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    Analysis of the Pedestrian System Indicators of City Blocks Based on the Real Walking Network: A Case Study of Jinan, China

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    %28ASCE%29UP.1943-5444.0000683.pdf (2.206Mb)
    Date
    6/1/2021
    Author
    Shengnan Niu
    Hao Tong
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    Abstract
    Over the last few decades, cities have been committed to building efficient, coordinated, and energy-saving transportation modes. As the most flexible and green travel mode, walking provides effective connections for various modes of transportation; therefore, it has received extensive attention from planners. A pedestrian network is a basic guarantee of walking, and its construction method has become a focus of many walking studies. However, due to the characteristics of flexibility and wide coverage, a pedestrian network often cannot be completely constructed, which causes problems with missing data in planning and research. This imperfect data has caused pedestrian network problems not to be effectively solved during the process of urban transformation. Taking Jinan's old city, Jinan, Shandong Province, China as an example, this study will use a variety of data to construct the real walking network (RWN) of Jinan's old city, which combines all the available roads in parks, squares, and residential quarters into the street network (SN). Compared with the SN, the RWN better reflects the real travel conditions of pedestrians. This study compares the common walking indicators of the SN and the RWN, which includes network connectivity and the service level of bus stops. The results show that there are significant differences between the RWN and the SN. The RWN that has more complete data is not significantly better than the SN, but the differences between the two will have an impact on the results of walking research. Therefore, RWN should be used in walking research. In addition, based on the comparison of the results, this study makes reasonable suggestions to improve the pedestrian system in Jinan's old city.
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    http://yetl.yabesh.ir/yetl1/handle/yetl/4270515
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