Enabling Sustainable Urban Transportation with Predictive Analytics and IoT
This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, us...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01179.pdf |
| _version_ | 1797248915734528000 |
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| author | Igorevich Rozhdestvenskiy Oleg Poornima E. |
| author_facet | Igorevich Rozhdestvenskiy Oleg Poornima E. |
| author_sort | Igorevich Rozhdestvenskiy Oleg |
| collection | DOAJ |
| description | This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, using empirical data gathered from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its correspondence with actual data. The data reveals a direct relationship between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of the models in forecasting congestion and AQI, thereby confirming their effectiveness. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban transportation, enabling more intelligent decision-making, and creating sustainable urban environments driven by data-driven insights and proactive actions. |
| first_indexed | 2024-04-24T20:22:11Z |
| format | Article |
| id | doaj.art-e5042816fc8448c9a193682950bcf8f4 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:22:11Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-e5042816fc8448c9a193682950bcf8f42024-03-22T08:05:26ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920117910.1051/matecconf/202439201179matecconf_icmed2024_01179Enabling Sustainable Urban Transportation with Predictive Analytics and IoTIgorevich Rozhdestvenskiy Oleg0Poornima E.1Lovely Professional UniversityDepartment of AIMLE, GRIETThis research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, using empirical data gathered from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its correspondence with actual data. The data reveals a direct relationship between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of the models in forecasting congestion and AQI, thereby confirming their effectiveness. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban transportation, enabling more intelligent decision-making, and creating sustainable urban environments driven by data-driven insights and proactive actions.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01179.pdfpredictive analyticsinternet of things (iot)urban transportationsustainabilitydata-driven interventions |
| spellingShingle | Igorevich Rozhdestvenskiy Oleg Poornima E. Enabling Sustainable Urban Transportation with Predictive Analytics and IoT MATEC Web of Conferences predictive analytics internet of things (iot) urban transportation sustainability data-driven interventions |
| title | Enabling Sustainable Urban Transportation with Predictive Analytics and IoT |
| title_full | Enabling Sustainable Urban Transportation with Predictive Analytics and IoT |
| title_fullStr | Enabling Sustainable Urban Transportation with Predictive Analytics and IoT |
| title_full_unstemmed | Enabling Sustainable Urban Transportation with Predictive Analytics and IoT |
| title_short | Enabling Sustainable Urban Transportation with Predictive Analytics and IoT |
| title_sort | enabling sustainable urban transportation with predictive analytics and iot |
| topic | predictive analytics internet of things (iot) urban transportation sustainability data-driven interventions |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01179.pdf |
| work_keys_str_mv | AT igorevichrozhdestvenskiyoleg enablingsustainableurbantransportationwithpredictiveanalyticsandiot AT poornimae enablingsustainableurbantransportationwithpredictiveanalyticsandiot |