Using traffic prediction models for providing predictive traveller information

2000 ◽  
Vol 20 (3/4) ◽  
pp. 326 ◽  
Author(s):  
Bin Ran
Keyword(s):  
Prediction Models ◽  
Traffic Prediction

scholarly journals Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shilpa P. Khedkar ◽  
R. Aroul Canessane ◽  
Moslem Lari Najafi
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Prediction Models ◽  
Forecast Model ◽  
Congestion Management ◽  
Traffic Prediction ◽  
Traffic Forecasting ◽  
Highly Sensitive ◽  
Iot Devices ◽  
Prediction Techniques

An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

scholarly journals Research and Application of the Beijing Road Traffic Prediction System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ruimin Li ◽  
Hongliang Ma ◽  
Huapu Lu ◽  
Min Guo
Keyword(s):  
Traffic Management ◽  
Web Application ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Prediction ◽  
Accurate Estimation ◽  
Prediction System ◽  
Traveler Information ◽  
Level Data ◽  
Function Modules

As an important part of the urban Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS), short-term road traffic prediction system has received special attention in recent decades. The success of ATMS and ATIS technology deployment is heavily dependent on the availability of timely and accurate estimation or prediction of prevailing and emerging traffic conditions. We studied a real-time road traffic prediction system developed for Beijing based on various traffic detection systems. The logical architecture of the system was presented, including raw data level, data processing and calculation level, and application level. Four key function servers were introduced, namely, the database server, calculation server, Geographic Information System (GIS) server, and web application server. The functions, function modules, and the data flow of the proposed traffic prediction system were analyzed, and subsequently prediction models used in this system are described. Finally, the prediction performance of the system in practice was analyzed. The application of the system in Beijing indicated that the proposed and developed system was feasible, robust, and reliable in practice.

scholarly journals Estimating the Impact of High-Fidelity Rainfall Data on Traffic Conditions and Traffic Prediction

2021 ◽  
pp. 036119812110263
Author(s):  
Anatolii Prokhorchuk ◽  
Nikola Mitrovic ◽  
Usman Muhammad ◽  
Aleksandar Stevanovic ◽  
Muhammad Tayyab Asif ◽  
...  
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Weather Conditions ◽  
Rainfall Data ◽  
Weather Data ◽  
Traffic Prediction ◽  
Percentage Error ◽  
Traffic Network ◽  
Radar Images ◽  
The Impact

Accurate prediction of network-level traffic parameters during inclement weather conditions can greatly help in many transportation applications. Rainfall tends to have a quantifiable impact on driving behavior and traffic network performance. This impact is often studied for low-resolution rainfall data on small road networks, whereas this study investigates it in the context of a large traffic network and high-resolution rainfall radar images. First, the impact of rainfall intensity on traffic performance throughout the day and for different road categories is analyzed. Next, it is investigated whether including rainfall information can improve the predictive accuracy of the state-of-the-art traffic forecasting methods. Numerical results show that the impact of rainfall on traffic varies for different rainfall intensities as well as for different times of the day and days of the week. The results also show that incorporating rainfall data into prediction models improves their overall performance. The average reduction in mean absolute percentage error (MAPE) for models with rainfall data is 4.5%. Experiments with downsampled rainfall data were also performed, and it was concluded that incorporating higher resolution weather data does indeed lead to an increase in performance of traffic prediction models.

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