Data Science for Transportation· 2026Q2
Dynamic Trajectory Planning for Urban Traffic Using Deep Learning-Based Traffic Prediction
- 1citations
- Q2SCImago
- 2026year
Short summary
A novel dynamic trajectory planning system integrates deep learning traffic prediction to continuously update optimal routes in real-time, significantly reducing travel time compared to static methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a dynamic trajectory planning system integrating deep learning traffic prediction.
- Utilizes neural networks (RNN, LSTM, TCN) for short-term traffic pattern forecasting.
- Continuously updates optimal trajectories in real-time based on predicted traffic states.
- Experimental results show significant enhancement in route optimality and reduction in travel time compared to static planning.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Traditional trajectory planning systems in navigation applications generate routes based on the current traffi conditions at the time of departure. However, traffi dynamics are highly variable, and aroute that is initially optimal may become congested during the trip, leading to suboptimal travel timesand traffi jams. This paper proposes a novel approach to dynamic trajectory planning by integratingdeep learning-based traffi prediction models into the routing process. We leverage neural networksdesigned for time series forecasting to predict short-term traffi patterns, incorporating factors suchas vehicle velocity and local traffi densities. By forecasting the future state of candidate routes, ourmethod continuously updates the optimal trajectory in real time, ensuring improved adaptability tochanging traffi conditions. We evaluate the performance of different time series models, including re-current neural networks, long short-term memory networks, and temporal convolutional networks, using real-world urban traffi datasets. Experimental results demonstrate that incorporating traffi predictionsignificantl enhances route optimality and reduces overall travel time compared to static trajectoryplanning approaches.
The authors' abstract, as published at the source. Data Science for Transportation, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Building and Construction
Building and ConstructionEngineering