权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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）

基本信息

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

项目摘要

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汽车使用量的增加造成了严重的环境和社会问题。在英国，城市交通系统面临着类似的问题，但通常规模较小，速度也要慢得多。然而，西方城市体系的物质、监管和社会基础设施存在着强大的内在惰性，这使得解决这一问题具有挑战性。用于个人机动性和货运的低碳车队有可能有助于减少城市交通对气候的影响，并改善当地交通和空气质量状况。然而，这种潜力的程度仍不清楚。在对车队服务的需求以及组织车队运营的最有效方式方面，仍存在大量不确定性，特别是在电动汽车方面，因为与电网的互动成为一个关键问题。与此同时，在普及资讯科技的推动下，营运城市货运及车队服务的一系列新商业模式正不断涌现。在此背景下，物流转型计划的整体目标是为多用途低碳车队提供综合规划及部署策略，并促进城市基础设施的发展，并设计营运商业模式，以确保经济可行性及环境效益。这一目标将通过解决4个关键研究挑战来实现：1)城市地区低碳交通规划的交通需求和网络建模工具已经被现实世界城市交通的实践创新所超越，需要与时俱进。特别是，城市机动性和货运模型之间需要更好地结合，在需求方面(例如，为了用送货上门取代购物旅行)和在运营方面(例如，考虑共享共同充电基础设施的电动客运和货运车辆)。进一步改善的地方包括把路边空间表述为影响车队停放和装载业务的稀缺和有限的资源，以及更好地描述车队服务客户的异质性(对于开发需求灵活性是必要的)；2)城市车队运营的新商业模式，特别是那些以“需求响应”模式运营和利用需求灵活性的业务模式，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。这在电动车队运营中尤其具有挑战性，因为车队服务的消费模式(货运和个人机动性)需要适应电动汽车充电业务，而此时依赖时间的电价和电网排放系数较低。3)大规模部署电动车队将对商业车队优化运营的联网基础设施的智能管理构成挑战，但在很大程度上没有得到充分研究。智能交通基础设施优化交通管理以及充电基础设施和智能充电算法优化环境和经济效益的要求也是如此，而商业车队的环境和经济效益尚未得到详细研究。4)为了使规模投资流入低碳交通，还需要新一代政策评估工具，能够处理联网的城市基础设施(交通、电力和IT)之间的相互依存关系。这类工具不仅必须考虑到技术和职能上的相互依存关系，而且还必须考虑到多个机构利益攸关方的存在，以及影响不同利益攸关方在不同时间范围内的成本和收益流动的重大不确定性。需要整合孤立的举措，以扩展现有的评估技术，例如通过整合实物期权理论的想法。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
期刊：
影响因子：
0
作者：
Blythe P
通讯作者：
Blythe P

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;
通讯作者：
D. Allinson;K. Irvine;J. Edmondson;A. Tiwary;G. Hill;Jonathan D. Morris;M. Bell;Z. Davies;

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