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Multimodal data modeling for efficiency assessment of social priority based urban bus route transportation system using GIS and data envelopment analysis

机译:基于GIS和数据包络分析的社会优先型城市公交路线运输系统效率评估的多模式数据建模。

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Multimodal data modeling is fast growing area of research. It may used to combine the information from the different sources. The research interest in multimodal data modeling is increasingly attracting the attention in the field of transportation planning. In this study, multi-modal data is used to assess and design a socially efficient public transport bus route plan for the Allahabad city of Uttar Pradesh state, India. Data envelopment analysis (DEA) method is used for the efficiency assessment of existing 24 public transport bus routes by taking access point to locations of social facilities like as hospitals, shopping malls, colleges, coaching centers, schools, banks and the population, near to the particular route. Geographical Information System (GIS) technology is used for multimodal data modeling to design new more socially efficient routes for the existing roads of the city. DEAP Solver software is used for the evaluation of efficiency and rank for social priority routes and route number 15 and 24 are relative efficient route among the existing 24 routes. Finally, the social efficiency of existing public bus transport routes and newly designed routes are compared. We suggested ways to improve the performance of bus routes based on the social perspectives using multimodal data.
机译:多模式数据建模是快速发展的研究领域。它可以用来合并来自不同来源的信息。在运输计划领域中,对多模式数据建模的研究兴趣日益引起人们的关注。在这项研究中,多模式数据用于评估和设计印度北方邦阿拉哈巴德市的具有社会效益的公共交通巴士路线计划。数据包络分析(DEA)方法用于通过访问到医院,购物中心,大学,教练中心,学校,银行和人口等社交设施的位置的访问点来对现有的24条公交路线的效率进行评估特定路线。地理信息系统(GIS)技术用于多模式数据建模,以为城市的现有道路设计更具社会效益的新路线。 DEAP Solver软件用于评估社会优先级路径的效率和等级,路径编号15和24是现有24条路径中相对有效的路径。最后,比较了现有公交路线和新设计路线的社会效率。我们基于使用多模式数据的社会观点,提出了改善公交路线性能的方法。

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