The Influence of Street Environments on Fuel Efficiency: Insights from Naturalistic Driving
Authors: Wang, X. G., C. Liu, L. Kostyniuk, Q. Shen, and B. Shan.
Report
Synopsis: Fuel consumption and greenhouse gas emissions in the transportation sector are a result of a “three-legged stool”: fuel types, vehicle fuel efficiency, and vehicle miles travelled (VMT). While there is a substantial body of literature that examines the connection between the built environment and total VMT, few studies have focused on the impacts of the street environment on fuel consumption rate. Ourresearch applied structural equation modeling to examine how driving behaviors and fuel efficiency respond to different street environments. We used a rich naturalistic driving dataset that recorded detailed driving patterns of 108 drivers randomly selected from the Southeast Michigan region. The results show that, some features of compact streets such as lower speed limit, higher intersection density, and higher employment density are associated with lower driving speed, more speed changes, and lower fuel efficiency; however, other features such as higher population density and higher density of pedestrian-scale retails improve fuel efficiency. The aim of our study is to gain further understanding of energy and environmental outcomes of the urban areas and the roadway infrastructure we plan, design, and build and to better inform policy decisions concerned with sustainable transportation.
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: Wang, X. G., C. Liu, L. Kostyniuk, Q. Shen, and B. Shan.
Report
Synopsis: Fuel consumption and greenhouse gas emissions in the transportation sector are a result of a “three-legged stool”: fuel types, vehicle fuel efficiency, and vehicle miles travelled (VMT). While there is a substantial body of literature that examines the connection between the built environment and total VMT, few studies have focused on the impacts of the street environment on fuel consumption rate. Ourresearch applied structural equation modeling to examine how driving behaviors and fuel efficiency respond to different street environments. We used a rich naturalistic driving dataset that recorded detailed driving patterns of 108 drivers randomly selected from the Southeast Michigan region. The results show that, some features of compact streets such as lower speed limit, higher intersection density, and higher employment density are associated with lower driving speed, more speed changes, and lower fuel efficiency; however, other features such as higher population density and higher density of pedestrian-scale retails improve fuel efficiency. The aim of our study is to gain further understanding of energy and environmental outcomes of the urban areas and the roadway infrastructure we plan, design, and build and to better inform policy decisions concerned with sustainable transportation.
Authors: Wang, X. G., C. Liu, L. Kostyniuk, Q. Shen, and B. Shan.
Report
Synopsis: Fuel consumption and greenhouse gas emissions in the transportation sector are a result of a “three-legged stool”: fuel types, vehicle fuel efficiency, and vehicle miles travelled (VMT). While there is a substantial body of literature that examines the connection between the built environment and total VMT, few studies have focused on the impacts of the street environment on fuel consumption rate. Ourresearch applied structural equation modeling to examine how driving behaviors and fuel efficiency respond to different street environments. We used a rich naturalistic driving dataset that recorded detailed driving patterns of 108 drivers randomly selected from the Southeast Michigan region. The results show that, some features of compact streets such as lower speed limit, higher intersection density, and higher employment density are associated with lower driving speed, more speed changes, and lower fuel efficiency; however, other features such as higher population density and higher density of pedestrian-scale retails improve fuel efficiency. The aim of our study is to gain further understanding of energy and environmental outcomes of the urban areas and the roadway infrastructure we plan, design, and build and to better inform policy decisions concerned with sustainable transportation.
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
The Influence of Street Environments on Fuel Efficiency: Insights from Naturalistic Driving
Authors: Wang, X. G., C. Liu, L. Kostyniuk, Q. Shen, and B. Shan.
Report
Synopsis: Fuel consumption and greenhouse gas emissions in the transportation sector are a result of a “three-legged stool”: fuel types, vehicle fuel efficiency, and vehicle miles travelled (VMT). While there is a substantial body of literature that examines the connection between the built environment and total VMT, few studies have focused on the impacts of the street environment on fuel consumption rate. Ourresearch applied structural equation modeling to examine how driving behaviors and fuel efficiency respond to different street environments. We used a rich naturalistic driving dataset that recorded detailed driving patterns of 108 drivers randomly selected from the Southeast Michigan region. The results show that, some features of compact streets such as lower speed limit, higher intersection density, and higher employment density are associated with lower driving speed, more speed changes, and lower fuel efficiency; however, other features such as higher population density and higher density of pedestrian-scale retails improve fuel efficiency. The aim of our study is to gain further understanding of energy and environmental outcomes of the urban areas and the roadway infrastructure we plan, design, and build and to better inform policy decisions concerned with sustainable transportation.