Abstract
Connected and autonomous vehicles (CAVs) are on the way to the field application. In the beginning stage, there will be a mixed traffic flow, containing the regular human-driven vehicles and CAVs with a low penetration rate. Recently, the discussion about the impact of a small proportion of CAVs in the mixed traffic is controversial. This paper investigated the possibility of applying the limited data from these lowly penetrated CAVs to estimate the average freeway link speeds based on the Kalman filtering (KF) method. First, this paper established a VISSIM-based microsimulation model to mimic the mixed traffic with different CAV penetration rates. The characteristics of this mixed traffic were then discussed based on the simulation data, including the sample size distribution, data-missing rate, speed difference, and fundamental diagram. Accordingly, the traditional KF-based method was introduced and modified to adapt data from CAVs. Finally, the evaluations of the estimation accuracy and the sensitive analysis of the proposed method were conducted. The results revealed the possibility and applicability of link speed estimation using data from a small proportion of CAVs.
Highlights
Autonomous vehicle (AV) technology is a hot and practical research spot
It is estimated that the market penetration rate of connected and autonomous vehicles (CAVs) might be able to reach between 24% and 87% by 2045 [4, 5]. erefore, there will be a long period of mixed traffic condition comprising CAVs and regular human-driven vehicles (RVs)
When CAVs account for 1%, m is far less than n
Summary
When AVs embedded with the feature to communicate with others including vehicles, roadside infrastructures, or traffic control centers, they are defined as connected and autonomous vehicles (CAVs). It is expected that CAVs can provide faster responses and keep shorter headways, which lead to an increased overall roadway capacity [1]. A majority of research works have been dedicated to the impact analysis of AVs/CAVs in the mixed traffic. Shladover et al proved that the Cooperative Adaptive Cruise Control (CACC) technology has the potential to increase lane throughput from the average 2000 veh/h to approximately 4000 veh/h with high market penetrations [7]. Friedrich found that the traffic volume could be increased to about 3900 veh/h/lane when AVs are in application compared with current designed capacity values of a lane of 2200 veh/h [8].
Published Version
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