Land use and transit ridership connections: Implications for state-level planning agencies
Land use and transit ridership connections: Implications for state-level planning agencies
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DOI：
10.1016/j.landusepol.2012.04.017
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发表时间：
2013-01-01
期刊：
影响因子：
7.1
通讯作者：
Mishra, Sabyasachee
中科院分区：
文献类型：
--
作者：
Chakraborty, Arnab;Mishra, Sabyasachee
在本文中，我们试图建立公交客流与土地利用和社会经济变量之间的联系，并预测不同情景下的未来客流。我们将美国马里兰州细分为1151个全州范围的模型区，并为基准年(2000)开发了一组变量。我们使用普通的最小二乘和空间误差建模方法估计了整个州的多个公交乘客模型。我们还测试了城市、郊区和农村类型中的乘车决定因素。我们发现，土地利用类型、交通可达性、收入和密度对全州和城市地区的公交乘客量具有很强的显著和稳健的预测作用。我们还发现，决定因素及其系数在城市、郊区和农村地区各不相同。接下来，我们使用了一套计量经济学、土地利用和其他模型来生成两组未来交通乘客情景--(A)一切照旧，(B)能源价格高企--时间跨度为30年。我们分析这些场景，以证明我们的方法对于州一级决策的价值。(C)2012爱思唯尔有限公司。保留所有权利。
In this article we attempt to establish the connections between transit ridership and land use and socioeconomic variables, and project future ridership under different scenarios. We subdivided the state of Maryland, USA into 1151 Statewide Modeling Zones and developed a set of variables for the base year (2000). We estimated multiple models of transit ridership - using ordinary least squares and spatial error modeling approaches - for the entire state. We also test for the determinants of ridership within urban, suburban and rural typologies. We find that land use type, transit accessibility, income, and density are strongly significant and robust predictors of transit ridership for the statewide and urban areas datasets. We also find that the determinants and their coefficients vary across urban, suburban and rural areas. Next we used a suite of econometric, land use and other models to generate two sets of future transit ridership scenarios under conditions of - (a) business as usual and (b) high energy price - for a 30-year horizon. We analyze these scenarios to demonstrate the value of our approach for state-level decision-making. (C) 2012 Elsevier Ltd. All rights reserved.