SCC-IRG Track 1: Socially Informed Services Conflict Governance through Specification, Detection, Resolution and Prevention

SCC-IRG 第 1 轨：通过规范、检测、解决和预防进行社会知情服务冲突治理

• 批准号: 1952096
• 负责人: Desheng Zhang
• 金额: $ 230万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952096&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Socially; Informed

Smart services are deeply embedded in modern cities aiming to enhance various aspects of citizens' lives, including safety, wellness, and quality of life. Examples include intelligent traffic control and air quality control. Given these services, monitoring a city's safety and performance collectively is crucial, yet also challenging due to many potential conflicts among the number increasing of services deployed. Researchers have accumulated abundant knowledge on how to design these services independently. However, underlying expected or unexpected couplings among services due to complex interactions of social and physical activities are under-explored, which leads to potential service conflicts. Developing approaches of reducing conflicts is essential for ensuring social inclusion and equity of city services because when conflicts occur, their impacts are likely to be concentrated in some sub-communities (e.g., specific geographic locations, specific user groups like patients with respiratory illness, etc.) meaning that some citizens will experience lower quality services than others due to the diversity. Put differently, service conflicts contribute to a digital divide in service provision.The key intellectual merit of the proposed project is the development of a socially aware conflict management theory and its deployment for smart cities, consisting of 5 sequential components as follows. (1) a novel, template-based requirements specification component/tool that integrates social and technical requirements to formally define a conflict; (2) a social diversity aware detection approach that utilizes machine learning and conflict correlations to detect conflicts in practice; (3) a multi-objective yet equity-centric resolution method that accounts for socially acceptable trade-offs, behavioral models, and control theory to resolve existing conflicts; (4) a participant-based conflict prevention solution that employs Game Theory and Reinforcement Learning in a scalable, decentralize fashion to prevent future conflicts; (5) a social intervention approach based on education outreach and professional training to disseminate the proposed technology to empower the community. The real-world implementation of this theory by working with the city partners in Newark NJ will show its effectiveness and broader impacts on a diverse set of stakeholders of conflict management from city operators, to service providers, to average citizens.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

智能服务已深深地嵌入现代城市中，旨在增强公民生活的各个方面，包括安全，健康和生活质量。示例包括智能交通管制和空气质量控制。鉴于这些服务，监视城市的安全性和绩效集体至关重要，但由于部署的服务数量增加了许多潜在的冲突，因此也充满挑战。研究人员积累了有关如何独立设计这些服务的丰富知识。但是，由于社会和体育活动的复杂互动而导致的服务之间的基本预期或意外耦合的探索不足，从而导致潜在的服务冲突。开发减少冲突的方法对于确保社会包容性和城市服务的平等至关重要，因为当发生冲突时，其影响可能集中在某些子社区（例如，特定的地理位置，呼吸道疾病患者等特定用户群体等特定的用户群体等）中，这意味着某些公民会比由于多样性而经历质量较低的质量服务。换句话说，服务冲突有助于服务提供的数字鸿沟。拟议项目的关键知识优点是开发具有社会意识的冲突管理理论及其对智能城市的部署，由5个顺序组成部分组成，如下所示。 （1）一种基于模板的新颖需求规范组件/工具，它集成了社会和技术要求以正式定义冲突； （2）一种社会多样性意识到的检测方法，利用机器学习和冲突相关性来检测实践中的冲突； （3）一种多目标但以股权为中心的解决方法，该方法解释了社会可接受的权衡，行为模型和控制理论以解决现有冲突； （4）基于参与者的冲突预防解决方案，采用游戏理论并以可扩展的，分散的方式来防止未来冲突的方式进行强化学习； （5）一种基于教育外展和专业培训的社会干预方法，以传播拟议的技术以增强社区的能力。通过与Newark NJ中的城市合作伙伴合作，将表现出其有效性和更广泛的影响，对从城市运营商到服务提供商，对普通公民的各种冲突管理的利益相关者，这一奖项反映了NSF的法规任务，并将其视为通过基金会的知识优点评估评估，这是对平均公民的现实实施。

项目成果

CoMiner: nationwide behavior-driven unsupervised spatial coordinate mining from uncertain delivery events

• DOI: 10.1145/3557915.3560944
• 发表时间: 2022-11
• 期刊: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
• 作者: Zhiqing Hong;Guang Wang;Wenjun Lyu;Baoshen Guo;Yi Ding;Haotian Wang;Shuai Wang;Yunhuai Liu;Desheng Zhang

MoCha: Large-Scale Driving Pattern Characterization for Usage-based Insurance

MoCha：基于使用的保险的大规模驾驶模式表征

• DOI: 10.1145/3447548.3467114
• 发表时间: 2021
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Fang, Zhihan;Yang, Guang;Zhang, Dian;Xie, Xiaoyang;Wang, Guang;Yang, Yu;Zhang, Fan;Zhang, Desheng
• 通讯作者: Zhang, Desheng

Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets

• DOI: 10.1109/icde51399.2021.00108
• 发表时间: 2021-04
• 期刊: 2021 IEEE 37th International Conference on Data Engineering (ICDE)
• 作者: Guang Wang;Shuxin Zhong;Shuai Wang;Fei Miao;Zheng Dong;Desheng Zhang

Softening Performance's Pitfalls by Integrating Context and Capacity: A Government Competitiveness Framework

通过整合环境和能力来软化绩效的陷阱：政府竞争力框架

• DOI: 10.1111/puar.13434
• 发表时间: 2022
• 期刊: Public Administration Review
• 影响因子: 8.3
• 作者: Christensen, Robert K.;Im, Tobin;Porumbescu, Gregory
• 通讯作者: Porumbescu, Gregory

Record: Joint Real-Time Repositioning and Charging for Electric Carsharing with Dynamic Deadlines

• DOI: 10.1145/3447548.3467112
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Guang Wang;Zhou Qin;Shuai Wang;Huijun Sun;Zheng Dong;Desheng Zhang