## 3 Applications of Computational Intelligence in Civil Engineering Research Domains
### Relevant Research Studies
#### 3.1.6 Transportation Engineering  
The domain of transportation engineering covers the subjects of road construction, pavements, traffic engineering, transport and terminal economics, transport statistics and error theory, airport planning, transport planning, railway, public transport evaluation, spatial planning, urban planning, city history, exporting, cartography data, photogrammetry data and environmental impacts from the construction and operation of roads. One of the central planning priorities of transportation projects is modeling short-term demand forecasts, which are usually focused on a horizon of less than one hour and are necessary for implementing dynamic transit control strategies. Suppose that airlines and other service providers have a good idea of how much demand they may anticipate. In that case, they can better prepare for demand spikes and mitigate their adverse effects on service quality and customer experience by using real-time management tactics. Predicting platform congestion and vehicle overcrowding is one of the most beneficial uses of transport demand forecasting models. This needs knowledge of origin and destination demands, giving a comprehensive picture of when, where and why customers join and leave a service. While some research has been performed in this area, it is limited and primarily concerned with forecasting passenger arrivals at stations. For many real-world uses, these data fall short [61; 62]. In [63], using advanced AI patterns, a scalable, real-time framework for demand forecasting in transportation systems was created. The proposed model was divided into three distinct sections: a multi-resolution spatial feature extraction section for capturing local spatial dependencies, an auxiliary coding section for external information and an area for tracking the temporal development of demand. Specifically, the order required at any given time is a square matrix that is processed in two different directions. Using the first fork, we can see patterns in the data that were not apparent in the raw demand data by decomposing it into its component time and frequency variations. A three-layer convolutional neural network was utilized in the second route to understand the demand's geographical relationships. After that, the market's temporal development was captured using a convolutional network with short-term memory. Two months of automated fare data from the Hong Kong mass transit train system were used in a case study to assess the methodology, demonstrating the suggested model's evident superiority over the other benchmark approaches. The flow of traffic is instantaneous. The notion of dynamic lane reversal (DLR), which allows vehicles to quickly switch lane directions to reflect their dynamics, has been tested on a broad scale in autonomously driven public transportation in recent years. DLR has been built to eliminate traffic bottlenecks, maximize the efficiency of road areas and prevent unused capacity. The effects of DLR and its ability to be implemented are, however, yet unknown. In [64], an ideal DLR strategy for a road segment with bidirectional stochastic traffic flow was investigated using a lane-based directional cell transmission model to explore DLR's efficacy, practicality and application. A regression analysis was carried out based on the gathered data to determine the influences of directional flow rate and multiple lanes on DLR-induced delay reductions. The findings suggest that, compared to conventional reversible lane strategies, DLR deployment may drastically cut the overall queuing time. DLR also achieved a superior performance on longer, multi-lane stretches and in situations when traffic was moving in opposite directions but relatively close together. It is also important to highlight how the suggested method helped identify the previously undetectable pattern border.