Improving the accuracy of predicting congested traffic flow road transport using random forest algorithm and compared with the naives bayes algorithm using machine learning
Through a comparison of the Naive Bayes algorithm and the Random Forest approach, the purpose of this research is to improve the accuracy of the former in order to anticipate the amount of traffic congestion that would occur on roadways. In order to conduct an analysis of the flow of traffic along a...
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| description | Through a comparison of the Naive Bayes algorithm and the Random Forest approach, the purpose of this research is to improve the accuracy of the former in order to anticipate the amount of traffic congestion that would occur on roadways. In order to conduct an analysis of the flow of traffic along a route, the research endeavourutilised the Random Forest technique with ten samples and the Naive Bayes algorithm with ten samples. The traffic flow road transport dataset may be utilised for the purpose of determining the flow of traffic on roads by utilising only a few factors from the dataset, as well as for the purpose of making forecasts. During times of congestion, the dataset contains a number of elements that may be utilised to forecast the flow of traffic. These features include the date, the time, the junction, and the id. The results of the statistical analysis conducted by SPSS indicate that there is a noteworthy distinction (p=0.0001; p |
| doi_str_mv | 10.1063/5.0233191 |
| format | Conference Proceeding |
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Rakesh ; Vijayan, V. ; Babu, A. B. Karthick Anand</contributor><creatorcontrib>Manimaran, A. ; Srinivasan, R ; Balasubramanian, PL ; Seenivasan, M ; Sharma, T. Rakesh ; Vijayan, V. ; Babu, A. B. Karthick Anand</creatorcontrib><description>Through a comparison of the Naive Bayes algorithm and the Random Forest approach, the purpose of this research is to improve the accuracy of the former in order to anticipate the amount of traffic congestion that would occur on roadways. In order to conduct an analysis of the flow of traffic along a route, the research endeavourutilised the Random Forest technique with ten samples and the Naive Bayes algorithm with ten samples. The traffic flow road transport dataset may be utilised for the purpose of determining the flow of traffic on roads by utilising only a few factors from the dataset, as well as for the purpose of making forecasts. During times of congestion, the dataset contains a number of elements that may be utilised to forecast the flow of traffic. These features include the date, the time, the junction, and the id. The results of the statistical analysis conducted by SPSS indicate that there is a noteworthy distinction (p=0.0001; p<0.05, 2-tailed) between the accuracy of the Random forest method, which is 89.54 percent, and the accuracy of the Naives Bayes algorithm, which is 49.13 percent. When it comes to predicting traffic congestion and road transport, the findings show that the Random forest approach works significantly better than the Naives Bayes algorithm. This is demonstrated by the fact that the methods are superior to one another.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0233191</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Accuracy ; Algorithms ; Datasets ; Machine learning ; Road transportation ; Roads & highways ; Statistical analysis ; Traffic congestion ; Traffic flow</subject><ispartof>AIP conference proceedings, 2024, Vol.3193 (1)</ispartof><rights>AIP Publishing LLC</rights><rights>2024 AIP Publishing LLC.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0233191$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>309,310,314,778,782,787,788,792,4500,23917,23918,25127,27911,27912,76141</link.rule.ids></links><search><contributor>Srinivasan, R</contributor><contributor>Balasubramanian, PL</contributor><contributor>Seenivasan, M</contributor><contributor>Sharma, T. Rakesh</contributor><contributor>Vijayan, V.</contributor><contributor>Babu, A. B. Karthick Anand</contributor><creatorcontrib>Manimaran, A.</creatorcontrib><title>Improving the accuracy of predicting congested traffic flow road transport using random forest algorithm and compared with the naives bayes algorithm using machine learning</title><title>AIP conference proceedings</title><description>Through a comparison of the Naive Bayes algorithm and the Random Forest approach, the purpose of this research is to improve the accuracy of the former in order to anticipate the amount of traffic congestion that would occur on roadways. In order to conduct an analysis of the flow of traffic along a route, the research endeavourutilised the Random Forest technique with ten samples and the Naive Bayes algorithm with ten samples. The traffic flow road transport dataset may be utilised for the purpose of determining the flow of traffic on roads by utilising only a few factors from the dataset, as well as for the purpose of making forecasts. During times of congestion, the dataset contains a number of elements that may be utilised to forecast the flow of traffic. These features include the date, the time, the junction, and the id. The results of the statistical analysis conducted by SPSS indicate that there is a noteworthy distinction (p=0.0001; p<0.05, 2-tailed) between the accuracy of the Random forest method, which is 89.54 percent, and the accuracy of the Naives Bayes algorithm, which is 49.13 percent. When it comes to predicting traffic congestion and road transport, the findings show that the Random forest approach works significantly better than the Naives Bayes algorithm. This is demonstrated by the fact that the methods are superior to one another.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Datasets</subject><subject>Machine learning</subject><subject>Road transportation</subject><subject>Roads & highways</subject><subject>Statistical analysis</subject><subject>Traffic congestion</subject><subject>Traffic flow</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpFUctqwzAQFKWFpmkP_QNBbwWnkmVJ1rGEPgKBXnLozWxkKVGwJVd2EvJP_cgqD-hll52dnWF3EXqkZEKJYC98QnLGqKJXaEQ5p5kUVFyjESGqyPKCfd-iu77fEJIrKcsR-p21XQw751d4WBsMWm8j6AMOFnfR1E4Px5YOfmX6wdR4iGCt09g2YY9jgBPi-y7EAW_7IzeVdWixDTFNYGhWIbph3eIEJ522gySL9wk6GXpwO9PjJRxS_CefpVrQa-cNbgxEn4B7dGOh6c3DJY_R4v1tMf3M5l8fs-nrPOsEoxlVSlirecpML7VhnNZCKFkyvqQSckJrYFJDDryUhdW5MLUuKS-FYqQwlo3R01k2XeZnm7aoNmEbfXKsGM2FlJwonljPZ1av3QCDC77qomshHipKquMzKl5dnsH-AOkvgAg</recordid><startdate>20241111</startdate><enddate>20241111</enddate><creator>Manimaran, A.</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20241111</creationdate><title>Improving the accuracy of predicting congested traffic flow road transport using random forest algorithm and compared with the naives bayes algorithm using machine learning</title><author>Manimaran, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p631-1996ffc51993cbce351d6697835b17a201da37ca2a5874fc26edc815869304ef3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Datasets</topic><topic>Machine learning</topic><topic>Road transportation</topic><topic>Roads & highways</topic><topic>Statistical analysis</topic><topic>Traffic congestion</topic><topic>Traffic flow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Manimaran, A.</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Manimaran, A.</au><au>Srinivasan, R</au><au>Balasubramanian, PL</au><au>Seenivasan, M</au><au>Sharma, T. Rakesh</au><au>Vijayan, V.</au><au>Babu, A. B. Karthick Anand</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Improving the accuracy of predicting congested traffic flow road transport using random forest algorithm and compared with the naives bayes algorithm using machine learning</atitle><btitle>AIP conference proceedings</btitle><date>2024-11-11</date><risdate>2024</risdate><volume>3193</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Through a comparison of the Naive Bayes algorithm and the Random Forest approach, the purpose of this research is to improve the accuracy of the former in order to anticipate the amount of traffic congestion that would occur on roadways. In order to conduct an analysis of the flow of traffic along a route, the research endeavourutilised the Random Forest technique with ten samples and the Naive Bayes algorithm with ten samples. The traffic flow road transport dataset may be utilised for the purpose of determining the flow of traffic on roads by utilising only a few factors from the dataset, as well as for the purpose of making forecasts. During times of congestion, the dataset contains a number of elements that may be utilised to forecast the flow of traffic. These features include the date, the time, the junction, and the id. The results of the statistical analysis conducted by SPSS indicate that there is a noteworthy distinction (p=0.0001; p<0.05, 2-tailed) between the accuracy of the Random forest method, which is 89.54 percent, and the accuracy of the Naives Bayes algorithm, which is 49.13 percent. When it comes to predicting traffic congestion and road transport, the findings show that the Random forest approach works significantly better than the Naives Bayes algorithm. 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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2024, Vol.3193 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_proquest_journals_3126775095 |
| source | AIP Journals Complete |
| subjects | Accuracy Algorithms Datasets Machine learning Road transportation Roads & highways Statistical analysis Traffic congestion Traffic flow |
| title | Improving the accuracy of predicting congested traffic flow road transport using random forest algorithm and compared with the naives bayes algorithm using machine learning |
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