Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding
Speeding remains a key factor in traffic fatalities, prompting transportation agencies to propose speed management solutions. While studies have examined speeding percentages above limits, few address its impact on individual journeys. Most studies rely on detector speed data, lacking route insights...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10354062/ |
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| author | Jorge Ugan Mohamed Abdel-Aty Zubayer Islam |
| author_facet | Jorge Ugan Mohamed Abdel-Aty Zubayer Islam |
| author_sort | Jorge Ugan |
| collection | DOAJ |
| description | Speeding remains a key factor in traffic fatalities, prompting transportation agencies to propose speed management solutions. While studies have examined speeding percentages above limits, few address its impact on individual journeys. Most studies rely on detector speed data, lacking route insights. This research employs connected vehicle trajectory data to analyze driver paths and variables, predicting speeding levels with various learning models. Extreme Gradient Boosting performed best, achieving 75.6% accuracy. This model elucidates how journey factors influence speeding and forecasts high-speed zones. Results reveal a driver’s total travel time significantly affects speeding, along with environmental features like residential area proportions. These findings aid transportation agencies in understanding trip-specific speeding factors, potentially informing better road safety measures. |
| format | Article |
| id | doaj-art-82a2cd6721344d5d920bfa14182e99c8 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-82a2cd6721344d5d920bfa14182e99c82025-01-24T00:02:38ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-015162810.1109/OJITS.2023.334196210354062Using Connected Vehicle Trajectory Data to Evaluate the Effects of SpeedingJorge Ugan0https://orcid.org/0000-0002-0830-0791Mohamed Abdel-Aty1https://orcid.org/0000-0002-4838-1573Zubayer Islam2https://orcid.org/0000-0002-8815-9117Department of Civil Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USADepartment of Civil Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USADepartment of Civil Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USASpeeding remains a key factor in traffic fatalities, prompting transportation agencies to propose speed management solutions. While studies have examined speeding percentages above limits, few address its impact on individual journeys. Most studies rely on detector speed data, lacking route insights. This research employs connected vehicle trajectory data to analyze driver paths and variables, predicting speeding levels with various learning models. Extreme Gradient Boosting performed best, achieving 75.6% accuracy. This model elucidates how journey factors influence speeding and forecasts high-speed zones. Results reveal a driver’s total travel time significantly affects speeding, along with environmental features like residential area proportions. These findings aid transportation agencies in understanding trip-specific speeding factors, potentially informing better road safety measures.https://ieeexplore.ieee.org/document/10354062/Probe vehicle dataconnected vehicle dataspeedingmachine learning |
| spellingShingle | Jorge Ugan Mohamed Abdel-Aty Zubayer Islam Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding IEEE Open Journal of Intelligent Transportation Systems Probe vehicle data connected vehicle data speeding machine learning |
| title | Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding |
| title_full | Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding |
| title_fullStr | Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding |
| title_full_unstemmed | Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding |
| title_short | Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding |
| title_sort | using connected vehicle trajectory data to evaluate the effects of speeding |
| topic | Probe vehicle data connected vehicle data speeding machine learning |
| url | https://ieeexplore.ieee.org/document/10354062/ |
| work_keys_str_mv | AT jorgeugan usingconnectedvehicletrajectorydatatoevaluatetheeffectsofspeeding AT mohamedabdelaty usingconnectedvehicletrajectorydatatoevaluatetheeffectsofspeeding AT zubayerislam usingconnectedvehicletrajectorydatatoevaluatetheeffectsofspeeding |