## 1 Introduction  
The presence of work zone areas in highway segments harms both traffic operation and traffic safety. Due to maintenance and construction activities, at least one lane has to be closed in work zones. Consequently, all the vehicles in the closed lanes are forced to merge into the open lanes upstream of work zones, where many merging maneuvers occur. However, frequent lane changes can cause significant disruption to the regular operation of vehicles in adjacent lanes, inducing traffic flow oscillation, capacity drop, and larger-scale congestion [1, 2]. Unreasonable merging maneuvers and complicated environments in work zones also increase the number of traffic conflicts and collision risks [3, 4]. For effective traffic flow control and crash risk reduction in work zones, the primary issue is understanding what merging characteristics vehicles have and what factors will affect drivers' merging behavior in reality. As one of the cases of lane changing behavior, merging in work zones has many similarities with lane changing at basic freeway segments. For example, merging in work zones is highly affected by traffic volume [5]. As traffic density increases, drivers may spend longer waiting in the initial lane due to difficulty finding a suitable gap. According to [PERSON] and [PERSON] [6], the limited opportunity could eventually make drivers postpone lane changing and further lead to difficulty in merging. Moreover, the merging behavior in work zones can be divided into lane-changing decision-making and implementation stages and applied with the lane-changing impact model [7]. However, merging behavior varies depending on location, traffic volume, speed limit, layout, and other properties of work zones, so general lane change models cannot be used directly to describe work zone merging behavior. There are warning signs far ahead of most work zones, so drivers have sufficient time to decide whether and where to change to the open lanes [8, 9]. Instead,other merge areas like the on-ramp merge and weaving sections only have a limited acceleration lane length. Merge control strategies, like early, late, and dynamic merge, are also applied in some highway work zones, which help regulate vehicle merge to the open lanes. Few recent studies have focused on comparing vehicle merging behavior in different work zones to determine influential factors. And most existing models extract features mainly from vehicle-level independent variables, including the speeds and gaps of the ego and surrounding vehicles. Variables in location level, containing the traffic states, number of lanes, warning signs, etc., were rarely considered. Therefore, before making a comprehensive merge behavior model in work zones, more influential factors and a more detailed investigation of vehicle merging characteristics should be considered. This study fills this gap by incorporating both vehicle-level and location-level variables to provide a more comprehensive analysis of merging behavior. We describe merging characteristics in work zones with different types and traffic states, aiming to discover influential factors by comparing lane-changing behavior between different scenarios. The amount of data also limits the development of the study. Due to the unforeseeable, flexible location and time, data on temporary highway work zone are difficult to obtain. In previous studies, most researchers extracted vehicle trajectory data from video cameras placed on a nearby overpass or traffic signs. Few collected data from driving simulators [10] and aerial videos [11]. For instance, using the data from video records collected at a short-term work zone site in Singapore, [PERSON] conducted a series of merging behavior models [12; 13; 14; 15]. [PERSON] et al. studied safety and headway characteristics in two highway work zones with automated speed enforcement [16].