Journal of Transportation Engineering, Part A: Systems

Road crashes are mainly caused by three concurrent factors: infrastructure, vehicles, and human factors. The interaction between infrastructure and human factors leads to the concept of geometric design consistency, which can be defined as how drivers’ expectations and road behavior fit. This paper presents a new global consistency model based on the difference between the inertial operating speed profile (${\mathit{V}}_{\mathit{i}}$) and the operating speed profile ($V_{85}$). The first is calculated as the weighted average speed of the previous road section and represents drivers’ expectations, whereas the second represents road behavior. A set of 71 homogeneous two-lane rural road segments located in Italy were used in the calibration of the model. As a result, a safety performance function based on this new consistency model was proposed to estimate the number of crashes on an entire road segment. Finally, the new model was compared with previous global consistency models, concluding that the new consistency parameter better explains the phenomenon than the previous ones. Therefore, the new consistency model is a useful tool for engineers that allows estimation of the number of crashes and incorporates road safety into the geometric design of both new two-lane rural roads and improvements to existing highways.

The purpose of the present study is to examine whether mining and analyzing social media data can be a powerful tool in the transportation domain. A survey of the literature based on the existing uses of social media in transportation is conducted; opportunities and barriers are also presented for the subject. Analysis of social media can provide valuable information regarding incident detection, mobility, and activity patterns as well as users’ opinions about different transport modes. The issues that need to be addressed are not few, with most important ones being the advanced mining and linguistic techniques required for the extraction of information, the reliability of the data collected, and the sample bias. The study concludes with recommendations in relation to the existing gaps in the literature, such as the need to create a transport-oriented lexicon to facilitate the process of collecting transport-related information from social media, use of social media during transport planning and operation, and finally, potential use of qualitative indicators on public transportation issues regarding the perceived level of service.

Access density is defined as the number of accesses per unit length along an arterial. Numerous studies conducted in various regions have indicated that access density has a significant influence on crash occurrences and severities. However, these research findings tend to simplify the relationship between access density and crash attributes and overlook the distinctive local roadway geometric and traffic flow characteristics. This study was conducted to quantitatively understand the impacts of various access densities on the safety performance of major arterials in New Mexico. A cluster analysis and a negative binomial model have been used through computational analysis to investigate the relationship between access density and crash rate. The analysis results demonstrate the piecewise relationship and verify that access density imposes heterogeneous influences on crash rates given different access density ranges, and lower public and commercial access rates are associated with lower crash rates. The impacts of other access features, such as access usage type and median opening type, on crash rates are also investigated. The research findings are helpful to improve safety performance on major arterials in urban metropolitan areas.