Publication

Can safety indicators assess and monitor road traffic risk in real-time? Investigation of two safety indicators on Swiss motorways

Related publications (31)

Social-Transmotion: Promptable Human Trajectory Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao, Kaouther Messaoud Ben Amor

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

2024

Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety

Jeffrey Huang, Simon Elias Bibri

In the context of smart cities, ensuring road safety is crucial due to increasing urbanization and the interconnected nature of contemporary urban environments. Leveraging innovative technologies is essential to mitigate risks and create safer communities. ...

Springernature2024

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Zhixiong Jin

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

MDPI2023

Pedestrian Crossings as a Means of Reducing Conflicts between Cyclists and Pedestrians in Shared Spaces

Evangelos Paschalidis

One significant and simultaneously interesting problem in urban mobility has to do with the study of shared spaces where various categories of users coexist and act together. This paper aims to examine the behavior and preferences of pedestrians and cyclis ...

2023

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

Simon Elias Bibri

In smart cities, ensuring road safety and optimizing transportation efficiency heavily relies on streamlined road condition monitoring. The application of Artificial Intelligence (AI) has notably enhanced the capability to detect road surfaces effectively. ...

Springernature2023

Haptics based collaborative steering control in automated driving environments

Tomohiro Nakade

A vehicle's steering is a particular system in that it is exposed to individual subjective reviews based on criteria that are hard to assess quantitatively. Haptic design of such systems is a prime concern that has been at the center of industrial developm ...

EPFL2023

Multivariate time series forecasting for freeway networks

Semin Kwak

This dissertation introduces traffic forecasting methods for different network configurations and data availability. Chapter 2 focuses on single freeway cases. Although its topology is simple, the non-linearity of traffic features makes this prediction sti ...

EPFL2022

Controlling pedestrian flows with moving walkways

Michel Bierlaire, Nicholas Alan Molyneaux

Moving walkways are pedestrian dedicated hardware which generally decrease pedestrian travel time. We propose the utilization of these devices to dynamically control pedestrian flows in order to improve pedestrian dynamics. Three variations of a control st ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Large-scale traffic signal control and multimodal network design

Dimitrios Tsitsokas

Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...

EPFL2022

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Alexandre Massoud Alahi, Taylor Ferdinand Mordan, Dongxu Guo

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

2022

Pedestrian intention prediction: A convolutional bottom-up multi-task approach

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestri ...

2021

Modelling transport time to trauma centres and 30-day mortality in road accidents in Switzerland: an exploratory study

Stéphane Joost

BACKGROUND: Rapid access to a trauma centre for severely injured road accident victims, conceptualised as the Golden Hour, links access time to definitive treatment within 1 hour of trauma with reduced risks of morbidity and mortality. Access times have no ...

E M H SWISS MEDICAL PUBLISHERS LTD2021

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. The ...

IEEE Institute of Electrical and Electronics Engineers2022

Travel Time Prediction for Congested Freeways With a Dynamic Linear Model

Nikolaos Geroliminis, Semin Kwak

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...

IEEE Institute of Electrical and Electronics Engineers2021

Dynamic control strategies for managing pedestrian flows

Nicholas Alan Molyneaux

Pedestrians, like drivers, generally dislike congestion. This is true for most pedestrian environments: trains stations, airports, or shopping malls. Furthermore, pedestrian congestion also influences the attractiveness of public transportation networks. T ...

EPFL2021

Empirical analysis of large-scale multimodal traffic with multi-sensor data

Nikolaos Geroliminis, Nan Zheng, Hui Fu

Recent advances in the network-level traffic flow modelling provide an efficient tool for analyzing traffic performance of large-scale networks. A relationship between density and flow at the network level is developed and widely studied, namely the macros ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Optimization-based Control, Estimation, and Identification of Urban Road Transport Systems

Isik Ilber Sirmatel

Urbanization intensifies as a global trend, exposing transportation networks to ever increasing levels of congestion. As network usage increases with available infrastructure, building new roads is not a solution. Design of intelligent transportation syste ...

EPFL2020

A Measure of Dynamical Efficiency for Multimodal Interconnected Urban Systems

Nikolaos Geroliminis, Leonardo Bellocchi

Complex network theory describes network performance mostly based on topological characteristics, like betweenness centrality. This work integrates concepts from complex networks and traffic engineering. We propose a new measure for spatial networks and, i ...

2020

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

IEEE2020

Model-based Identification, Estimation, and Control for Large-scale Urban Road Networks

Nikolaos Geroliminis, Isik Ilber Sirmatel

Network-level road traffic control remains a challenging problem. Macroscopic fundamental diagram (MFD) based dynamical models of large-scale urban networks enable development of model predictive perimeter control methods, which represent an efficient cong ...

IEEE2020