The objective of this research effort is to develop an advanced Transit Signal Priority (TSP) system within a connected vehicle (CV) environment. Specifically, the research effort develops a CV-enabled TSP system that provides equitable priority for buses (priority with minimum disruption to surrounding traffic) that is effective for near-side, far-side, and mid-block bus stops.
The proposed research extends the Eco-Cooperative Adaptive Cruise Control (Eco-CACC) system previously developed by the research team on light duty vehicles (LDVs) to heavy duty vehicles (HDVs) (diesel and hybrid electric buses).
This research will use a mathematical model to determine the most efficient patrol coverage area to improve emergency response to incidents such as accidents, debris in the roadway and disabled vehicles.
The expected outcome of the research is model formulation and solution algorithms for the time dependent vehicle routing problem with time windows and documentation of the results of model test, sensitivity analysis and a small case study.
This project seeks to develop general-purpose methods for optimizing the selection, sequencing, and scheduling of interrelated improvement projects in transportation networks, with special emphasis on relatively dense urban networks.
As American cities have seen a change in land uses in urban areas, with stores moving to suburban areas increasing the reliance on cars, many urban areas are left with a lack of accessible quality food options.
The proposed research will develop prediction models for passenger demand by incorporating the traditional APC data and the newly developed user data from mobile APP to improve prediction accuracy. The proposed research will also develop state-of-art prediction models for bike share system based on the latest popular machine learning algorithms.