# Research on a Collaborative Control Strategy of an Urban Expressway Merging Bottleneck Area
## 5 Discussion and Conclusions  
Based on the investigation and analysis of the merging bottleneck, which is the key part that restricts the operation efficiency of the urban expressway, this paper establishes an expressway traffic state prediction model considering the characteristics of the merging bottleneck. Combined with the mainline variable speed limit and ramp merging control algorithm, the cooperative control strategy of expressway merging bottleneck is studied based on the idea of a predictive model control. The main research results and prospects are summarized as follows:  
By analyzing the limitations of the traditional CTM applied to the merging bottleneck, the traditional cell transmission model is improved from three aspects: cell space division, the basic diagram of the merging cell traffic flow, and the update of the on-ramp state, so as to establish the expressway traffic state prediction model considering the merging bottleneck. The parameter calibration based on GA and the validity verification method based on MAPE value are proposed, which makes the model have higher accuracy in simulating the traffic flow. The expressway traffic state prediction model constructed is based on the macroscopic traffic flow theory. Although it has the advantage of a fast solution, it lacks the description of microscopic driving behaviors such as lane change and car-following. With the development of an artificial intelligence algorithm, the next step can be considered to predict the change of expressway traffic state through autonomous learning of agents so as to realize the overall consideration of macro and micro characteristics of traffic flow. Based on the traffic flow characteristics and control purposes at the merging bottleneck, the appropriate mainline and ramp control algorithms are selected and optimized. For the variable speed limit of the mainline, the MTFC-VSL is used for speed limit control, and the speed limit value constraints are set according to the actual road conditions. For ramp merging control, the PI-ALINEA with a better control effect on bottleneck segments is adopted, and the defects of constant expected occupancy and queue overflow are improved. After that, aiming at the characteristics of rapid deterioration of traffic state in the short term, the model predictive control theory is introduced. The MPC is used to combine the improved CTM with the independent control algorithm, and the joint control target based on efficiency and safety is proposed. A cooperative control strategy of merging bottlenecks based on improved CTM is established. Taking the survey segment of the East 3 rd Ring Road in Xi'an City, China, as an actual case, the results show that the collaborative control strategy is applied during the peak period, the overall travel time and turnover of the road segment are improved, the total travel time is reduced by 7.34%, and the total turnover is increased by 6.06%, indicating that the overall operational efficiency of the expressway under the collaborative control strategy is effectively improved. The time speed difference under cooperative control is between \(-6.9\) km/h (a decrease of 54.30%) and 4.98 km/h (a decrease of 34.47%), the range is 11.88 km/h (a decrease of 47.67%), and the standard deviation is 2.6614 (a decrease of \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Min** & **Median** & **Max** & **Range** & **Standard Deviation** \\ \hline speed difference (km/h) & \(-9.7\) & \(1.95\) & \(10.16\) & \(19.86\) & \(4.9042\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Results of space speed difference under coordinated control. 25.89%). The overall spatial speed difference of the segment under coordinated control is between \(-9.7\) km/h (a decrease of 41.85%) and 10.16 km/h (a decrease of 30.79%), the range is 19.86 km/h (a decrease of 36.67%), and the standard deviation is 4.9042 (a decrease of 33.85%), which is lower than that under uncontrolled conditions.