Analyzing historical vehicle traffic data has many applications including urban planning and intelligent in-vehicle route prediction. A common practice to acquire this data is through roadside sensors. This approach is expensive because of infrastructure and planning costs and cannot be easily applied to new routes. A Web mining approach is proposed to address these limitations. The proposed system gathers information about vehicle commute times, accidents, and weather reports from heterogeneous Web sources. This information is combined to support vehicle traffic analytics. Clustering analysis is performed on historical data that investigates the traffic patterns of highways and arterial roads with factors having the most impact on commute time. A commute time prediction model is built on historical vehicle traffic data analytics. Commute time prediction model is trained with the traffic problems faced in the past and forecasts the commute time incorporating the impact of external factors such as weather and accidents.