阿拉巴马州伯明翰市人口特征和土地使用模式的时空模式及其对微型交通工具乘客数量的影响

Mostafa Jafarzadehfadaki , Virginia P. Sisiopiku , Wencui Yang , Dimitra Michalaka , Kweku Tekyi Brown , William J. Davis , Jalal Khalil , Da Yan
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引用次数: 0

摘要

近年来,共享经济的兴起导致交通服务的提供方式发生变化,包括引入微型交通服务。为了更好地了解微交通模式选择的决定因素及其对交通运营的影响,需要进行案例研究。本研究利用阿拉巴马州伯明翰市微型交通试点项目的数据,分析了时空需求变化,并探讨了微型交通乘客数量与人口特征和土地使用模式之间的相关性。利用时空模式挖掘技术,确认了微型交通使用的时空变化,观察到周五、周六和周日、下午和傍晚时段以及温暖月份是使用高峰期。空间分析采用了核密度技术,发现微移动出行的出发地集中在铁路公园、市中心、阿拉巴马大学伯明翰分校(UAB)校园和南五点社区等高密度地区。我们使用聚类方法和多层次负二叉模型研究了伯明翰微型交通系统乘客数量与人口特征和土地使用模式之间的相关性。该模型发现,微型交通工具的乘坐率与年轻人群(18-34 岁)之间存在明显的正相关关系,而与 45-54 岁年龄组之间存在负相关关系,这表明老年人群的使用率有所下降。在土地使用方面,模型结果显示,公园区域的存在与商业、住宅、工业用地以及大学校园之间存在显著的正相关关系。此外,国家步行指数和停车设施也呈正相关,而与市中心距离的增加则与微型交通的乘客减少有关。这项研究提供了宝贵的见解,可以帮助伯明翰以及其他中等城市的决策者和政策制定者规划和实施符合当地需求的微型交通项目。
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Spatiotemporal patterns and influences of demographic characteristics and land use patterns on micromobility ridership in Birmingham, Alabama

The rise of the sharing economy in recent years led to changes in transportation service delivery, including the introduction of micromobility services. Case studies are needed to better understand determinants of micromobility mode choice and its impacts on transportation operations. This study used data from a micromobility pilot program in Birmingham, Alabama to analyze spatiotemporal demand variations and explore correlations between micromobility ridership and demographic characteristics and land use patterns. Using space-time pattern mining techniques, temporal and spatial variations in micromobility usage were confirmed, with peak usage observed on Fridays, Saturdays and Sundays, during afternoon and evening hours, and during warmer months. Spatial analysis employed Kernel Density techniques and revealed concentrated micromobility trip origins in high-density areas such as Railroad Park, downtown, the University of Alabama at Birmingham (UAB) campus, and the Five Points South neighborhood. Correlations between Birmingham micromobility ridership and demographic characteristics and land use patterns were studied using clustering approaches and a multilevel negative binomial model. The model identified significant positive associations between micromobility ridership and the younger population (18–34 years of age), with a negative association in the 45–54 age group, signaling a decline in usage among older individuals. Regarding land uses, the model results showed significant positive correlations with the presence of park areas and commercial, residential, and industrial land uses, and the university campus. Furthermore, a positive correlation was observed with the National Walkability Index and parking facilities, whereas increased distance from the city center was associated with reduced micromobility ridership. The study offers valuable insights that can assist decision and policymakers in Birmingham as well as other medium-sized cities, in planning, and implementing micromobility programs that serve the local needs.

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