Application of surrogate safety measures in higher levels of automated vehicles simulation studies: A review of the state of the practice.

Objective: Surrogate safety measures (SSMs) are developed and applied as alternatives or complements of safety analyses mainly due to important road crash data availability and reliability limitations. Automated vehicles (AVs) have recently emerged as a prominent solution to mitigate transport externalities and increase road traffic safety. Due to the novelty of the technology and the lack of real-world data, traffic simulation combined with SSMs is the most common approach to quantify their impact.

Multinomial logistic regression for prediction of vulnerable road users risk injuries based on spatial and temporal assessment.

Urban area's rapid growth often leads to adverse effects such as traffic congestion and increasing accident risks due to the expansion in transportation systems. In the frame of smart cities, active modes are expected to be promoted to improve living conditions. To achieve this goal, it is necessary to reduce the number of vulnerable road users (VRUs) injuries.