[HTML][HTML] Trajectory data-based traffic flow studies: A revisit
In this paper, we review trajectory data-based traffic flow studies that have been conducted
over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest …
over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest …
[HTML][HTML] About calibration of car-following dynamics of automated and human-driven vehicles: Methodology, guidelines and codes
A comprehensive literature review reveals that there exist lots of ambiguities, confusion and
even contradictions in setting a car-following calibration problem. In particular, confusion …
even contradictions in setting a car-following calibration problem. In particular, confusion …
Synthetic data in machine learning for medicine and healthcare
Synthetic data in machine learning for medicine and healthcare | Nature Biomedical Engineering
Skip to main content Thank you for visiting nature.com. You are using a browser version with …
Skip to main content Thank you for visiting nature.com. You are using a browser version with …
A flow feedback traffic prediction based on visual quantified features
J Chen, M Xu, W Xu, D Li, W Peng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traffic flow prediction methods commonly rely on historical traffic data, such as traffic volume
and speed, but may not be suitable for high-capacity expressways or during peak traffic …
and speed, but may not be suitable for high-capacity expressways or during peak traffic …
Vehicle trajectory prediction using LSTMs with spatial–temporal attention mechanisms
Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system
applications ranging from traffic simulations to driver assistance. The need for this ability is …
applications ranging from traffic simulations to driver assistance. The need for this ability is …
[HTML][HTML] Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives
Automated vehicles (AVs) are widely considered to play a crucial role in future transportation
systems because of their speculated capabilities in improving road safety, saving energy …
systems because of their speculated capabilities in improving road safety, saving energy …
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 …
Design and experimental validation of deep reinforcement learning-based fast trajectory planning and control for mobile robot in unknown environment
This article is concerned with the problem of planning optimal maneuver trajectories and
guiding the mobile robot toward target positions in uncertain environments for exploration …
guiding the mobile robot toward target positions in uncertain environments for exploration …
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
Short-term passenger demand forecasting is of great importance to the on-demand ride
service platform, which can incentivize vacant cars moving from over-supply regions to over …
service platform, which can incentivize vacant cars moving from over-supply regions to over …
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 …