Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.

Objectives: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe injury. The number of conflicts in different severity levels categorized by the safety index is used as an explanatory variable for developing statistical models for pro-actively estimating crashes.

Methods: Video-derived conflicts in different severity levels between left-turning vehicles and opposing through vehicles, a well-recognized severe injury crash typology at signalized intersections, were identified by jointly integrating the indicators of frequency and severity, using an autoencoder neural network integration method to develop anomaly scores.

Road safety performance measures and AADT uncertainty from short-term counts.

Objective: The objective of this paper is to enable better risk analysis of road safety performance measures by creating the first knowledge base on uncertainty surrounding annual average daily traffic (AADT) estimates when the estimates are derived by expanding short-term counts with the individual permanent counter method.

Background: Many road safety performance measures and performance models use AADT as an input. While there is an awareness that the input suffers from uncertainty, the uncertainty is not well known or accounted for.

Value of a statistical life in road safety: a benefit-transfer function with risk-analysis guidance based on developing country data.

We model a value of statistical life (VSL) transfer function for application to road-safety engineering in developing countries through an income-disaggregated meta-analysis of scope-sensitive stated preference VSL data. The income-disaggregated meta-analysis treats developing country and high-income country data separately. Previous transfer functions are based on aggregated datasets that are composed largely of data from high-income countries.

Performance measures and input uncertainty for pedestrian crossing exposure estimates.

Pedestrian safety performance measures often use estimates of annual crossing exposure as inputs-but relatively little information exists on the uncertainty associated with these inputs. This research considers two sources of temporal information for expanding short-term counts: (1) a composite of pedestrian counts from other cities, and (2) local vehicle counts. A database of pedestrian flows from video review covering 12 months and including over 350,000 pedestrian observations provides a known reference annual volume and a set of short-term counts for expansion and testing.