Abstract
In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.
Highlights
With autonomous vehicles gradually being accepted by the public, the precision and communication capabilities between autonomous vehicles and cloud-computers will allow new traffic behaviors to enhance the capacity of the road network
With the background that autonomous vehiclesvehicles will gradually humanWith the background that autonomous will graduallyreplace replace humandriven vehicles the we decades, we establish the shared traffic models based on driven vehicles in the nextin decades, establish the shared road road traffic flowflow models based using calculus and difference methods
The main conclusions and contributions are as follows: If p ∈ (0,1), the mixed traffic flow q-k curve is in the region between the q-k curve for fullytraffic humanflow and autonomous traffic; If p ∊ (0,1), the mixed q-k curve is in the region between the q-k curve for
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Increasing the proportion of autonomous vehicles will lead to a change of travel mode from human vehicles to autonomous vehicles, which will bring significant variation to traditional traffic and have a great influence on traffic flow characteristics, affecting the overall optimization design of the road network. With autonomous vehicles gradually being accepted by the public, the precision and communication capabilities between autonomous vehicles and cloud-computers will allow new traffic behaviors to enhance the capacity of the road network. The emergence of autonomous vehicles could realize some idealized assumptions of previous traffic models, for example, autonomous vehicles could keep a constant speed and deal with traffic behavior completely rationally
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