Transport Reviews最新文献

The increasing uptake of electric vehicles (EVs) has raised concerns about the impact a large fleet could have on electricity markets and distribution grids alike. Charging strategies have emerged as a means to provide flexibility, especially to electricity distribution grids, by controlling the EV charging process. This paper presents a typification of charging strategies and introduces a conceptual framework for appraising their flexibility in distribution grids. This is underpinned by data collected through an aggregative systematic literature review. The framework is derived from an exploratory qualitative content analysis of the sampled data and encompasses four flexibility dimensions: time, duration, quantity, and location of charging. Structural elements of a charging strategy are also explored, complementing the framework. Finally, the paper also presents a quantitative data analysis assessing the level of flexibility provided by each charging strategy. Results show that flexibility dimensions are not equally exploited, direct control strategies do not strictly outperform other control alternatives and innovative charging structures are yet to thrive for flexibility supply to increase. These findings contribute to better-informed, evidence-based policy interventions.

Identifying and assessing the likelihood and consequences of maritime accidents has been a key focus of research within the maritime industry. However, conventional methods utilised for maritime risk assessment have been dominated by a few methodologies each of which have recognised weaknesses. Given the growing attention that supervised machine learning and big data applications for safety assessments have been receiving in other disciplines, a comprehensive review of the academic literature on this topic in the maritime domain has been conducted. The review encapsulates the prediction of accident occurrence, accident severity, ship detentions and ship collision risk. In particular, the purpose, methods, datasets and features of such studies are compared to better understand how such an approach can be applied in practice and its relative merits. Several key challenges within these themes are also identified, such as the availability and representativeness of the datasets and methodological challenges associated with transparency, model development and results evaluation. Whilst focused within the maritime domain, many of these findings are equally relevant to other transportation topics. This work, therefore, highlights both novel applications for applying these techniques to maritime safety and key challenges that warrant further research in order to strengthen this methodological approach.

It is believed that shared transport services (STSs) can reduce transport poverty and social exclusion. This paper proposes a definition of “social acceptability” and “social acceptance” and examines whether vulnerable groups accept STSs. The notions “acceptability” and “acceptance” were distinguished and four necessary conditions, especially for vulnerable groups, or the 4As were identified: “availability”, “accessibility”, “affordability”, and “attractability”. In the context of STSs, “social acceptability” is defined as the degree to which an individual intends to use a STS before experiencing it in everyday travel based on the expected availability, accessibility, affordability, and attractability of the service, while “social acceptance” also incorporates the use of a STS after experiencing it in everyday travel based on a minimum level of perceived availability, accessibility, affordability, and attractability. This paper further reviews the scientific literature in transport research regarding the “acceptability” or “acceptance” of STSs by vulnerable groups. While several studies include socio-economic and demographic variables (e.g. age, gender) to explain the “acceptability” of STSs, only a few studies specifically focus on vulnerable groups. More research on the “social acceptance” of STSs, especially shared scooters, ride-sharing, and apps and Mobility as a Service (MaaS), by vulnerable groups is needed.

Governments worldwide are investing in innovative transport technologies to foster their development and widespread adoptions. Since accurate predictions are essential for evaluating public policies, great efforts have been devoted to forecast the potential demand and adoption times of these innovations. However, this proves to be challenging, and it often fails to deliver accurate predictions. Learning a lesson to guide future work is critical but difficult because forecast figures depend on modelling methods and assumptions, and exhibit a great variability in methodologies, data and contexts. This paper provides a critical review of the models and methods employed in the literature to forecast the demand for electric vehicles (EVs), with a focus on the methods for incorporating choice behaviour into diffusion modelling. The review complements and extends previous works in three ways: (1) it focuses specifically on the ways in which fuel type choice has been incorporated into diffusion models or vice-versa; (2) it includes a discussion on forecast accuracy, contrasting the predictions with the actual figures available and estimating an average root mean square error and (3) it compares models and methods in terms of their strengths and limitations, and their implications in forecasting accuracy. In doing that, it also contributes discussing the literature published between 2019 and 2021. The analysis shows that EV demand estimation requires solving the non-trivial issue of jointly modelling the factors that induce diffusion in a social network and the instrumental and psychological elements that might favour household adoption considering the available alternatives. Mixed models that integrate disaggregate micro-simulation tools to capture social interaction and discrete choice models for individual behaviour appear as an interesting approach, but like almost all methods analysed failed to deliver satisfactory results or accurate predictions even when using sophisticated modelling techniques. Further improvement in various components is still needed, in particular in the input data, which regardless of the method used, is key to the accuracy of any forecasting exercise.

Recent technological and methodological advances have led to the possibility of a wider range of data being incorporated into travel choice models. In particular, physiological data such as eye-tracking information, skin conductance, heart rate recordings and electroencephalogram (EEG) have emerged as promising sources of information that could be used to gain insights into the decision-making process as well as the decision-maker's state of mind. However, research on methodologies to utilise these data sources and to integrate them with mobility data for advancing state-of-the-art travel behaviour models is still very limited. In this paper, we discuss the key benefits of using these emerging sources of physiological data, review applications of different types of physiological data and highlight their strengths and weaknesses. Particular attention is paid to two different generic frameworks for integrating these types of data into econometric choice models of travel behaviour. The first framework involves using physiological sensor data as indicators of latent variables while in the second framework, they are used as exogenous variables. We identify the research gaps and outline the directions for future methodological and applied research required to better utilise the physiological data for travel choice models.

The advent of autonomous vehicles (AV) is expected to significantly impact the built environment in the long-term. However, the mechanism through which these effects would occur is not known. This study aims to develop conceptual frameworks in the form of causal loop diagrams to enhance understanding through a systematic scoping review of the literature. The review process followed the PRISMA framework and 82 eligible studies were sourced from the Scopus and Web of Science databases. Data were extracted for six attributes of the built environment (parking, density, land use diversity, destination accessibility, urban sprawl and street design). Both qualitative/speculative and quantitative findings are presented stratified by AV types (i.e. shared-autonomous vehicle and private autonomous vehicles), and geographical contexts (i.e. citywide, suburbs and central business district). The findings show that the long-term effects of AVs on the built environment would not be uniformly distributed across the city and vary by AV types. Built environment effects would occur through changes in accessibility, the redistributive demand for parking spaces and other mechanisms. The study provides a knowledge repository and identifies gaps in knowledge for researchers and practitioners interested in the long-term effects of AVs on the built environment.

The impacts of shared e-scooters on modal shifts have received increased attention in recent years. This study provides a review of the literature for modal shifts in the US and other countries. The profile of shared e-scooter users is rather similar to that of station-based and free-floating bikeshare programs. The empirical data reveal that people use shared e-scooters in place of cars at substantial rates, especially in many US cities, which suggests that in many locations shared e-scooters may be a good strategy for reducing car dependence. The use of shared e-scooters as a complement to public transit varies highly by city, highlighting how technology, regulations, and incentives may be needed in some cities to ensure modal integration and harvest the potential societal benefits from the introduction of shared e-scooters.

Knowledge about how implemented policy instruments have performed is important for designing effective and efficient policy instruments that contribute to reductions of greenhouse gas emissions. This paper carries out a meta-evaluation of ex-post evaluations of climate policy instruments in the freight transport sector. By analysing the outcomes and quality of evaluations, the aim is to identify whether estimated effects of policy instruments can be compared between evaluations and if the results are appropriate to use for evidence-based decision making. To analyse these aspects, commonly applied evaluation criteria are assessed and classified according to an assessment scale. We confirm that few ex-post evaluations are carried out and that there is a gap between evaluation theory and how ex-post policy evaluations are performed in practice, where evaluation criteria recommended in policy evaluation guidelines are found to often be neglected in evaluations. The result is a lack of systematic climate policy evaluation which hinders reliable conclusions about the effect of policy instruments. There is a need for more systematic monitoring and evaluation of implemented policy instruments and we suggest that evidence-based decision making can be improved by adjusting current policy evaluation guidelines and by introducing an evaluation obligation.

Recently, a new shared micromobility service has become popular in cities. The service is supplied by a new vehicle, the e-scooter, which is equipped with a dockless security system and electric power assistance. The relatively unregulated proliferation of these systems driven by the private sector has resulted in numerous research questions about their repercussions. This paper reviews scientific publications as well as evaluation reports and other technical documents from around the world to provide insights about these issues. In particular, we focus on mobility, consumer perception and environment. Based on this review, we observe several knowledge needs in different directions: deeper comprehension of use patterns, their function in the whole transport system, and appropriate policies, designs and operations for competitive and sustainable shared e-scooter services.