# Review of Traffic Assignment and Future Challenges
## 5 Discussion
### Promising Directions  
In light of the aforementioned developments in intelligent transport systems, we will now outline two key directions that show promise for traffic assignment:  
* **Mixed traffic digital twin**: The cohabitation of a multitude of systems would require either new macroscopic and mesoscopic traffic models or the use of microscopic models coupled with an agent-based approach [76; 169]. The latter option appears better suited to accommodate the diverse objectives of road users, especially in the context of mixed traffic: human drivers with navigation systems, human drivers familiar with the road, autonomous shuttles, optimized freight transport, etc. Furthermore, employing microscopic models could enable us to address the challenge of finding an equilibrium between diverse equitable routes and the costs induced by conflicts. However, it is worth noting that the use of microscopic traffic models for computing the travel times is resource-intensive in terms of computation resources and time. Another option is to investigate the implementation of digital twins [170; 171] with different levels of granularity of the traffic entities. This digital twin should be capable of assimilating data from the cloud, ranging from macroscopic quantities such as road flow and occupancy rates to more detailed user profiles at the vehicle level, including departure times and itineraries. Harnessing _A1_ techniques is crucial [172] for generating a comprehensible map of road usage for decision-makers (e.g., identifying saturated intersections, assessing environmental impact, improving safety, and offering the forecasts needed by cities). More importantly, _A1_ can also be used to propose solutions (e.g., the necessity for new transit services and a new toll policy). As emphasized throughout this article, the problem is undeniably intricate. Nevertheless, the substantial advancements in machine learning technologies, data analysis techniques, and cloud computing hold the promise of highly innovative approaches. The data has previously been employed for a range of tasks related to traffic assignment, including estimating the origin-destination (_o-d_) matrix [173; 174; 175], evaluating travel times [176], and calibrating models and traffic assignment results [177; 178; 179; 180]. This utilization encompasses the data of cell phones, information systems, and magnetic loops. The development of the mixed traffic digital twin aims to transcend these contributions, representing a significant stride towards exploring advanced artificial-intelligence (_AI_) techniques for traffic management. An illustrative foundation for data-driven traffic assignment is established in the study presented in [181]. Instead of assuming user behavior, the authors showcased the feasibility of precisely estimating road demand by directly learning flow patterns from the available data. This innovative approach lays the groundwork for incorporating _AI_ into traffic forecasting. The extension introduces additional parameters, such as traffic control strategies and new transportation scenarios. Moreover, the mixed traffic digital twin can be designed to be proactive, not only highlighting problems, such as those proposed in ref. [182], but also proposing solutions to the decision-makers. To this end, the mixed traffic digital twin may be trained by using \"classical traffic\" assignment approaches. * **New generation of navigation systems**: The evolving uses of _CAV_ and associated regulatory systems will likely challenge current traffic assignment approaches. These vehicles will need to plan their routes in real time based on received demands and communicate their estimated arrival times as accurately as possible. Although [PERSON]'s equilibrium assumption does not allow for the perfect rationalization of vehicle usage, it remains desirable for fairness reasons. Indeed, it seems evident that no user would want to take a shuttle service that significantly takes longer than other shuttles without financial compensation.