Maintenance work zones on the road network have impacts on the normal travelling of vehicles, which increase the risk of traffic accidents. The traffic characteristic analysis in maintenance work zones is a basis for maintenance work zone related research such as layout design, traffic control and safety assessment. Due to the difficulty in vehicle microscopic behavior data acquisition, traditional traffic characteristic analysis mainly focuses on macroscopic characteristics. With the development of data acquisition technology, it becomes much easier to obtain a large amount of microscopic behavior data nowadays, which lays a good foundation for analyzing the traffic characteristics from a new point of view. This paper puts forward a method for expressing and displaying the vehicle behavior distribution in maintenance work zones. Using portable vehicle microscopic behavior data acquisition devices, lots of data can be obtained. Based on this data, an endpoint detection technology is used to automatically extract the segments in behavior data with violent fluctuations, which are segments where vehicles take behaviors such as acceleration or turning. Using the support vector machine classification method, the specific types of behaviors of the segments extracted can be identified, and together with a data combination method, a total of ten types of behaviors can be identified. Then the kernel density analysis is used to cluster different types of behaviors of all passing vehicles to show the distribution on maps. By this method, how vehicles travel through maintenance work zones, and how different vehicle behaviors distribute in maintenance work zones can be displayed intuitively on maps, which is a novel traffic characteristic and can shed light to maintenance work zone related researches such as safety assessment and design method.