Marco Dozza

2025

Understanding factors influencing e-scooterist crash risk: A naturalistic study of rental e-scooters in an urban area

When is it Safe to Complete an Overtaking Maneuver? Modeling Drivers' Decision to Return After Passing a Cyclist

Naturalistic micromobility data: opportunities and threats

2024

Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads

2024

Understanding the interaction between cyclists and motorized vehicles at unsignalized intersections: Results from a cycling simulator study

How do drivers interact with cyclists at unsignalized intersections? A driving simulator study

Micromobility: new road-user interactions in the urban landscape

The right turn: Investigating interactions between drivers and e-scooter riders

Seeing is Believing: How Artificial Eyes Are Making Micromobility Safer

2023

Drivers passing cyclists: How does sight distance affect safety? Results from a naturalistic study

2023

How do drivers pass cyclists on European roads? Toward a common regulation for overtaking across Europe

2023

How do expert and non-expert drivers interact with cyclists at unsignalized intersections? Results from naturalistic data

2023

Modelling Braking and Steering Avoidance Maneuvers for Micromobility

2023

How do cyclists interact with motorized vehicles at unsignalized intersections? Modeling cyclists’ yielding behavior using naturalistic data

E-Scooters: Transport or leisure? Findings from naturalistic data collection

2023

Modeling collision avoidance maneuvers for micromobility vehicles

Micromobility: an opportunity for future transport and a challenge for intelligent vehicles (Keynote)

The effect of speed on driver behavior when overtaking cyclists: Results from driving-simulator and test-track data

2023

Data and data collection methodologies for the development of computational models of AV/VRU interaction and their integration into virtual simulation testing of AV : Deliverable 2.3 in the EC ITN project SHAPE-IT

2023

Driver response to take-over requests in real traffic

2023

How do different micro-mobility vehicles affect longitudinal control? Results from a field experiment

2022

Drivers’ and cyclists’ safety perceptions in overtaking maneuvers

2022

A data-driven framework for the safe integration of micro-mobility into the transport system: Comparing bicycles and e-scooters in field trials

2022

Understanding the interaction between cyclists and automated vehicles: Results from a cycling simulator study

2022

Modeling the Braking Behavior of Micro-Mobility Vehicles

2022

On the evaluation of visual nudges to promote safe cycling: Can we encourage lower speeds at intersections?

2022

Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Data Augmentation via Neural-Style-Transfer for Driver Distraction Recognition

2022

It’s about time! Earlier take-over requests in automated driving enable safer responses to conflicts

2022

Automation aftereffects: the influence of automation duration, test track and timings

2022

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer

2022

Drivers overtaking cyclists on rural roads: How does visibility affect safety? Results from a naturalistic study

2021

Driver conflict response during supervised automation: Do hands on wheel matter?

Social-IWSTCNN: A social interaction-weighted spatio- temporal convolutional neural network for pedestrian trajectory prediction in urban traffic scenarios

Modelling duration of car-bicycles overtaking manoeuvres on two-lane rural roads using naturalistic data

Users’ response to critical situations in automated driving: rear-ends, sideswipes, and false warnings

2021

The development of cycling in european countries since 1990

2020

How do drivers negotiate intersections with pedestrians? The importance of pedestrian time-to-arrival and visibility

2020

How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data

2020

How do oncoming traffic and cyclist lane position influence cyclist overtaking by drivers?

2020

Modelling cyclists’ comfort zones from obstacle avoidance manoeuvres

2020

What is the relation between crashes from crash databases and near crashes from naturalistic data?

2020

Modelling discomfort: How do drivers feel when cyclists cross their path?

Editorial: Cycling Safety

2020

A Bayesian reference model for visual time-sharing behaviour in manual and automated naturalistic driving

2020

A computational driver model to predict driver control at unsignalised intersections

Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data

2020

Modelling Interaction between Cyclists and Automobiles - Final Report

2020

A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres

Modelling overtaking strategy and lateral distance in car-to-cyclist overtaking on rural roads: A driving simulator experiment

Drivers’ Decision Making of Overtaking Strategies of Cyclists on Rural Roads–A Driving Simulator Experiment

E-bikers’ braking behavior: Results from a naturalistic cycling study

2019

A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance

Drivers overtaking cyclists in the real-world: evidence from a naturalistic driving study

Modelling how drivers respond to a bicyclist crossing their path at an intersection: How do test track and driving simulator compare?

Definition of run-off-road crash clusters - for safety benefit estimation and driver assistance development

Investigating cycling kinematics and braking maneuvers in the real world: e-bikes make cyclists move faster, brake harder and experience new conflicts

A new framework for modelling road-user interaction and evaluating active safety systems

Cycling safety

Safety Science Special Issue on Cycling Safety

Crash Risk: How Cycling Flow Can Help Explain Crash Data

An Open-Source Data Logger for Field Cycling Collection: Design and Evaluation

On the use of naturalistic methods to examine safety-relevant behaviours amongst children and evaluate a cycling education program

Car drivers overtaking cyclists: A European perspective using naturalistic driving data

BikeModel: modeller för cyklistbeteende - Slutrapport

Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems

Using Wireless Communication to Control Road-user Interactions in the Real World

How do drivers negotiate intersections with pedestrians? Fractional factorial design in an open-source driving simulator

2016

Using naturalistic data to assess e-cyclist behavior

2016

Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses

NoRisk2Bike - Slutrapport

What is the relation between crashes from crash databases and near-crashes from naturalistic data?

Evaluation of a new narrow and tiltable electric tricycle (e-trike) concept

2016

How do drivers overtake cyclists?

Combining Data Sets for Analysis of Cycling Safety: How Detailed Exposure Data Can Help Explain Crash Data

2015

Real-world effects of using a phone while driving on lateral and longitudinal control of vehicles

2015

How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2

e-BikeWay Slutrapport

2015

Driving context influences drivers' decision to engage in visual-manual phone tasks: evidence from a naturalistic driving study

Safety-critical events in everyday cycling - Interviews with bicyclists and video annotation of safety-critical events in a naturalistic cycling study

2015

On the Potential of Accelerating an Electrified Lead Vehicle to Mitigate Rear-End Collisions

Do cyclists on e-bikes behave differently than cyclists on traditional bicycles?

Integrating road safety data for single-bicycle crash causation

Platform Enabling Intelligent Safety Applications for Vulnerable Road Users

e-BikeSAFE (TRV2013/14367) Slutrapport

Understanding Bicycle Dynamics and Cyclist Behavior from Naturalistic Field Data

Introducing naturalistic cycling data: What factors influence bicyclists' safety in the real world?

2014

Driving context and visual-manual phone tasks influence glance behavior in naturalistic driving

Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk

BikeSAFE (TRV2012/13373) Slutrapport

Safer Glances, Driver Inattention, and Crash Risk: An Investigation Using the SHRP 2 Naturalistic Driving Study

2013

Chunking: a procedure to improve naturalistic data analysis

Farlig å blande trafikanter

What factors influence drivers' response time for evasive maneuvers in real traffic?

BikeSAFE – Analysis of Safety-Critical Events from Naturalistic Cycling Data

BikeCOM – A cooperative safety application supporting cyclists and drivers at intersections

What is the Relation between Bicycle Dynamics and Safety in the Real World?

Dialling, texting, and reading in real world driving: When do drivers choose to use mobile phones?

Understanding driver self-regulating behavior: how does phone use influence vehicle control in real world?

“Please, bike and tell me when you feel unsafe!” – Analysis of safety-critical situations from a cyclist’s perspective in a naturalistic cycling study

Recognising safety critical events in real traffic: Can automatic video processing be used to improve naturalistic data analyses?

BikeSAFER: Final Report

Initial Analyses from the SHRP 2 Naturalistic Driving Study - Addressing Driver Performance and Behavior in Traffic Safety

e-BikeSAFE: A Naturalistic Cycling Study to Understand how Electrical Bicycles Change Cycling Behaviour and Influence Safety.

Collection of naturalistic bicycling data is now ongoing

Set-up and real-traffic assessment of an active-safety platform for vulnerable-road-users

preBikeSAFE (TRV 2011 86383) Slutrapport

2012

Piloting the Naturalistic Methodology on Bicycles

Investigating visually distracted driver reactions in rear-end crashes and near crashes based on 100-car study data

Recognizing Safety-critical Events from Naturalistic Driving Data

2012

Deliverable D3.3: Data management in euroFOT

Timing Matters: Visual behaviour and crash risk in the 100‐car on‐line data

On data security and analysis platforms for analysis of naturalistic driving data

An Open Customizable Modular Platform For Analysis of Human Movement in Laboratory and Outdoors

What is the most effective type of audio-biofeedback for postural motor learning?

FOTware: a modular, customizable software for analysis of multiple-source fieldoperational- test data

euroFOT: constrains and trade-offs in testing hypotheses

Chunking: a Method to Increase Robustness of Naturalistic Field-Operational-Test Data Analysis

SAFER100Car: a toolkit to analyze data from the 100 Car Naturalistic Driving Study

Vibrotactile biofeedback improves tandem gait in patients with unilateral vestibular loss.

Effects of practicing tandem gait with and without vibrotactile biofeedback in subjects with unilateral vestibular loss.

Auditory biofeedback substitutes for loss of sensory information in maintaining stance.

Effects of linear versus sigmoid coding of visual or audio biofeedback for the control of upright stance.

Audio-biofeedback improves balance in patients with bilateral vestibular loss.

Audio-biofeedback for balance improvement: an accelerometry-based system.

Influence of a portable audio-biofeedback device on structural properties of postural sway.