scholarly journals Factors Affecting Road Rating

2020 ◽  
Vol 3 (1) ◽  
pp. 17
Author(s):  
Fei Wang ◽  
Bokuan Zhang ◽  
Ruishu Gong
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Traffic Congestion ◽  
Queuing Theory ◽  
Vehicle Speed ◽  
Analytic Hierarchy ◽  
Factors Affecting ◽  
Important Research Topic ◽  
Learning Machine ◽  
The Stability

The decision of traffic congestion degree is an important research topic today. In severe traffic jams, the speed of the car is slow, and the speed estimate is very inaccurate.This paper first uses the data collected by Google Maps to reclassify road levels by using analytic hierarchy process. The vehicle speed, road length, normal travel time, traffic volume, and road level are selected as the input features of the limit learning machine, and the delay coefficient is selected. As the limit learning machine as the output value, 10-fold cross-validation is used. Compared with the traditional neural network, it is found that the training speed of the limit learning machine is 10 times that of the traditional neural network, and the mean square error is 0.8 times that of the traditional neural network. The stability of the model Significantly higher than traditional neural networks.Finally, the delay coefficient predicted by the extreme learning machine and the normal travel time are combined with the knowledge of queuing theory to finally predict the delay time.

Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

2022 ◽  
Vol 24 (2) ◽  
pp. E74-E86
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Tiziana Campisi ◽  
Lagouge Kwanda Tartibu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Time Of Day ◽  
Vehicle Speed ◽  
Road Transportation ◽  
Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

Freight Vehicle Travel Time Analysis: A Case Study of Birgunj Nagdhunga Corridor

2021 ◽  
Vol 08 (02) ◽  
pp. 1-13
Author(s):  
Abhishek Jha ◽  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
License Plate ◽  
Monitoring Method ◽  
Factors Affecting ◽  
Average Travel Time ◽  
Major Factors ◽  
Vehicle Travel Time ◽  
Freight Vehicles

This study covers the freight vehicle, which clears the custom clearance process for Kathmandu and transports the same goods to Kathmandu from Birgunj. In this study average travel time for freight vehicles from Birgunj to Nagdhunga has been studied, along with the factors affecting the travel time from Birgunj to Nagdhunga. License plate monitoring method of the freight vehicles was done to find the average travel time and a questionnaire survey was done to identify the factors affecting travel time of the freight vehicle. The travel time from Birgunj to Nagdhunga is different for different types of, vehicle and good. The fastest average travel time is of fixed container of 40 feet size with 23.2 hours and longest average time is for fixed container of 20 feet size with 28.95 hours. The average travel time for non-degradable goods is 26.5 hours and for degradable goods is 22.38 hours. Major factors affecting the travel time are traffic congestion along the route, bad road condition along the route and hilly road with sharp bends, turns and grade.

Estimation of Vehicle Pre-Braking Speed

2012 ◽  
Vol 151 ◽  
pp. 165-169
Author(s):  
Wen Kung Tseng ◽  
S. X. Liao
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Rbf Neural Network ◽  
Training Data ◽  
Vehicle Speed ◽  
Training Time ◽  
Factors Affecting ◽  
Single Formula ◽  
The Relationship ◽  
Suitable System

An expert system has been proposed to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. Since the length of the skid mark varies with many factors, there is no a single formula or equation which can represent the relationship between the vehicle pre-braking speed and the length of the skid mark. Therefore in this paper an expert system is built to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. The radial basis function (RBF) neural network is used for the expert system due to its shorter training time and higher accuracy. There are many factors affecting the skid mark. In this paper we choose 7 factors, i.e. brand of vehicle, vehicle displacement, year of manufacture, vehicle weight, vehicles with and without ABS, roadway surface, and vehicle speed for the training in the RBF neural network. The total number of the training data for the RBF neural network is 2619. The results showed that high accuracy is obtained for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark. Thus the expert system proposed in this paper is demonstrated to be a suitable system for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark.

scholarly journals The stability analysis of dam walls using artificial neural network

2019 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rock Slopes ◽  
Factors Affecting ◽  
Network Algorithm ◽  
Before And After ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
The Stability ◽  
Artificial Neural Network Algorithm

The stability of rock slopes of the walls of Roodbar dam in Lorestan is investigated using multi-layer Perceptron of artificial neural network algorithm. Then, the stability of rock slopes is studied by considered factors affecting stability at before and after impounding dam. The calculation is done on the factors affecting stability using artificial neural network algorithm. Finally, the results show that rock slopes of the walls of Roodbar dam in Lorestan in a dry state are stable at seventeen modes and unstable at three modes. Also, in a saturated state are stable at fourteen modes and unstable at six modes, furthermore have generally a little stability. The results of this paper indicated that the calculation are augmentable with experimental results.

scholarly journals An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

2021 ◽  
Vol 38 (4) ◽  
pp. 1229-1235
Author(s):  
Derya Avci ◽  
Eser Sert
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Optical Illusions ◽  
Factors Affecting ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Layer ◽  
Elm Classifier

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

scholarly journals Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2971 ◽  
Author(s):  
Chong Yu ◽  
Shuaizong Si ◽  
Hongye Guo ◽  
Hai Zhao
Keyword(s):  
Analytical Model ◽  
Traffic Safety ◽  
Queuing Theory ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Ieee 802.11P ◽  
Communication Model ◽  
Vehicle Speed ◽  
Vehicle Communication ◽  
The Stability

Road capacity, traffic safety, and energy efficiency can be extremely improved by forming platoons with a small intra-vehicle spacing. Automated controllers obtain vehicle speed, acceleration, and position through vehicular ad hoc networks (VANETs), which allows the performance of platoon communication to make a significant impact on the stability of the platoon. To the best of our knowledge, there is not much research relating to packet delay and packet dropping rate of platoon communication based on the IEEE 802.11p broadcasting. In this paper, we introduce platoon structure model, vehicle control model, and communication model for a single platoon scenario. By utilizing Markov process and M/G/1/K queuing theory, we put forward an analytical model to assess the property of intra-vehicle communication. The analytical model is validated by simulations and the influence of communication parameters on intra-vehicle communication performance are discussed. In addition, the experimental results demonstrate that the IEEE 802.11p-based intra-vehicle communication guarantee the stability of platoon.

Statistical analysis of the stability of bus vehicles based on GPS trajectory data

2019 ◽  
Vol 33 (03) ◽  
pp. 1950015 ◽  
Author(s):  
Hui Zhang ◽  
Baiying Shi ◽  
Shuguang Song ◽  
Quanman Zhao ◽  
Xiangming Yao ◽  
...  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Dwell Time ◽  
Trajectory Data ◽  
Running Time ◽  
Bus Bunching ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Bus Service ◽  
The Stability

High quality bus service is considered as an efficient way to mitigate traffic congestion in big cities. Global positioning system (GPS) data provide sufficient sources to evaluate the performance of bus vehicles that both passengers and operator concern about. This paper aims to propose a framework to assess the operational performance of bus routes based on the GPS trajectory data collected from Jinan, China. Several important indicators of bus operation including travel time of routes, section running time, dwell time and bus bunching have been studied. The results show that the travel time of routes follow right skewed distributions. Moreover, section running time between two consecutive stations varies in different time period and it is larger in evening peak hours. Additionally, the dwell time has been discussed and the results show that there is no big variation in most stations except some stations, which provides a help to identify the key stations. Furthermore, we propose an approach to detect the bunching points. The results indicate the bunching points are easy to occur in the peak hours and the congested road section.

scholarly journals Parametric Study of Factors Affecting Lateral Stability of a Public Transportation Vehicle

2021 ◽  
Vol 4 (2) ◽  
pp. 79-86
Author(s):  
Vania Katherine Mulia ◽  
◽  
Fitri Endrasari ◽  
Djati Wibowo ◽  
Ibham Veza ◽  
...  
Keyword(s):  
Public Transport ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Lateral Stability ◽  
Factors Affecting ◽  
The Public ◽  
The Stability ◽  
Transportation Vehicle ◽  
Indirect Relationship ◽  
Rear Tire

The availability of public transport is one of the solutions to traffic congestion in Jakarta. Focusing on angkot, one of the public transport types in Jakarta, this study discusses a model and simulations to investigate several factors that affect its lateral stability. Those factors include rear tire inflation pressure, passenger configuration, velocity, and downhill inclination angle. The results show that the stability of an angkot is proportional to the rear tires cornering stiffness. It also has an indirect relationship with the passenger configuration within the angkot. Moreover, the stability of an angkot decreases as its velocity and the angle of the inclined road increase. In general, this study is expected to have a contribution to the development of public transport in Jakarta, especially angkot.

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