Uday R R Manepalli

Longitudinal Evaluation of a GIS Laboratory in a Transportation Engineering Course

This paper focuses on the potential impact of student-centered feedback for enhancing the learning experience of civil engineering students that used a geographic information system (GIS) based tutorial in a transportation engineering course. The tutorial was implemented in a laboratory environment developed as a self-guided activity supported by a web-based learning system. The formative research proposed in this study includes a series of four successive implementations of this laboratory. Students’ performance, beliefs, and perceptions were monitored by using a mixed-methods design approach and weaknesses identified from early implementations were addressed before the next implementation of the laboratory activity. The students’ performance was found to improve when the GIS web-based tutorial was complemented with an instructor-driven short introduction that anchored the laboratory activity in traffic safety. In addition, students’ feedback in both quantitative and qualitative format indicated weakness...

Identification of Crash-Contributing Factors: Effects of Spatial Autocorrelation and Sample Data Size

Sample sets of crash data are used to examine the similarities in crash-contributing factors among various counties in the state of Arkansas that have similar effects on spatial autocorrelation. Morans I and Getis–Ord Gi* statistics were used to determine the correlation, and multinomial logistic regression was used to identify the crash-contributing factors. Seventy-five counties were divided into five categories on the basis of the Z-values of the Getis–Ord Gi* statistic. Depending on the sample data size, for each category crash data from a county or a group of counties were used, and crash-contributing factors were identified on the basis of the crash severity index. Results indicated that most of the crash-contributing factors identified for each category were also identified by the sample crash data from a county or a group of counties in that category. Pulaski County, with the highest Z-value from the first category, had the largest cluster of crashes and identified the highest percentage (55%) of factors that contributed to crashes in the category by using the sample crash data. From the sample data used, the multinomial logistic regression indicated the following factors to be positively associated with crash severity: nighttime driving, driving under the influence of alcohol, roadway gradient, alignment on a curve, rural areas, and collision types head-on and sideswipe-same-direction. The results of this research can be used for better allocation of funds by departments of transportation by analyzing smaller sets of data to identify crash-contributing factors associated with higher levels of crash severity.

A composite rank measure based on principal component analysis for hotspot identification on highways

ABSTRACT Identification of hotspots or high-crash locations on highways is important to ensure public safety and minimize loss of life, risk of injury and/or trauma; reduce crash cost to society; and any interruption to traffic flow. Many performance measures are available for use in identification of hotspots. Current performance measures, however, suffer from lack of accounting for traffic volume, crash injury severity, justification of weights used for ranking of sites, and issues with statistical distribution of crash data. This article proposes a composite rank measure based on principal component analysis to overcome limitations of existing measures for network screening. The results of a proposed measure called Composite Principal Rank Measure (CPRM) is demonstrated with interstate, U.S. and state highway data and compared against the commonly used sum-of-rank (SOR) measure. CPRM and SOR are evaluated based on several empirical and simulated data tests. CPRM was found to be robust and outperformed the SOR measure and is recommended for identification of hotspots. To ensure extensive evaluation of the measures, the entire highway routes are examined in this article.

Application of Spatial Statistics in Transportation Engineering

Everything is related to everything else, but near things are more related than distant things” is the first law of geography. It can be hypothesized that spatially, occurrence of a crash can exhibit similarities. To identify spatial patterns of crashes, this chapter presents spatial autocorrelation techniques such as Moran’s I and the Getis-Ord Gi*statistics; spatial interpolation such as kriging; and nonparametric probability density function and kernel density (K). The aim of this chapter is to provide application of spatial statistics in transportation engineering specifically to identify crash concentrations and patterns of clusters in a study area.