To use efficiently the infrastructure of transportation networks, control strategies have been developed with the aim to reduce negative externalities, such as congestion and pollutant emissions. Previous works demonstrated that the maximum performance achievable by traffic control policies depends on the number and location of controllers employed, which implies the need to determine a set of controllers capable of fully controlling the underlying transportation network. Various approaches have been explored in the literature to locate controllers on networks, however a gap remains in terms of scalability as the methods proposed often exhibit heavy computational complexity. In this paper we aim to propose an approach capable of locating pricing controllers on transportation networks that is scalable, such that it can be applied on large instances, such as city-sized or regional networks. For this purpose, we propose a topology-based approach, adapted from the sensor location problem, as both problems share similar characteristics. We validate our proposed approach by analyzing the performance of controller sets produced on a wide range of artificially generated network ensembles. The analysis we provide reveals that the method proposed, while being easily applicable on large instances, is capable to locate an efficient controller set, and to redirect flows on the network so as to reduce the total time spent by road users.

To be used efficiently, transportation networks require control strategies to optimize flow distribution. However, the location and type of controllers employed in a transportation network directly impact the level of performance reachable by traffic control policies. In order to guarantee that the highest level of performance is reachable, fully exploiting the available network capacity and reducing negative externalities, we explore methods to identify relevant controller locations in transportation networks while minimizing the number of controllers used. Previous work provided an exact approach to identify controller locations on complex networks. However, said approach exhibits complications when applied to transportation networks containing bi-directional links. We therefore propose simple heuristic algorithms relying only on topological information to solve this problem, while also avoiding heavy computation, thus being able to determine a solution to the minimal controller location set problem on large networks. Based on the existing framework, we aim to provide an experimental setup, with diverse network sizes and configurations to analyze the performances of different heuristic methods, in order to develop an efficient algorithm.

The maximum level of performance reachable by traffic control policies is directly affected by the locations and types of controllers employed in the network. We therefore investigate methods to identify locations and types of controllers that guarantee that the highest possible level of performance can be reached, fully exploiting the available network capacity, while minimizing the overall installation costs. This condition is known as full controllability. In this work, based on an existing methodological contribution that allows to determine the optimal number and locations of Pricing controllers required to reach the maximal level of controllability in transportation networks, we investigate instances and conditions in which the same results can be achieved through Traffic Light controllers, which represent a more realistic and widespread technology. Due to the different impact of traffic lights and pricing controllers on the network, however, optimal locations for one set might differ from the optimal locations for the other. Consequently, replacing pricing controllers by traffic lights is not a straightforward task. Moreover, an enumeration of every possible combination of traffic light positions will result in combinatorial explosion. In order to avoid this problematic, in this work we study simple, atomic subnetwork structures, widely occurring in real-life networks, and assess whether it is possible to maintain the maximum level of controllability while replacing pricing controllers with traffic light controllers. We validated our approach on a set of increasing sized networks showcasing that our proposed substitution rule yields satisfactory results in more than 90 percents of instances. This first empirical step acts as the basis to study larger and more realistic transportation networks, therefore helping to develop a general methodology to substitute pricing controllers with traffic lights.