## 1 Introduction
Compared with conventional speed limits based on VSL signs, the popular CAVs are more accurate and flexible for traffic management. In addition, the traffic management of super-long-span bridges faces numerous challenges, including inaccurate traffic flow predictions, difficulties in managing congestion, and delays in accident response times. The introduction of CAVs can effectively alleviate these issues. CAVs enhance the accuracy of traffic flow predictions by collecting and transmitting real-time traffic data, allowing traffic management systems to dynamically adjust signals and speed limits, thereby optimizing traffic flow and reducing congestion. Additionally, cooperative communication among CAVs improves driving safety, lowers the likelihood of accidents, and strengthens emergency response capabilities, ensuring the efficient and safe operation of super-long-span bridges. There are also several control measures employed to mitigate the traffic bottleneck: ramp management (in particular ramp metering) and mainstream control (including speed control, lane change control, congestion warning, keep-lane instructions, and so on). However, considering the special traffic characteristics of the long segment in which the road has only one entrance and one exit, the bottleneck can only be mitigated by VSL. With the technology of vehicle sensing and communications apparatus, the VSL control strategy can be implemented precisely for every CAV, which will maximize the control strategy's effectiveness. Therefore, a multi-segment variable speed predictive control (MVSPC) strategy is proposed in this paper to solve the problem of lane-drop bottleneck. The proposed strategy can delay vehicles reaching the bottleneck point and avoid the aggravation of congestion in the bottleneck segment and is only useful for long road segments with only one entry and exit or segments with no ramps in the manage and control zone. Unlike other research, the proposed strategy is based on an expanded macro-traffic flow model that considers detailed traffic flow characteristics and operates on an event-triggered basis. This strategy is implemented within the in-vehicle navigation system (IVNS) under the Intelligent Vehicle Infrastructure Cooperative System (IVICS) framework. Real-time and dynamic traffic control and management strategies are promptly employed for vehicles in each segment, based on predictions of the development status of traffic flow. The main contributions of this work are summarized as follows:  
(1) Considering the moving bottleneck with uncertainties, an event-triggered strategy with dynamic threshold parameters is proposed for a long-segment freeway. The control law is not updated at every step, but only when a specific set of conditions is met. This approach not only allows for efficient intervention during lane drops but also conserves computing resources when traffic flow is restored. (2) A state-compensation flow model based on an extension of the MetaNet is first proposed and matches the special traffic characteristics of the long segment without an on/off-ramp. Considering the particularity of its traffic flow, only the traffic flow characteristics of the main line can be adjusted. (3) Based on the MPC framework, the aforementioned traffic flow model is used as a traffic flow prediction model to construct a control strategy. The control strategy contains two types of control variables. One is used to control the vehicle's speed in different road segments, and another is used to control the density of the corresponding segment. The controller has an almost instantaneous computation time, and tuning the parameters is easier than deep learning methods. (4) The MVSPC strategy, a real-time and dynamic traffic control and management method, has dramatically improved both environmental sustainability and traffic performance under different levels of traffic flow. The rest of this paper is structured as follows: Section 2 presents the classification of the VSL method.