The cause for the majority of road crashes can be attributed to drivers' behavior. Recent in-vehicle monitoring technologies enable continuous and high resolution measurements of drivers' behavior. We analyze the information received from a novel in-vehicle technology which identifies the occurrence of undesirable driving events such as extreme braking and accelerating, sharp cornering and sudden lane changing. We use maximum likelihood estimation to fit a Negative-Binomial model to Events Frequency (EF) in a trip (N=117,195). Negative-Binomial regression results show higher EF in trip edges (trip beginning and trip end) than in the middle of the trip, higher EF for males than for females and higher EF at nighttime than at daytime. In addition, it shows a significant interaction between gender and time of the day-the increased EF at night was more extreme for males than females.