Browsing by Author "Hong, Jinhyun"

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    How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities
    (Journal of Transport and Land Use, 2012) Zhang, Lei; Hong, Jinhyun; Nasri, Arefeh; Shen, Qing
    Mixed findings have been reported in previous research regarding the impact of built environment on travel behavior—i.e., statistically and practically significant effects found in a number of empirical studies and insignificant correlations shown in many other studies. It is not clear why the estimated impact is stronger or weaker in certain urban areas and how effective a proposed land use change/policy will be in changing certain travel behavior. This knowledge gap has made it difficult for decision makers to evaluate land use plans and policies according to their impact on vehicle miles traveled (VMT), and consequently, their impact on congestion mitigation, energy conservation, and pollution and greenhouse gas emission reduction. This research has several objectives: (1) re-examine the effects of built-environment factors on travel behavior, in particular, VMT in five US metropolitan areas grouped into four case study areas; (2) develop consistent models in all case study areas with the same model specification and data sets to enable direct comparisons; (3) identify factors such as existing land use characteristics and land use policy decision-making processes that may explain the different impacts of built environment on VMT in different urban areas; and (4) provide a prototype tool for government agencies and decision makers to estimate the impact of proposed land use changes on VMT. The four case study areas include Seattle, WA; Richmond-Petersburg and Norfolk-Virginia Beach, VA; Baltimore, MD; and Washington, DC. Our empirical analysis employs Bayesian multilevel modeling method with various person-level socioeconomic and demographic variables, and five built-environment factors including residential density, employment density, entropy (measuring level of mixed-use development), average block size (measuring transit/walking friendliness), and distance to city center (measuring decentralization and level of infill development). Our findings show that promoting compact, mixed-use, small-block, and infill developments can be effective in reducing VMT per person in all four case study areas. However, the effectiveness of land use plans and policies encouraging these types of land development is different both across case study areas and within the same case study area. We have identified several factors that potentially influence the connection between built environment shifts and VMT changes including urban area size, existing built environment characteristics, transit service cover- age and quality, and land use decision-making processes.
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    Non-linear influences of the built environment on transportation emissions: Focusing on densities
    (Journal of Transport and Land Use, 2017) Hong, Jinhyun
    Compact development is often recommended to reduce auto-dependency thereby decreasing related energy consumptions and transportation emissions. However, there could be a non-linear relationship between density and transportation emissions because of a possible non-linear association between density and vehicle miles travelled (VMT); low travel speed due to congestion; and the relationship between neighborhood characteristics and vehicle characteristics (e.g., vehicle type and age). In addition, the self-selection issue can exist in the land use-transportation emissions analysis because transportation emissions are often estimated based on travel behavior. Using the 2006 Puget Sound Regional Council (PSRC) Household Activity survey, the follow-up stated preference survey, the Motor Vehicle Emission Simulator (MOVES) data, and the GIS network data, this study investigates the non-linear effects of densities on CO2 equivalent (CO2e) emissions with the consideration of self-selection. Specifically, quadratic forms of population and employment densities, different population density group indicators, and attitudinal factors are employed in the regression models. The results indicate that people living in denser neighborhoods tend to generate fewer CO2e emissions. However, this effect becomes insignificant as population density reaches a certain level.