The grouping of heavy goods vehicles phenomenon. A case study based on Weigh-in-Motion data on a French highway
This study deals with the manual grouping phenomenon of heavy vehicles (over 3.5 tons), which refers to vehicles following a vehicle of the same group (rigid box truck, bobtail truck…) in the same lane with time gaps less than or equal to 2 s and which presents a speed differential of zero, or more...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Transportation Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666691X25000624 |
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| Summary: | This study deals with the manual grouping phenomenon of heavy vehicles (over 3.5 tons), which refers to vehicles following a vehicle of the same group (rigid box truck, bobtail truck…) in the same lane with time gaps less than or equal to 2 s and which presents a speed differential of zero, or more or less equal to the margin of error of the measuring instruments. By focusing precisely on these time gaps, we sought to determine the share of heavy truck drivers who play with the net headway imposed by the highway code in close-following conditions and who might also the most likely to adopt platooning technology given the similarity between the two situations. The data explored in this study was recorded by weigh-in-motion (WIM) systems located on several French high-speed roads. Each time a vehicle passes, the system increments an indicator and for each truck records: time, total weight and axle weight, number of axles, and instantaneous speed… We worked on a database containing about two million records, generated by a WIM system located in the south of France during the calendar year 2015. After a presentation of how we completed, organized and pre-processed the information in this database not designed for this purpose, we indicate how we constructed relevant variables from these data and conducted linear regression model on inter-distance and K-means classification in order to distinguish subgroups of truck drivers in the close following situations. The results show that this phenomenon which is rare on this type of road, is more correlated with the level of heavy vehicle traffic than with general traffic. |
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| ISSN: | 2666-691X |