# Special Issue on Future Intelligent Transportation System (ITS) for Tomorrow and Beyond
## 2 Contributions  
Decreasing fuel utilization, carbon dioxide (CO\({}_{2}\)), and other air contamination emanations has long been a pressing issue for the transportation area, particularly the automobile business, which is currently mired in a deadlock over oil constraint and environmental concerns. Along these lines, authorities and automakers have been testing a variety of tactics and technologies to limit fuel utilization and emissions. The introduction of the model predictive control (MPC) algorithm into the adaptive cruise control (ACC) system has lately brought limelight to academic interest as a new research issue. The feasibility of adopting a hybrid MPC framework to vehicle tracking control investigated in [11] is based on the architecture of the intelligent ACC system. The ACC framework was proposed in view of eco-driving in ref. [12], and the results showed the adequacy and modern feasibility of the created algorithm. The MPC approach is used to develop a multi-objective coordinated optimization strategy for the ACC system which includes tracking capabilities, driving comfortability, and decreasing fuel usage. However, the distinctness of the cut-in driving circumstances and related specifics of developing an MPC-based ACC system was not fully defined. In addition to this, the suggested MPC-based ACC system's specific fuel efficiency performance gain over the classic controller-based ACC system was unclear. The MPC method is used to develop the ACC system in order to achieve the goals of eco-driving, driving safety, comfortability, and tracking capabilities. The proposed MPC-based ACC framework was assessed and contrasted with the standard proportional-integral-derivative controller-based ACC framework. Based on the simulation results, the MPC-based ACC system improved the fuel economy by 12-13%. With the growth of urbanization, traffic congestion remains a major challenge for large cities. Drivers' route-based choices are primarily based on their limited perspectives in the absence of complete real-time traffic information. As a result of these options on improvati and non-cooperative routes, the efficiency of road network resource use is degraded. Some nations are focused on enthusiastically fostering ITS to accomplish a proficient traffic flow. Traffic prediction is an essential part of ITS, particularly on highways with high traffic volumes and high-speed driving. To deal with the intricacy of gigantic traffic information [13], robust traffic models are required, considering traffic information for the space-time connection to predict the traffic circumstances. These techniques are rehearsed to anticipate the traffic flow and volumes. All known machine learning approaches fail to grasp the spatial-temporal aspects of a traffic network when dealing with large amounts of data. Researchers used deep learning approaches to utilize historical traffic circumstances for predicting future traffic situations due to their excellent feature learning capabilities. A novel multi-branching technique to regulate the spatial-temporal aspects by modeling traffic flow using spatial correlations and various 3D volume layers is presented in ref. [14]. This methodology is helpful for investigating temporal dependencies through the 3D CNN. Mobile gadgets have exploded in popularity in recent years. The proliferation of smartphones and GPS-enabled portable devices have resulted in an unanticipated increase in traffic trajectory data. Modeling the trajectory and forecasting the destination are important not only for monitoring urban traffic but also for a variety of other exciting applications, such as targeted advertising based on destination, location-based social networks (LBSNs), intelligent traveling schedules, and reducing travel costs, as well as energy consumption-optimized scheduling strategies [15]. The study of forecasting the future destination is a well-studied human mobility application for minimizing traffic congestion and improving the performance of the electronic dispatching system.