Influence of CAV clustering strategies on mixed traffic flow characteristics: An analysis of vehicle trajectory data

Abstract

Being one of the most promising applications enabled by connected and automated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC) is expected to be deployed in the near term on public roads. Thus far, the majority of the CACC studies have been focusing on the overall network performance with limited insights on the potential impacts of CAVs on human-driven vehicles (HVs). This paper aims to quantify such impacts by studying the high-resolution vehicle trajectory data that are obtained from microscopic simulation. Two platoon clustering strategies for CACC-an ad hoc coordination strategy and a local coordination strategy-are implemented. Results show that the local coordination outperforms the ad hoc coordination across all tested market penetration rates (MPRs) in terms of network throughput and productivity. According to the two-sample Kolmogorov-Smirnov test, however, the distributions of the hard braking events (as a potential safety impact) for HVs change significantly under local coordination strategy. For both of the clustering strategy, CAVs increase the average lane change frequency for HVs. The break-even point for average lane change frequency between the two strategies is observed at 30% MPR, which decreases from 5.42 to 5.38 per vehicle. The average lane change frequency following a monotonically increasing pattern in response to MPR, and it reaches the highest 5.48 per vehicle at 40% MPR. Lastly, the interaction state of the car-following model for HVs is analyzed. It is revealed that the composition of the interaction state could be influenced by CAVs as well. One of the apparent trends is that the time spent on approaching state declines with the increasing presence of CAVs.

Traffic Flow Characteristics and Lane Use Strategies for Connected and Automated Vehicles in Mixed Traffic Conditions

“…ey observed substantial safety benefits in terms of reduction in traffic conflicts: 12-47% at 25% MPR to 90-94 % at 100% MPR. In [41], TTC was also used to assess the safety conditions for HVs when CAV local clustering strategy was employed. Ali et al found that drivers with advanced traffic information enabled by connectivity tend to wait longer and maintained a larger space on mandatory lane change (the communication delay for lane merging assistance was unnoticeable when it was less than 1.5 s).…”

“…e primary reason for using a simply synthetic network is to limit variables for the simulation. Note that the driving behavior parameters for the Wiedemann carfollowing model (for HVs) are the same as those in previous studies [31,41,64,65], representing a subset of the calibrated driving behavior in the I-66 segment in northern Virginia. e demand originated on the mainline is deliberately set higher than usual to create a congested network.…”

Application of cluster analysis in the behaviour of traffic participants relating to the use of safety systems and mobile phones

“…Numerous research studies deal with cluster analysis in traffic. The study presented in [4] shows an analysis of data originating from vehicle trajectories obtained by simulation. Two strategies were implemented: -Two platoon clustering strategies for CACC; an ad hoc coordination strategy and a local coordination strategy‖.…”

Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

“…As a result, location-based services, such as path planning [1] and customized Points of Interest (POIs) recommendations [2], have produced a large amount of trajectory data. In turn, trajectory data offer a wealth of information and knowledge that can be applied to many sectors, such as location services [3,4], traffic management [5,6], urban planning [7,8], and animal welfare [9,10]. It is important to accurately discover the semantic information behind the original trajectory data and to interpret the human movement actions described by the trajectory from a semantic perspective.…”