# Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time

arXiv: https://arxiv.org/abs/2011.01771 · HTML: https://mlsanity.com/paper/2011.01771/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Road Planning for Slums via Deep Reinforcement Learning](https://mlsanity.com/paper/2305.13060/) (2023)
- [Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes](https://mlsanity.com/paper/2401.08669/) (2024)
- [PathletRL++: Optimizing Trajectory Pathlet Extraction and Dictionary Formation via Reinforcement Learning](https://mlsanity.com/paper/2412.03715/) (2024)
- [Network-Constrained Policy Optimization for Adaptive Multi-agent Vehicle Routing](https://mlsanity.com/paper/2510.26089/) (2025)
- [Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning](https://mlsanity.com/paper/2409.00754/) (2024)
- [Governing Automated Strategic Intelligence](https://mlsanity.com/paper/2509.17087/) (2025)
- [Real-DRL: Teach and Learn in Reality](https://mlsanity.com/paper/2511.00112/) (2025)
- [RL-Ballast: Ship Ballast Water Path Planning and Clog Prediction via Reinforcement Learning](https://mlsanity.com/paper/2607.04906/) (2026)

## Abstract (arXiv)

Route planning is important in transportation. Existing works focus on finding the shortest path solution or using metrics such as safety and energy consumption to determine the planning. It is noted that most of these studies rely on prior knowledge of road network, which may be not available in certain situations. In this paper, we design a route planning algorithm based on deep reinforcement learning (DRL) for pedestrians. We use travel time consumption as the metric, and plan the route by predicting pedestrian flow in the road network. We put an agent, which is an intelligent robot, on a virtual map. Different from previous studies, our approach assumes that the agent does not need any prior information about road network, but simply relies on the interaction with the environment. We propose a dynamically adjustable route planning (DARP) algorithm, where the agent learns strategies through a dueling deep Q network to avoid congested roads. Simulation results show that the DARP algorithm saves 52% of the time under congestion condition when compared with traditional shortest path planning algorithms.