Rapid technological advances, especially in telematics and Big Data analytics, as well as the increasing 2 penetration and use of information technology by drivers (e.g. smartphones), provide new capabilities for 3 monitoring and analyzing driving behavior. This paper examines the impact of driver feedback delivered 4 through a smartphone application on driving behavior risk indicators, within a 21-month multiphase 5 naturalistic driving experiment involving a sample of 175 car drivers. First, a preliminary analysis 6 utilizing summary statistics and Wilcoxon signed-rank test shed light upon the effects of upgraded 7 feedback features on key risk indicators across experiment phases. Subsequently, Structural Equation 8 Models (SEM) on a 73,869 trip dataset provided significant insights into how feedback mechanisms and 9 exposure factors influence driving behaviors. Results indicate that the examined feedback mechanisms 10 are effective in reducing the percentage of speeding time and harsh braking events, although there is a 11 slight increase in harsh accelerations, which may require further refinement of the feedback system. The 12 scorecard feature has the highest positive effect, indicating its crucial role in modifying driving habits, 13 with gamification (competition and challenges) being the second most influential feedback mechanism. 14 Regarding the exposure indicators, morning peak is associated with more aggressive driving, while 15 afternoon peak tends to be less risky. Additionally, results showcase strong positive correlations between 16 speeding, harsh braking, and harsh accelerations highlight the interconnected nature of aggressive driving 17 behaviors. These findings may be beneficial for insurance companies, fleet management applications, and 18 policymakers, enabling them to leverage results to improve traffic safety and driver behavior.
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