Ensuring adequate pavement cross-slope on highways can improve driver safety by reducing the potential for ponding to occur or vehicles to hydroplane. Mobile laser scanning (MLS) systems provide a rapid, continuous, and cost-effective means of collecting accurate 3D coordinate data along a corridor in the form of a point cloud. This study provides an evaluation of MLS systems in terms of the accuracy and precision of collected cross-slope data and documentation of procedures needed to calibrate, collect, and process this data. Mobile light detection and ranging (LiDAR) data were collected by five different vendors on three roadway sections. The results indicate the difference between ground control adjusted and unadjusted LiDAR derived cross-slopes, and field surveying measurements less than 0.19% at a 95% confidence level. The unadjusted LiDAR data incorporated corrections from an integrated inertial measurement unit and high-accuracy real-time kinematic GPS, however it was not post-processed adjusted with ground control points. This level of accuracy meets suggested cross-slope accuracies for mobile measurements (±0.2%) and demonstrates that mobile LiDAR is a reliable method for cross-slope verification. Performing cross-slope verification can ensure existing pavement meets minimum cross-slope requirements, and conversely is useful in identifying roadway sections that do not meet minimum standards, which is more desirable than through crash reconnaissance where hydroplaning was evident. Adoption of MLS would enable the South Carolina Department of Transportation (SCDOT) to address cross-slope issues through efficient and accurate data collection methods.

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Although several studies have been undertaken on the association between built environmental characteristics and travel patterns in western societies, the impacts of the local built environment on individuals’ travel behavior considering the specific conditions of developing nations have remained largely unknown. Thus, this paper investigates the travel behavior effects of local planning and design in three residential neighborhoods of Shiraz, a city in the southwest of Iran. The data on land use and built environment characteristics were extracted primarily from an existing digital map and GIS, whereas the data on individuals’ socioeconomics and their daily travel behavior were purposefully collected using a field questionnaire survey (n=393). A nested logit model (NLM) based on the microeconomic utility concept was then applied to discover the impacts of personal characteristics and built environment factors on the choice mode of the individuals. The results and the associated policy implications can be helpful in defining a strategic agenda for neighborhood design and planning.