# Review of Traffic Assignment and Future Challenges
## 5 Discussion
### Intelligent Transportation Systems  
To illustrate the claim of limitations of the classical approaches, let us begin by examining current intelligent transportation systems. Many road users rely on navigation applications [143; 144; 145] to choose their routes in major cities. These applications provide optimal routes based solely on real-time traffic information without necessarily seeking equilibrium for obvious computational time reasons [146; 147].  
Another widely adopted intelligent transportation system is adaptive traffic signals [148]. Signal regulation is based on measured flows, favoring the most significant flows by extending the duration of the green light and providing sequences of green waves. This could challenge the assumption of increasing travel time based on demand. Additionally, these optimizations raise questions about the multitude of itineraries, as it may be more beneficial to define efficient corridors and reduce conflicts that lead to congestion [149]. Furthermore, other adaptive control systems exist to streamline traffic outside conflict zones, such as speed limit control systems in smart segments, aiming to prevent the formation of traffic congestion.  
Whether it is the increasing use of navigation systems in major cities or adaptive traffic control systems, current intelligent transportation systems challenge the models used in static assignment. Specifically, it is crucial to leverage microscopic simulation to incorporate the new traffic supply and demand behaviors.  
We can expect the future widespread integration of connected and autonomous vehicles (\(CAV\)), with technological shifts and new transportation behaviors across various aspects. \(CAV\) is anticipated to streamline the utilization of road infrastructure, leading to a reduction in the need for parking spaces and lanes. Consequently, the challenge of traffic assignment becomes pivotal in realizing urban space efficiencies and designing autonomous shuttle services. Hence, traffic assignment approaches must effectively tackle this upcoming traffic.  
These vehicles would require new modes of driving and regulation. For instance, leveraging their communication capabilities and trajectory control, they can self-organize in conflict zones without the need for traffic lights or priority signs [150; 151]. They determine the sequence of passage (who goes first, second, etc.) and adjust their speed accordingly. A highly active scientific community is working on this mode of regulation, commonly referred to as an 'autonomous intersection management system'. This technology is also set to be applied to ordinary vehicles equipped with connectivity, thanks in particular to green-light optimal speed advice (\(GLOSA\)) [152; 153; 154; 155]. In addition to autonomous intersections, other driving systems are conceivable, such as cooperative platooning [156], connected merging systems [157; 158; 159; 160], dynamic lane reversal [161; 162; 163; 82], etc.  
Intelligent transport systems are not limited to vehicle and infrastructure equipment. The domain of smart cities opens up new perspectives. Cities have access to the cloud, where real travel data can be analyzed. These data come from the GPS tracks of mobile applications, increasingly precise roadside sensors (cameras with number-plate detection), WiFi hot spots on public transportation tracking cellphones' MAC addresses, and GPS-equipped vehicles (privacy preservation needs to be considered carefully in this context.). The data can feed traffic assignment approaches not only to calculate \(o\)-\(d\) matrices but also to calibrate both supply and demand models. With these technologies, one can expect that the city's traffic models, including traffic assignment models, would be improved to become increasingly accurate. Thanks to the progress in \(AI\) techniques and data science, the data may also be sufficient alone to carry out traffic assessments and traffic projections [164; 165; 166; 167; 168].