five

A Comprehensive Study on Autonomous Vehicle Integration for Tırport

收藏
DataONE2024-04-14 更新2024-10-19 收录
下载链接:
https://search.dataone.org/view/sha256:e7f35a5af2f21c45478199ea0528d44324f63cb3e52e88f0a7bab8e7306dbb6c
下载链接
链接失效反馈
资源简介:
Although regular road transportation is convenient, it can be difficult for transportation companies to operate. Most issues stem from timetable delays, poor routing plans, and greater transportation costs, including wages, fuel, and operations expenditures. Wind resistance during high-speed transportation increases fuel use, and human error causes safety incidents that increase costs and environmental damage. Therefore, the platooning system is a strategically designed alternative to keep trucks in a convoy using sensors. Thus, the goal of this research is to determine optimized platooning routes in Turkey’s motorway network and the ideal locations for hubs. Also, we would like to explore the potential of platooning technology to reduce fuel consumption, carbon footprint, and transportation time with mathematical modeling and Python code. According to our results, the 5 hubs should be located in Adana, Ankara, Manisa, Istanbul, and Bursa. The greatest percentage saving is achieved by 4 truck platooning systems with an average of 11% and there is reduction of millions kg of CO2 emission in a day. In addition, we conducted a what-if-analysis with a future motorway in Turkey which resulted in an increase of profit to 12%. Finally, we implemented the waiting times of trucks for each other when forming convoys in a hub and according to our results, we discovered that it can be disregarded in each scenario because they are less than 20 minutes. And also even in the worst case, there is a reduction of total empty mileages by up to 1 in 3.
创建时间:
2024-09-24
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

Beijing Traffic

The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.

Papers with Code 收录

FER2013

FER2013数据集是一个广泛用于面部表情识别领域的数据集,包含28,709个训练样本和7,178个测试样本。图像属性为48x48像素,标签包括愤怒、厌恶、恐惧、快乐、悲伤、惊讶和中性。

github 收录

中国交通事故深度调查(CIDAS)数据集

交通事故深度调查数据通过采用科学系统方法现场调查中国道路上实际发生交通事故相关的道路环境、道路交通行为、车辆损坏、人员损伤信息,以探究碰撞事故中车损和人伤机理。目前已积累深度调查事故10000余例,单个案例信息包含人、车 、路和环境多维信息组成的3000多个字段。该数据集可作为深入分析中国道路交通事故工况特征,探索事故预防和损伤防护措施的关键数据源,为制定汽车安全法规和标准、完善汽车测评试验规程、

北方大数据交易中心 收录

Breast Cancer Dataset

该项目专注于清理和转换一个乳腺癌数据集,该数据集最初由卢布尔雅那大学医学中心肿瘤研究所获得。目标是通过应用各种数据转换技术(如分类、编码和二值化)来创建一个可以由数据科学团队用于未来分析的精炼数据集。

github 收录

HazyDet

HazyDet是由解放军工程大学等机构创建的一个大规模数据集,专门用于雾霾场景下的无人机视角物体检测。该数据集包含383,000个真实世界实例,收集自自然雾霾环境和正常场景中人工添加的雾霾效果,以模拟恶劣天气条件。数据集的创建过程结合了深度估计和大气散射模型,确保了数据的真实性和多样性。HazyDet主要应用于无人机在恶劣天气条件下的物体检测,旨在提高无人机在复杂环境中的感知能力。

arXiv 收录