Street Networks, knitted in the urban fabric, facilitate spatial movement and control the flow of urbanization. The interrelation between a city’s spatial network and how the residents travel over it has always been ...Street Networks, knitted in the urban fabric, facilitate spatial movement and control the flow of urbanization. The interrelation between a city’s spatial network and how the residents travel over it has always been of high interest to scholars. Over the years, multifaceted visualization methods have emerged to better express this travel trend from small to large scale. This study proposes a novel approach to 1) visualize city-wide travel patterns with respect to the street network orientation and 2) analyze the discrepancies between travel patterns and streets to evaluate network usability. The visualizations adopt histograms and rose diagrams to provide several insights into network-wide traffic flows. The visualization of four New York City (NYC) boroughs including Queens, Brooklyn, Bronx, and Staten Island was generated for the daily traffic and the average hourly flows in the morning and evening rush hours. Then the contrasts between built-in street network topology and travel orientation were drawn to show where people travel over the network, travel demand, and finally which segments experience high or light traffic, revealing the true picture of network usability. The findings of the study provide an insight into the novel and innovative approach that can help better understand the travel behavior lucidly and assist policymakers in decision making to maintain a balance between urban topology and travel demands. In addition, the study demonstrates how to further investigate city street networks and urbanization from different diverse dimensions.展开更多
轨道交通网络中乘客的出行受网络结构和运营状况变化的影响,个体出行偏好对这些变化的响应也各异。为分析轨道交通远郊区段计划性停运对常乘客的出行转移影响,本文提出考虑转移类型和转移比例的乘客出行特征刻画方法,结合时段属性生成...轨道交通网络中乘客的出行受网络结构和运营状况变化的影响,个体出行偏好对这些变化的响应也各异。为分析轨道交通远郊区段计划性停运对常乘客的出行转移影响,本文提出考虑转移类型和转移比例的乘客出行特征刻画方法,结合时段属性生成乘客特征—时序(FeatureTemporal,F-T)矩阵;通过改进的欧氏距离计算F-T矩阵间的相似性,实现F-T矩阵的相似性度量;提出一种基于相似度矩阵的K-Means聚类和层次聚类相结合的两步聚类方法(Two-step Clustering of K-Means Clustering and Hierarchical Clustering,KMHC)划分乘客影响群体,分析影响乘客出行转移的因素;以新冠肺炎疫情期间上海轨道交通11号线昆山段停运作为实例,对本文方法进行验证。研究结果表明:昆山段停运后,常乘客呈现出5种主要的出行转移影响群体,占常乘客总数的94.4%;各影响群体的转移距离、通勤时间和出行频率差异明显,是影响区段停运后常乘客出行选择的重要因素。本文方法可为其他计划性停运场景提供借鉴和参考,也可为区段停运后的网络客流变化预测,行车和客运组织方案优化提供支撑。展开更多
It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the bous...It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .展开更多
文摘Street Networks, knitted in the urban fabric, facilitate spatial movement and control the flow of urbanization. The interrelation between a city’s spatial network and how the residents travel over it has always been of high interest to scholars. Over the years, multifaceted visualization methods have emerged to better express this travel trend from small to large scale. This study proposes a novel approach to 1) visualize city-wide travel patterns with respect to the street network orientation and 2) analyze the discrepancies between travel patterns and streets to evaluate network usability. The visualizations adopt histograms and rose diagrams to provide several insights into network-wide traffic flows. The visualization of four New York City (NYC) boroughs including Queens, Brooklyn, Bronx, and Staten Island was generated for the daily traffic and the average hourly flows in the morning and evening rush hours. Then the contrasts between built-in street network topology and travel orientation were drawn to show where people travel over the network, travel demand, and finally which segments experience high or light traffic, revealing the true picture of network usability. The findings of the study provide an insight into the novel and innovative approach that can help better understand the travel behavior lucidly and assist policymakers in decision making to maintain a balance between urban topology and travel demands. In addition, the study demonstrates how to further investigate city street networks and urbanization from different diverse dimensions.
文摘轨道交通网络中乘客的出行受网络结构和运营状况变化的影响,个体出行偏好对这些变化的响应也各异。为分析轨道交通远郊区段计划性停运对常乘客的出行转移影响,本文提出考虑转移类型和转移比例的乘客出行特征刻画方法,结合时段属性生成乘客特征—时序(FeatureTemporal,F-T)矩阵;通过改进的欧氏距离计算F-T矩阵间的相似性,实现F-T矩阵的相似性度量;提出一种基于相似度矩阵的K-Means聚类和层次聚类相结合的两步聚类方法(Two-step Clustering of K-Means Clustering and Hierarchical Clustering,KMHC)划分乘客影响群体,分析影响乘客出行转移的因素;以新冠肺炎疫情期间上海轨道交通11号线昆山段停运作为实例,对本文方法进行验证。研究结果表明:昆山段停运后,常乘客呈现出5种主要的出行转移影响群体,占常乘客总数的94.4%;各影响群体的转移距离、通勤时间和出行频率差异明显,是影响区段停运后常乘客出行选择的重要因素。本文方法可为其他计划性停运场景提供借鉴和参考,也可为区段停运后的网络客流变化预测,行车和客运组织方案优化提供支撑。
文摘It is difficult to solve complete coverage path planning directly in the obstructed area. Therefore, in this paper, we propose a method of complete coverage path planning with improved area division. Firstly, the boustrophedon cell decomposition method is used to partition the map into sub-regions. The complete coverage paths within each sub-region are obtained by the Boustrophedon back-and-forth motions, and the order of traversal of the sub-regions is then described as a generalised traveling salesman problem with pickup and delivery based on the relative positions of the vertices of each sub-region. An adaptive large neighbourhood algorithm is proposed to quickly obtain solution results in traversal order. The effectiveness of the improved algorithm on traversal cost reduction is verified in this paper through multiple sets of experiments. .
