Online Multimodal Transportation Planning using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2105.08374v1
• Date: Tue, 18 May 2021 09:01:44 GMT
• Title: Online Multimodal Transportation Planning using Deep Reinforcement Learning
• Abstract summary: We propose a Deep Reinforcement Learning approach to solve a multimodal transportation planning problem. While traditional planning methods work "offline" (i.e., they take decisions for a batch of containers before the transportation starts), the proposed approach is "online" Planning transportation online helps to effectively respond to unforeseen events that may affect the original transportation plan.
• Score: 2.0088802641040604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we propose a Deep Reinforcement Learning approach to solve a multimodal transportation planning problem, in which containers must be assigned to a truck or to trains that will transport them to their destination. While traditional planning methods work "offline" (i.e., they take decisions for a batch of containers before the transportation starts), the proposed approach is "online", in that it can take decisions for individual containers, while transportation is being executed. Planning transportation online helps to effectively respond to unforeseen events that may affect the original transportation plan, thus supporting companies in lowering transportation costs. We implemented different container selection heuristics within the proposed Deep Reinforcement Learning algorithm and we evaluated its performance for each heuristic using data that simulate a realistic scenario, designed on the basis of a real case study at a logistics company. The experimental results revealed that the proposed method was able to learn effective patterns of container assignment. It outperformed tested competitors in terms of total transportation costs and utilization of train capacity by 20.48% to 55.32% for the cost and by 7.51% to 20.54% for the capacity. Furthermore, it obtained results within 2.7% for the cost and 0.72% for the capacity of the optimal solution generated by an Integer Linear Programming solver in an offline setting.

Related papers

• Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework [8.212024590297895]
  This paper introduces a novel reinforcement learning framework, termed Reward-Guided Conservative Q-learning (RG-CQL) We propose an offline training and online fine-tuning RL framework to learn the optimal operational decisions of the multimodal transportation systems. Our innovative offline training and online fine-tuning framework offers a remarkable 81.3% improvement in data efficiency.
  arXiv  Detail & Related papers  (2025-01-24T03:05:04Z)
• Rethinking Optimal Transport in Offline Reinforcement Learning [64.56896902186126]
  In offline reinforcement learning, the data is provided by various experts and some of them can be sub-optimal. To extract an efficient policy, it is necessary to emphstitch the best behaviors from the dataset. We present an algorithm that aims to find a policy that maps states to a emphpartial distribution of the best expert actions for each given state.
  arXiv  Detail & Related papers  (2024-10-17T22:36:43Z)
• Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph [0.0]
  We propose a novel deep learning-based methodology for a decision support system that enables public transport planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching.
  arXiv  Detail & Related papers  (2023-08-24T14:37:55Z)
• Rethinking Closed-loop Training for Autonomous Driving [82.61418945804544]
  We present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents. We propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines.
  arXiv  Detail & Related papers  (2023-06-27T17:58:39Z)
• iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning [57.24340061741223]
  We introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios. Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
  arXiv  Detail & Related papers  (2023-06-09T20:12:02Z)
• Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control [8.020856741504794]
  We study online control algorithms for autonomous mobility-on-demand systems. We develop a novel hybrid enriched machine learning pipeline which learns online dispatching and rebalancing policies. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches.
  arXiv  Detail & Related papers  (2023-02-08T09:40:30Z)
• Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network [0.0]
  This study uses Graph Convolutional Neural Networks (GCNs) to assist paratransit operators in researching various operational scenarios. The study was carried out by using a randomized simulated dataset to help determine the decision to make in terms of fleet composition and capacity.
  arXiv  Detail & Related papers  (2022-05-21T05:27:45Z)
• A Deep Reinforcement Learning Approach for Constrained Online Logistics Route Assignment [4.367543599338385]
  It is crucial for the logistics industry on how to assign a candidate logistics route for each shipping parcel properly. This online route-assignment problem can be viewed as a constrained online decision-making problem. We develop a model-free DRL approach named PPO-RA, in which Proximal Policy Optimization (PPO) is improved with dedicated techniques to address the challenges for route assignment (RA)
  arXiv  Detail & Related papers  (2021-09-08T07:27:39Z)
• Estimating the Robustness of Public Transport Systems Using Machine Learning [62.997667081978825]
  Planning public transport systems is a highly complex process involving many steps. Integrating robustness from a passenger's point of view makes the task even more challenging. In this paper, we explore a new way of such a scenario-based robustness approximation by using methods from machine learning.
  arXiv  Detail & Related papers  (2021-06-10T05:52:56Z)
• Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark [133.46066694893318]
  We evaluate the performance of neural network-based solvers for optimal transport. We find that existing solvers do not recover optimal transport maps even though they perform well in downstream tasks.
  arXiv  Detail & Related papers  (2021-06-03T15:59:28Z)
• End-to-end Interpretable Neural Motion Planner [78.69295676456085]
  We propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios. We design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America.
  arXiv  Detail & Related papers  (2021-01-17T14:16:12Z)
• PassGoodPool: Joint Passengers and Goods Fleet Management with Reinforcement Learning aided Pricing, Matching, and Route Planning [29.73314892749729]
  We present a demand aware fleet management framework for combined goods and passenger transportation. Our proposed model is deployable independently within each vehicle as this minimizes computational costs associated with the growth of distributed systems.
  arXiv  Detail & Related papers  (2020-11-17T23:15:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.