Understanding the Multiple Dimensions of Residential Choice

At the time of publication, X. Fu was at the Shanghai Jiao Tong University, C.R. Bhat at the University of Texas at Austin, R.M. Pendyala at Georgia Institute of Technology, S. Vladlamani and V.M Garikapati at Arizona State University.Residential choice may be characterized as a household’s simultaneous decisions of location, neighborhood, and dwelling. Traditional models do not account for the latent unmeasured constructs which capture individuals’ preferences for and attitudes towards residence and mode choice. This paper employs Bhat’s (2014) Generalized Heterogeneous Data Model (GHMD) to accommodate five inter-related residential choice dimensions, including residential location, neighborhood land-use pattern, public transportation availability, housing type, and dwelling ownership. Four latent variables including pro-driving, pro-public transportation, facility availability, and residential spaciousness are constructed to capture individuals’ attitudes towards travel modes and preferences for residential features. The inclusion of these latent constructs helps account for self-selection effects in residential choice processes. The determination of relationships among multiple dimensions of residential choice behavior, socio-demographics, and latent attitudes and preferences is critical to integrated land use – transport modeling and the formulation of policies as well as urban residential and neighborhood environments that cater to individual preferences and enhance quality of life.Civil, Architectural, and Environmental Engineerin

Identifying large truck hot spots using crash counts and PDOEs

Large trucks are involved in a disproportionately small fraction of the total crashes but a disproportionately large fraction of fatal crashes. Large truck crashes often result in significant congestion due to their large physical dimensions and from difficulties in clearing crash scenes. Consequently, preventing large truck crashes is critical to improving highway safety and operations. This study identifies high risk sites (hot spots) for large truck crashes in Arizona and examines potential risk factors related to the design and operation of the high risk sites. High risk sites were identified using both state of the practice methods (accident reduction potential using negative binomial regression with long crash histories) and a newly proposed method using Property Damage Only Equivalents (PDOE). The hot spots identified via the count model generally exhibited low fatalities and major injuries but large minor injuries and PDOs, while the opposite trend was observed using the PDOE methodology. The hot spots based on the count model exhibited large AADTs, whereas those based on the PDOE showed relatively small AADTs but large fractions of trucks and high posted speed limits. Documented site investigations of hot spots revealed numerous potential risk factors, including weaving activities near freeway junctions and ramps, absence of acceleration lanes near on-ramps, small shoulders to accommodate large trucks, narrow lane widths, inadequate signage, and poor lighting conditions within a tunnel