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Enhance the performance of navigation: A two-stage machine learning approach [article]

Yimin Fan, Zhiyuan Wang, Yuanpeng Lin, Haisheng Tan
2020 arXiv   pre-print
In this paper, we adopt the ideas of ensemble learning and develop a two-stage machine learning model to give accurate navigation results.  ...  However, real traffic navigation is still considered to be a particularly challenging problem because of the time-varying patterns of the traffic flow and unpredictable accidents/congestion.  ...  Using machine learning models to enhance the performance of navigation system is a great topic in real applications.  ... 
arXiv:2004.00879v1 fatcat:kpm5pi6qlvdtjim2xf2ud64s2e

The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review

Aneri M., Rutvij H.
2018 International Journal of Computer Applications  
In this paper, we build survey on existing research work carried out for various applications of machine learning to IoT.  ...  In order to provide smarter environment, their need to be implement IoT with machine learning.  ...  This scheme based on real time wealth information of vast numbers of animals in wild. This automatic extraction reduces the cost of system.  ... 
doi:10.5120/ijca2018916609 fatcat:ia3lxjerd5a4lczw4yqwtfc43i

Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere

Naonori Ueda, Futoshi Naya
2018 APSIPA Transactions on Signal and Information Processing  
Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow.  ...  Machine learning is a promising technology for analyzing diverse types of big data.  ...  To solve this problem, we developed a new method based on a machine learning approach.  ... 
doi:10.1017/atsip.2018.4 fatcat:5anb5fwe4vbnvkqtcgrmgdlika

Smart Town Traffic Management System using LoRa and Machine Learning Mechanism

Seung Byum Seo, Dhananjay Singh
2018 IEEE Technology Policy and Ethics  
Based on the training set, the algorithm learns how to predict the real-valued output. The main inputs we use for the training set are traveling time and density.  ...  As long as solid ITMS is built, any applications including adaptive navigation system can be implemented based on ITMS [4] .  ...  Jones worked briefly for Panasonic Communications and Systems Company as a district sales manager providing application engineering and product support to distributors in a five-state area prior to starting  ... 
doi:10.1109/ntpe.2018.9778109 fatcat:w54p4yefubbobaz7t7bz5xx4lm

Intelligent Transport System: A Progressive Review

Aditi Zear, Pradeep Kumar Singh, Yashwant Singh
2016 Indian Journal of Science and Technology  
Hence Intelligent Transport System (ITS) has been emerged as a solution to various transport related issues. The aim of this research paper is to conduct systematic analysis on ITS.  ...  ITS have combined various technologies such as Data collection, Communication, Data Mining, Machine Learning, Artificial Intelligence and Database Management.  ...  This algorithm uses real situations. The sensors send the traffic flow information on a computer, and then based on Genetic Algorithm (GA) timing of green light is adjusted.  ... 
doi:10.17485/ijst/2016/v9i32/100713 fatcat:jtjeluppfjaxrlmpk7s3dzwvme

Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

Tu Peng, Xu Yang, Zi Xu, Yu Liang
2020 Sustainability  
In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks.  ...  on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm.  ...  Unlike traditional navigation systems, which are commonly based on static urban road networks without real-time analysis of changes in traffic networks or are focused on reducing the traveling time or  ... 
doi:10.3390/su12198118 fatcat:dqjthfz2yzd4bjsheqx7a62fum

Machine Learning with Internet of Things: A Comprehensive Survey

Akanksha Kochhar, Prerna Sharma
2019 Zenodo  
In this paper a review is conducted on the existing work done by the researchers in using Machine learning with IOT which includes the application areas.  ...  Also the major challenges which are faced in using Machine Learning with IOT are briefly discussed.  ...  After the analysis the congestion on roads is predicted using Linear Regression, a Machine Learning algorithm.  ... 
doi:10.5281/zenodo.4743556 fatcat:ypvoaptpgbc4bn4lgoxauebjay

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
ReFOCUS+: Multi-Layers Real-Time Intelligent Route Guidance System With Congestion Detection and Avoidance M. Rezaei, H. Noori, M. M. Razlighi, and M.  ...  Nickray This article proposes a new dynamic multilayer and fogcloud-based advance route guidance system architecture in order to detect and ease the road congestion.  ...  Real-Time Embedded Vision System for the Watchfulness Analysis of Train Drivers C. A. Avizzano, P. Tripicchio, E. Ruffaldi, A. Filippeschi, and J. M.  ... 
doi:10.1109/tits.2020.3044830 fatcat:mmamywaconchdnynve3qm6eip4

Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting [article]

Xiangpeng Wan, Hakim Ghazzai, Yehia Massoud
2020 arXiv   pre-print
We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time  ...  In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets.  ...  real-time traffic navigation map.  ... 
arXiv:2004.13823v1 fatcat:brcfm72bvzajpa7whmcer3oqra


Natalija Stojanović, Dragan Stojanović
2020 Facta Universitatis Series Automatic Control and Robotics  
real time.  ...  Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of  ...  They present a novel approach based on the system model characterized by essential input and output parameters and provide a self-adaptation of the navigation system based on an analysis of streaming data  ... 
doi:10.22190/fuacr2002087s fatcat:dsud4exf3zedlnh6hea6dcv3lu

Smart Parking Tools Suitability for Open Parking Lots: A Review

Vijay Paidi, Hasan Fleyeh, Johan Håkansson, Roger G. Nyberg
2018 Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems  
No application provided real time parking occupancy information of open parking lots, which is a necessity to guide them along the shortest route to free space.  ...  However, this paper suggests a combination of machine vision, fuzzy logic or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions  ...  Therefore, in order to address this challenge further research in the use of deep learning and multiagent systems would help to provide real time parking occupancy information along with navigational directions  ... 
doi:10.5220/0006812006000609 dblp:conf/vehits/PaidiFHN18 fatcat:spxyyujrlrbd5hxtyouf3mjhqm

A Review of Machine Learning and IoT in Smart Transportation

Fotios Zantalis, Grigorios Koulouras, Sotiris Karabetsos, Dionisis Kandris
2019 Future Internet  
As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application.  ...  From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications.  ...  Reinforcement learning is mainly used for applications like AI gaming, skill acquisition, robot navigation, and real-time decisions. [9] .  ... 
doi:10.3390/fi11040094 fatcat:6xneyx7ynrgn7p2yl5efy76cee

Intelligent Traffic Management Using Big Data Analytics and IOT

Tharun Palla
2021 International Journal for Research in Applied Science and Engineering Technology  
This paper proposes a system using data analytics, machine learning algorithms, Internet of things to predict the traffic flow, generate precise data about real time traffic congestions at that instant  ...  Keywords: data analytics, machine learning, GPS, image analysis, intelligent traffic management system, Internet of things  ...  The technique is used with machine learning to enhance item recognition and classification based on their form and motion.  ... 
doi:10.22214/ijraset.2021.38650 fatcat:yknb4aokpzbzhavtgpolmeomku

Environment Feature and Obstacle Position Prediction Using Long Short-Term Memory

Samir N. Ajani, Salim Y. Amdani
2022 International Journal of Scientific Research in Science and Technology  
Congestion management and procedural knowledge require obstacle prediction of network based on large amounts of dataset.  ...  Traditional time series forecasting approaches struggle to create effective prediction models since time series analysis in prediction of network traffic is very unstable time parameter and is also non  ...  system analysis etc.  ... 
doi:10.32628/ijsrst229151 fatcat:q3ar5vb2qrgt5jgaiebvmrdi7e

Multi-agent based vehicular congestion management

Prajakta Desai, Seng W. Loke, Aniruddha Desai, Jack Singh
2011 2011 IEEE Intelligent Vehicles Symposium (IV)  
The paper classifies the multi-agent techniques based on the locus of decision control intelligence and focuses on their suitability of application in congestion management.  ...  Existing congestion management techniques in Intelligent Transportation Systems (ITS) have not been very effective due to lack of autonomous and collaborative behavior of the constituent traffic control  ...  The results showed an overall decrease in waiting time of 26% for complex routes. 2 ) Machine Learning Models The authors in [13] and [14] describe a Machine Learning approach in which an action  ... 
doi:10.1109/ivs.2011.5940493 dblp:conf/ivs/DesaiLDS11 fatcat:hl6wvy6n2rdnhn67j4dtzztchy
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