A comparative study of traffic detection using Google map (GPS) and IoV
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.
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| Γλώσσα: | English |
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Brac University
2023
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| Διαθέσιμο Online: | http://hdl.handle.net/10361/22020 |
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10361-220202023-12-21T21:02:44Z A comparative study of traffic detection using Google map (GPS) and IoV Alam, Mohd. Touhedul Al Nayeem, Harun Kazi, Sadia Hamid Shakil, Arif Department of Computer Science and Engineering, Brac University Traffic detection Google map IoV GPS Navigation Computer communication systems. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-41). Everyone knows that time is money and nowadays people spend quite a large amount of time on the roads in their daily life. The reason behind this is nothing but ‘Traffic’. Traffic detection and navigation has gone through many phases over the years. Once we had to depend on traffic prediction and now, we can check real time traffic updates by the means of services like Google Map. Google Map uses GPS to detect traffic. However, the problem with it is, Map uses historical traffic data and the individual GPS device count to detect the congestion of traffic. This may affect the accuracy in terms of vehicles. Depending only on the device count and past records, a proper real time data cannot be generated. The very important aspect of vehicle count, and vehicle size are totally ignored in this approach. Moreover, a real time representation of traffic will also require 24/7 surveillance. Another promising approach to detect traffic is using IoV (Internet of Vehicles) to count the number of connected vehicles rather than counting people in an area. As the world is moving towards IoT, traffic detection can evolve to a great extent with the introduction of IoV as well. In this work, we will be analysing the existing system google uses for traffic detection and compare it with a possible model to be used with the inclusion of IoV. To provide a verdict on which system will be better, we will be using traffic detection reports of google Map as preliminary data and possible outcome of the IoV model analysed. Mohd. Touhedul Alam Harun Al Nayeem B.Sc. in Computer Science 2023-12-21T06:04:28Z 2023-12-21T06:04:28Z 2021 2021-06 Thesis ID: 17301230 ID: 17301209 http://hdl.handle.net/10361/22020 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 41 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Traffic detection Google map IoV GPS Navigation Computer communication systems. |
| spellingShingle |
Traffic detection Google map IoV GPS Navigation Computer communication systems. Alam, Mohd. Touhedul Al Nayeem, Harun A comparative study of traffic detection using Google map (GPS) and IoV |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. |
| author2 |
Kazi, Sadia Hamid |
| author_facet |
Kazi, Sadia Hamid Alam, Mohd. Touhedul Al Nayeem, Harun |
| format |
Thesis |
| author |
Alam, Mohd. Touhedul Al Nayeem, Harun |
| author_sort |
Alam, Mohd. Touhedul |
| title |
A comparative study of traffic detection using Google map (GPS) and IoV |
| title_short |
A comparative study of traffic detection using Google map (GPS) and IoV |
| title_full |
A comparative study of traffic detection using Google map (GPS) and IoV |
| title_fullStr |
A comparative study of traffic detection using Google map (GPS) and IoV |
| title_full_unstemmed |
A comparative study of traffic detection using Google map (GPS) and IoV |
| title_sort |
comparative study of traffic detection using google map (gps) and iov |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/22020 |
| work_keys_str_mv |
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