A cavy dedicated lane layout optimization method based on travel time reliability

By constructing an extended road network and traffic flow assignment model, and combining Monte Carlo sampling and genetic algorithms, the layout of CAV dedicated lanes was optimized, solving the problem of reliability assessment and optimization of road network travel time in mixed traffic scenarios, and realizing scientific decision-making on road network travel time.

CN121982917BActive Publication Date: 2026-07-03BEIJING UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING UNIV OF TECH
Filing Date
2026-02-06
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

In mixed-traffic scenarios, existing technologies lack precise methods for quantifying and evaluating the reliability of road network travel time, and it is difficult to improve the reliability of road network travel time through the optimized layout of CAVs.

Method used

An extended road network including conventional and virtual road segments is constructed. Based on vehicle following patterns and safe headway, traffic capacity is calculated, and a mixed traffic flow allocation model is established. Travel time samples are obtained through Monte Carlo random sampling, and a CAV dedicated lane layout optimization model is established. The optimization objectives are to minimize the total travel time and the total buffer time exponent. A fast elite multi-objective genetic algorithm is used to solve the optimization model.

Benefits of technology

It enables quantitative assessment and optimization of road network travel time reliability, provides scientific CAV lane layout decisions, and supports traffic managers in making a scientific trade-off between travel efficiency and reliability.

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Abstract

This invention discloses a CAV dedicated lane layout optimization method based on travel time reliability, comprising: constructing an extended road network including regular road segments and virtual road segments; calculating the capacity of regular road segments and virtual road segments based on the extended road network, according to vehicle following patterns and corresponding safe headway; establishing a mixed traffic flow allocation model based on the capacity, and solving the mixed traffic flow allocation model to obtain the road network equilibrium flow distribution; obtaining a travel time sample set of road segments and paths through Monte Carlo random sampling based on the equilibrium flow distribution; calculating the road network buffer time index based on the travel time sample set, the buffer time index characterizing the travel time reliability of the road network; and establishing a CAV dedicated lane layout optimization model with the number of CAV dedicated lanes on virtual road segments as decision variables, and with the minimum total travel time and the minimum total buffer time index of the road network as optimization objectives, wherein the model is used to output the CAV dedicated lane layout optimization scheme.
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