Road Traffic Prediction Model Using Extreme Learning Machine: The Case Study of Tangier, Morocco

Jiber, Mouna; Mbarek, Abdelilah; Yahyaouy, Ali; Sabri, My Abdelouahed; Boumhidi, Jaouad

doi:10.3390/info11120542

Cited by 15 publications

(11 citation statements)

References 29 publications

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“…The results of the evolution of model performance as a function of hidden neuron numbers demonstrate that the ideal number of hidden neurons with accurate results for our training dataset is 250 neurons. This approach has been detailed in a recent study [7]. For the purpose of Bagging base estimators, the number of ELM estimators was set to 10 since it shows better accuracy and requires less computation time.…”

Section: Extreme Learning Machinementioning

confidence: 99%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

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<span lang="EN-US">Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.</span>

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Section: Extreme Learning Machinementioning

confidence: 99%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

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“…The confidence is 1 for a rule (𝐴 → 𝐵) if the consequent and antecedent always occur together. It is expressed by (2): The lift of the rule (A→B) is the confidence of the rule divided by the support of the consequent B. A higher lift value between two variables means a higher correlation between them.…”

Section: Association Rule Miningmentioning

confidence: 99%

Accident black spots identification based on association rule mining

Mbarek,

Jiber,

Yahyaouy

et al. 2024

Bulletin EEI

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This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

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“…When comparing the model findings to the actual results, it was shown that the model can meet the criteria for bus arrival prediction. Jiber et al [9] conducted a road traffic forecast study in Morocco using data from the Moroccan center for road studies and research. They merged Ensemble-based ELM approaches based on the number of neurons in the hidden layer.…”

Section: Related Workmentioning

confidence: 99%

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Jalaney¹,

Ganesh²

2023

Intelligent Automation &Amp; Soft Computing

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Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle speed affecting traffic are used for the analysis. The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area. Multiple ELMs (MELM) for explicitly training dynamic, road and trajectory information are used in the proposed approach. A large-scale dataset (historical data) obtained from Kerala State Road Transport Corporation is used for training. Simulations are carried out by using MATLAB R2021a. The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week. It can manage huge volumes of data with less human intervention at greater learning speeds. It is found MELM yields prediction with accuracy in the range of 96.7% to 99.08%. The MAE value is between 0.28 to 1.74 minutes with the proposed approach. The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy. The research has proved that MELM is superior for arrival time predictions in terms of accuracy and error, compared with other approaches.

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Road Traffic Prediction Model Using Extreme Learning Machine: The Case Study of Tangier, Morocco

Cited by 15 publications

References 29 publications

Black spots identification on rural roads based on extreme learning machine

Black spots identification on rural roads based on extreme learning machine

Accident black spots identification based on association rule mining

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

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