# Special Issue on Future Intelligent Transportation System (ITS) for Tomorrow and Beyond
## 2 Contributions
A neural network approach to forecast the next destination according to taxi driver behavior is studied in ref. [16]. In this method, encoder and decoder are the essential units of the transformer. With the assistance of LBSN, the topographical data in view of visited semantic areas is encoded. The model is specifically trained to anticipate the future destination, using exact longitude and latitude data. The trials were run through two real-world datasets, Porto and Manhattan, and it was found that functioning is far better than earlier models. This study has the potential to minimize client wait times for rides and driver wait times to pick up customers. People have become more connected as cellphones have turned out to be more widespread, and this has made everyday order delivery service much more convenient. Customers can be dropped/picked up by a cab at any time using apps like Kakao, Ola, and Uber, and in the same way food can be transported to the address of consumers in a relatively short time by utilizing food-ordering apps like Zomato, Meituan, and Freshhema. The delivery service routing system, which directs automobiles and couriers to convey orders, has played a critical role in serving large number of requests. A study on the delivery service sharing (DSS) and flexible time windows (DSS-Fle) variation with adjustable time frames, which allows orders to be shared and served at the same time, is carried out in ref. [17]. The practical DSS-Fle problem was investigated in this study, where client orders have variable drop-off time periods and can be served in a shared fashion. The suggested results showed with thousands of regions and client orders that the DSS-Fle algorithm is efficient in both enhancing order rate and adapting to city-scale scenarios. Economy, mobility, environment, population, lifestyle, and organization are all cornerstones of a city. The goal of a smart city is to save costs and improve organization and the well-being of its residents. Unmanned aerial vehicles (UAVs) are now used in a wide range of everyday applications and are acquainted further to develop street traffic effectiveness [18]. The UAVs have a certain level of intelligence in most of applications, allowing them to be utilized as high-performance sensors, data collectors, or even communication relays, especially when ground cover is insufficient. The advancements in cooperative ITS technology, as well as the intriguing qualities of UAVs, present an ideal background for introducing a UAV into a use case involving a car and a pedestrian. In [19], the UAV is sup posed to control the situation, gather data, and provide directions to the automobile driver in order to avoid a collision between the car and the pedestrian. The authors underline that the UAV might aid in improving both road safety and energy consumption. Skyline localization is a crucial branch of optical geolocation, particularly in fields with few feature points. The location of the observation can be established by extracting features from photos or videos and comparing them to feature databases. The difficulties of detecting the skyline in hilly terrain, including lush vegetation and proximity to the skyline, is investigated in ref. [20]. This study proposes a new skyline localization approach that includes the enhanced angle chain code and skyline lapel point, as well as other matching technique that include the skyline pyramid and the skyline distance heatmap. The test results showed that this technique has high localization precision in hilly regions. However, the exactness of the technique is low when adjacent trees severely hide the skyline, or when the mountain in front of the camera is too close. Electric vehicles (EVs) have become a fundamental part in the transportation system to diminish reliance on fossil fuel sources and grabbed the attention of researchers investigating different sorts of eco-friendly power assets in the microgrid (MG). Considering time-varying load demand similar to the work in ref.