Abstract
This paper explores the potential of moving array 'probes' to collect traffic data. This application simulates the prospect of mining environmental data on traffic conditions to present a cheap and potentially widespread source of traffic conditions. Based on three different simulations, we measure the magnitude and trends of probe error (comparing the probe's `subjective' or time-weighted perception with an `objective' observer) in density, speed, and flow in order to validate the proposed model and compare the results with loop detectors. From these simulations, several conclusions were reached. A single probe's error follows a double hump trend due to an interplay between the factors of traffic heterogeneity and shockwaves. Reduced visibility of the single probe does not proportionately increase the error. Multiple probes do not tend to increase accuracy significantly, which suggests that the data will be still useful even if probes are sparsely distributed. Finally, probes can measure the conditions of oncoming traffic more accurately than concurrent traffic. Further research is expected to consider more complex road networks and develop methods to improve the accuracy of moving array samples.
Highlights
The fast-approaching advent of Autonomous Vehicles (AVs) will have many benefits as well as unforeseen disadvantages and risks (Davidson and Spinoulas, 2015; Levinson and Krizek, 2017; Liu et al, 2017; Morando et al, 2018)
The space– time plots in Figure 3 illustrate how a single probe measures data under different road coverage levels (10, 30, 70%), where the red solid line indicates the trajectory of the single probe and the blue dashed lines represent the trajectories of non-probe vehicles
It has been seen that there is potential in the use of autonomous vehicles as moving array probes
Summary
The fast-approaching advent of Autonomous Vehicles (AVs) will have many benefits as well as unforeseen disadvantages and risks (Davidson and Spinoulas, 2015; Levinson and Krizek, 2017; Liu et al, 2017; Morando et al, 2018). It is expected that AVs will be on the market and on public roads. Given that such a shift in transport will probably require important changes in transport networks (Fagnant and Kockelman, 2015), AVs’ potential in supplying accurate and detailed traffic data may be invaluable in facilitating these changes. One of the neglected side-benefits of AVs is their potential to act as moving array “probes” collecting traffic data which can be utilized to map real-time traffic conditions as well as long-term traffic information for planning and other traffic engineering purposes. Unlike traditional vehicle probes, moving array probes can collect data on their surroundings as well as their own state. While traditional vehicles can in theory be equipped as moving array probes, autonomous vehicles will be such by default, providing increasing streams of data
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