Multimodal strategies at the local, corridor, and network level: using connected vehicles for traffic monitoring and control
DR. Monica Menendez
Director of research group Traffic Engineering (SVT)
Institute for Transport Planning and Systems (IVT)
Department of Civil, Environmental and Geomatic Engineering (D-BAUG)
Swiss Federal Institute of Technology (ETHZ)
To effectively manage the transportation systems of tomorrow, it will be necessary to properly monitor, model and control the operations of multiple modes (e.g., buses and cars), as they are sharing and often competing for the same space. Fortunately, as new technologies emerge, we are getting access to more possibilities for new multimodal transportation management schemes, as well as new and improved information sources.
In this presentation, we will discuss the use of advanced signaling strategies (i.e., pre-signals - additional signals upstream of the main signal at the intersection) to provide priority to buses, while minimizing the disruptions to cars at the local level. This will be extended into a new analytical framework that allows us to model pre-signals as well as other multimodal flexible sharing strategies at the corridor level. With a focus on the future transition period, we will then propose new signal control algorithms for dealing with different types of technology (e.g., conventional, connected, and automated vehicles). The goal is to develop monitoring schemes, control algorithms, and management strategies that evolve as the proportion of vehicles with different levels of automation changes. Some features of these algorithms are further extended to the network level with a two-level optimization. The objective is to minimize congestion in an urban area through a perimeter control, while reducing delay at the perimeter intersections. This two-level optimization will also benefit from on-going research on the 3D-Macroscopic Fundamental Diagram (3D-MFD), relating the network density (or accumulation) of cars and public transportation vehicles to the total network flow, for either vehicles or passengers. In particular, we will present the first empirical estimate of a 3D-MFD at the network level, using data from loop detectors and automatic vehicle location devices (AVL) from the public transportation vehicles in the city of Zurich, Switzerland. Other approaches for obtaining either the MFD or the 3D-MFD with limited information will also be discussed.