Unreliability of travel times in urban areas is partly caused by traffic incidents. Traffic operations can be further hindered by the occurrence of secondary incidents and associated traffic delays. Understanding the characteristics of incidents that occur on urban freeways and forecasting their impacts can help decision-makers select better operational strategies. Using roadway inventory and traffic incident data provided by the Hampton Roads Traffic Operations Center, this study analyses traffic incidents and presents an online tool (called iMiT-incident management integration tool) that can dynamically predict incident durations, secondary incident occurrence and associated incident delays. This prediction tool was developed based on rigorous statistical models for incident duration and secondary incident occurrence, and uses a theoretically based deterministic queuing model to estimate associated delays; iMiT relies on available inputs about the roadway conditions, and incoming incident information, for example, location, time of day and weather conditions. It can aid incident management by generating information about primary and secondary incidents and help effectively assign incident management resources.