Yasir Ali

Lecturer

Loughborough University

United Kingdom

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Yasir Ali is a Lecturer at Loughborough University in the United Kingdom. His research areas include machine learning applications in traffic flow modeling, driver behavior analysis, and the impact of emerging vehicle technologies. Recent articles highlight his work on the dynamics of autonomous vehicle lane changing, pedestrian crash risk analysis utilizing artificial intelligence, and the effects of congestion charging on car ownership.

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2021

Collaborators (7)

He Haitao

Lecturer / Assistant Professor

Loughborough University

UNITED KINGDOM

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MD. MAZHARUL HAQUE

Queensland University of Technology

AUSTRALIA

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Michiel Bliemer

Professor of Transport Planning and Modelling

University of Sydney

AUSTRALIA

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Zuduo Zheng

Professor

University of Queensland

AUSTRALIA

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Shamsunnahar Yasmin

Queensland University of Technology

AUSTRALIA

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Mehmet Yildirimoglu

Associate Professor

University of Queensland

AUSTRALIA

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Craig Morton

Senior Lecturer

Loughborough University

UNITED KINGDOM

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Yasir Ali

Research Interests

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Articles (26)

A physics-informed risk force theory for estimating pedestrian crash risk by severity using artificial intelligence-based video analytics

A systematic review of machine learning-based microscopic traffic flow models and simulations

Modelling gap selection of mobile phone distracted drivers at roundabouts: A mixed multinomial logit with heterogeneity-in-means approach

How do the drivers react to different C-V2X-based communication conditions in dilemma zones? A driving simulator study

Autonomous vehicle lane-changing dynamics and impact on the immediate follower

Examining collision avoidance behavior of distracted drivers: A correlated grouped random parameters accelerated failure time model with heterogeneity-in-means

Empirical research on car-following and lane-changing: Recent developments, emerging vehicle technologies’ impact, and future research needs

The impact of congestion charging on car ownership: Evidence from a quasi-natural experiment

Effects of sample size on pedestrian crash risk estimation from traffic conflicts using extreme value models

Pedestrian crash risk analysis using extreme value models: New insights and evidence

Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data

Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review

Before-after safety evaluation of part-time protected right-turn signals: An extreme value theory approach by applying artificial intelligence-based video analytics

Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Empirical evidence of connected environment driving on fuel consumption and emissions

Modelling speed reduction behaviour on variable speed limit-controlled highways considering surrounding traffic pressure: A random parameters duration modelling approach

Calibrating lane-changing models: Two data-related issues and a general method to extract appropriate data

Assessing a Connected Environment’s Safety Impact During Mandatory Lane-Changing: A Block Maxima Approach

Effects of partial replacement of fine aggregates with crumb rubber on skid resistance and mechanical properties of cement concrete pavements

Quantifying the Differential Phase Angle Behaviour of Asphalt Concrete Mixtures Using Artificial Neural Networks

Modelling lane-changing execution behaviour in a connected environment: A grouped random parameters with heterogeneity-in-means approach

Alternative Approach for Predicting the Phase Angle Characteristics of Asphalt Concrete Mixtures Based on Recurrent Neural Networks

Stop or go decisions at the onset of yellow light in a connected environment: A hybrid approach of decision tree and panel mixed logit model

An eXtreme Gradient Boosting model for predicting dynamic modulus of asphalt concrete mixtures

CLACD: A complete LAne-Changing decision modeling framework for the connected and traditional environments

Collaborators (7)

He Haitao

MD. MAZHARUL HAQUE

Michiel Bliemer

Zuduo Zheng

Shamsunnahar Yasmin

Mehmet Yildirimoglu

Craig Morton

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