Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis
The volume and availability of data in the Intelligent Transportation System (ITS) result in the
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
[HTML][HTML] Modeling the car-following behavior with consideration of driver, vehicle, and environment factors: A historical review
J Han, X Wang, G Wang - Sustainability, 2022 - mdpi.com
Car-following behavior is the result of the interaction of various elements in the specific
driver-vehicle-environment aggregation. Under the intelligent and connected condition, the …
driver-vehicle-environment aggregation. Under the intelligent and connected condition, the …
Human-like autonomous car-following model with deep reinforcement learning
This study proposes a framework for human-like autonomous car-following planning based
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …
A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach
It has been well recognized that human driver's limits, heterogeneity, and selfishness
substantially compromise the performance of our urban transport systems. In recent years, in …
substantially compromise the performance of our urban transport systems. In recent years, in …
Imitating driver behavior with generative adversarial networks
The ability to accurately predict and simulate human driving behavior is critical for the
development of intelligent transportation systems. Traditional modeling methods have …
development of intelligent transportation systems. Traditional modeling methods have …
A recurrent neural network based microscopic car following model to predict traffic oscillation
This paper proposes a recurrent neural network based microscopic car following model that
is able to accurately capture and predict traffic oscillation. Neural network models have …
is able to accurately capture and predict traffic oscillation. Neural network models have …
A car-following model considering asymmetric driving behavior based on long short-term memory neural networks
Asymmetric driving behavior is a critical characteristic of human driving behaviors and has a
significant impact on traffic flow. In consideration of the asymmetric driving behavior, this …
significant impact on traffic flow. In consideration of the asymmetric driving behavior, this …
Simultaneous modeling of car-following and lane-changing behaviors using deep learning
Car-following (CF) and lane-changing (LC) behaviors are two basic movements in traffic
flow which are generally modeled separately in the literature, and thus the interaction …
flow which are generally modeled separately in the literature, and thus the interaction …
Analysis of recurrent neural networks for probabilistic modeling of driver behavior
J Morton, TA Wheeler… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
The validity of any traffic simulation model depends on its ability to generate representative
driver acceleration profiles. This paper studies the effectiveness of recurrent neural networks …
driver acceleration profiles. This paper studies the effectiveness of recurrent neural networks …