以下《城市信息学》专刊现已开放供投稿。提交截止日期为2024年3月31日。
专刊主题：GeoAI for Smart Urban Mobility: Emerging Technologies and Applications
客座主编：
涂伟，深圳大学；邮箱：tuwei@szu.edu.cn
徐阳，香港理工大学；邮箱：yang.ls.xu@polyu.edu.hk
钟晨，伦敦大学学院；邮箱：c.zhong@ucl.ac.uk
黄啸，埃默里大学；邮箱：xiao.huang2@emory.edu
目标和范围：
城市交通不断演化，迈向绿色交通、智慧交通。地理大数据、传感器和地理空间人工智能 (GeoAI)（包括图深度学习、可解释深度学习、生成式人工智能）日新月异，正在深刻改变城市移动性的理解和研究范式。不断产生的人类移动观测数据（例如车辆轨迹、手机数据、社交媒体、街景图像、短视频和充电数据等），为开发和训练创新性的 GeoAI 模型，为基于GeoAI的城市移动性解决方案提供了新要素。基于此，本期专刊旨在讨论、展示和展望 GeoAI中人类流动的新兴技术和应用，支撑智慧城市、未来城市。
可能的主题：
1.城市交通的新兴数据源和传感技术；
2.基于数据驱动的多模式交通；
3.机器学习/深度学习用于城市交通预测和出行需求预测；
4.基于图学习的智慧出行；
5.基于个体/群体流动的时空人工智能模型；
6.自动驾驶汽车（AV）和电动汽车（EV）的GeoAI解决方案；
7.共享和按需出行；
8.城市出行研究中的隐私保护和伦理问题；
9.生成式人工智能（例如AIGC、ChatGPT）在城市交通的应用及其影响；
10.可持续城市交通的先进解决方案
The following special issue in Urban Informatics is open for submissions. The submission deadline is Mar 31, 2024.
Theme: GeoAI for Smart Urban Mobility: Emerging Technologies and Applications
Guest editors:
Dr. Wei Tu, Shenzhen University, China; Email: tuwei@szu.edu.cn
Dr. Yang Xu, The Hong Kong Polytechnic University, China; Email: yang.ls.xu@polyu.edu.hk
Dr. Chen Zhong, University College London, UK; Email: c.zhong@ucl.ac.uk
Dr. Xiao Huang, Emory University, USA; Email: xiao.huang2@emory.edu
Aim & Scope:
Urban transport is continuously evolving towards future green and smart mobility. Recent advancements in big geo-data, sensing technologies, and geospatial artificial intelligence (GeoAI) (i.e., graph deep learning, explainable deep learning, generative AI) are transforming the ways we study and understand human mobility in cities. The increasing availability of massive mobility observations, including, vehicle trajectories, mobile phone data, social media, street-view images, short videos and recharging data, has provided new ingredients for developing innovative and GeoAI-empowered models and solutions. The purpose of this special issue is to discuss, showcase and envision emerging technologies and applications in GeoAI for human mobility in smart cities and future cities.
Possible topics:
1.Emerging data sources and sensing technologies for urban mobility;
2.Data-driven multi-mode transportation;
3.Machine learning/Deep learning for urban traffic prediction and travel demand forecast;
4.Graph learning for smart mobility;
5.Spatio-temporal AI models for individual and collective human mobility;
6.GeoAI solutions for autonomous vehicles (AV) and electric vehicles (EV);
7.Shared and mobility-on-demand systems;
8.Privacy protection and ethics in urban mobility research;
9.Impact and applications of generative AI (e.g., AIGC, ChatGPT) on urban mobility research;
10.Advanced solutions for sustainable urban mobility
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