☆ 4.3 Article

Temporal pattern mining of urban traffic volume data: a pairwise hybrid clustering method

TRANSPORTMETRICA B-TRANSPORT DYNAMICS (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Transportation

DLW-Net model for traffic flow prediction under adverse weather

Ronghan Yao et al.

Summary: The study developed a hybrid deep learning model DLW-Net to predict traffic flow under adverse weather, with target analysis and global analysis parts using various neural network models. The results showed different impacts of different adverse weather conditions on traffic flow, with DLW-Net performing the best under all conditions.

TRANSPORTMETRICA B-TRANSPORT DYNAMICS (2022)

添加到收藏夹

Article Engineering, Civil

Network-Wide Traffic State Estimation and Rolling Horizon-Based Signal Control Optimization in a Connected Vehicle Environment

Azadeh Emami et al.

Summary: The study proposes an innovative method to adaptively optimize traffic signal plans based on the estimation of various penetration rates of Connected Vehicles (CVs). By using Kalman filter and Neural Network algorithms to predict and update traffic situation, the methodology outperforms conventional actuated-coordinated traffic signal plans at lower than 100% penetration rates. The results show that the proposed method can achieve maximum benefits even at 60% penetration rate, indicating its effectiveness in traffic signal optimization.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

添加到收藏夹

Article Transportation

Functional clustering and missing value imputation of traffic flow trajectories

Pai-Ling Li et al.

Summary: The research introduces a data-adaptive clustering approach to explore traffic flow patterns and a unified algorithm for imputing missing values in incomplete traffic flow trajectories. By combining probabilistic functional clustering with functional principal component analysis, a mixture prediction is proposed for missing value imputation. These methods effectively reveal daily traffic flow patterns and enhance the accuracy of missing value imputation in traffic data analysis.

TRANSPORTMETRICA B-TRANSPORT DYNAMICS (2021)

添加到收藏夹

Article Engineering, Civil

Daily Traffic Flow Forecasting Through a Contextual Convolutional Recurrent Neural Network Modeling Inter- and Intra-Day Traffic Patterns

Dongfang Ma et al.

Summary: This paper proposes a deep-learning-based method for daily traffic flow forecasting, which integrates contextual factors and traffic flow patterns. The approach achieves over 90% prediction accuracy and outperforms existing benchmark methods, demonstrating robust forecasting performance under various scenarios.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

添加到收藏夹

Article Engineering, Civil

Quantifying the Traffic Impacts of the COVID-19 Shutdown

Zixuan Liu et al.

Summary: The article discusses the disruption of transportation and travel patterns due to the COVID-19 pandemic, focusing on the impact of stay-at-home orders in the Minneapolis-St. Paul, Minnesota area. It found a nearly 50% reduction in traffic volume and identified key dates that led to significant reductions in travel. The study suggests that while stay-at-home orders played a role in reducing traffic, other factors also contributed to changes in travel patterns before and after the orders.

JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS (2021)

添加到收藏夹

Article Transportation

Incorporating congestion patterns into spatio-temporal deep learning algorithms

Neil Leiser et al.

Summary: This research enhances deep learning algorithms for traffic prediction by exploiting spatial dependencies in road networks. It identifies traffic patterns through graph theory and traffic flow fundamentals, integrates them with deep learning algorithms, and enables better predictions in congested areas of road networks. The proposed enhanced models significantly outperform the original deep learning models that consider the whole network as the prediction domain, as demonstrated by case studies in New York and Amsterdam networks.

TRANSPORTMETRICA B-TRANSPORT DYNAMICS (2021)

添加到收藏夹

Article Engineering, Civil

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

Ling Zhao et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

添加到收藏夹

Article Ergonomics

Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis

Amir Bahador Parsa et al.

ACCIDENT ANALYSIS AND PREVENTION (2020)

添加到收藏夹

Article Geography

Fuzzy inference approach in traffic congestion detection

Maja Kalinic et al.

ANNALS OF GIS (2019)

添加到收藏夹

Article Thermodynamics

Analysis of traffic state variation patterns for urban road network based on spectral clustering

Senyan Yang et al.

ADVANCES IN MECHANICAL ENGINEERING (2017)

添加到收藏夹

Article Computer Science, Information Systems

DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN

Erich Schubert et al.

ACM TRANSACTIONS ON DATABASE SYSTEMS (2017)

添加到收藏夹

Article Engineering, Civil

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Yisheng Lv et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2015)

添加到收藏夹

Article Engineering, Civil

Deriving Traffic Flow Patterns from Historical Data

Francesc Soriguera

JOURNAL OF TRANSPORTATION ENGINEERING (2012)

添加到收藏夹

Article Engineering, Civil

Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance

Vikrant Vaze et al.

TRANSPORTATION RESEARCH RECORD (2009)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

The effect of pre-smoothing functional data on cluster analysis

David B. Hitchcock et al.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION (2007)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.