Mining Truck Platooning Patterns Through Massive Trajectory Data
- URL: http://arxiv.org/abs/2010.05142v1
- Date: Sun, 11 Oct 2020 02:44:48 GMT
- Title: Mining Truck Platooning Patterns Through Massive Trajectory Data
- Authors: Xiaolei Ma, Enze Huo, Haiyang Yu, Honghai Li
- Abstract summary: Properly planning platoons and evaluating the potential of truck platooning are crucial to trucking transportation authorities.
This study proposes a series of data mining approaches to learn spontaneous truck platooning patterns from massive trajectories.
We leverage real data collected from truck fleeting systems in Liaoning Province, China, to evaluate platooning performance and successfully extract platooning patterns.
- Score: 9.559029665817656
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Truck platooning refers to a series of trucks driving in close proximity via
communication technologies, and it is considered one of the most implementable
systems of connected and automated vehicles, bringing huge energy savings and
safety improvements. Properly planning platoons and evaluating the potential of
truck platooning are crucial to trucking companies and transportation
authorities. This study proposes a series of data mining approaches to learn
spontaneous truck platooning patterns from massive trajectories. An enhanced
map matching algorithm is developed to identify truck headings by using digital
map data, followed by an adaptive spatial clustering algorithm to detect
instantaneous co-moving truck sets. These sets are then aggregated to find the
network-wide maximum platoon duration and size through frequent itemset mining
for computational efficiency. We leverage real GPS data collected from truck
fleeting systems in Liaoning Province, China, to evaluate platooning
performance and successfully extract spatiotemporal platooning patterns.
Results show that approximately 36% spontaneous truck platoons can be
coordinated by speed adjustment without changing routes and schedules. The
average platooning distance and duration ratios for these platooned trucks are
9.6% and 9.9%, respectively, leading to a 2.8% reduction in total fuel
consumption. We also distinguish the optimal platooning periods and space
headways for national freeways and trunk roads, and prioritize the road
segments with high possibilities of truck platooning. The derived results are
reproducible, providing useful policy implications and operational strategies
for large-scale truck platoon planning and roadside infrastructure
construction.
Related papers
- Enhancing UAV Path Planning Efficiency Through Accelerated Learning [3.216130900831975]
This study aims to develop a learning algorithm for the path planning of UAV wireless communication relays.
It can reduce storage requirements and accelerate Deep Reinforcement Learning (DRL) convergence.
arXiv Detail & Related papers (2025-01-17T12:05:24Z) - Multi-Agent Deep Reinforcement Learning for Distributed and Autonomous Platoon Coordination via Speed-regulation over Large-scale Transportation Networks [2.4919288454226796]
Truck platooning technology enables a group of trucks to travel closely together, with which the platoon can save fuel, improve traffic flow efficiency, and improve safety.
In this paper, we consider the platoon coordination problem in a large-scale transportation network, to promote cooperation among trucks and optimize the overall efficiency.
arXiv Detail & Related papers (2024-12-02T03:21:40Z) - Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment.
Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field.
We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
arXiv Detail & Related papers (2024-10-10T10:10:03Z) - Optimization of Multi-Agent Flying Sidekick Traveling Salesman Problem over Road Networks [10.18252143035175]
We introduce the multi-agent flying sidekick traveling salesman problem (MA-FSTSP) on road networks.
We propose a mixed-integer linear programming model and an efficient three-phase algorithm for this NP-hard problem.
Our approach scales to more than 300 customers within a 5-minute time limit, showcasing its potential for large-scale, real-world logistics applications.
arXiv Detail & Related papers (2024-08-20T20:44:18Z) - Exploring the Effects of Population and Employment Characteristics on
Truck Flows: An Analysis of NextGen NHTS Origin-Destination Data [2.6842755963997926]
This study includes zone-level population and employment characteristics from the US Census Bureau.
The final data set was used to train a machine learning algorithm-based model, Extreme Gradient Boosting (XGBoost)
Results showed that the distance between the zones was the most important variable and had a nonlinear relationship with truck flows.
arXiv Detail & Related papers (2024-02-02T15:47:01Z) - MSight: An Edge-Cloud Infrastructure-based Perception System for
Connected Automated Vehicles [58.461077944514564]
This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles.
MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction.
Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
arXiv Detail & Related papers (2023-10-08T21:32:30Z) - Drone navigation and license place detection for vehicle location in
indoor spaces [55.66423065924684]
This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates.
All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map.
arXiv Detail & Related papers (2023-07-19T17:46:55Z) - A Big-Data Driven Framework to Estimating Vehicle Volume based on Mobile
Device Location Data [0.40631409309544836]
Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance and more.
Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations.
This paper presents a big-data driven framework that can ingest terabytes of Device Location Data and estimate vehicle volume at a larger geographical area with a larger sample size.
arXiv Detail & Related papers (2023-01-20T16:23:17Z) - Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset [103.35624417260541]
Decentralized vehicle coordination is useful in understructured road environments.
We collect the Berkeley DeepDrive Drone dataset to study implicit "social etiquette" observed by nearby drivers.
The dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and for computer vision in remote sensing settings.
arXiv Detail & Related papers (2022-09-19T05:06:57Z) - An Intelligent Self-driving Truck System For Highway Transportation [81.12838700312308]
In this paper, we introduce an intelligent self-driving truck system.
Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real-world deployment.
We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap.
arXiv Detail & Related papers (2021-12-31T04:54:13Z) - Learning to Localize Using a LiDAR Intensity Map [87.04427452634445]
We propose a real-time, calibration-agnostic and effective localization system for self-driving cars.
Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space.
Our full system can operate in real-time at 15Hz while achieving centimeter level accuracy across different LiDAR sensors and environments.
arXiv Detail & Related papers (2020-12-20T11:56:23Z)
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.