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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 ...

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. ...

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 ...

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 ...

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. ...

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 ...

Residential ventilative cooling via natural ventilation is influenced by outdoor air pollution. However, relative to climate, outdoor air pollution is not comprehensively considered in determining the ventilative cooling potential of buildings. To assess t ...

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 ...

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 ...

This work evaluates the aerosol oxidative potential (OP) and its changes from modified air pollution emissions during the COVID-19 lockdown period in 2020, with the intent of elucidating the contribution of aerosol sources and related components to aerosol ...

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 ...

Modeling urban traffic on the level of network is a wide research area oriented to the development of ITS. In this thesis properties of models based on MFD (Macroscopic Fundamental Diagram) are studied. The idea behind MFD is to say that the state of the t ...

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 ...

Most logic synthesis algorithms work on graph representations of logic functions with nodes associated with arbitrary logic expressions or simple logic functions and iteratively optimize such graphs. While recent multilevel logic synthesis efforts focused ...

This paper proposes an integrated methodological framework to design a spatially heterogeneous bus route network and time-dependent service headways to serve travel demand that varies over time and space. Travelers choose to use either the transit mode or ...

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 ...

The new era of sharing information and "big data" has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the e ...

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 ...

Most perimeter control methods in literature are the model-based schemes. However, accurate modeling of the traffic flow system is hard and time-consuming. On the other hand, macroscopic traffic flow patterns show heavily similarity between days, and data ...

Most perimeter control methods in literature are the model-based schemes designing the controller based on the available accurate macroscopic fundamental diagram (MFD) function with well known techniques of modern control methods. However, accurate modelin ...

Modeling and Simulating ITS Applications with iTETRIS

Chattez avec Graph Search

GreenEVT: Greensboro Electric Vehicle Testbed

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

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

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

Modeling and Optimization of Ridesplitting Operations

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

Influence of outdoor air pollution on European residential ventilative cooling potential

Large-scale traffic signal control and multimodal network design

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Impact of COVID-19 Lockdown on Oxidative Potential of Particulate Matter: Case of Athens (Greece)

Travel Time Prediction for Congested Freeways With a Dynamic Linear Model

The influence of trip length distribution on urban traffic in network-level models

Dynamic control strategies for managing pedestrian flows

Three-Input Gates for Logic Synthesis

Dedicated bus lane network design under demand diversion and dynamic traffic congestion: An aggregated network and continuous approximation model approach

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

On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment

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

Multi-Region Network Perimeter Control via Model Free Adaptive Iterative Learning Control

Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks