Chao Liu

Mapping Opportunity: A Critical Assessment

Authors: Eli Knaap, Gerrit-Jan Knaap, and Chao Liu
Synopsis: A renewed interest has emerged on spatial opportunity structures and their role in shapinghousing policy, community development, and equity planning. To this end, many have triedto quantify the geography of opportunity and quite literally plot it in a map. In this paperwe explore the conceptual foundations and analytical methods that underlie the currentpractice of opportunity mapping. We find that opportunity maps can inform housing policyand metropolitan planning but that greater consideration should be given to the variablesincluded, the methods in which variables are geographically articulated and combined, andthe extent to which the public is engaged in opportunity mapping exercises.

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
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

Changing Urban Growth Patterns in a Pro-Smart Growth State: The Case of Maryland, 1973-2002

Authors: Qing Shen, Chao Liu, Joe Liao, Feng Zhang, Chris Dorney (2007)
Synopsis: This paper presents a study of recent urban growth patterns in the state of Maryland, which is known as a leader in the current smart growth movement. Five research questions are addressed in this study. First, what have been the trends in urban growth and land use in Maryland for the past 30 years? Second, to what extent have recent urban development patterns in Maryland matched the typical characterization of sprawl? Third, how have the intensity of urban land uses and the physical forms of urban growth in this state varied among its counties? Fourth, have the smart growth initiatives, especially the “Smart Growth Area Act,” significantly affected urban development patterns? Fifth, does the effectiveness of smart growth initiatives vary significantly across local jurisdictions? To answer these research questions, we measure, analyze, and model urban development patterns in Maryland using land use and land cover (LULC) and demographic data for 1973, 1992, 1997, 2000, and 2002. By calculating several important indicators of urban development patterns, we find that for the past three decades population densities have continued to decrease for the state as a whole. However, this trend has slowed since 1997, when the state implemented the smart growth programs. The land conversion rate has somewhat decreased, which indicates that smart growth initiatives have helped, in a limited way, curtail the growing demand for urban land and residential space. Further, we find that the patterns of urban growth and land use have generally become slightly less fragmented and more continuous since 1997. Additionally, we find significant variations in urban development patterns among local jurisdictions. In general, higher densities, higher levels of compactness, and lower levels of fragmentation are observed in the more urbanized counties. Moreover, by estimating a series of logit models of land conversion, we find that Maryland’s “Smart Growth Area Act” has generally increased the probability of land use change from non-urban to urban for areas designated as “Priority Funding Areas.” The effectiveness of this program, however, varies significantly across the counties. We discuss the implications of these findings and identify the directions for future research. 

Reclassification of Sustainable Neighborhoods: An Opportunity Indicator Analysis in Baltimore Metropolitan Area

Authors: Chao Liu, , Eli Knaap, Gerrit Knaap
Synopsis: The “Sustainable neighborhoods” has become widely proposed objective of urban planners, scholars, and local government agencies. However, after decades of discussion, there is still no consensus on the definition of sustainable neighborhoods (Sawicki and Flynn, 1996; Dluhy and Swartz 2006; Song and Knaap,2007; Galster 2010). To gain new information on this issue, this paper develops a quantitative method for classifying neighborhood types. It starts by measuring a set of more than 100 neighborhood sustainable indicators. The initial set of indicators includes education, housing, neighborhood quality and social capital, neighborhood environment and health, employment and transportation. Data are gathered from various sources, including the National Center for Smart Growth (NCSG) data inventory, U.S. Census, Bureau of Economic Analysis (BEA), Environmental Protection Agency (EPA), many government agencies and private vendors. GIS mapping is used to visualize and identify variations in neighborhood attributes at the most detailed level (e.g census tracts). Factor analysis is then used to reduce the number of indicators to a small set of dimensions that capture essential differences in neighborhood types in terms of social, economic, and environmental dimensions. These factors loadings are used as inputs to a cluster analysis to identify unique neighborhood types. Finally, different types of neighborhoods are visualized using a GIS tool for further evaluation.The proposed quantitative analysis will help illustrate variations in neighborhood types and their spatial patterns in the Baltimore metropolitan region.  This framework offers new insights on what is a sustainable neighborhood.

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
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
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.

How to Increase Rail Ridership in Maryland? Direct Ridership Models (DRM) for Policy Guidance

Authors: Liu, Chao, Sevgi Erdogan, Ting Ma, and Frederick W. Ducca
Synopsis: The state of Maryland aims to double its transit ridership by the end of 2020. The Maryland Statewide Transportation Model (MSTM) has been used to analyze different policy options at a system-wide level. Direct ridership models (DRM) estimate ridership as a function of station environment and transit service features rather than using mode‐choice results from large‐scale traditional models. They have been particularly favored for estimating the benefits of smart growth policies such as Transit Oriented Development (TOD) on transit ridership and can can be used as complementary to the traditional four-step models for analyzing smart growth scenarios at a local level and can provide valuable information that a system level analysis cannot provide. In this study, we developed DRMs of rail transit stations, namely light rail, commuter rail, Baltimore metro, and Washington D.C. metro for the state of Maryland. Data for 117 rail stations were gathered from a variety of sources and categorized by transit service characteristics, station built environment features and social-demographic variables. The results suggest that impacts of built environment show differences for light rail and commuter rail. For light rail stations, employment at half-mile buffer areas, service level, feeder bus connectivity, station location in the CBD, distance to the nearest station, and terminal stations are significant factors affecting ridership. For commuter rail stations only feeder bus connection is found to be significant. The policy implications of the results are discussed.