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Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership
Given the concerns about urban mobility, traffic congestion, and greenhouse gas (GHG) emissions, extensive research has explored the relationship between the built environment and transit ridership. However, the nature of aggregation and the cross-sectional approach of the research rarely provide essential clues on the potential of a transit system as a sustainable mobility option. From the perspective of longitudinal sustainability, this paper develops regression models for rail transit stations in the Los Angeles Metro system. These models attempt to identify the socio-demographic characteristics and land use features influencing longitudinal transit ridership changes. Step-wise ordinary least square (OLS) regression models are used to identify factors that contribute to transit ridership changes. Those factors include the number of dwelling units, employment-oriented land uses such as office and commercial land uses, and land use balance. The models suggest a negative relationship between job and population balance with transit ridership change. They also raise a question regarding the 0.4 km radius commonly used in transit analysis. The models indicate that the 0.4 km radius is too small to capture the significant influence of the built environment on transit ridership.
Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership
Given the concerns about urban mobility, traffic congestion, and greenhouse gas (GHG) emissions, extensive research has explored the relationship between the built environment and transit ridership. However, the nature of aggregation and the cross-sectional approach of the research rarely provide essential clues on the potential of a transit system as a sustainable mobility option. From the perspective of longitudinal sustainability, this paper develops regression models for rail transit stations in the Los Angeles Metro system. These models attempt to identify the socio-demographic characteristics and land use features influencing longitudinal transit ridership changes. Step-wise ordinary least square (OLS) regression models are used to identify factors that contribute to transit ridership changes. Those factors include the number of dwelling units, employment-oriented land uses such as office and commercial land uses, and land use balance. The models suggest a negative relationship between job and population balance with transit ridership change. They also raise a question regarding the 0.4 km radius commonly used in transit analysis. The models indicate that the 0.4 km radius is too small to capture the significant influence of the built environment on transit ridership.
Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership
Dohyung Kim (author) / Yongjin Ahn (author) / Simon Choi (author) / Kwangkoo Kim (author)
2016
Article (Journal)
Electronic Resource
Unknown
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