全生命周期成本分析框架下的电动公交充电器部署和车队管理

尽管城市交通电气化快速发展,但电动公交车(EB)车队的系统规划和管理进展却很落后。本研究首先综述了影响新生EB系统的基本问题,包括充电站部署、电池尺寸、公交车调度和生命周期分析。目前,EB系统的规划和运行是按顺序进行的,公交车调度是在公交车队和基础设施部署完毕后进行的,这导致了资源利用率低和资源浪费。对此,本文提出了混合整数规划模型,以尽可能低的生命周期成本(LCC)整合充电站部署和公交车队管理,包括所有权、运营、维护和排放费用,从而缩小了最佳规划和运营之间的差距。进一步引入了分支定价算法,以减少寻找最佳解决方案所需的计算工作量。本研究还通过分析真实案例,与目前的公交运营策略和充电站布局相比,一条公交线路的LCC可显著降低30.4%。提出的研究不仅可以进行生命周期分析,还可以为交通当局和运营商提供可靠的充电站部署及单线和多线服务的公交时刻表,这两者都是未来具有高电气化普及率的交通系统中决策支持的关键要求,本研究也有助于推动可持续交通的发展。

海运数据驱动建模——关键问题、挑战和解决方案

1.Uncertainty in maritime transportation Maritime transportation plays a central role in global logistics systems.Over 80%of international trade is carried out via the maritime transportation network[1],which has received widespread attention from academia and industry.In the shipping network,ports are the vertices where large numbers of activities occur,including cargo loading,unloading,and transshipment.Ships sailing between different ports travel along routes that form the links of the network.Shipping operation studies usually cover ship routing,schedule design,fleet deployment,and network design.

Implications of the EU’s Inclusion of Maritime Transport in the Emissions Trading System for Shipping Companies

1.Introduction Maritime transport is the backbone of international trade.The amount of total international maritime trade in million tonnes loaded was 8408 in 2012 and had increased to 11076 by 2019,for an average annual increase of 3.12%.In early 2020,the world fleet contained 98140 ships of 100 gross tonnes and above with 2.06 million dead weight tonnage of capacity[1].The greenhouse gas(GHG)emissions from shipping activities are not negligible.According to the fourth GHG study commissioned by the International Maritime Organization(IMO),in 2018,global shipping emitted a total of 1056 million tonnes of carbon dioxide(CO_(2)),accounting for around 2.89%of global anthropogenic CO_(2) emissions[2].Due to the international nature of shipping,efforts to control CO_(2) emissions from ships are absent from the Kyoto Protocol and the Paris Agreement.In an attempt to phase out carbon emissions from shipping entirely,the IMO formulated a strategy to cut the total annual GHG emissions from shipping by at least 50%from their 2008 levels by 2050[3];however,no mandatory rules have been promulgated since the release of this strategy.

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact,necessitating the adoption of green logistics practices to mitigate environmental effects.The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis.Operations research provides a means to balance environmental concerns and costs,thereby enhancing the management of logistical activities.This paper presents a comprehensive review of studies integrating operations research into green logistics.A systematic search was conducted in the Web of Science Core Collection database,covering papers published until June 3,2023.Six keywords(green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation)were used to identify relevant papers.The reviewed studies were categorized into five main research directions:Green waste logistics,the impact of costs on green logistics,the green routing problem,green transport network design,and emerging challenges in green logistics.The review concludes by outlining suggestions for further research that combines green logistics and operations research,with particular emphasis on investigating the long-term effects of the pandemic on this field.

Modelling follow up time at a single-lane roundabout

The follow up time is an important parameter for estimating the entry capacity of roundabouts. However, its variability and contributing factors have long been ignored in the literatures. In this study, 171 follow up samples and contributing factors （traffic volume, vehicle position, waiting vehicles behind, vehicle type, and drivers＇ gender） are collected at a roundabout in Pacific Pines, Australia. It is found that the follow up time is indeed significantly affected by traffic volume, waiting vehicles behind, vehicle type, and drivers＇ gender. In order to establish the relationship between the follow up time and its contributing factors, an inverse Gaussian regression model is further developed. This relationship could be applied to estimate the entry capacities by taking into account the variability of follow up samples. According to the model, the traffic volume and vehicle types are the most important contributing factors.

Operation management of green ports and shipping networks: overview and research opportunities

Global ports and maritime shipping networks are important carriers for global supply chain networks, but they are also the main sources of energy consumption and pollution. To limit ship emissions in ports and offshore areas, the International Maritime Organization, as well as some countries, has issued a series of policies. This study highlights the importance and necessity of investigating emergent research problems in the operation management of green ports and maritime shipping networks. Considerable literature related to this topic is reviewed and discussed. Moreover, a comprehensive research framework on green port and shipping operation management is proposed for future research opportunities. The framework mainly comprises four research areas related to emission control and grading policies. This review may provide new ideas to the academia and industry practitioners for improving the performance and efficiency of the operation management of green ports and maritime shipping networks.

On the urban-rural bus transit system with passenger-freight mixed flow

1.Introduction Providing low-cost,accessible transportation systems connecting rural to urban areas is essential for creating a richer rural-urban partnership.Low density rural populations,coupled with low trip frequency and larger geography makes bus-based travel the only reasonable transit option for rural residents to access critical services(e.g.,hospitals,schools,markets,and parks),many of which are largely situated in urban areas.

Replacing urban trucks via ground-air cooperation

The advent of drones is leading to a paradigm shift in courier services,while their large-scale deployment is confined by a limited range.Here,we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles.With this,we propose a ground-air cooperation(GAC)based business model for parcel delivery in an urban environment.As per our case study using real-world data in Beijing,the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost,but also reduce road traffic by 8.6%,leading to an annual social benefit of 6.67 billion USD for Beijing.The proposed model utilizes the currently“wasted or unused”rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers,thus fundamentally addressing the congestion,noise,pollution,and road wear and tear problems caused by trucks,and bringing in immense social benefit.

Communications in Transportation Research:Vision and scope

Transportation is a vibrant,practical and public-oriented field that has undergone rapid and sustained growth.Traditionally,this growth was largely due to urbanization and increased trade and travel between countries and regions across the world.At most research universities worldwide,transportation engineering,as a sub-discipline under civil engineering,mainly analyses relationships among travelers,vehicles,transport infrastructure,and environment,using highly aggregated data(e.g.,origin-destination matrix,traffic volume,speed,density).In recent years,thanks to the rapid advancement in data collection and processing(e.g.,high-resolution individualized real-time data),vehicular technologies(e.g.,connected,automated and electric vehicles),and computational and communication enhancement(e.g.,real time automatic control,cloud and edge computing),transportation engineering keeps expanding its borders,encompassing more and more emerging interdisciplinary components(e.g.,shared mobility,modular vehicles,flying cars,hyperloop,boring).Not surprisingly,more and more researchers with a diverse background are involved in transportation related studies,including but not limited to computer science,mechanical engineering,electrical engineering,control engineering,psychology,urban planning,business,law.This is because transportation is essentially interdependent with a number of other large-scale systems,including electricity grid(e.g.,via electric vehicles),communication networks(e.g.,via connected vehicles),emergency management systems(e.g.,via emergency vehicles),and even societal systems(e.g.,via residential development and house valuation),which overhauls human mobility/travel behaviors,infrastructure systems,and societal awareness.Indeed,our next generation urban mobility system will become a system of multiple systems.We believe that the future transport discipline can be characterized as an inter-disciplinary system of systems with emerging components that are enabled and empowered by huge amount of data that are collected,communicated,and processed in real time and in various forms.This journal Communications in Transportation Research(COMMTR)is focused on the above characteristics.

Paradox of international maritime organization's carbon intensity indicator

The 76th session of the Marine Environment Committee(MEPC 76)of the International Maritime Organization adopted several mandatory measures in June 2021 to reduce carbon emissions from ships.One of the measures is the carbon intensity indicator(CII),which is the carbon emissions per unit transport work for each ship.Several options of CIIs are available and none of them is chosen to be applied yet.We prove that,at least in theory,requiring the attained annual CII of a ship to be less than a reference value,no matter which CII option is applied,may increase its carbon emissions.Therefore,more elaborate models,combined with real data,should be developed to analyze the effectiveness of each CII option and possibly to design a new CII.

Model on empirically calibrating stochastic traffic flow fundamental diagram

This paper addresses two shortcomings of the data-driven stochastic fundamental diagram for freeway traffic.The first shortcoming is related to the least-squares methods which have been widely used in establishing traffic flow fundamental diagrams.We argue that these methods are not suitable to generate the percentile-based stochastic fundamental diagrams,because the results generated by least-squares methods represent weighted sample mean,rather than percentile.The second shortcoming is widespread use of independent modeling methodology for a family of percentile-based fundamental diagrams.Existing methods are inadequate to coordinate the fundamental diagrams in the same family,and consequently,are not in alignment with the basic rules in probability theory and statistics.To address these issues,this paper proposes a holistic modeling framework based on the concept of mean absolute error minimization.The established model is convex,but non-differentiable.To efficiently implement the proposed methodology,we further reformulate this model as a linear programming problem which could be solved by the state-of-the-art solvers.Experimental results using real-world traffic flow data validate the proposed method.

Emerging approaches applied to maritime transport research:Past and future

Maritime transport is the backbone of international trade and globalization.Maritime transport research can be roughly divided into two categories,namely the shipping side and the port side.Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge,while few of them are based on historical data accumulated from practice.In recent years,emerging approaches,which we refer to as machine learning and deep learning techniques in this essay,have been receiving a wider attention to solve practical problems.As a relatively conservative industry,there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector.The objective of this essay is to review the application of emerging approaches to maritime transport research.The main research topics in maritime transport and classic methods developed to solve them are first presented.The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed.Related existing studies are then reviewed according to problem settings,main data sources,and emerging approaches adopted.Challenges and solutions in the process are also discussed from the perspectives of data,model,users,and targets.Finally,promising future research directions are identified.This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.

Bi-level optimization model applications in managing air emissions from ships:A review

Ship air emissions are recognized as one of the key concerns of the maritime industry.Competent authorities have issued various regulations to manage air emissions from ships.Although the authorities are policy makers,the effectiveness of policies is up to the shipping industry who operates the vessels and terminals to fulfill maritime transportation works.Given this characteristic,bi-level optimization model has been widely adopted in studies that optimize policy design or evaluate its effectiveness.The framework of a typical bi-level optimization model for ship emission management problem is given to show the basic structure of similar issues.A series of applications of bi-level optimization model in managing ship emissions is reviewed,including cases of Energy Efficiency Design Index,Emissions Control Area,Market Based Measure,Carbon Intensity Indicator,and Vessel Speed Reduction Incentive Program.We hope this paper can enlighten scholars interested in this area and provide help for them.

Data and Code Disclosure and Sharing Policy of communications in transportation research

1.Introduction Replicability is an essential requirement for scientific publications,as it ensures the high reliability and soundness of new scientific methods and discoveries.Replicability is particularly crucial for current research in Transportation Engineering,as most such research is built on sophisticated computational models and/or empirical/experimental data analyses.Examples include machine learning for demand/traffic state prediction and optimization;statistical and econometric models for traffic safety and travel behavior analysis;complex mathematical models and optimization approaches for transport planning,operation design,and controls;and numeric simulations.Because of the complexity of the mathematical methods and big traffic data,it is challenging to include all technical details(e.g.,all parameter settings of algorithms and simulation setups)in a manuscript.

Three potential benefits of the EU and IMO’s landmark efforts to monitor carbon dioxide emissions from shipping

International shipping accounts for around 2.2%of global carbon dioxide(C0_(2))emissions(Smith et al.,2014).Emissions from international shipping are expected to increase by 50%-250%by 2050,mainly due to the growth of the world maritime trade(Smith et al.,2014).However,efforts to control C0_(2)emissions from the global shipping industry are absent from the Kyoto Protocol and Paris Agreement.Therefore,the International Maritime Organization(IMO)has released an ambitious strategy to cut the total annual greenhouse gas emissions of shipping by at least 50%by 2050,compared with 2008(IMO,2018).