A congestion-driven placement framework with local congestion prediction

2005 ◽  
Author(s):  
Qinghua Liu ◽  
Malgorzata Marek-Sadowska
Keyword(s):  
Congestion Prediction

scholarly journals TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA

2019 ◽  
Vol 20 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Wiam Elleuch ◽  
Ali Wali ◽  
Adel M. Alimi
Keyword(s):  
Traffic Congestion ◽  
Prediction Method ◽  
Traffic Information ◽  
Gps Data ◽  
Traffic Patterns ◽  
Dynamic Changes ◽  
Traffic Conditions ◽  
Congestion Prediction ◽  
Different Types ◽  
Nonparametric Techniques

ABSTRACT: The prediction of accurate traffic information such as speed, travel time, and congestion state is a very important task in many Intelligent Transportations Systems (ITS) applications. However, the dynamic changes in traffic conditions make this task harder. In fact, the type of road, such as the freeways and the highways in urban regions, can influence the driving speeds and the congestion state of the corresponding road. In this paper, we present a NNs-based model to predict the congestion state in roads. Our model handles new inputs and distinguishes the dynamic traffic patterns in two different types of roads: highways and freeways. The model has been tested using a big GPS database gathered from vehicles circulating in Tunisia. The NNs-based model has shown their capabilities of detecting the nonlinearity of dynamic changes and different patterns of roads compared to other nonparametric techniques from the literature. ABSTRAK: Ramalan maklumat trafik yang tepat seperti kelajuan, masa perjalanan dan keadaan kesesakan adalah tugas yang sangat penting dalam banyak aplikasi Sistem Pengangkutan Pintar (ITS). Walau bagaimanapun, perubahan keadaan lalu lintas yang dinamik menjadikan tugas ini menjadi lebih sukar. Malah, jenis jalan raya, seperti jalan raya dan lebuh raya di kawasan bandar, boleh mempengaruhi kelajuan memandu dan keadaan kesesakan jalan yang sama. Dalam makalah ini, kami membentangkan model berasaskan NN untuk meramalkan keadaan kesesakan di jalan raya. Model kami mengendalikan input baru dan membezakan corak trafik dinamik dalam dua jenis jalan raya yang lebuh raya dan jalan raya. Model ini telah diuji menggunakan pangkalan data GPS yang besar yang dikumpulkan dari kenderaan yang beredar di Tunisia. Model berasaskan NNs telah menunjukkan keupayaan mereka untuk mengesan ketiadaan perubahan dinamik dan pola jalan yang berbeza berbanding dengan teknik nonparametrik yang lain dari kesusasteraan.

scholarly journals A Review of Traffic Congestion Prediction Using Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mahmuda Akhtar ◽  
Sara Moridpour
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Traffic Congestion ◽  
Historical Data ◽  
Research Area ◽  
Short Term ◽  
Real Time Traffic ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Congestion Prediction

In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively. Traffic congestion prediction, especially short-term traffic congestion prediction is made by evaluating different traffic parameters. Most of the researches focus on historical data in forecasting traffic congestion. However, a few articles made real-time traffic congestion prediction. This paper systematically summarises the existing research conducted by applying the various methodologies of AI, notably different machine learning models. The paper accumulates the models under respective branches of AI, and the strength and weaknesses of the models are summarised.

scholarly journals Traffic congestion prediction based on Estimated Time of Arrival

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0238200
Author(s):  
Noureen Zafar ◽  
Irfan Ul Haq
Keyword(s):  
Traffic Congestion ◽  
Rapid Expansion ◽  
Time Of Arrival ◽  
Random Forest Classification ◽  
Traffic Forecasting ◽  
Real Time Traffic ◽  
Forest Classification ◽  
Time Period ◽  
Congestion Prediction ◽  
Google Map Api

With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively.

The Evolution of the Traffic Congestion Prediction and AI Application

2021 ◽  
pp. 19-31
Author(s):  
Badr-Eddine Soussi Niaimi ◽  
Mohammed Bouhorma ◽  
Hassan Zili
Keyword(s):  
Traffic Congestion ◽  
Congestion Prediction

Congestion Prediction System With Artificial Neural Networks

2020 ◽  
Vol 12 (3) ◽  
pp. 28-43
Author(s):  
Fatma Gumus ◽  
Derya Yiltas-Kaplan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Prediction Algorithm ◽  
Learning Stage ◽  
Network Algorithms ◽  
Prediction Scheme ◽  
Machine Learning Approach ◽  
Congestion Prediction ◽  
Nonlinear Autoregressive

Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.

Deep Learning Based Congestion Prediction Using PROBE Trajectory Data

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Jingqiu Guo ◽  
Yangzexi Liu ◽  
Yibing Wang ◽  
Ken Yang
Keyword(s):  
Deep Learning ◽  
Trajectory Data ◽  
Congestion Prediction
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