Learning the representation of surrogate safety measures to identify traffic conflict.

Traffic conflict can be identified by the presence of evasive actions or the amount of temporal (spatial) proximity measures like time-to-collision (TTC). However, it is not enough to use only one kind of measures in some scenarios and it is hard to set a threshold for those measures. This paper proposed a method to identify traffic conflict by learning the representation of TTC and driver maneuver profiles with deep unsupervised learning and clustering the representations into traffic conflict and non-conflict clusters.

Developing a safety heatmap of uncontrolled intersections using both conflict probability and severity.

This paper presents a method to assess the safety of uncontrolled intersections considering two major properties of traffic conflicts-conflict probability and severity. This method assesses both the safety level of the entire intersection in addition to the distribution of safety within it. Intersections are modeled by a two-dimensional Cartesian coordinate system and the internal space of intersections is divided into cells.

Investigating the underreporting of pedestrian and bicycle crashes in and around university campuses - a crowdsourcing approach.

In this paper, the non-motorized traffic safety concerns in and around three university campuses are evaluated by comparing police-reported crash data with traffic safety information sourced from the campus communities themselves. The crowdsourced traffic safety data comprise of both self-reported crashes as well as perceived hazardous locations. The results of the crash data analysis reveal that police-reported crashes underrepresent non-motorized safety concerns in and around the campus regions.

After-effects and disabilities in traffic crash victims in northern Brazil.

Objectives: The objective of this study was to identify the characteristics related to crash and victim, as well as the after-effects/disabilities and consequences arising from traffic crashes occurring in the city of Rio Branco-Acre.

Methods: This is an analytical descriptive cross-sectional study conducted in the City of Rio Branco-Acre. The study population consisted of 405 residents of the city who were victims of traffic crashes, of all age groups and genders, who were hospitalized for the first time as a result of the crash in public hospitals and the health system network, as recorded in the Hospital Information System, and who were discharged between January 1 and December 31, 2010.

Dynamic programming-based hot spot identification approach for pedestrian crashes.

Network screening techniques are widely used by state agencies to identify locations with high collision concentration, also referred to as hot spots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile collisions. In comparison, pedestrian hot spot detection has typically focused on analyzing pedestrian crashes in specific locations, such as at/near intersections, mid-blocks, and/or other crossings, as opposed to long stretches of roadway.