Low Carbon Transitions of Fleet Operations in Metropolitan Sites (LC TRANSFORMS)

大都市车队运营的低碳转型（LC TRANSFORMS）

• 批准号: EP/N010612/1
• 负责人: Phil Blythe
• 金额: $ 102.6万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN010612%2F1
• 关键词: Low; Carbon; Transitions; Fleet; Operations

The rapid urbanisation and increase in vehicle use in East Asia has created substantial environmental and social problems. In the UK, urban transport systems face similar issues, but generally at a smaller scale and at a much lower pace. However, a strong built-in inertia within physical, regulatory and societal infrastructure in western urban systems makes this challenging to tackle. Low carbon vehicle fleets for personal mobility and freight have the potential to contribute to reduction of the climate impact from urban transport as well as to improve local traffic and air quality conditions. The extent of this potential is however still unclear. Ample uncertainties remain regarding both the demand for fleet services and the most effective way to organise fleet operations, especially in the case of electric vehicles where interaction with the power grid becomes a critical issue. At the same time, a range of new business models for the operation urban freights and fleet services are emerging, enabled by pervasive ICT.Against this background, the overall aim of the LC TRANSFORMS project is to provide an integrated planning and deployment strategy for multi-purpose low carbon fleets and enabling urban infrastructure and to devise operational business models ensuring economic viability and environmental effectiveness. This aim will be attained by addressing 4 key research challenges:1) Transport demand and network modelling tools for low carbon transport planning in urban areas have been outpaced by practical innovation in real-world urban transport and need to be brought up to speed. In particular, better integration is needed between urban mobility and freight modelling on the demand side (e.g. to capture substitution of shopping trips by home deliveries) and on the operations side (e.g. accounting for electric passenger and freight vehicles sharing a common charging infrastructure). Further improvement areas include representation of kerb space as a scarce and constrained resource affecting parking and loading operations of vehicle fleets, and better characterisation of fleet service customer heterogeneity (necessary for demand flexibility exploitation); 2) The new business models in urban fleet operation, in particular those operating in "demand responsive" modes and exploiting demand flexibility require the development of new operational management algorithms that ensure high quality of service, economic and environmental performances. This is particularly challenging in electric fleet operation where patterns of consumption of fleet services (freight and personal mobility), need to accommodate electric vehicle charging operations, when time-dependent prices of electricity and grid emissions factors are low.3) The large scale deployment of electric fleets will pose challenges for the intelligent management of networked infrastructure for optimal operation of commercial fleets is largely understudied. This is true for intelligent transport infrastructure to optimise traffic management as well as the requirements of charging infrastructure and of smart charging algorithms to optimise environmental and economic benefits which have not been studied in detail for commercial for commercial fleets.4) For scale investments to flow into low carbon transport, there is also a need for a new generation of policy appraisal tools that can deal with the interdependencies among networked urban infrastructures, (transport, power and IT). Such tools must take account not only of technical and functional interdependencies but also of the existence of multiple institutional stakeholders and of the substantial uncertainties affecting the flow of costs and benefits to different stakeholder over different time horizons. Consolidation of isolated initiatives to extend existing appraisal techniques, e.g. by the integration of ideas from Real Options Theory are required.

东亚的快速城市化和车辆使用的增加造成了重大的环境和社会问题。在英国，城市运输系统面临类似的问题，但通常情况下的速度较小，速度要低得多。但是，西方城市系统中物理，监管和社会基础设施内的强大内置惯性使得解决这个问题。低碳车队的个人流动性和货运有可能促进城市运输的气候影响以及改善当地交通和空气质量条件的影响。但是，这种潜力的程度仍不清楚。关于舰队服务的需求和组织车队运营的最有效方式，尤其是在与电网互动成为关键问题的电动汽车的情况下，仍然存在充分的不确定性。同时，通过普遍的ICT启用了一系列针对城市货运和车队服务的新业务模型。通过应对4个关键研究挑战，将实现此目标：1）在现实世界中，城市运输的实际创新超过了城市地区低碳运输计划的运输需求和网络建模工具，并且需要提高速度。特别是，在需求方面，城市流动性和货运建模之间需要更好的整合（例如，捕获房屋交货替代购物旅行）和运营方面（例如，对电动乘客和分享共同充电基础设施的电动乘客和货运车辆的核算）。进一步的改进领域包括将路缘空间的代表作为稀缺和限制的资源，影响了车队的停车和装载操作，以及更好地表征舰队服务客户异质性（对于需求灵活性开发所必需的）； 2）城市车队运营中的新业务模型，尤其是那些以“需求响应”模式运行的人和利用需求灵活性的人需要开发新的运营管理算法，以确保高质量的服务，经济和环境表现。这在电动车队的运营中尤其具有挑战性，在该操作中，机队服务的消费模式（货运和个人流动性）需要适应电动汽车充电操作，当电力和电网排放因素的时间相关价格较低时。3）3）电动车队的大规模部署将对网络结构的智能管理，以使其对商业舰队进行智能管理的智能管理，以使网络结构的智能管理成为最佳的士兵。智能运输基础设施以优化交通管理以及充电基础设施和明智的充电算法的要求是正确的，以优化未经详细研究的环境和经济利益，这些算法尚未详细研究商业企业的商业投资4）。然后）。这样的工具不仅必须考虑到技术和功能相互依存关系，而且还必须考虑到多个机构利益相关者以及影响不同时间范围内不同利益相关者的成本和收益流动的实质性不确定性。整合孤立的举措以扩展现有的评估技术，例如通过从真实选择理论中整合思想。

项目成果

A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data

• DOI: 10.1016/j.aap.2018.10.015
• 发表时间: 2019-01-01
• 期刊: ACCIDENT ANALYSIS AND PREVENTION
• 影响因子: 5.9
• 作者: Bao, Jie;Liu, Pan;Ukkusuri, Satish V.
• 通讯作者: Ukkusuri, Satish V.

Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests

• DOI: 10.1007/s11067-017-9366-x
• 发表时间: 2017-12-01
• 期刊: NETWORKS & SPATIAL ECONOMICS
• 影响因子: 2.4
• 作者: Bao, Jie;Xu, Chengcheng;Wang, Wei
• 通讯作者: Wang, Wei

Autonomous Vehicles: Some thoughts on Consumer Engagement

自动驾驶汽车：关于消费者参与的一些想法

• DOI: 10.1049/ic.2015.0065
• 发表时间: 2015
• 作者: Blythe P

Estimating level of service of mid-block bicycle lanes considering mixed traffic flow

• DOI: 10.1016/j.tra.2017.04.031
• 发表时间: 2017-07-01
• 期刊: TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
• 影响因子: 6.4
• 作者: Bai, Lu;Liu, Pan;Li, Zhibin
• 通讯作者: Li, Zhibin

Measurement and analysis of household carbon: The case of a UK city

• DOI: 10.1016/j.apenergy.2015.11.054
• 发表时间: 2016-02
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: D. Allinson;K. Irvine;J. Edmondson;A. Tiwary;G. Hill;Jonathan D. Morris;M. Bell;Z. Davies