the bus route scheduling gene algorithm constructed in this study is stable under different situations.

並列關鍵字

Bus route network scheduling problem ； Passenger properties ； Multi-Objective ； Pareto Front ； Genetic Algorithm

參考文獻

英文文獻

Abido, M. (2003). A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch. International Journal of Electrical Power & Energy Systems, 25(2), 97-105.

Amin-Naseri, M. R., & Baradaran, V. (2014). Accurate estimation of average waiting time in public transportation systems. Transportation Science, 49(2), 213-222.

Anand, E., & Panneerselvam, R. (2016). A study of crossover operators for genetic algorithm and proposal of a new crossover operator to solve open shop scheduling problem. American Journal of Industrial and Business Management, 6(06), 774.

Arostegui Jr, M. A., Kadipasaoglu, S. N., & Khumawala, B. M. (2006). An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems. International Journal of Production Economics, 103(2), 742-754.