From the 1 of 6 linked papers with an AI index.
6 papers
Q-SCM: A Quantum-Sequential Choice Model for Driver Mental State Evolution
Rulla Al-Haideri, Bilal Farooq, Karim Ismail
The paper introduces a model that uses quantum concepts to represent how drivers' mental states evolve in response to traffic cues, capturing the influence of cue history and order…
Assessment of Latent Pedestrian-Vehicle Interaction Risk Profiles at Midblock Crossing in VR
Rulla Al-Haideri, Bilal Farooq, Elisabetta Cherchi
Pedestrian safety at midblock crossings is a critical concern in mixed traffic environments where autonomous vehicles (AVs) and human-driven vehicles (HDVs) share the road. Pedestr…
From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction
Rulla Al-Haideri, Bilal Farooq
High frequency pedestrian motion forecasting when interacting with autonomous vehicles (AVs) can be enhanced through the use of behavioural frameworks, such as discrete choice mode…
Modelling Pedestrian Behaviour in Autonomous Vehicle Encounters Using Naturalistic Dataset
Rulla Al-Haideri, Bilal Farooq
Understanding how pedestrians adjust their movement when interacting with autonomous vehicles (AVs) is essential for improving safety in mixed traffic. This study examines micro-le…
Latent Class Logit Kernel Framework for Surrogate Safety: Identifying Behavioural Thresholds through Conflict Indicator Profiles
Rulla Al-Haideri, Changhe Liu, Karim Ismail +2
Crash data objectively characterize road safety but are rare and often unsuitable for proactive safety management. Traffic conflict indicators such as time-to-collision (TTC) provi…
Towards the Safety-Relevant Dimension of Driver Behaviour: A Dual-State Model
Rulla Al-Haideri, Karim Ismail, Bilal Farooq +1
We make a methodological contribution by introducing a new dimension of traffic conflict severity: the probability that a driver is in a defensive state. This behavioural probabili…