Titolo della tesi: Dynamic simulation of route choice and congestion phenomena on public transport networks
The transportation sector faces significant challenges, including traffic congestion, air pollution, and limited accessibility, which have negative impacts on society and the environment. While investing in new infrastructure and vehicles may seem like a solution, optimizing the existing transportation system is a more cost-effective approach. One of the most effective ways to do this is to expand and enhance public transportation, which is critical for providing access to employment, education, and other opportunities, especially for those who lack personal vehicles.
However, public transportation systems are often underutilized due to issues such as inadequate accessibility and unreliable services. To enhance the efficiency and reliability of public transportation, transit operators can use forecasting tools based on data analytics and network simulations. One effective methodology to optimize public transportation is based on Dynamic Transit Assignment (DTA) models that allocate passenger demand to available services and provide insights on the current state of the network, accounting for the time of day.
Unfortunately, existing Dynamic Transit Assignment (DTA) models have limitations in terms of their scope and performance. They do not always consider various phenomena that impact the performance of public transport systems, such as onboard overcrowding, queuing at stops, and strict capacity constraints. Moreover, existing models are often not able to run in real time, which limits their usefulness for real-time decision-making.
To address these limitations, this Ph.D. thesis proposes the Hyper Run Assignment Model (HRAM) and its real-time extension, HRAM-RT, which are macroscopic simulation models for dynamic transit assignment that use a user equilibrium method to allocate dynamic travel demand to a transit transportation system. HRAM and HRAM-RT can aid decision-makers in optimizing routes, schedules, and capacity utilization to make public transport more attractive to users, leading to increased ridership and a reduction in car dependency, congestion, and emissions.
HRAM adopts a run-based framework, representing using single runs in an event-based diachronic graph. It also considers only attractive connections and uses implicit hyperpaths enumeration to limit graph size while providing direct volumes of passengers on runs. It simulates congestion phenomena, such as onboard overcrowding and strict capacity constraints, modeled using BPR-like congestion functions and fail-to-board hyperarcs. The use of a reduced gradient projection algorithm improves convergence on congested networks.
HRAM-RT incorporates real-time measurement and events. We use HRAM-RT in a rolling-horizon framework to provide short-term forecasts of passenger volumes, making it more accurate in predicting passenger volumes and better able to forecast the evolution of congestion throughout the day. This approach is implemented in PTV Optima Transit, an innovative software prototype developed by PTV Group, designed to enhance the reliability and accessibility of public transportation systems.
The numerical experiments conducted evaluated HRAM and HRAM-RT in various scenarios, showing that the methodology can model congestion phenomena and rapidly converge on a medium-sized network. Moreover, the tests showed that the HRAM-RT approach is effective in accounting for real-time events and measurements. Further investigation and improvement of the models under the European project "Unleashing the Potential of Public Transport in Europe (UPPER)" will help refine their behavior and enhance their usefulness in addressing the challenges facing the transportation sector.