Estimating potential reductions in externalities from rail-road substitution in Trans-European freight transport corridors

This paper develops a method for analysing and estimating savings in externalities that could be achieved by substituting truck with rail freight services in a given Trans-European freight transport corridor. The externalities affected include energy consumption, emissions of greenhouse gases, noise, congestion, and traffic incidents/accidents. The European Commission transport policy aims to provide an institutional framework for the medium- to long-term sustainable development of the transport sector. An important aspect of this policy is to stimulating the modal shift from truck to rail freight transport in inland Trans-European corridors.     

振华启东船厂产能升级布局规划

振华启东船厂前身为成立于2007年的道达船厂,针对厂区内各功能模块之间的能力匹配问题凸显、部分设施破损、部分设施严重制约厂区生产能力的问题,通过厂区现状平面布局、工艺流程系统梳理,根据企业发展定位和关键生产要素潜能制定科学合理的规划目标,以生产设施产能分析计算分析为理论支撑,提出一套各区域产能相匹配且适应未来规模化、精益化、规范化、安全化发展的产能升级布局规划方案,达到各功能模块布局合理、生产能力匹配、生产工艺流畅、生产设施规范的目的。针对既有港区的产能升级规划,采用以定量计算为支撑的规划方法,可供类似工程参考与借鉴。     

地铁与市郊铁路过轨运营下列车开行方案研究

在分析地铁与市郊铁路过轨运营特点的基础上,以乘客节省的总旅行时间最大化和地铁运营企业增加的总费用最小化为目标函数,建立了过轨运营下列车开行方案双目标规划模型,并设计了基于模糊数学规划的求解算法.以某两条轨道交通线路为算例,研究过轨运营开行方案并对相关参数进行灵敏度分析.研究结果表明,地铁与市郊铁路过轨运营既可节省乘客总旅行时间又可增加运营企业收益,且过轨客流需求越大、过轨站换乘时间越长、过轨方缴纳的过轨费用越少、过轨列车运营组织方案越合理,过轨运营效果越显著.     

Genetics of traffic assignment models for strategic transport planning

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic assignment models can be described by three genes. The first gene determines the spatial capability (unrestricted, capacity restrained, capacity constrained, and capacity and storage constrained) described by four spatial assumptions (shape of the fundamental diagram, capacity constraints, storage constraints, and turn flow restrictions). The second gene determines the temporal capability (static, semi-dynamic, and dynamic) described by three temporal assumptions (wave speeds, vehicle propagation speeds, and residual traffic transfer). The third gene determines the behavioural capability (all-or-nothing, one shot, and equilibrium) described by two behavioural assumptions (decision-making and travel time consideration). This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature. It further allows for comparing different models in terms of functionality, and paves the way for developing novel traffic assignment models.     

Intermodal transport in freight distribution: a literature review

There has been significant growth in research on intermodal transport in freight distribution since the 1990s. Differentiating itself from previously published literature reviews, this paper evaluates the current state of this research using Systematic Literature Review methodology. The complementary aims are: (a) to identify the research lines developed and to propose a criterion for classifying the literature, and (b) to discuss the empirical evidence that identifies existing interrelationships. The analysis has enabled three main lines of research to be identified. The first research line, basic principles of intermodal transport, groups together works related to the definition of intermodal transport and the results obtained using this transportation system. The second, improvements to the way that intermodal transport systems work, frames elements and variables that impact intermodal transport systems’ logistics efficiency, such as quality of service, information and communication systems, and freight planning and linkages among system operators to provide an adequate service. Finally, the third line, intermodal transport system modelling, identifies the main variables used to optimise these transport systems, the different focuses and approaches used in modelling, and the advantages and disadvantages of each focus. These research lines take in more specific sublines that incorporate articles that develop related research questions. Lastly, the discussion of the content of each of these research sublines enables us to identify gaps in the literature and comment on directions for future research.     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

A planning tool for evaluating vehicles miles travelled and traffic safety forecasts of growth management scenarios: A case study of Baton Rouge and New Orleans

This study describes an adaptable planning tool that examines potential change in vehicle miles travelled (VMT) growth and corresponding traffic safety outcomes in two urbanized areas, Baton Rouge and New Orleans, based on built environment, economic and demographic variables. This model is employed to demonstrate one aspect of the potential benefits of growth management policy implementation aimed at curbing VMT growth, and to establish targets with which to measure the effectiveness of those policies through a forecasting approach. The primary objective of this research is to demonstrate the need to break with current trends in order to achieve future goals, and to identify specific policy targets for fuel prices, population density, and transit service within the two study regions. Models indicate based on medium growth scenarios, Baton Rouge will experience a 9 percent increase in VMTs and New Orleans will experience 10 percent growth. This translates to corresponding increases in crashes, injuries and fatalities. The paper provides forecasts for planners and engineers to consider an alternative future, based on desired goals to reduce VMTs and therefore improve safety outcomes. A constrained-forecast model shows a cap on VMTs and crash rates is achievable through policy that increases fuel prices, population density and annual transit passenger miles per capita at reasonable levels through a growth management approach.     

Situation analysis forecasting: the case of European maritime clusters

Within the literature concerned with aspects of competitiveness, innovation and strategic management of industrial clusters, the body regarding forecasting of strategic management is still nascent. This work aspires to render a contribution within the domain of strategic management forecasting, through the indicative case of European maritime clusters. For this end, a two-tier model is formulated. A quantitative strengths, weaknesses, opportunities, and threats (SWOT) methodology that derives from the fusion of a quondam situation analysis and crosstabs’ theory is generated. This leads to the utilization of the crosstab’s conditional probabilities as transition probabilities; the latter are called to compile the transition matrix of a Markov chain. Through this methodology, and the successive Markov chain’s transition matrices, strategic forecasts are extracted for a devised European maritime cluster case. This work relinquishes a novel application with respect to strategic management forecasting, that provides a dichotomy of practical interpretations and scenarios for quantitative situation analysis. Thus, it may enable effective real-time decision-making for strategic management and/or policy drafting. The situation analysis forecasting model may find applicability in a plethora of practical and theoretical cases, wherein forecasting may be desirable. It may as well pertain to an intrinsic methodology for situation analysis forecasting of maritime clusters.     

燃气蒸汽式弹射内弹道研究

燃气-蒸汽式弹射方式以其结构简单、温度适中、压力输出平稳等优点,被越来越广泛地应用到水下运载器的发射系统中,深入研究燃气-蒸汽式弹射装置的内流场对弹射装置的设计、改进具有重要意义.本文通过采用3种不同仿真模型对燃气-蒸汽式弹射内弹道进行计算,结果表明:对流场进行仿真计算时应同时引入汽化模型和组分输运模型,其仿真结果与实际情况更为相符.汽化模型对流场温度的影响比对压力和速度的影响更为明显.研究成果可为燃气-蒸汽式弹射装置的设计与改进提供理论支撑.