轨道交通自动售检票系统国产化现状与展望

论述近10年来AFC系统的国产化取得的显著成绩和存在的问题:从起初基本依赖进口,到目前国产化率达到70%,但是现场终端设备(主要包括自动售票机及自动检票机)中的几个关键模块仍以进口为主,国产化遇到瓶颈。对AFC系统的现状进行论述,并提出对国产化的展望。     

网络化城市轨道交通AFC系统票务流转分析

为更好地完成车票管理工作、提高票务管理效率,根据管理方式将车票分为回收类和非回收类,分析这两类轨道交通专用车票的应用特点.在研究网络化运营环境下车票流转的运行模式的基础上,以中央清结算中心、线路中心和车站三级管理流程为例,详细分析回收类车票和非回收类车票的流转全过程,为今后网络化运营下AFC系统的车票流通管理提供理论参...     

城市轨道交通票款清分方法的研究

针对＂无障碍换乘＂模式网络化运营条件下的票款清分问题,提出一种基于广义出行代价的多路径选择概率模型,考虑不同出行时段、换乘代价对乘客路径选择的影响。在票款清分比例确定时,除考虑各线路运营里程外,还考虑分配换乘里程的因素,从而使得各线路的收益分配更趋合理。     

How the cap limit for food-crop-based biofuels may affect France’s stakeholders by 2030? A range-based multi-actor multi-criteria analysis

From 2012 to 2016, the long-term signal in the biofuel market changed almost once per year, leading to a drastic decrease in investments and contributing to multiple production unit closures in France and around Europe. The European Commission proposed a new renewable energy directive that includes a 3.8% cap limit on the contribution of food-crop biofuels by 2030. Given the role of biofuels in green growth, the bioeconomy, and renewable energy incorporation targets, how will this measure affect the stakeholders by 2030? Will it lead to, contribute to, or hamper their sustainability criteria? This paper aims to contribute to this debate by studying the case of France. To this end, our methodology—the range-based multi-actor multi-criteria analysis—aims to (1) explicitly consider the stakeholder groups and their sustainability criteria; (2) evaluate and compare how the cap limit will affect these sustainability criteria whether or not advanced biofuels are deployed by 2030; (3) capture the uncertainty of the context evolution and biofuel capacity to fulfil the stakeholders’ sustainability criteria by means of a Monte Carlo. The results suggest that the cap limit is a double-edged sword for the stakeholders and their sustainability criteria. Shifting towards advanced biofuels while limiting the food-crop biofuels is the better alternative for most of the stakeholders. Nevertheless, given biofuel policy instability and the lost confidence of investors, such a shift may not occur by 2030. In such a case, this paper demonstrates that the cap limit may highly and negatively affect the stakeholders and their sustainability targets, whereas fostering French food-crop biofuel production at its full capacity level constitutes a better alternative. As no alternative is suited to all actors simultaneously, this paper also studies the strengths and weaknesses of these alternatives from each stakeholder groups’ perspective.     

The desulphurisation of shipping: Past,present and the future under a global cap

As from January 2020, the International Maritime Organization (IMO) is implementing a global 0.5% limit on the sulphur content of fuel, commonly known as the global sulphur cap. This limit is the latest policy in the efforts to reduce sulphur emissions from shipping, following the designation of emission control areas (ECAs) and other regional regulations. In this paper, a literature review is conducted of academic studies that have dealt with issues relating to the reduction of maritime sulphur emissions. Various recurring research themes are identified, spanning the areas of operations research, maritime economics and transport policy. The effects and implications of available compliance options are then analyzed from the perspectives of ship operators, shippers and consumers. Using lessons learned from the enforcement of ECA regulations, this is followed by an appraisal of various potential issues related to the enforcement of these new global regulations. It is found that a homogeneous enforcement regime is required to ensure a level playing field amongst ship operators and that the global sulphur cap may lead to serious market distortion, due to the potential short term rise of fuel prices. The paper concludes with a set of recommendations for future research on sulphur emissions from shipping in the aftermath of the global cap and, looking forward, to its relationship to the IMO strategy on the reduction of greenhouse gas (GHG) shipping emissions.     

Key beliefs and attitudes for sea-level rise policy

This article reviews key measures of public opinion on sea-level rise (SLR): beliefs, attitudes, issue prioritization, and policy support. To do so, we first assess the influence of SLR beliefs and attitudes on issue prioritization and policy support using state-level data. Then, we compare the state findings to other surveys conducted in a hot spot of rising coastal waters, the U.S. Mid-Atlantic, to better understand the landscape of public opinion. Our findings indicate that, as in studies of climate change public opinion, belief certainty that SLR is happening and attitudes about its consequences significantly influence issue prioritization and policy support. Compared to climate change, SLR demonstrates less salience, but is similarly a low public priority. Nevertheless, the public supports governmental policies that address the issue, preferring strategies that discourage new construction in high risk areas and employ “soft” protection through natural barriers. Among the least popular approaches are those that implement hard barriers to defend against encroaching seas. Communication programs and public consultation by governments can benefit from the use of survey data to support evidence-based decision-making.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.     

Are technology myths stalling aviation climate policy?

Emissions from aviation will continue to increase in the future, in contradiction of global climate policy objectives. Yet, airlines and airline organisations suggest that aviation will become climatically sustainable. This paper investigates this paradox by reviewing fuel-efficiency gains since the 1960s in comparison to aviation growth, and by linking these results to technology discourses, based on a two-tiered approach tracing technology-focused discourses over 20 years (1994–2013). Findings indicate that a wide range of solutions to growing emissions from aviation have been presented by industry, hyped in global media, and subsequently vanished to be replaced by new technology discourses. Redundant discourses often linger in the public domain, where they continue to be associated with industry aspirations of ‘sustainable aviation’ and ‘zero-emission flight’. The paper highlights and discusses a number of technology discourses that constitute ‘technology myths’, and the role these ‘myths’ may be playing in the enduring but flawed promise of sustainable aviation. We conclude that technology myths require policy-makers to interpret and take into account technical uncertainty, which may result in inaction that continues to delay much needed progress in climate policy for aviation.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

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.