The topic for this paper is Artificial Intelligence (AI) Transportation. There are several problems that have reared in AI transportation. However, this paper will be discussing the five major problems that have been identified. Problems with AI transportation involve both qualitative and quantitative data. Using qualitative data justifies the use of experts when addressing AI transportation problems. A second problem is that transportation systems with difficult behavior to model using traditional approaches because either interactions between these system components are not completely understood or because there is a lot of uncertainty that stems from the human component of the system. Because this is so complex, empirical model development based on observed data may be the only viable option in this situation.
A third problem is that problems with transportation could potentially lead to challenging situations during optimization that are difficult to solve with traditional mathematical programs. This is due either to analytical relationships being difficult to specify or because the size of the problem is too large for computational intractability where alternative solutions must be presented. A fourth problem is that transportation systems are very complex and can be difficult to fix using standard programs. A fifth problem is that the behavior of transportation systems typically emerges throughout system components making ABM techniques appropriate for assessing the system’s behavior.

These are but a few problems that can be encountered with AI transportation. Transportation systems are so complex utilizing many components that these traditional methods may not be able to be met. However, there are solutions to fix these problems using complex methods to treat problems in a complex system. Artificial Intelligence is equipped to handle these types of complex problems and are more effective and efficient in getting these problems fixed quickly and correctly.