Social and Economic Implications of Transport Sharing and Automation

这项研究将把自动化和平台经济带来的工作性质的变化与区域基础设施规划和运输业务联系起来，特别是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安排和通勤模式的差异，非标准的工作出差模式，甚至取消某些工作岗位和创造新的工作岗位，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(如自动驾驶和联网车辆)，基础设施和运营预计将发生巨大变化。该项目将在区域一级对由于这些考虑而改变的工作性质作出估计，以实现得出运输和区域基础设施规划结果的目标。该项目将使用劳动力市场调查数据以及私人持有的就业、技能和行业劳动力市场数据，根据地区行业和职业组合，估计由于这些趋势而产生的地区差异。这些变化将与空间城市数据系统(SUDS)相联系，这是UBDC内正在开发的一个全英国范围的地理空间数据基础设施，其中包含交通基础设施和运营条件。，它最近被用来确定英国各地的交通贫困地区，以及我们将通过与该项目的行业合作伙伴的合作扩大到什么程度。使用这些数据源，我们将识别由于独特的行业和技能集中而导致的区域自动化风险，并得出运输和基础设施规划的影响。在这一背景下，我们还将使用专业的交通模拟模型来评估自动驾驶车辆在潜在不同通勤模式下的作用。我们将进一步开发专门的交通模拟模型，以确定哪些“最后一英里”的交通解决方案(低能耗站点汽车、自动驾驶汽车、共享交通、主动出行和需求响应服务)可能在有可能改变工作性质的地区带来高质量、可持续和社会公平的交通便利形式。然后，我们将综合各种模型情景的结果，使用集合预测方法，利用贝叶斯模型平均或相关技术来确定哪些包裹更有可能在选定地区带来高质量的交通便利。

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.