Social and Economic Implications of Transport Sharing and Automation

这项研究将把自动化和平台经济所带来的工作性质的变化与区域基础设施规划和运输运营联系起来，并具体探讨运输自动化在这一背景下的作用。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和通勤模式的差异，非标准的工作旅行模式，甚至消除某些工作和创造新的工作，对区域基础设施规划和运输业务产生重大影响。与此同时，由于运输部门的大规模自动化（例如自动驾驶和联网车辆），预计基础设施和运营将发生巨大变化。该项目将对区域一级由于这些考虑而发生的工作性质的变化作出估计，以达到得出运输和区域基础设施规划后果的目标。该项目将利用劳动力市场调查数据以及私人持有的关于工作、技能和行业的劳动力市场数据，根据区域行业-职业组合，估计这些趋势造成的区域差异。这些变化将与空间城市数据系统（SUDS）相关联，该系统是UBDC内部正在开发的全英国地理空间数据基础设施，包含交通基础设施和运营条件。，最近已被用于确定整个英国的交通贫困领域，以及我们将通过与该项目的工业合作伙伴合作扩大的程度。利用这些数据源，我们将确定由于独特的行业和技能集中而导致的区域自动化风险，并得出交通和基础设施规划的影响。在此背景下，我们还将使用专业的交通仿真模型评估自动驾驶汽车在潜在不同通勤模式下的作用。我们将进一步开发专业的交通模拟模型，以确定哪些“最后一英里”交通解决方案（低能耗站用汽车、自动驾驶汽车、共享交通、主动出行和需求响应服务）可能会带来高质量、可持续和社会公平的交通方式在面临工作性质改变风险的地区实现交通无障碍。然后，我们将联合收割机结合我们的各种模型方案的结果，使用集合预测方法，利用贝叶斯模型平均或相关技术，以确定哪些方案更有可能在选定地区带来高质量的交通可达性。

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.