Approaches and Techniques for Modelling CO2 Emissions from Road Transport

Abstract

Transport accounts for around a quarter of CO₂ emissions globally. Transport modelling provides a useful means to explore the dynamics, scale and magnitude of transport-related emissions. This paper explores the modelling tools available for analysing the emissions of CO₂ from transport. Covering a range of techniques from transport microsimulation to global techno-economic models, this review provides insights into the various advantages and shortcomings of these tools. The paper also examines the value of having a broad range of perspectives for analysing emissions from transport. The paper concludes by suggesting that the broad range of models creates a rich environment for exploring a spectrum of policy questions around the emissions from transport, and the potential for combining modelling approaches further enhances the understanding that can be attained.     

Incorporating equity as part of the wider impacts in transport infrastructure assessment: an application of the SUMINI approach

The state of the art in appraisal of transport infrastructure (particularly for developed countries) is moving towards inclusivity of a set of wider impacts than has traditionally been the case. In appraisal frameworks generally Multi-Criteria Analysis (MCA), features as either an alternative to, or complementary with, Cost Benefit Analysis (CBA) particularly when assessing a wider set of distributional and other impacts. In that respect it goes some way towards addressing an identified weakness in conventional CBA. This paper proposes a new method to incorporate the wider impacts into the appraisal framework (SUMINI) based upon a composite indicator and MCA. The method is illustrated for a particular example of the wider set of impacts, i.e. equity, through the ex-post assessment of two large EU transport infrastructure (TEN-T) case studies. The results suggest that SUMINI assesses equity impacts well and the case studies highlight the flexibility of the approach in reflecting different policy or project objectives. The research concludes that this method should not be viewed as being in competition with traditional CBA, but that it could be an easily adopted and complementary approach. The value in the research is in providing a new and significant methodological advance to the historically difficult question of how to evaluate equity and other wider impacts. The research is of strong international significance due to the publication of the TEN-Ts review by the European Commission, as well as the transnational nature of large scale interurban transport schemes, the involvement of national and transnational stakeholder groups in the approval and funding of those schemes, the large numbers of population potentially subject to equity and other wider impacts and the degree of variation in the regional objectives and priorities for transport decision makers.     

Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

How do local actions affect VMT? A critical review of the empirical evidence

In this paper, we present a discussion of the challenges for research on the topic of vehicle miles traveled. We then summarize and critique evidence from the US on the association between 14 distinct factors and vehicle miles traveled. Our results quantify how much vehicle miles traveled can be expected to change in response to changes in policy or land use factors, including residential density and land use mix, as well as specific transport policies and programs such as transit improvements, road pricing, and programs aimed at changing people’s travel choices. Overall, though individual studies differ as to exact effect sizes, it is clear that local-level policymakers can take actions that are likely to affect vehicle miles traveled. However, we highlight gaps in the knowledge base at a time when decision makers at the local level are being increasingly called upon to take action to reduce vehicle miles traveled. Variation in effect size based on local context or interaction with related policies and programs has been left largely unexplored. In addition, experimental research designs that can identify causal direction are rare, and appropriate data that quantifies vehicle miles traveled are often lacking.     

Promoting Cycling for Transport: Research Needs and Challenges

Abstract

Near future travel-time information is one of the most critical factors that travellers consider before making trip decisions. In efforts to provide more reliable future travel-time estimations, transportation engineers have examined various techniques developed in the last three decades. However, there have not been sufficiently systematic and through reviews so far. In order to effectively support various transportation strategies and applications including Intelligent Transportation Systems (ITS), it is necessary to apply appropriate forecasting methods for matching circumstances in a timely manner. This paper conducts a comprehensive review study focusing on literatures, including modern techniques proposed recently, related to travel time and traffic condition predictions that are based on ‘data-driven' approaches. Based on the underlying mechanisms and theoretical principles, different approaches are categorized as parametric (linear regression and time series) and non-parametric approaches (artificial intelligence and pattern searching). Then, the approaches are analysed for their strengths, potential weaknesses, and performances from five main perspectives that are prediction range, accuracy, efficiency, applicability, and robustness.     

World Maritime University

The issue of quality assurance in higher education is at the top of the agenda of universities throughout the world. For the World Maritime University, an institution with highly international staff and student bodies, a complex base of voluntary funding and isolated from any national or European system of education, the problem of defining quality and instituting a quality management system has been a particularly complex and vital task. Our quality standards are the key to our survival.     

The relationship between the built environment and nonwork travel: A case study of Northern California

Many studies have found that residents living in suburban neighborhoods drive more and walk less than their counterparts in traditional neighborhoods. This evidence supports the advocacy of smart growth strategies to alter individuals’ travel behavior. However, the observed differences in travel behavior may be more of a residential choice than a travel choice. Applying the seemingly unrelated regression approach to a sample from Northern California, we explored the relationship between the residential environment and nonwork travel frequencies by auto, transit, and walk/bicycle modes, controlling for residential self-selection. We found that residential preferences and travel attitudes (self-selection) significantly influenced tripmaking by all three modes, and also that neighborhood characteristics (the built environment and its perception) retained a separate influence on behavior after controlling for self-selection. Both preferences/attitudes and the built environment itself played a more prominent role in explaining the variation in non-motorized travel than for auto and transit travel. Taken together, our results suggest that if cities use land use policies to offer options to drive less and use transit and non-motorized modes more, many residents will tend to do so.     

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以英国M42高速公路线圈交通数据为输入,建立预测模型,提出神经网络OD矩阵预测方法. 将预测结果与该公路3a号至7号交叉口间各支路上自动车牌识别装置测得的车辆数据进行对比,验证其有效性. 解决了以下关键问题：利用线圈数据实现OD矩阵预测的可操作性,该类数据的特殊性是否影响模型构建,通过变异数稳定数据转换能否改善模型性能,能否同时进行单个OD预测. 得到基于训练数据平方根代换的最佳计算结果和单个OD预测模型.     

Joint road toll pricing and capacity development in discrete transport network design problem

The paper demonstrates a method to determine road network improvements that also involve the use of a road toll charge, taking the perspective of the government or authority. A general discrete network design problem with a road toll pricing scheme, to minimize the total travel time under a budget constraint, is proposed. This approach is taken in order to determine the appropriate level of road toll pricing whilst simultaneously addressing the need for capacity. The proposed approach is formulated as a bi-level programming problem. The optimal road capacity improvement and toll level scheme is investigated with respect to the available budget levels and toll revenues.