Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

Duhayyim, Mesfer Al; Albraikan, Amani Abdulrahman; Al‐Wesabi, Fahd N.; Burbur, Hiba M.; Alamgeer, Mohammad; Hilal, Anwer Mustafa; Hamza, Manar Ahmed; Rizwanullah, Mohammed

doi:10.32604/cmc.2022.022692

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…3 and Fig. 8 shows the predictive results achieved by SMOBGRU-TP model and other recent models under distinct time durations [27][28][29]. The experimental results exhibit that the proposed SMOBGRU-TP model accomplished the least MAPE under all-time durations.…”

Section: Smo Based Hyperparameter Optimizationmentioning

confidence: 99%

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Hamza¹,

Alsolai²,

Alamgeer³

et al. 2022

Computers, Materials &Amp; Continua

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Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning (DL) is a kind of ML technique which yields effective performance on data classification and prediction tasks. With this motivation, the current study introduces a novel Slime Mould Optimization (SMO) model with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, data preprocessing is performed to normalize the input data in the range of [0, 1] using minmax normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Moreover, the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method. The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model's superior performance in terms of prediction compared to existing techniques.

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Section: Smo Based Hyperparameter Optimizationmentioning

confidence: 99%

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Hamza¹,

Alsolai²,

Alamgeer³

et al. 2022

Computers, Materials &Amp; Continua

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“…Chen et al proposed a two-stage clustering algorithm to identify candidate areas in urban space, integrating taxi trajectory data to estimate taxi travel routes and destinations [21]. Various regression models are employed in studies predicting the ftting of taxi trajectories to passenger mobility [45,46]. By ftting the distribution of passenger boarding points, these models forecast spatiotemporal changes and waiting times for passengers in hotspot areas [47].…”

Section: Application Of Taxi Trajectory Datamentioning

confidence: 99%

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

Chen,

Liu,

Gao

2024

Journal of Advanced Transportation

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Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

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“…Aqib et al [18] proposed a multifaceted approach to a vehicular assumption at a massive ratio instantaneously besides integrating four parallel innovations; huge information, expert systems, data processing, and graphics processing unit. Duhayyim et al [19] introduce an Artificial Intelligence Traffic Flow Prediction with Weather Conditions (AITFP-WC) for smart cities. The Elman Neural Network (ENN) method is an aspect of the proposed AITFP-WC technique and is used to forecast traffic flow in smart cities.…”

Section: Non-parametric Modelmentioning

confidence: 99%

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Anjaneyulu¹,

Kubendiran²

2023

Computers, Materials &Amp; Continua

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Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed a novel hybrid algorithm utilizing Principal Component Analysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory (LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCA removes unwanted information from the dataset and selects essential features. Secondly, SAE is used to reduce the dimension of input data using onehot encoding so the model can be trained with better speed. Thirdly, LSTM takes the input from SAE, where the data is sorted in ascending order based on the important features and generates the derived value. Finally, KNN Regressor takes information from LSTM to predict traffic flow. The forecasting performance of the PALKNN model is investigated with Open Road Traffic Statistics dataset, Great Britain, UK. This paper enhanced the traffic flow prediction for every hour of a day with a minimal error value. An extensive experimental analysis was performed on the benchmark dataset. The evaluated results indicate the significant improvement of the proposed PALKNN model over the recent approaches such as KNN, SARIMA, Logistic Regression, RNN, and LSTM in terms of root mean square error (RMSE) of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE) of 2.04%.

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Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

Cited by 9 publications

References 18 publications

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

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