scholarly journals Comparison of Logistics Time of R3A and R3B for Transportation of Lychee Product

2021 ◽  
pp. 15-25
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Prediction Method ◽  
Transportation Safety ◽  
Traffic Density ◽  
Traffic Data ◽  
Management Application ◽  
Heavy Burden ◽  
Chinese Markets ◽  
Local Farmers

This research presents the logistics management information system (LMIS) for the supply chain of lychee products of Phayao Province, Thailand. The main aim of this research is to develop a management application for Phayao’s agricultures to improve their competitive abilities on Chinese markets by utilizing a prediction method for traffic congestion based on both real-time and anticipated road traffic. The loss of productivity caused by traffic congestion has become a huge and increasingly heavy burden on Phayao farmers. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion is of great significance to solve this problem. By using traffic data obtained by distance, road conditions, transportation safety, traffic density, and customs clearance, the local farmers in Phayao can deliver lychee products on time and reduce the loss of high emissions and environmental pollution caused by traffic congestion effectively.

scholarly journals Detection of Road Traffic Anomalies Based on Computational Data Science

2022 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Road Traffic ◽  
Smart Devices ◽  
Traffic Data ◽  
Forecasting Accuracy ◽  
Traffic Efficiency ◽  
Computational Data

Abstract The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

scholarly journals DEVELOPMENT OF WEBGIS BASED REAL TIME ROAD TRAFFIC INFORMATION SYSTEM

2018 ◽  
Vol IV-5 ◽  
pp. 37-45
Author(s):  
H. Shankar ◽  
M. Sharma ◽  
K. Oberai ◽  
S. Saran
Keyword(s):  
Information System ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Cost ◽  
Traffic Information ◽  
Traffic Density ◽  
Vehicle To Vehicle ◽  
Decision Making Processes ◽  
Inductive Loop Detector

<p><strong>Abstract.</strong> Rapid increase in road traffic density results into a serious problem of Traffic Congestion (TC) in cities. During peaks hours TC is very high and hence public search least congested path for their journeys in order to minimize ravel time and hence transportation cost. In this study, a new empirical model was developed to estimate congestion levels using real time road Traffic Parameters (TPs) such as vehicle density, speed, class and vehicle-to-vehicle (V2V) gap. These real time road TPs were collected using latest generation Inductive Loop Detector (ILD) technology. Further, a WebGIS based Road Traffic Information System (RTIS) for Dehradun city was developed for real time TD analyses and visualisation. This RTIS is very useful for public and user departments for planning and decision making processes. No other such system is available in India, which handles multiple traffic parameters simultaneously to provide solution of day-to-day problems.</p>

Traffic Problems of Linjiao’s Turning Passage in Lhasa City from the Perspective of Traffic Sociology

2021 ◽  
Vol 7 (5) ◽  
pp. 4672-4681
Author(s):  
Shuai Lai ◽  
Jinfeng Wu
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Traffic Data ◽  
Learning Network ◽  
Accurate Judgment ◽  
Congestion Prediction ◽  
The Common ◽  
Deep Learning Network

Objectives: The transportation problem of Linjiao transit passage in Lhasa City from the perspective of traffic sociology is studied. Methods: Firstly, the research history and current situation of experts in the field of traffic congestion prediction are studied. The common parameters and models of congestion prediction are analyzed. Results: Combined with the complexity of road traffic structure and the possession of a large number of high-dimensional traffic data records, the use of a prediction model is finally determined based on RNN-RBM deep learning network. Through the research and analysis of all-day road traffic flow data, accurate judgment and prediction of traffic congestion status are made. Conclusion: In this paper, the role of the RNN model on the time axis and the state judgment of the RBM network are used to predict the traffic congestion based on the characterization of the congestion sequence.

scholarly journals Performance Evaluation of a Hybrid PSO Enhanced ANFIS Model in Prediction of Traffic Flow of Vehicles on Freeways: Traffic Data Evidence from South Africa

2021 ◽  
Vol 7 (1) ◽  
pp. 2
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Lagouge Kwanda Tartibu ◽  
Fabio Arena ◽  
Ziya Cakici
Keyword(s):  
South Africa ◽  
Traffic Flow ◽  
Rural Areas ◽  
Traffic Congestion ◽  
Significant Rise ◽  
Traffic Density ◽  
Vehicular Traffic ◽  
Traffic Data ◽  
Inference System ◽  
Vehicular Traffic Flow

In the last few years, there has been a significant rise in the number of private vehicles ownership, migration of people from rural areas to urban cities, and the rise in the number of under-maintained freeways; all these have added to the perennial problem of traffic congestion. Traffic flow prediction has been recognized as the solution in alleviating and reducing the problem of traffic congestion. In this research, we developed an adaptive neuro-fuzzy inference system trained by particle swarm optimization (ANFIS-PSO) by performing an evaluative performance of the model through traffic flow modelling of vehicles on five freeways (N1,N3,N12,N14 and N17) using South Africa Transportation System as a case study. Six hundred and fifty (650) traffic data were collected using inductive loop detectors and video cameras from the five freeways. The traffic data used for developing these models comprises traffic volume, traffic density, speed of vehicles, time, and different types of vehicles. The traffic data were divided into 70% and 30% for the training and validation of the model. The model results show a positively correlated optimal performance between the inputs and the output with a regression value R2  of 0.9978 and 0.9860 for the training and testing. The result of this research shows that the soft computing model ANFIS-PSO used in this research can model vehicular traffic flow on freeways. Furthermore, the evidence from this research suggests that the on-peak and off-peak hours are significant determinants of vehicular traffic flow on freeways. The modelling approach developed in this research will assist urban planners in developing practical ways to tackle traffic congestion and assist motorists and pedestrians in travel behaviour decision-making. Finally, the approach used in this study will assist transportation engineers in making constructive and safety dependent guidelines for drivers and pedestrians on freeways.

Video Stream Mining for On-Road Traffic Density Analytics

2012 ◽  
pp. 182-194
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

scholarly journals The Marginal Cost of Traffic Congestion and Road Pricing: Evidence from a Natural Experiment in Beijing

2020 ◽  
Vol 12 (1) ◽  
pp. 418-453 ◽  
Author(s):  
Jun Yang ◽  
Avralt-Od Purevjav ◽  
Shanjun Li
Keyword(s):  
Traffic Congestion ◽  
Natural Experiment ◽  
Road Pricing ◽  
Traffic Density ◽  
Traffic Data ◽  
City Center ◽  
Urban Centers ◽  
Restriction Policy ◽  
The Relationship ◽  
The City

Severe traffic congestion is ubiquitous in large urban centers. This paper provides the first causal estimate of the relationship between traffic density and speed and optimal congestion charges using real-time fine-scale traffic data in Beijing. The identification relies on plausibly exogenous variation in traffic density induced by Beijing’s driving restriction policy. Optimal congestion charges range from 5 to 39 cents per km depending on time and location. Road pricing would increase traffic speed by 11 percent within the city center and lead to an annual welfare gain of ¥1.5 billion from reduced congestion and revenue of ¥10.5 billion. (JEL H23, O18, P25, R41, R48)

Video Stream Mining for On-Road Traffic Density Analytics

2013 ◽  
pp. 1019-1030
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

scholarly journals A Peak Traffic Congestion Prediction Method Based on Bus Driving Time

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 709 ◽  
Author(s):  
Zhao Huang ◽  
Jizhe Xia ◽  
Fan Li ◽  
Zhen Li ◽  
Qingquan Li
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Short Term Memory ◽  
Road Traffic ◽  
Prediction Method ◽  
Transportation Systems ◽  
Percentage Error ◽  
Driving Time ◽  
Congestion Prediction ◽  
Congestion Index

Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses’ GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error ( M A P E ¯ ) and root mean square error ( R M S E ¯ ) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles.

scholarly journals Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xu Yang ◽  
Shixin Luo ◽  
Keyan Gao ◽  
Tingting Qiao ◽  
Xiaoya Chen
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Data Science ◽  
Vehicular Networks ◽  
Rapid Development ◽  
Prediction Method ◽  
Urban Traffic ◽  
Motor Vehicles ◽  
Car Accidents ◽  
Heavy Burden

In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method.

scholarly journals IoT based Road Traffic Management System for Metropolitan Cities

2020 ◽  
Vol 8 (12s3) ◽  
pp. 75-79
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Density ◽  
The Road ◽  
Metropolitan Cities ◽  
Traffic Management System ◽  
Road Traffic Congestion ◽  
Jamming Problems

Many cities in the world face jamming problems in road traffic, particularly in metropolitan cities. At present the traffic controlling systems aresemiautomatic in nature. With the introduction of IoT in road traffic management systems, it revolutionizes the field of road traffic management system and improves the road traffic congestion problem.This paper proposes an IoT-based road traffic management system for metropolitan cities. The proposed system provides the hassle free movement of the vehicles to avoid inconvenience and reroute the higher priority vehicles. Experimental results show that the proposed system gives higher success rate for the low traffic density in the lane.

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