{"title":"利用遗传算法对马尼拉大都会公共交通系统进行综合优化调度","authors":"Cyrill O. Escolano, E. Dadios, Alexis M. Fillone","doi":"10.1109/HNICEM.2014.7016207","DOIUrl":null,"url":null,"abstract":"Selection of dispatching modes for a transit system is a very important aspect of the schedule problem. This paper aims to optimize and monitor the scheduling and dispatching of public utility vehicles (PUV) plying along EDSA. Using passenger and vehicle data, the system will analyze an optimal scheduling pattern for dispatching PUVs in terminals that covers EDSA routes. The scheduling is based on passenger demand and congestion along the route. The scheduling system will be based on the dispatch system used by the Bus Rapid Transit. There are three modes of dispatch scheduling: normal scheduling, zone scheduling and express scheduling. It seeks to optimize the dispatch system in such a way that the transfer time of passengers at the transfer nodes is minimized while the operational constraints such as the traffic demand, departure time and maximum (minimum) headway are satisfied. Mathematical model illustrates the dynamics and behaviour of the system under different constraints. Genetic algorithm is used as the optimization tool. Data necessary for the generation of the algorithm came from transportation surveys. The code was written using C++ program. Effectiveness, accuracy and robustness of the system are evident by the results.","PeriodicalId":309548,"journal":{"name":"2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An integrated and optimal scheduling of a public transport system in metro Manila using genetic algorithm\",\"authors\":\"Cyrill O. Escolano, E. Dadios, Alexis M. Fillone\",\"doi\":\"10.1109/HNICEM.2014.7016207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Selection of dispatching modes for a transit system is a very important aspect of the schedule problem. This paper aims to optimize and monitor the scheduling and dispatching of public utility vehicles (PUV) plying along EDSA. Using passenger and vehicle data, the system will analyze an optimal scheduling pattern for dispatching PUVs in terminals that covers EDSA routes. The scheduling is based on passenger demand and congestion along the route. The scheduling system will be based on the dispatch system used by the Bus Rapid Transit. There are three modes of dispatch scheduling: normal scheduling, zone scheduling and express scheduling. It seeks to optimize the dispatch system in such a way that the transfer time of passengers at the transfer nodes is minimized while the operational constraints such as the traffic demand, departure time and maximum (minimum) headway are satisfied. Mathematical model illustrates the dynamics and behaviour of the system under different constraints. Genetic algorithm is used as the optimization tool. Data necessary for the generation of the algorithm came from transportation surveys. The code was written using C++ program. Effectiveness, accuracy and robustness of the system are evident by the results.\",\"PeriodicalId\":309548,\"journal\":{\"name\":\"2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HNICEM.2014.7016207\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HNICEM.2014.7016207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An integrated and optimal scheduling of a public transport system in metro Manila using genetic algorithm
Selection of dispatching modes for a transit system is a very important aspect of the schedule problem. This paper aims to optimize and monitor the scheduling and dispatching of public utility vehicles (PUV) plying along EDSA. Using passenger and vehicle data, the system will analyze an optimal scheduling pattern for dispatching PUVs in terminals that covers EDSA routes. The scheduling is based on passenger demand and congestion along the route. The scheduling system will be based on the dispatch system used by the Bus Rapid Transit. There are three modes of dispatch scheduling: normal scheduling, zone scheduling and express scheduling. It seeks to optimize the dispatch system in such a way that the transfer time of passengers at the transfer nodes is minimized while the operational constraints such as the traffic demand, departure time and maximum (minimum) headway are satisfied. Mathematical model illustrates the dynamics and behaviour of the system under different constraints. Genetic algorithm is used as the optimization tool. Data necessary for the generation of the algorithm came from transportation surveys. The code was written using C++ program. Effectiveness, accuracy and robustness of the system are evident by the results.