Understanding the Role of Built Environment in Reducing Vehicle Miles Traveled Accounting for Spatial Heterogeneity
Authors: Ding, Chuan, Yaowu Wang, Binglei Xie, and Chao Liu
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Synopsis: In recent years, increasing concerns over climate change and transportation energy consumption have sparked research into the influences of urban form and land use patterns on motorized travel, notably vehicle miles traveled (VMT). However, empirical studies provide mixed evidence of the influence of the built environment on travel. In particular, the role of density after controlling for the confounding factors (e.g., land use mix, average block size, and distance from CBD) still remains unclear. The object of this study is twofold. First, this research provides additional insights into the effects of built environment factors on the work-related VMT, considering urban form measurements at both the home location and workplace simultaneously. Second, a cross-classified multilevel model using Bayesian approach is applied to account for the spatial heterogeneity across spatial units. Using Washington DC as our study area, the home-based work tour in the AM peak hours is used as the analysis unit. Estimation results confirmed the important role that the built environment at both home and workplace plays in affecting work-related VMT. In particular, the results reveal that densities at the workplace have more important roles than that at home location. These findings confirm that urban planning and city design should be part of the solution in stabilizing global climate and energy consumption.
Authors: Ding, Chuan, Chao Liu, Yaoyu Lin, and Yaowu Wang
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Synopsis: The empirical data were selected from Washington-Baltimore Regional Household Travel Survey in 2007-2008, including all the trips from home to workplace during the morning hours. The model parameters were estimated using the simultaneous estimation approach and the integrated model turns out to be superior to the traditional multinomial logit (MNL) model accounting for the impact of employer attitudes towards green commuting. The direct and indirect effects of socio-demographic attributes and employer attitudes towards green commuting were estimated. Through the structural equation modelling with mediating variable, this approach confirmed the intermediary nature of car ownership in the choice process.
Authors: Wang, Yaowu, Chuan Ding, Chao Liu, and Binglei Xie
Report
Synopsis: Freight mode choice is a critical part in modeling freight demand. Due to limited freight data, considerably less research has been conducted on freight mode choice than that in passenger demand analysis. This paper investigates unobserved factors influencing freight mode choices, including truck and rail. Revealed preference data is collected from Freight Analysis Framework database and aggregated to be used in this study. Binary probit and logit models are developed to compare the modal behavior and to verify the differences of mode choice behavior among the three zones in Maryland. Different factors which are significantly influencing the freight mode choice can be found for the shipments originated from these zones. Identifying these factors may help the freight modelers to establish and calibrate better freight demand models for Maryland, and can help the policy makers to take actions to reduce highway congestion and air pollution which is caused by trucks
Authors: Ding, Chuan, Yaowu Wang, Binglei Xie, and Chao Liu
Report
Synopsis: In recent years, increasing concerns over climate change and transportation energy consumption have sparked research into the influences of urban form and land use patterns on motorized travel, notably vehicle miles traveled (VMT). However, empirical studies provide mixed evidence of the influence of the built environment on travel. In particular, the role of density after controlling for the confounding factors (e.g., land use mix, average block size, and distance from CBD) still remains unclear. The object of this study is twofold. First, this research provides additional insights into the effects of built environment factors on the work-related VMT, considering urban form measurements at both the home location and workplace simultaneously. Second, a cross-classified multilevel model using Bayesian approach is applied to account for the spatial heterogeneity across spatial units. Using Washington DC as our study area, the home-based work tour in the AM peak hours is used as the analysis unit. Estimation results confirmed the important role that the built environment at both home and workplace plays in affecting work-related VMT. In particular, the results reveal that densities at the workplace have more important roles than that at home location. These findings confirm that urban planning and city design should be part of the solution in stabilizing global climate and energy consumption.
The Impact of Employer Attitude to Green Commuting Plans on Reducing Car Driving: A Mixed Method Analysis
Authors: Ding, Chuan, Chao Liu, Yaoyu Lin, and Yaowu Wang
Report
Synopsis: The empirical data were selected from Washington-Baltimore Regional Household Travel Survey in 2007-2008, including all the trips from home to workplace during the morning hours. The model parameters were estimated using the simultaneous estimation approach and the integrated model turns out to be superior to the traditional multinomial logit (MNL) model accounting for the impact of employer attitudes towards green commuting. The direct and indirect effects of socio-demographic attributes and employer attitudes towards green commuting were estimated. Through the structural equation modelling with mediating variable, this approach confirmed the intermediary nature of car ownership in the choice process.
An analysis of Interstate freight mode choice between truck and rail: A case study of Maryland, United States
Authors: Wang, Yaowu, Chuan Ding, Chao Liu, and Binglei Xie
Report
Synopsis: Freight mode choice is a critical part in modeling freight demand. Due to limited freight data, considerably less research has been conducted on freight mode choice than that in passenger demand analysis. This paper investigates unobserved factors influencing freight mode choices, including truck and rail. Revealed preference data is collected from Freight Analysis Framework database and aggregated to be used in this study. Binary probit and logit models are developed to compare the modal behavior and to verify the differences of mode choice behavior among the three zones in Maryland. Different factors which are significantly influencing the freight mode choice can be found for the shipments originated from these zones. Identifying these factors may help the freight modelers to establish and calibrate better freight demand models for Maryland, and can help the policy makers to take actions to reduce highway congestion and air pollution which is caused by trucks