Land Use and Transit Ridership Connections: Implications for State-level Planning Agencies
Authors: Arnab Chakraborty and Sabyasachee Mishra (2013)
Report
Synopsis: Land use and neighborhood characteristics have long been linked to transit ridership. Large-scale agencies, such as state departments of transportations, often make decisions that affect land use pattern and transit services. However, the interdependencies between them are seldom harnessed in decision-making. In this article, we develop and apply a transit ridership model based on land use and other neighborhood characteristics for an entire state. We then discuss its implications for regional and state-level decision-making. We chose the state of Maryland as our study area. Using a number of criteria, we subdivided the state into 1151 statewide modeling zones (SMZs) and, for each zone in the base year (2000), developed a set of variables, including developed land under different uses, population and employment densities, free-flow and congested speeds, current transport capacities, and accessibility to different transport modes. We estimated two sets of OLS-regression models for the base year data: one on the statewide SMZs dataset and other on subsets of urban, suburban and rural typologies. We find that characteristics of land use, transit accessibility, income, and density are strongly significant and robust for the statewide and urban areas datasets. We also find that determinants and their coefficients vary across urban, suburban and rural areas suggesting the need for finely tuned policy. Next we used a suite of econometric and land use models to generate two scenarios for the horizon year (2030) – business as usual and high-energy price – and estimated ridership changes between them. We use the resulting scenarios to show how demand could vary by parts of the state and demonstrate the framework’s value in large-scale decision-making.
A Case for Increased State Role in Transit Planning: Analyzing Land Use and Transit Ridership Connections Using Scenarios
Authors: Arnab Chakraborty and Sabyasachee Mishra (2011)
Report
Synopsis: Land use and neighborhood characteristics have long been linked to transit ridership. Large-scale agencies, such as state departments of transportations, often make decisions that affect land use pattern and transit services. However, the interdependencies between them are seldom harnessed in decision-making. In this article, we develop and apply a transit ridership model based on land use and other neighborhood characteristics for an entire state. We then discuss its implications for regional and state-level decision-making. We chose the state of Maryland as our study area. Using a number of criteria, we subdivided the state into 1151 statewide modeling zones (SMZs) and, for each zone in the base year (2000), developed a set of variables, including developed land under different uses, population and employment densities, free-flow and congested speeds, current transport capacities, and accessibility to different transport modes. We estimated two sets of OLS-regression models for the base year data: one on the statewide SMZs dataset and other on subsets of urban, suburban and rural typologies. We find that characteristics of land use, transit accessibility, income, and density are strongly significant and robust for the statewide and urban areas datasets. We also find that determinants and their coefficients vary across urban, suburban and rural areas suggesting the need for finely tuned policy. Next we used a suite of econometric and land use models to generate two scenarios for the horizon year (2030) – business as usual and high-energy price – and estimated ridership changes between them. We use the resulting scenarios to show how demand could vary by parts of the state and demonstrate the framework’s value in large-scale decision-making.
Planning Support Systems and Planning Across Scales: Comparing Scenarios Using Multiple Regional Delineations and Projections
Authors: Arnab Chakraborty, Sabyasachee Mishra, and Yong Wook Kim (2012)
Report
Synopsis: Planning support systems often employ urban models that simulate and evaluate impacts of plans. Their application to plan making is however, challenging when issues transcend local jurisdictions, and model assumptions are contested by the stakeholders. Neglecting the role of such specifications, especially when they are important and uncertain, can diminish the efficacy of plans. In this paper, we use the principles of scenario analysis to illustrate the impacts of two such important considerations – forecasts and regional boundaries – on model outcomes and related decisions. We use Montgomery County, MD as a case and leverage a model developed for a larger region, i.e. the state of MD and vicinity. We develop two sets of scenarios – one where the county (a local government) freely competes with its neighboring jurisdictions for development and another where a higher (i.e. a regional or state) level agency controls the extent of development that the county can receive. The scenarios are constructed using different specifications for regional boundaries and also results in different amount of growth in the County – both rare practices in scenario analysis with models. We then compare the outcomes on a set of indicators and draw implications for planning. We conclude with the argument that planning agencies should compare future scenarios not just with different desirability but different sets of assumptions and regional formulations.
Authors: Arnab Chakraborty and Sabyasachee Mishra (2013)
Report
Synopsis: Land use and neighborhood characteristics have long been linked to transit ridership. Large-scale agencies, such as state departments of transportations, often make decisions that affect land use pattern and transit services. However, the interdependencies between them are seldom harnessed in decision-making. In this article, we develop and apply a transit ridership model based on land use and other neighborhood characteristics for an entire state. We then discuss its implications for regional and state-level decision-making. We chose the state of Maryland as our study area. Using a number of criteria, we subdivided the state into 1151 statewide modeling zones (SMZs) and, for each zone in the base year (2000), developed a set of variables, including developed land under different uses, population and employment densities, free-flow and congested speeds, current transport capacities, and accessibility to different transport modes. We estimated two sets of OLS-regression models for the base year data: one on the statewide SMZs dataset and other on subsets of urban, suburban and rural typologies. We find that characteristics of land use, transit accessibility, income, and density are strongly significant and robust for the statewide and urban areas datasets. We also find that determinants and their coefficients vary across urban, suburban and rural areas suggesting the need for finely tuned policy. Next we used a suite of econometric and land use models to generate two scenarios for the horizon year (2030) – business as usual and high-energy price – and estimated ridership changes between them. We use the resulting scenarios to show how demand could vary by parts of the state and demonstrate the framework’s value in large-scale decision-making.
Authors: Arnab Chakraborty and Sabyasachee Mishra (2011)
Report
Synopsis: Land use and neighborhood characteristics have long been linked to transit ridership. Large-scale agencies, such as state departments of transportations, often make decisions that affect land use pattern and transit services. However, the interdependencies between them are seldom harnessed in decision-making. In this article, we develop and apply a transit ridership model based on land use and other neighborhood characteristics for an entire state. We then discuss its implications for regional and state-level decision-making. We chose the state of Maryland as our study area. Using a number of criteria, we subdivided the state into 1151 statewide modeling zones (SMZs) and, for each zone in the base year (2000), developed a set of variables, including developed land under different uses, population and employment densities, free-flow and congested speeds, current transport capacities, and accessibility to different transport modes. We estimated two sets of OLS-regression models for the base year data: one on the statewide SMZs dataset and other on subsets of urban, suburban and rural typologies. We find that characteristics of land use, transit accessibility, income, and density are strongly significant and robust for the statewide and urban areas datasets. We also find that determinants and their coefficients vary across urban, suburban and rural areas suggesting the need for finely tuned policy. Next we used a suite of econometric and land use models to generate two scenarios for the horizon year (2030) – business as usual and high-energy price – and estimated ridership changes between them. We use the resulting scenarios to show how demand could vary by parts of the state and demonstrate the framework’s value in large-scale decision-making.
Authors: Arnab Chakraborty, Sabyasachee Mishra, and Yong Wook Kim (2012)
Report
Synopsis: Planning support systems often employ urban models that simulate and evaluate impacts of plans. Their application to plan making is however, challenging when issues transcend local jurisdictions, and model assumptions are contested by the stakeholders. Neglecting the role of such specifications, especially when they are important and uncertain, can diminish the efficacy of plans. In this paper, we use the principles of scenario analysis to illustrate the impacts of two such important considerations – forecasts and regional boundaries – on model outcomes and related decisions. We use Montgomery County, MD as a case and leverage a model developed for a larger region, i.e. the state of MD and vicinity. We develop two sets of scenarios – one where the county (a local government) freely competes with its neighboring jurisdictions for development and another where a higher (i.e. a regional or state) level agency controls the extent of development that the county can receive. The scenarios are constructed using different specifications for regional boundaries and also results in different amount of growth in the County – both rare practices in scenario analysis with models. We then compare the outcomes on a set of indicators and draw implications for planning. We conclude with the argument that planning agencies should compare future scenarios not just with different desirability but different sets of assumptions and regional formulations.