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Automated vehicles are expected to influence human drivers’ behavior. Accordingly, capturing such changes is critical for planning and operation purposes. With regard to car-following behavior, a key question is whether existing car-following models can replicate these changes in human behavior. Using a data set that was collected from the car-following behavior of human drivers when following automated vehicles, this paper offers a robust methodology based on the concept of dynamic time warping to investigate the critical parameters that can be used to capture changes in human behavior. The results indicate that spacing can best substantiate such changes. Moreover, calibration and validation of the intelligent driver model (IDM) suggest its inability to capture changes in human behavior in response to automated vehicles. Thus, an extension of the IDM that explicitly models stochasticity in the behavior of individual drivers is applied, and the results show such a model can identify a reduction in uncertainty when following an automated vehicle. This finding also has implications for a stochastic extension to other models when analyzing and simulating a mixed-autonomy traffic flow environment.
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Modeling and Control Using Connected and Automated Vehicles with Chained Asymmetric Driver Behavior under Stop-and-Go Oscillations
This paper presents a behavioral car following model, named the chained asymmetric behavior model, that improves on the asymmetric behavior model. This model is inspired by the empirical observation that vehicles react proportionately to the magnitude of disturbance experienced when traversing through a stop-and-go oscillation, deviating from a constant following behavior observed in equilibrium conditions. Findings from simulation experiments suggest that this “second-order” effect significantly affects traffic throughput and evolution under disturbances. Knowledge obtained from the model is leveraged toward designing control for connected automated vehicles in mixed traffic streams.
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- Award ID(s):
- 1932921
- PAR ID:
- 10200135
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2675
- Issue:
- 1
- ISSN:
- 0361-1981
- Format(s):
- Medium: X Size: p. 342-355
- Size(s):
- p. 342-355
- Sponsoring Org:
- National Science Foundation
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