Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data
Vehicular incidents, especially those involving tractor trailers, are increasing in number every year. These events are extremely costly for fleets, in terms of damage or loss of property, loss of efficiency, and certainly in terms of loss of life. Although the U.S. Department of Transportation (DOT...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Safety |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-576X/9/4/72 |
| _version_ | 1797379413214494720 |
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| author | Amy Moore Vivek Sujan Adam Siekmann Hyeonsup Lim Shiqi (Shawn) Ou Sarah Tennille |
| author_facet | Amy Moore Vivek Sujan Adam Siekmann Hyeonsup Lim Shiqi (Shawn) Ou Sarah Tennille |
| author_sort | Amy Moore |
| collection | DOAJ |
| description | Vehicular incidents, especially those involving tractor trailers, are increasing in number every year. These events are extremely costly for fleets, in terms of damage or loss of property, loss of efficiency, and certainly in terms of loss of life. Although the U.S. Department of Transportation (DOT) is responsible for performing inspections, and fleet managers are encouraged to maintain their fleet and participate in regular inspections, it is uncertain whether these inspections are occurring at a frequency that is necessary to prevent incidents. The Federal Motor Carrier Safety Administration (FMCSA) of the DOT manages and maintains the Motor Carrier Management Information System (MCMIS) dataset, which contains all incident and inspection data regarding commercial vehicles in the U.S. The purpose of this preliminary analysis was to explore the MCMIS dataset through spatiotemporal analyses, to uncover findings that may hint at potential improvements in the DOT inspection process and highlight location-specific trends in the dataset. These analyses are novel, as previous research using the MCMIS dataset only examined the data at the state or county level, not at a national scale. The results from the analyses pinpointed specific major metropolitan areas, namely Harris County (Houston), Texas, and three of the New York boroughs (Kings, Queens, and the Bronx), which were found to have increasing incident rates during the study period (2016–2020). An overview of potential causal factors contributing to this increase are provided as well as an overview of the inspection process, and suggestions for improvement relative to the highlighted locations in Texas and New York are also provided. Ultimately, it is suggested that the incorporation of advanced technology and automation may prove beneficial in reducing the occurrence of events that lead to incidents and may also help in the inspection process. |
| first_indexed | 2024-03-08T20:22:52Z |
| format | Article |
| id | doaj.art-37f5d01579704dad96c5da5cf9743dd6 |
| institution | Directory Open Access Journal |
| issn | 2313-576X |
| language | English |
| last_indexed | 2024-03-08T20:22:52Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Safety |
| spelling | doaj.art-37f5d01579704dad96c5da5cf9743dd62023-12-22T14:39:45ZengMDPI AGSafety2313-576X2023-10-01947210.3390/safety9040072Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection DataAmy Moore0Vivek Sujan1Adam Siekmann2Hyeonsup Lim3Shiqi (Shawn) Ou4Sarah Tennille5Oak Ridge National Laboratory, Oak Ridge, TN 37830, USAOak Ridge National Laboratory, Oak Ridge, TN 37830, USAOak Ridge National Laboratory, Oak Ridge, TN 37830, USAOak Ridge National Laboratory, Oak Ridge, TN 37830, USAOak Ridge National Laboratory, Oak Ridge, TN 37830, USAOak Ridge National Laboratory, Oak Ridge, TN 37830, USAVehicular incidents, especially those involving tractor trailers, are increasing in number every year. These events are extremely costly for fleets, in terms of damage or loss of property, loss of efficiency, and certainly in terms of loss of life. Although the U.S. Department of Transportation (DOT) is responsible for performing inspections, and fleet managers are encouraged to maintain their fleet and participate in regular inspections, it is uncertain whether these inspections are occurring at a frequency that is necessary to prevent incidents. The Federal Motor Carrier Safety Administration (FMCSA) of the DOT manages and maintains the Motor Carrier Management Information System (MCMIS) dataset, which contains all incident and inspection data regarding commercial vehicles in the U.S. The purpose of this preliminary analysis was to explore the MCMIS dataset through spatiotemporal analyses, to uncover findings that may hint at potential improvements in the DOT inspection process and highlight location-specific trends in the dataset. These analyses are novel, as previous research using the MCMIS dataset only examined the data at the state or county level, not at a national scale. The results from the analyses pinpointed specific major metropolitan areas, namely Harris County (Houston), Texas, and three of the New York boroughs (Kings, Queens, and the Bronx), which were found to have increasing incident rates during the study period (2016–2020). An overview of potential causal factors contributing to this increase are provided as well as an overview of the inspection process, and suggestions for improvement relative to the highlighted locations in Texas and New York are also provided. Ultimately, it is suggested that the incorporation of advanced technology and automation may prove beneficial in reducing the occurrence of events that lead to incidents and may also help in the inspection process.https://www.mdpi.com/2313-576X/9/4/72heavy-duty truckincidentsinspectionsMCMISFAF |
| spellingShingle | Amy Moore Vivek Sujan Adam Siekmann Hyeonsup Lim Shiqi (Shawn) Ou Sarah Tennille Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data Safety heavy-duty truck incidents inspections MCMIS FAF |
| title | Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data |
| title_full | Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data |
| title_fullStr | Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data |
| title_full_unstemmed | Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data |
| title_short | Spatio-Temporal Assessment of Heavy-Duty Truck Incident and Inspection Data |
| title_sort | spatio temporal assessment of heavy duty truck incident and inspection data |
| topic | heavy-duty truck incidents inspections MCMIS FAF |
| url | https://www.mdpi.com/2313-576X/9/4/72 |
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