Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO

2012 ◽  
Vol 28 (4) ◽  
pp. 297-315 ◽  
Author(s):  
Yiannis Kamarianakis ◽  
Wei Shen ◽  
Laura Wynter
Keyword(s):  
Real Time ◽  
Regime Switching ◽  
Road Traffic ◽  
Space Time ◽  
Adaptive Lasso ◽  
Traffic Forecasting

A real-time push system on road traffic information

2014 ◽  
Author(s):  
Yang Xu ◽  
Zhang Zhenjiang ◽  
Liu Yun
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Information

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

scholarly journals Reasoning-predicting Model based on Strong Relevant Logic in Road Traffic Forecasting

2012 ◽  
Vol 33 ◽  
pp. 1105-1110 ◽  
Author(s):  
Dancheng Li ◽  
Zhiliang Liu ◽  
Cheng Liu ◽  
Binsheng Liu ◽  
Wei Zhang
Keyword(s):  
Road Traffic ◽  
Relevant Logic ◽  
Predicting Model ◽  
Traffic Forecasting ◽  
Model Based

Machine Learning and statistic predictive modeling for road traffic flow

2021 ◽  
Vol 03 (01) ◽  
pp. 17-24
Author(s):  
Nadia Slimani ◽  
Ilham Slimani ◽  
Nawal Sbiti ◽  
Mustapha Amghar
Keyword(s):  
Road Traffic ◽  
Smart Cities ◽  
Moving Average ◽  
Ann Model ◽  
Important Place ◽  
Traffic Forecasting ◽  
Absolute Relative Error ◽  
History Of ◽  
The One ◽  
Motorized Vehicles

Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart cities. This work is in line with this perspective and aims to solve this problem. The proposed methodology plans to forecast day-by-day traffic stream using three different models: the Multilayer Perceptron of Artificial Neural Networks (ANN), the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Support Machine Regression (SMOreg). Using those three models, the forecast is realized based on a history of real traffic data recorded on a road section over 42 months. Besides, a recognized traffic manager in Morocco provides this dataset; the performance is then tested based on predefined criteria. From the experiment results, it is clear that the proposed ANN model achieves highest prediction accuracy with the lowest absolute relative error of 0.57%.

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