# Intersection passing strategies for human-driven and autonomous vehicles in mixed traffic using DEA

## TL;DR

This paper introduces a new method to optimize traffic flow at intersections by balancing delays and travel times for both human and autonomous vehicles.

## Contribution

A novel multi-objective DEA-based model is proposed to optimize intersection passing strategies in mixed traffic environments.

## Key findings

- The proposed strategy reduces per capita delay and travel time compared to existing methods.
- The model outperforms actuated control and First-Come, First-Served strategies in SUMO simulations.
- Weights for optimization objectives are determined using the Crossing Efficiency Evaluation Method (CREE).

## Abstract

In this paper, we propose a right-of-way optimization model considering multi-objective DEA evaluation for intersections in mixed driving environments with automated and human driving. Considering average speed, number of cars, penetration of automated vehicles, queuing pattern, left-turn rate, and number of buses as factors influencing intersection rights-of-way. Comprehensively consider the per capita delay, travel time and traffic volume as the optimization objectives, and then determine the weights of the three optimization objectives for each strand of traffic flow, and calculate the cross-benefit by interchanging the weight evaluation through the Crossing Efficiency Evaluation Method (CREE) to determine the optimal order of traffic flow in each direction at the intersection. In this paper, the optimization strategy is compared with existing benchmarks (e.g., actuated control) using SUMO simulation software, and the simulation results show that the proposed optimization strategy is able to shorten the per capita delay and travel time at intersections in order to improve the efficiency of the traffic flow compared to actuated control and the First-Come, First-Served strategy.