Social and Economic Implications of Transport Sharing and Automation

交通共享和自动化的社会和经济影响

• 批准号: ES/S001875/1
• 负责人: Long Chen
• 金额: $ 38.52万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FS001875%2F1
• 关键词: Social; Economic; Implications; Transport; Sharing

This study will link the changing nature of jobs due to automation and the platform economy to regional infrastructure planning and transport operations, and the role specifically of transport automation within this context. The patterns and forms of jobs are changing due to many different reasons, leading to non-traditional work schedules and differences in commuting patterns, non-standard work travel patterns, and even elimination of certain jobs and creation of new ones, with significant implications for regional infrastructure planning and transport operations. At the same time, there are enormous changes anticipated in infrastructure and operations, due to large-scale automation in the transport sector (eg autonomous and connected vehicles). This project will make estimates of the changing nature of jobs due to these considerations at the regional level towards the goal of deriving the transport and regional infrastructural planning consequences. The project will use labour market survey data as well as privately-held labour market data on jobs, skills and industry to estimate regional variations due to these trends, given regional industry-occupation mix. These changes will be linked to the Spatial Urban Data System (SUDS), which is a UK-wide geospatial data infrastructure under development within UBDC containing transport infrastructural and operational conditions. , and which has been recently used to identify areas of transport poverty throughout the UK and the extent to which and which we will expand through work with the project's industrial partners. Using these data sources, we will identify regional automation risks due to unique industry and skill concentrations and derive transport and infrastructure planning implications. Within this context, we will also evaluate the role of autonomous vehicles given potentially different commuting patterns using specialist transport simulation models. We will further develop specialist transport simulation models to ascertain which packages of "last-mile" transport solutions (low-energy station cars, autonomous vehicles, shared transport, active travel and demand-response services) are likely to bring about high-quality, sustainable and socially-equitable forms of transport accessibility in areas at risk of changing nature of jobs. We will then combine the results of our various model scenarios, using ensemble forecasting methods utilising Bayesian Model Averaging or related techniques to ascertain which packages are more likely to bring about high-quality transport accessibility in the selected areas.

这项研究将把由于自动化和平台经济引起的工作的不断变化与区域基础设施计划和运输运营联系起来，以及在这种情况下特别是运输自动化的作用。由于许多不同的原因，工作的模式和形式正在发生变化，导致非传统的工作时间表以及通勤模式，非标准的工作旅行模式的差异，甚至消除了某些工作和新工作的创造，对区域基础设施规划和运输运营产生了重大影响。同时，由于运输部门的大规模自动化（例如自动驾驶汽车和连接的车辆），基础设施和运营中预计会发生巨大变化。由于这些考虑因素在区域层面上，该项目将估计工作性质的变化，以推导运输和区域基础设施计划后果。考虑到区域行业占领组合，由于这些趋势，该项目将使用劳动力市场调查数据以及有关就业，技能和行业的私人劳动力市场数据来估计区域变化。这些更改将与空间城市数据系统（SUDS）相关联，该系统是涵盖UBDC中包含运输基础设施和运营条件的UBDC中正在开发的英国地理空间数据基础设施。 ，最近被用来确定整个英国的运输贫困地区以及通过与项目的工业合作伙伴合作的程度以及我们将在多大程度上扩展的程度。使用这些数据源，我们将由于独特的行业和技能集中而确定区域自动化风险，并获得运输和基础设施计划的影响。在这种情况下，我们还将使用专业运输模拟模型来评估在潜在的通勤模式下的自动驾驶汽车的作用。我们将进一步开发专业的运输模拟模型，以确定哪些“最后一英里”运输解决方案（低能量车站汽车，自动驾驶汽车，共享运输，积极的旅行和需求响应服务）可能会带来高质量，可持续和社会公平和具有社会公平的运输形式，这些形式的运输形式在有改变工作性质的风险的领域风险。然后，我们将使用贝叶斯模型平均或相关技术的集合预测方法结合各种模型方案的结果，以确定哪些软件包更有可能在所选区域实现高质量的运输可及性。

项目成果

Modelling and Predicting Individual Salaries in United Kingdom with Graph Convolutional Network

• DOI: 10.1007/978-3-030-14347-3_7
• 发表时间: 2018-12
• 期刊: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence
• 作者: Long Chen;Yeran Sun;P. Thakuriah

Predicting Taxi Demand in NYC with Wavenet

使用 Wavenet 预测纽约市的出租车需求

• 发表时间: 2019
• 作者: Long Chen