C. Liu

Investigating the Neighborhood Effect on Hybrid Car Adaptation

Authors: X. Zhu and C. Liu
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Synopsis: Utilizing the 2009 Florida NHTS add-on data, we conducted a cluster analysis to display the spatial clusters of the households who purchased the HEVs during the period of 2005-2008. We found that more clusters of HEVs emerged overtime. Geographic patterns also demonstrated the increasing number of clusters featured by households with hybrid vehicles and the surrounding hybrid vehicle adopters. Further, the relationship between the hybrid vehicle adoption and neighborhood effects (NE) and social-demographic factors are analyzed using Binary Logit models with and without weight. Neighborhood effects are confirmed to be significant in both urban and rural models, especially the factors of 1-mile and 5-mile neighborhood coverage. This indicates that potential buyers are more likely to purchase the HEVs when there are more HEVs exposures surrounding, and this measurement of exposure is proved to be reasonable for neighborhood effect. Among the social and demographic attributes, household income is the most significant variable and plays a dominant role in affecting the propensity to buy hybrid vehicles. Other factors, such as vehicle ownership, household structure and education attainment level also significantly affect households’ choice of hybrid cars. Vehicle usage is a controversial factor in this study because of the interactive correlation between the type of vehicle owned and corresponding usage.

 

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.
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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.

 

Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach

Authors: Ding, C., Y. Wang, and C. Liu
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Synopsis: Understanding travel behavior and its relationship to urban form is vital for the sustainableplanning strategies aimed at automobile dependency reduction. The objective of this studyis twofold. First, this research provides additional insights to examine the effects of builtenvironment factors measured at both home location and workplace on tour-based modechoice behavior. Second, a cross-classified multilevel probit model using Bayesianapproach is employed to accommodate the spatial context in which individuals maketravel decisions. Using Washington, D.C. as our study area, the home-based work(Home-work) tour in the AM peak hours is used as the analysis unit. The empirical datawas gathered from the Washington-Baltimore Regional Household Travel Survey2007–2008. For parameter estimation, Bayesian estimation method integrating MarkovChain Monte Carlo (MCMC) sampling is adopted. Our findings confirmed the important rolethat the built environment at both home location and work ends plays in affecting commutermode choice behavior. Meanwhile, a comparison of different model results showsthat the cross-classified multilevel probit model offers significant improvements over thetraditional probit model. The results are expected to give a better understanding on therelationship between the built environment and commuter mode choice behavior.