A methodology for setting credible speed limits based on numerical analyses and driving simulator experiments

Montella, Alfonso; Calvi, Alessandro; D'Amico, Fabrizio; Ferrante, Chiara; Galante, Francesco; Mauriello, Filomena; Rella Riccardi, Maria; Scarano, Antonella

doi:10.1016/j.trf.2023.12.008

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…However, the high reporting of speeding non-compliance may indicate that the speed limits are not perceived as credible or consistent with drivers' expectations. This perception could be influenced by roadway geometric features and other external factors, which may suggest that higher speeds on the roads are feasible than those indicated by the speed limit signs, thus prompting drivers to travel at higher speeds [46]. Nevertheless, in the local context of Yaoundé, most urban and highway roads typically feature only one lane in the direction of travel, and the overall quality of the infrastructure generally does not support very high speeds.…”

Section: Discussionmentioning

confidence: 99%

Self-Reported Speed Compliance and Drivers Speeding Behaviour in Cameroon

Fondzenyuy,

Fowo Fotso,

Feudjio

et al. 2024

Future Transportation

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Speeding is a significant global issue, with disparities in speed and safety outcomes between low- and middle-income countries (LMICs) and high-income countries (HICs). This study aims to address speed research gaps in LMICs by examining the prevalence of self-reported speeding and factors influencing drivers’ speeding behavior using the Theory of Planned Behavior (TPB). An online survey involving 387 anonymous drivers was conducted in Yaoundé. Results showed that all drivers reported exceeding speed limits, with 81% exceeding them by 5 km/h, 12% by 10 km/h, and 7% by more than 10 km/h in urban areas. On highways, 54% reported exceeding the limit by more than 10 km/h. Age, driver’s license, and gender showed significant associations with speed limit non-compliance. This study’s results suggest that the TPB can be useful in assessing speeding behavior, as the inclusion of TPB variables led to a more than 50% proportionate increase in speeding behavior variance. Structural equation modeling revealed attitude towards speeding as the strongest predictor of speeding intention, along with certain demographics indirectly influencing speeding behavior through speeding intention. Perceived behavioral control and speeding intention directly influenced speeding behavior. These findings emphasize the potential of interventions targeting attitude, intentions, and perceived behavioral control to modify speeding behavior and improve road safety.

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Section: Discussionmentioning

confidence: 99%