Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata

Anticipating ability is a skill that drivers count on to handle risky tasks in the traffic. This paper explores how the drivers of lane changing vehicle and its immediately car follower anticipate surrounding vehicles’ movements and adjust their manoeuvers during vehicle inserting process. The drivers’ anticipating mechanisms are modelled in the framework of structural equation model and estimated from field data. Results show that the change of lane changing type or traffic signal affects the drivers’ anticipation. Increased vehicle speed impels subject driver to anticipate driving condition in further future, but the stimulus is lower than the one coming from the kinematic comparisons of subject vehicle and other vehicles. The drivers care more about the vehicles’ interactions with which they are personally involved than the one to which they are only onlookers. The drivers’ responses to the counterpart vehicle’s movements depend on the progress of vehicle insertion and their roles in vehicle interactions.

Section: Discussionmentioning

confidence: 99%

The Roles of Car Following and Lane Changing Drivers’ Anticipations during Vehicle Inserting Process: A Structural Equation Model Approach

Zhang

et al. 2018

“…Different combinations of manual vehicles, AVs, and CAVs (0-100 % by 10% gap). [19] Develop an improved cellular automaton as an AV modeling platform.…”

Section: Idmmentioning

confidence: 99%

“…The lane capacity increases from 2,046 to 6,450 vehicles/hour/lane with CAVs increases from 0% to 100%. [19] Manual vehicle, AV Throughput…”

Section: Intelligent Driver Model (Idm)mentioning

confidence: 99%

“…Researchers acknowledge that the development of CAVs will generate significant changes in our daily life and society as a whole. To estimate the impacts of CAVs, the vast majority 2 Journal of Advanced Transportation of researchers have been conducting a simulation-based analysis because (i) the real field data on the CAV's performance are limited [14][15][16] and (ii) many studies deal with high market shares for CAVs [17][18][19], which is hypothetical, far from the current reality. It is crucial to understand the impacts of CAVs early in their development to avoid costly mistakes before their widespread implementation.…”

Section: Introductionmentioning

confidence: 99%

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Simulation-Based Connected and Automated Vehicle Models on Highway Sections: A Literature Review

Rouhani

Miranda-Moreno

2019

This study provides a literature review of the simulation-based connected and automated intelligent-vehicle studies. Media and car-manufacturing companies predict that connected and automated vehicles (CAVs) would be available in the near future. However, society and transportation systems might not be completely ready for their implementation in various aspects, e.g., public acceptance, technology, infrastructure, and/or policy. Since the empirical field data for CAVs are not available at present, many researchers develop micro or macro simulation models to evaluate the CAV impacts. This study classifies the most commonly used intelligent-vehicle types into four categories (i.e., adaptive cruise control, ACC; cooperative adaptive cruise control, CACC; automated vehicle, AV; CAV) and summarizes the intelligent-vehicle car-following models (i.e., Intelligent Driver Model, IDM; MICroscopic Model for Simulation of Intelligent Cruise Control, MIXIC). The review results offer new insights for future intelligent-vehicle analyses: (i) the increase in the market-penetration rate of intelligent vehicles has a significant impact on traffic flow conditions; (ii) without vehicle connections, such as the ACC vehicles, the roadway-capacity increase would be marginal; (iii) none of the parameters in the AV or CAV models is calibrated by the actual field data; (iv) both longitudinal and lateral movements of intelligent vehicles can reduce energy consumption and environmental costs compared to human-driven vehicles; (v) research gap exists in studying the car-following models for newly developed intelligent vehicles; and (vi) the estimated impacts are not converted into a unified metric (i.e., welfare economic impact on users or society) which is essential to evaluate intelligent vehicles from an overall societal perspective.

“…Numerous studies have been conducted by acclaimed researchers and practitioners to interpret the complex dynamics of combined traffic movements [1][2][3][4][5][6]. While these studies transcended in conceiving the levels of impact of automated driving technologies through simplified to complex macroscopic and mesoscopic modeling, the motivation of the present was shaped by the need of modeling microscopic car-following behavior in heterogeneous traffic in order to study macroscopic consequences from mobility, safety, and environmental perspectives.…”

Section: Introductionmentioning

confidence: 99%

Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making

Seraj

Qiu

2018

Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations.