{"title":"利用 GIS 处理数据估算公交站点需求的空间统计方法","authors":"Yaiza Montero-Lamas , Rubén Fernández-Casal , Francisco-Alberto Varela-García , Alfonso Orro , Margarita Novales","doi":"10.1016/j.jtrangeo.2024.103906","DOIUrl":null,"url":null,"abstract":"<div><p>This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits.</p></div>","PeriodicalId":48413,"journal":{"name":"Journal of Transport Geography","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0966692324001157/pdfft?md5=20156fc87f12228093f3868ef3ce5437&pid=1-s2.0-S0966692324001157-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A spatial statistical approach to estimate bus stop demand using GIS-processed data\",\"authors\":\"Yaiza Montero-Lamas , Rubén Fernández-Casal , Francisco-Alberto Varela-García , Alfonso Orro , Margarita Novales\",\"doi\":\"10.1016/j.jtrangeo.2024.103906\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. 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引用次数: 0
摘要
本研究综合了地理学、城市交通规划和统计学习等领域的知识,开发出一套复杂的方法,用于预测车站一级的公交需求。它采用了广义相加模型,该模型能捕捉非线性关系并结合空间依赖性,从而改进了传统方法。在验证过程中,该模型显示出很高的预测能力,其伪 R 方为 0.79,确保了对新观察结果的强大解释力。该方法分析了大量变量,包括土地使用特征、社会经济因素和过境供应。这些广泛可用的预测因子有助于将该方法应用到其他城市地区。公交供给预测因子考虑了每个站点和区域的年出行次数,以及站点沿服务线路的位置。通过对数据进行 GIS 处理,可以计算出每个站点影响范围内的变量,这些变量是通过步行街网络获得的。在案例研究中,大学、医院和住宿区的存在,以及居民和公交出行比例都对公交需求产生了积极影响。这一地理分析过程采用了精确的分类数据,如每栋建筑的用途信息,以及将当地社会经济信息分配到住宅建筑的方法。这项研究强调了公交线路沿线和地理邻近的公交网络站点位置、公交供应及其周边因素之间的复杂关系。结果表明,相距不足 1.15 千米的站点存在空间依赖性。所开发的方法为公交网络规划者提供了可靠的决策信息。具体而言,该方法有助于设计新路线、优化站点位置以及估算公交网络或城市规划变化对公交需求的影响。所有这些改进措施都能促进城市交通的可持续发展,从而产生环境和社会效益。
A spatial statistical approach to estimate bus stop demand using GIS-processed data
This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits.
期刊介绍:
A major resurgence has occurred in transport geography in the wake of political and policy changes, huge transport infrastructure projects and responses to urban traffic congestion. The Journal of Transport Geography provides a central focus for developments in this rapidly expanding sub-discipline.