Disruptive Transportation Technologies and Services

在大流行后的世界，颠覆性交通技术和服务（DTTS），如互联和自动化车辆、城市空中交通、智能电网上的电动汽车、基于区块链的交通服务、微交通和按需交通服务，有可能成为智慧城市生态系统中不可或缺的一部分。为了在我们未来的交通系统中可持续地采用、运营、设计和规划DTTS，我们必须主动了解与之相关的新挑战和机遇。拟议的CRC计划将开展新颖的研究，探索智慧城市中与DTTS相关的新问题。利用虚拟现实（VR）、数字孪生、先进的机器学习和计量经济模型，该项目将进一步提高我们对影响城市范围内可持续采用dts出行行为的关键推动和拉动因素的理解。该计划开发的新型旅行行为模型将特别侧重于利用因果推理理论捕捉决策过程。该项目还将开发新技术、算法和建模/仿真工具，帮助DTTS解决与气候变化、公平和包容、隐私、算法/数据偏差和网络安全相关的问题。为此，该计划将探索区块链、分布式学习和多目标深度强化学习的使用。未来五年的研究将使人们更深入地了解dts对人类流动性和活动的影响，并开发新的模型、算法和解决方案，以解决新出现的问题。该计划将为交通工程和智慧城市研究的关键领域带来基本的方法和技术贡献。这种独一无二的基于VR的数字孪生将极大地增强现有知识和理解dts相关移动行为的能力。本项目将开发并与研究界分享的创新行为模型将有助于改进旅游需求预测文献。该项目产生的创新优化算法和技术将有助于研究社区对交通系统有害排放的研究，并为减少这些排放提供系统的分析。

In the post-pandemic world, Disruptive Transportation Technologies and Services (DTTS), e.g., connected and automated vehicles, urban air mobility, electric vehicles on the smart-grid, blockchain based mobility services, micromobility, and on-demand mobility services, have the potential to become an integral and ubiquitous part of the smart city ecosystem. For sustainable adoption, operations, design, and planning of DTTS in our future transportation systems, one must proactively understand the emerging challenges and opportunities associated with them. The proposed CRC program will develop novel research that will explore emerging issues related to DTTS in smart cities. Using virtual reality (VR), digital twin, and advanced machine learning and econometric modelling, the program will further improve our understanding of the key push- and pull-factors affecting mobility behaviour for the sustainable adoption of DTTS at urban scale. The novel travel behavioural models developed in the program will particularly focus on capturing the decision-making process using causal inference theories. The program will also develop novel technologies, algorithms, and modelling/simulation tools that can help DTTS address issues related to climate change, equity and inclusion, privacy, algorithm/data bias, and cybersecurity. For this purpose, the program will explore the use of blockchain, distributed learning, and multi-objective deep reinforcement learning. The research conducted in the next five years will result in a deeper understanding of the effects of DTTS on human mobility and activities, and the development of new models, algorithms, and solutions that can solve emerging issues. The program will bring fundamental methodological and technological contributions to key areas in transportation engineering and smart cities research. The one-of-a-kind VR based digital twin will greatly enhance existing knowledge and the ability to understand DTTS-related mobility behaviour. The innovative behavioural models that will be developed in this program and shared with the research community will contribute to the improvement of travel demand prediction literature. The innovative optimization algorithms and technologies arising from this program will contribute to the research community working on harmful emissions from transportation systems, and provide a systematic analysis of the capacity of DTTS to reduce them.