返回
Smart Urban Mobility: When Mobility Systems Meet Smart Data
DOI:10.1109/TITS.2021.3084907.png)
摘要
En 中文
Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and casts an average of US $482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.
Keyword:
Artificial intelligence
autonomous vehicles
connected vehicles
electric mobility
intelligent transportation systems
Internet of Things
open data
routing vehicle
smart mobility
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
Isolated Giant Smooth Muscle Fibres in Beroe Ovata: Ionic Dependence of Action Potentials Reveals two Distinct Types of Fibre 卵子中的孤立的巨大平滑肌纤维: 动作电位的离子依赖性揭示了两种不同类型的纤维
Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection
SOFT COMPUTING
IF2.5
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems6g无线系统的愿景: 应用,趋势,技术和开放研究问题
IEEE NETWORK
IF6.3

