Analytically Modeling Unmanaged Intersections with Microscopic Vehicle Interactions

TL;DR

This paper presents an analytical model for unmanaged intersections that efficiently captures microscopic vehicle interactions and delays, enabling better policy comparison, optimization, and traffic prediction.

Contribution

It introduces a novel event-driven stochastic dynamic model that combines microscopic vehicle behaviors with macroscopic delay analysis, improving efficiency over traditional simulations.

Findings

Model accurately predicts intersection delays.

Efficiently compares different traffic policies.

Supports policy and system optimization.

Abstract

With the emergence of autonomous vehicles, it is important to understand their impact on the transportation system. However, conventional traffic simulations are time-consuming. In this paper, we introduce an analytical traffic model for unmanaged intersections accounting for microscopic vehicle interactions. The macroscopic property, i.e., delay at the intersection, is modeled as an event-driven stochastic dynamic process, whose dynamics encode the microscopic vehicle behaviors. The distribution of macroscopic properties can be obtained through either direct analysis or event-driven simulation. They are more efficient than conventional (time-driven) traffic simulation, and capture more microscopic details compared to conventional macroscopic flow models. We illustrate the efficiency of this method by delay analyses under two different policies at a two-lane intersection. The proposed model allows for 1) efficient and effective comparison among different policies, 2) policy optimization, 3) traffic prediction, and 4) system optimization (e.g., infrastructure and protocol).