Traffic Prediction Using Machine Learning (2025)

Traffic Prediction for Intelligent Transportation System Using Machine Learning

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

This Document aims to develop a tool for predicting correct and timely traffic flow info. Traffic surroundings involves everything which will have an effect on the traffic flowing on the road, whether or not it's traffic signals, accidents, rallies, even repairing of roads which will cause a jam. If we've got previous info that is extremely close to approximate regarding all the higher than and many more lifestyle things which may have an effect on traffic then, a driver or rider will create an knowing decision. Also, it helps within the way forward for autonomous vehicles. within the current decades, traffic information are generating exponentially, and that we have stirred towards the large information ideas for transportation. Available prediction ways for traffic flow use some traffic prediction models and are still dissatisfactory to handle real-world applications. This reality impressed us to figure on the traffic flow forecast problem build upon the traffic information and models. It is cumbersome to forecast the traffic flow accurately as a result of the info on the market for the transportation is insanely vast. during this work, we tend to planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyse the big-data for the transportation with much-reduced quality. Also, Image process algorithms are concerned in traffic sign recognition, that eventually helps for the correct training of autonomous vehicles.

Traffic Prediction for Intelligent Transportation Systems Using Machine Learning

International Journal of Scientific Research in Science, Engineering and Technology, 2023

The goal of this project is to provide a platform for forecasting accurate and timely traffic data. Traffic conditions include things that can affect road traffic speeds, such as: B. Traffic lights, accidents, protests and even road repairs that can cause traffic jams. Motorists or drivers should make informed decisions when they have very accurate prior knowledge of all of the above approximations and more real-world conditions that may affect traffic. I can. can be lowered. It is also useful for the development of self-driving cars. Transportation data has increased dramatically over the past decades and is evolving towards the concept of transportation big data. Current traffic prediction approaches use specific traffic prediction models that are still inadequate to handle real-world situations. Therefore, we tackled the problem of traffic prediction using traffic data and models. Due to the vast amount of data available in transportation systems, it is difficult to accurately predict traffic flows. In this study, we wanted to use machine learning, genetics, soft computing, and deep learning techniques to evaluate vast amounts of data in transportation systems while greatly reducing complexity. In addition, it uses image processing algorithms to recognize traffic signs and ultimately help train self-driving cars properly.

Traffic Prediction For Intelligent Transport System Using Machine Learning

International Journal of Scientific Research in Science, Engineering and Technology, 2023

Automobile manufacturers have developed various safety features to mitigate the risk of traffic accidents, but accidents continue to occur frequently in both urban and rural areas. To prevent accidents and improve safety measures, it is necessary to develop accurate prediction models that can identify patterns associated with different scenarios. By using these models, we can cluster accident scenarios and develop effective safety measures. We aim to achieve the maximum possible reduction in accidents using low-budget resources through scientific measures.To achieve this goal, we need to collect and analyze a vast amount of data related to traffic accidents, such as accident location, time, weather conditions, and road features. Machine learning algorithms can be used to automatically identify patterns in the data and predict accident scenarios based on these patterns. These models can then be used to cluster accidents into different categories and develop safety measures tailored to each category. By using this approach, we can develop cost-effective safety measures that can be implemented in a variety of settings. We believe that this approach has the potential to significantly reduce the number of traffic accidents and improve safety for drivers, passengers, and pedestrians alike.

The Prediction of Traffic Flow with Regression Analysis

Advances in Intelligent Systems and Computing, 2018

A Comparative Study for the Traffic Predictions in Smart Cities Using AI

2024

In recent years, the number of vehicles on the road has increased substantially, and as a result, traffic congestion has become a big issue. Future traffic prediction is one of the most effective techniques to reduce traffic congestion. It is undeniable that technology has a role in many aspects of our lives. Since Artificial Intelligence's inception in the late 1970s, the discipline of traffic prediction research has progressed significantly. Its models have recently attracted the attention of researchers because of their strength and adaptability. Therefore, we enter the era of machine and deep learning as theoretical and technological developments emerge. Machine learning and deep learning gained popularity due to their enormous prediction capacity, which may be attributed to their complex and deep structure. Hence, this work proposes a literature review to address the problem of evaluating traffic road congestion prediction algorithms in terms of efficiency while considering the application field. Several relevant works will then be analysed and compared to each other.

Traffic Flow Prediction Using Machine Learning

Third International Conference “Transport for Today’s Society “, 2021

–The main objective of this research was to define and verify the methodology of predicting the volume and structure of traffic flows, based on the building and application of a supervised machine learning models. The proposed methodology was applied in the case study of the prediction of traffic flows on selected routes in the Republic of Serbia. Keywords – Machine learning, Big data analytics, Traffic flow

Machine Learning and statistic predictive modeling for road traffic flow

International Journal of Traffic and Transportation Management, 2021

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 r...

Traffic Prediction Architecture based on Machine Learning Approach for Smart Cities

Res. Comput. Sci., 2020

It is common to see more and more people dealing with traffic due to excessive population growth in cities. The traffic has become one of the main topics of interest is used for smart cities approaches. Accordingly, this study presents the development and implementation of a new architecture to predict the traffic flow in a city and the strategy used in this scheme. The proposal considers the use of machine learning, computer vision, deep learning, and neuronal networks to implement the solution. The architecture is composed of four main components; (1) A Machine Algorithm System (MASY) that works using pattern recognition of the traffic; (2) A Neuronal Artificial System (NASY) helps with the traffic classification; (3) A Web user application (WeUsAP) to present the results, and process entry user data and finally, (4) A Car Counting Wizard (CCW) video capturing component based on computer vision to create a statistical analysis of vehicles. Consequently, some results and comparativ...

A Review of Traffic Prediction Using Time Series Analysis

International Journal of Advance Research and Innovative Ideas in Education, 2021

Traffic data is significant in planning an intelligent or smart city. Presentlya day's numerous clever vehicle frameworks utilize current advancements to foresee traffic stream, to limit mishaps on street, to anticipate speed of a vehicle and so forth the traffic stream expectation is an engaging examination field. We are utilizing deep learning calculations to conjecture genuine traffic data. At the point when traffic data turns out to be big data, a few strategies to improve the exactness of traffic forecast are additionally talked about with the assistance of big data examination.

Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data

International Journal of Distributed Sensor Networks, 2017

Traffic prediction in smart cities is an essential way for intelligent transportation system. The objective of this article is designing and implementing a traffic prediction scheme which can forecast the traffic flow with high efficiency and accuracy in Hong Kong. One problem in traffic prediction is how to balance the importance of historical traffic data and realtime traffic data. To make use of the real-time data as well as the history records, our ideas are combining data-driven approaches with model-driven approaches. First, the limitations of two baseline approaches auto-regressive integrated moving average and periodical moving average model are discussed. Second, artificial neural network is applied in the hybrid prediction model to balance between the two models. The training of neural network enables the artificial neural network to weight between real-time traffic data and traffic patterns revealed by historical traffic data. Furthermore, an emergency strategy using the Bayesian network is added to the prediction scheme to handle with the traffic accident or other emergent situation. The emergency prediction strategy on unexpected traffic situation considers the traffic condition of nearby links to predict the speed change on the link. Finally, experimental results of short-term and long-term predictions demonstrate the efficiency and accuracy of the proposed scheme.