How fast would you (or should you) drive here? Investigation of relationships between official speed limit, perceived speed limit, and preferred speed

Ambros, Jiří; Turek, Richard; Šragová, Eva; Petr, Kryštof; Šucha, Matúš; Frič, Jindřich

doi:10.1016/j.trf.2021.09.003

Cited by 3 publications

(1 citation statement)

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“…No multicollinearity issues were observed for the speeding intention and speed behavior models, as the VIF (Variance Inflation Factor) of all predictors fell between 1 and 3. VIFs below 10 are often recommended [43,44]. According to the results of Table 4, in Step 1, the overall model accounted for 18% of the variance in the intention of speeding with sex, education level, and driver experience, demonstrating a significant relationship with speeding intention (at p < 0.05).…”

Section: Results Of Hierarchical Multiple Regression Analysismentioning

confidence: 94%

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: Results Of Hierarchical Multiple Regression Analysismentioning

confidence: 94%