traffic

Traffic sensors serve as one of the most important sources for such data by providing an accurate and reliable general picture of the traffic system. In this dissertation, a new sensor location model is developed to maximize the observability of link densities in a dynamic traffic network described using a piecewise linear ordinary differential equation system.

In this paper, the authors present a new sensor location model that aims to maximize observability of link densities in a dynamic traffic network described using a piecewise linear system of ordinary differential equations.

In this research, the team studied a dynamic on-demand food delivery system and proposed a rolling horizon-based optimization approach integrated with adaptive large neighborhood search (ALNS) to efficiently obtain high-quality solutions. The system-level evaluation shows that on-demand food delivery has great potential to reduce dining-related VMT, resulting in significant reductions of fuel consumption and emissions, especially with Multi-R delivery policy.

This paper develops a customized method for computational solution of equilibrium in the basic bathtub model with smooth preferences that exploits the mathematical structure of the problem. The researchers then apply their model to traffic congestion.

A robust rideshare system needs to take uncertainties such as traffic congestion and passenger cancellations into account. In this report, the authors propose a data-driven stochastic rideshare system that integrates those sources of uncertainties.

The potential for automated vehicles (AVs) to reduce parking in central cities has generated much excitement among urban planners. However, a reduction in parking could be accompanied by increased demand for curbside DO/PU space with related movements to enter and exit the flow of traffic.This study uses a microscopic road traffic model with local travel activity data to simulate personal AV parking scenarios in San Francisco’s downtown central business district (CBD).

This report uses a microscopic road traffic model with local travel activity data to simulate personal AV parking scenarios in San Francisco's downtown central business district.