Vehicular Traffic Flow Prediction Model Deep Learning

Abstract

Efficient traffic flow prediction is crucial for effective traffic management and congestion reduction in urban areas. However, traditional statistical models often struggle to accurately capture the intricate dynamics of vehicular traffic flow, particularly under dynamic conditions. In this research project, we propose a novel approach that leverages deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, AdaBoost, and gradient descent, to enhance the accuracy of traffic flow predictions .By harnessing historical traffic data, our model generates precise predictions for the next time step, empowering traffic managers to optimize signal timings and proactively reroute traffic. To boost the model\'s performance, we incorporate AdaBoost, which integrates LSTM predictions as additional input features. We evaluate the accuracy of our model using mean absolute error (MAE) and R2 score techniques, comparing the predicted traffic flow against the actual traffic flow .Experimental results demonstrate that our proposed model outperforms traditional statistical models, exhibiting lower MAE and higher R2 scores. This indicates its efficacy in accurately predicting traffic flow and presents a promising solution for traffic management and congestion reduction. Our research contributes to the advancement of traffic flow prediction models by offering a more reliable and accurate approach. Future work may explore the integration of real- time data streams and external factors, such as weather conditions and events, to further enhance prediction accuracy and effectively address dynamic traffic situations. By optimizing traffic management strategies, reducing congestion, and improving overall traffic flow efficiency, our proposed model holds significant potential for improving urban traffic conditions.

Introduction

I. INTRODUCTION

Traditional statistical models have been widely employed for traffic flow prediction, but they often struggle to capture the temporal dependencies and long-term trends inherent in traffic data. These models often fail to adapt to the non-linear and dynamic nature of traffic, leading to suboptimal predictions and limited effectiveness in real-world scenarios. As a result, there is a growing need to explore advanced computational techniques capable of handling the complexity and variability of traffic flow data.

In recent years, deep learning has emerged as a promising approach for modelling and predicting sequential data. Specifically, Long Short-Term Memory (LSTM) neural networks have demonstrated remarkable capabilities in capturing long-term dependencies and patterns in time series data. LSTM networks employ a memory cell structure, enabling them to retain information over extended periods and effectively learn temporal dependencies

The core idea behind our proposed model is to utilize historical traffic flow information as input to the LSTM neural network. By training the network on a vast amount of past data, it learns to capture the underlying patterns and dependencies in the traffic flow dynamics. This enables the model to generate more precise predictions for future time steps, enabling traffic managers to make informed decisions regarding signal timings, route optimization, and resource allocation.

The potential benefits of accurate traffic flow prediction are vast. Transportation authorities can proactively identify congestion-prone areas, optimize traffic signal timings, and dynamically adjust traffic management strategies to minimize delays and improve overall traffic flow efficiency. Commuters can benefit from real-time traffic information, enabling them to make informed decisions about travel routes and avoid congestion-prone areas, leading to reduced travel times and improved road safety.

II. IMPLEMENTATION

With the progress of urbanization and the popularity of automobiles, transportation problems are becoming more and more challenging: traffic flow is congested, accidents are frequent, and the traffic environment is deteriorating. To address this issue, it becomes essential to implement an Vehicular Traffic Flow Prediction Model. Such a system would enable early detection of traffic. To optimize the performance of the system in terms of computational complexity and accuracy.

In the proposed methodology, the first step nstall cameras and sensors near traffic junctions to collect real-time traffic data. Collect the dataset at each junction, including the time and the number of vehicles flowing through the junction.

Clean the collected data by removing any outliers or inconsistent values. Perform feature engineering if necessary, such as extracting additional features from the timestamp (e.g., hour of the day, day of the week).Split the dataset into training and testing sets. Implement machine learning algorithms like AdaBoost and decision tree to predict traffic flow among different junctions. Utilize the LSTM (Long Short-Term

Memory algorithm for time-series data analysis, as traffic data typically exhibits temporal data . dependencies. Train and evaluate the models using appropriate evaluation metrics (e.g., accuracy, mean squared error) to assess their performance. Optimize the models by tuning hyperparameters and selecting the best-performing model. Apply the trained model to new incoming data to predict future traffic flow at each junction. Utilize the predictions for early detection of potential traffic congestion or abnormal traffic patterns.

III. ARCHITECTURE

Majorly 1 Deep Learning algorithm and 1 type of supervised Machine Learning algorithm are being used in implementing the project. They are

o LSTM Algorithm

o AdaBoost Regression

LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is designed to process and predict sequences of data. It is particularly effective in handling problems involving sequential or time-dependent data, such as natural language processing, speech recognition, and time series analysis. Unlike traditional feed-forward neural networks, LSTM networks have a more complex structure that allows them to capture long-term dependencies in sequential data. They achieve this by introducing memory cells, which store and update information over time. Each memory cell has three main components: an input gate, a forget gate, and an output gate.

The LSTM network processes the input sequence step by step, and at each step, it updates the memory cell based on the input, forgets or retains information from previous steps, and generates the output for that step. The memory cells allow the network to remember important information from distant past steps, making it capable of capturing long-range dependencies.

The training of an LSTM involves optimizing the weights and biases associated with its gates through the use of backpropagation and gradient descent. This enables the network to learn patterns and relationships in the input data, allowing it to make predictions or generate sequences based on the learned information.

LSTM networks have proven to be effective in various tasks involving sequential data, including time series prediction, language modelling, machine translation, and speech recognition. They have become a popular choice in many deep learning applications where temporal dependencies are critical.

AdaBoost Regression, also known as Adaptive Boosting Regression, is a machine learning algorithm used for regression tasks. It is an extension of the AdaBoost algorithm, which is primarily used for classification.

In AdaBoost Regression, the goal is to build a strong regression model by combining multiple weak regressors. These weak regressors are typically simple models, such as decision trees, that are trained on subsets of the training data.

The algorithm then updates the weights of the training instances, giving higher weights to the instances that were poorly predicted by the previous weak regressors. This adaptive

weak regressors are combined into a strong ensemble model by assigning weights to each regressor based on their performance. More accurate weak regressors are given higher weights in the ensemble During the prediction phase, the final ensemble model is used to make predictions by aggregating the predictions of the weak regressors, weighted by their respective weights. The ensemble model leverages the collective knowledge of the weak regressors to produce accurate predictions for unseen data.

AdaBoost Regression is particularly useful when dealing with complex relationships and outliers in the data. By emphasizing difficult instances and adjusting the weights accordingly, AdaBoost Regression can effectively handle challenging regression problems.

VII. ACKNOWLEDGEMENT

The help and materials offered by Malla Reddy University for this research are acknowledged with heartfelt thanks by the authors.

Conclusion

we conclude , that from our observation we used LSTM algorithm that gave us the best accuracy score. It highlights the model\'s potential to contribute to improved traffic management, enhanced road safety, and increased overall traffic efficiency. With the increase in the population of human beings, travel is exponentially growing With this rapid rate of increase in vehicle, management of movement of vehicles is very critical. Having precise background information is crucial for creating a precise vehicular management system. The objectives are to increase overall traffic efficiency, decrease congestion, and improve traffic management tactics .While there are several advantages to using the deep learning models to predict traffic flow individually, there are significant disadvantagesalso.

References

[1] Dong, X., Lei, T., Jin, S., & Hou, Z. (2018). Short-term traffic flow prediction based on XG- Boost. 7th Data Driven Control and Learning Systems Conference, 25-27 May 2018. https://doi.org/10.1109/DDCLS.2018.8516114 [Google Scholar] [2] Duan, Y., Lv, Y., & Wang, F. Y., “Performance evaluation of the deep learning approach for traffic flow prediction at different times,” Proc. - 2016 IEEE Int. Conf. Serv. [3] Erhan, D., Bengio, Y., Courville, A., Manzagol, P., Vincent, P., & Bengio, S. (2010). Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research. [4] Essien, A., Petrounias, I., Sampaio, P., & Sampaio, S. (2021). A deep-learning model for urban traffic flow prediction with traffic events mined from twitter. World Wide Web, 24, 1345–1368 . [5] Fu, R., Zhang, Z., & Li, L. (2016). Using LSTM and GRU neural network methods for traffic flow prediction. 31st Youth Academic Annual Conference of Chinese Association of Automation, 11-13 Nov. 2016. IEEE: Wuhan.

Copyright

Copyright © 2023 M. Srinath, M. Suma, Y. Cholavi, M. Srinithish. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.