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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级城市公交路线运输系统效率评价多峰数据建模

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摘要

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增长的研究领域。它可以用于将信息与不同来源组合。多模式数据建模的研究兴趣越来越多地引起交通规划领域的关注。在这项研究中,多模态数据用于评估和设计印度北方邦北方邦北方市的社会有效的公共交通总线路线。数据包络分析(DEA)方法用于通过作为医院,购物中心,学院,教练中心,学校,银行和人口等社会设施的访问点,借鉴了现有的24公共交通总线路线的效率评估。特定路线。地理信息系统(GIS)技术用于多模式数据建模,为城市现有道路设计新的更具社会有效路线。 DEAP Solver软件用于评估效率,社会优先路由路由的等级和路线15和24是现有24路线之间相对有效的路由。最后,比较了现有公共巴士运输路线和新设计的路线的社会效率。我们建议根据使用多模式数据的社交视角来提高公交路线的性能的方法。

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