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

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

GreenEVT: Greensboro Electric Vehicle Testbed

Nils Gustav Nilsson

The ongoing electrification of the transportation fleet will increase the load on the electric power grid. Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures wil ...

Ieee-Inst Electrical Electronics Engineers Inc2024

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Nikolaos Geroliminis, Can Chen

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

Informs2024

The Strong Integral Input-to-State Stability Property in Dynamical Flow Networks

Nils Gustav Nilsson

Dynamical flow networks serve as macroscopic models for, e.g., transportation networks, queuing networks, and distribution networks. While the flow dynamics in such networks follow the conservation of mass on the links, the outflow from each link is often ...

Ieee-Inst Electrical Electronics Engineers Inc2024

A hybrid microscopic model for multimodal traffic with empirical observations from aerial footage

Nikolaos Geroliminis, Georgios Anagnostopoulos

Microscopic traffic flow models can be distinguished in lane-based or lane-free depending on the degree of lane-discipline. This distinction holds true only if motorcycles are neglected in lane-based traffic. In cities, as opposed to highways, this is an o ...

2023

Modeling and Optimization of Ridesplitting Operations

Caio Vitor Beojone

Ridesourcing has driven a lot of attention in recent years with the expansion of companies like Uber, Lift, and many others around the world. Companies use mobile applications connected through the internet to match drivers and their passengers real-time. ...

EPFL2023

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

Optimizing the utilization of existing vehicle flows in last-mile passenger transport and logistics systems

Patrick Stefan Adriaan Stokkink

The new era of shared economy has raised our expectations to make mobility more sustainable through better utilization of existing resources and capacity. In this thesis, we focus on the design of transport systems that stimulate multi-purpose trips with t ...

EPFL2023

Worst-Case Delay Analysis of Time-Sensitive Networks with Network Calculus

Seyed Mohammadhossein Tabatabaee

Time-sensitive networks, as in the context of IEEE Time-Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet), offer deterministic services with guaranteed bounded latency in order to support safety-critical applications. In this thesis, we ...

EPFL2023

Cycling Interaction Rituals in the Conflict against the Car. From the Bike Subculture to the City Scale and Beyond

Alexandre André Robert Rigal

Mobility scholars have long been interested in challenging the automobile's hegemony in the street, particularly by highlighting how to develop urban cycling. The article contributes to this task by explaining how one type of conflict between the bike and ...

Taylor & Francis Ltd2023