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Title: Parking Demand Forecasting Using Asymmetric Discrete Choice Models with Applications
Creator: Zhang, Ji
Date: 2023
Description: Using discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The...
Show moreUsing discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The most used discrete choice models have fairly simple mathematical expressions, such as the probit and logit models. The application of simple models helps release the computational burdens brought by parameter estimation tasks in practice, but the cost is the unwanted properties of classic models such as the “symmetry property” that we argue is often undesirable in many fields. To some extent, the symmetry property of related models limits the shape of curves that makes the model fitting less flexible technically. This study addresses the following question: “Can discrete choice models with asymmetry property outperform classic models with symmetry property in forecasting travelers’ parking location choices?” The contributions of this study include: (1) providing a new perspective of using asymmetric discrete choice models to explain and forecast individual’s parking location choice; and (2) completing the travel demand forecasting process from choices of the destination zone centroid to the parking location, enabling parking choice forecasting. This provides a generalized framework to calibrate and validate asymmetric discrete choice models with the field observed parking facility-specific arrival profile data integrated into a large-scale, high-fidelity regional travel demand model. Further, an experimental study is conducted to compare the performance of the proposed asymmetric discrete choice models in the parking demand forecasting framework. The results suggest that asymmetric discrete choice models for individual’s parking choice modeling outperform the symmetric discrete choice models such as the logit models owing largely to their flexibility of parameter fitting and training using the available dataset.
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Title: Parking Demand Forecasting Using Asymmetric Discrete Choice Models with Applications
Creator: Zhang, Ji
Date: 2023
Description: Using discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The...
Show moreUsing discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The most used discrete choice models have fairly simple mathematical expressions, such as the probit and logit models. The application of simple models helps release the computational burdens brought by parameter estimation tasks in practice, but the cost is the unwanted properties of classic models such as the “symmetry property” that we argue is often undesirable in many fields. To some extent, the symmetry property of related models limits the shape of curves that makes the model fitting less flexible technically. This study addresses the following question: “Can discrete choice models with asymmetry property outperform classic models with symmetry property in forecasting travelers’ parking location choices?” The contributions of this study include: (1) providing a new perspective of using asymmetric discrete choice models to explain and forecast individual’s parking location choice; and (2) completing the travel demand forecasting process from choices of the destination zone centroid to the parking location, enabling parking choice forecasting. This provides a generalized framework to calibrate and validate asymmetric discrete choice models with the field observed parking facility-specific arrival profile data integrated into a large-scale, high-fidelity regional travel demand model. Further, an experimental study is conducted to compare the performance of the proposed asymmetric discrete choice models in the parking demand forecasting framework. The results suggest that asymmetric discrete choice models for individual’s parking choice modeling outperform the symmetric discrete choice models such as the logit models owing largely to their flexibility of parameter fitting and training using the available dataset.
Show less

Title: Parking Demand Forecasting Using Asymmetric Discrete Choice Models with Applications
Creator: Zhang, Ji
Date: 2023
Description: Using discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The...
Show moreUsing discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The most used discrete choice models have fairly simple mathematical expressions, such as the probit and logit models. The application of simple models helps release the computational burdens brought by parameter estimation tasks in practice, but the cost is the unwanted properties of classic models such as the “symmetry property” that we argue is often undesirable in many fields. To some extent, the symmetry property of related models limits the shape of curves that makes the model fitting less flexible technically. This study addresses the following question: “Can discrete choice models with asymmetry property outperform classic models with symmetry property in forecasting travelers’ parking location choices?” The contributions of this study include: (1) providing a new perspective of using asymmetric discrete choice models to explain and forecast individual’s parking location choice; and (2) completing the travel demand forecasting process from choices of the destination zone centroid to the parking location, enabling parking choice forecasting. This provides a generalized framework to calibrate and validate asymmetric discrete choice models with the field observed parking facility-specific arrival profile data integrated into a large-scale, high-fidelity regional travel demand model. Further, an experimental study is conducted to compare the performance of the proposed asymmetric discrete choice models in the parking demand forecasting framework. The results suggest that asymmetric discrete choice models for individual’s parking choice modeling outperform the symmetric discrete choice models such as the logit models owing largely to their flexibility of parameter fitting and training using the available dataset.
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Title: MOBILITY IMPROVEMENT BENEFIT ANALYSIS OF SIGNAL TIMING OPTIMIZATION FOR URBAN STREET NETWORK
Creator: Zhang, Ji
Date: 2015, 2015-05
Description: The traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the...
Show moreThe traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the United States during the past few decades. In general, insufficient capacity can be solved by system expansion. However, expanding system is not feasible anymore because of the land scarcity in urban areas and its high cost. From this point of view, transportation operations that lead to the optimal system usage are more preferable thanks to their relatively low cost and remarkable consequences. Several performance indices were used in order to assess the effects of a given transportation operation. This study introduces a new method for evaluating the mobility performance of the transportation system before and after a transportation operation. And the mobility benefit is converted into monetary value. Further, a Life-Cycle Benefit Analysis is conducted to expand the evaluation process to the time dimension. An experimental study is performed to apply this method on the urban street network in Chicago downtown area that contains 917 intersections and 1675 roadway segments before and after a network-wide signal timing optimization treatment. Based on this application, the results indicate a few potential advantages and disadvantages of this system-wide signal timing optimization methodology.
M.S. in Civil, Architectural and Environmental Engineering, May 2015
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Title: MOBILITY IMPROVEMENT BENEFIT ANALYSIS OF SIGNAL TIMING OPTIMIZATION FOR URBAN STREET NETWORK
Creator: Zhang, Ji
Date: 2015, 2015-05
Description: The traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the...
Show moreThe traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the United States during the past few decades. In general, insufficient capacity can be solved by system expansion. However, expanding system is not feasible anymore because of the land scarcity in urban areas and its high cost. From this point of view, transportation operations that lead to the optimal system usage are more preferable thanks to their relatively low cost and remarkable consequences. Several performance indices were used in order to assess the effects of a given transportation operation. This study introduces a new method for evaluating the mobility performance of the transportation system before and after a transportation operation. And the mobility benefit is converted into monetary value. Further, a Life-Cycle Benefit Analysis is conducted to expand the evaluation process to the time dimension. An experimental study is performed to apply this method on the urban street network in Chicago downtown area that contains 917 intersections and 1675 roadway segments before and after a network-wide signal timing optimization treatment. Based on this application, the results indicate a few potential advantages and disadvantages of this system-wide signal timing optimization methodology.
M.S. in Civil Engineering, May 2015
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