arXiv research

Framework classifies urban congestion into recurrent and non-recurrent types.

Paper uses DeePC to improve urban traffic lights, reducing congestion and emissions.

Some optimization or equilibrium problems involving somehow the concept of optimal transport are presented in these notes, mainly devoted to applications to economic and game theory settings. A variant model of transport, taking into account traffic congestion effects is the first topic, and it shows various links with…

Because of the prominent position of urban rail in reducing urban transport-related problems, such as congestion and air pollution, insights into the costs of possible new urban rail projects is very relevant for those involved with cost estimations, policy makers, cost-benefit analysts, and other target groups. Knowle…

GraphSVR forecasts urban air pollution robustly across stations and seasons.

This study develops methods to coordinate travel routes to reduce congestion.

Proposes a dynamic model for urban traffic volume prediction.

Paper predicts urban dispersal events using deep survival analysis on mobility data.

In this study, we present a machine learning approach to infer the worker and student mobility flows on daily basis from static censuses. The rapid urbanization has made the estimation of the human mobility flows a critical task for transportation and urban planners. The primary objective of this paper is to complete i…

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the ot…

Agent-based simulation assesses tradable credit schemes for congestion reduction.

Intelligent AQM uses ECN to predict and control network congestion.

Survey of deep learning applications in traffic congestion detection, prediction, and alleviation.

Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is…

Predicts routing congestion in FPGA designs using machine learning.

Model traffic congestion events using multi-modal data and attention-based neural networks.

The paper tackles Nash-regret minimization in congestion games with bandit feedback.

Study of congestion in negative curvature manifolds using fair-division algorithms.

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such cont…

New method estimates traffic congestion delays using statistical causality.

Wasserstein GANs with Gradient Penalty compute a different optimal transport problem called congested transport.

Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic …

MVFST-RL tackles real-time network congestion control with RL.

Due to their ubiquitous and pervasive nature, Wi-Fi networks have the potential to collect large-scale, low-cost, and disaggregate data on multimodal transportation. In this study, we develop a semi-supervised deep residual network (ResNet) framework to utilize Wi-Fi communications obtained from smartphones for the pur…

NAC-FL optimizes model updates in FL systems by adapting compression to network congestion.

New algorithm for traffic routing in congested conditions.

Study predicts traffic congestion based on population mobility data.

VAE models generate urban street networks from data.

New framework analyzes LLM personalization trade-offs under congestion.

UrbanRhythm reveals urban dynamics from mobility data.

Predicts morning traffic congestion using social media data from the previous evening.

Survey of urban flows prediction methods using various datasets.

Graph embeddings from commute networks identify socioeconomic disparities in urban areas.

Study proposes CNN for reconstructing high-res urban DEMs.

CityTFT models urban building energy using a data-driven approach.

We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically, within day and from day to day. The second shortcoming is the wide-spread use of Gaussian probability dens…

Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The …

Proposes a model to understand urban dynamics from mega-metropolises.

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multic…

Dynamic linear models improve travel time prediction for congested freeways.

Urban transformations within large and growing metropolitan areas often generate critical dynamics affecting social interactions, transport connectivity and income flow distribution. We develop a statistical-mechanical model of urban transformations, exemplified for Greater Sydney, and derive a thermodynamic descriptio…

Model predicts traffic speed using urban incidents.

Novel graph model forecasts urban traffic with reduced spatial complexity.

Paper explores multimodal methods for detecting urban micro-events.

Online learning improves traffic congestion prediction over time.

hood2vec identifies urban area similarity via mobility networks.

The paper uses GIS data to predict urban sprawl.