Transport Reviews最新文献

This literature review is the first to explore the predictors of the ownership, mode choice, and use of private motorcycles for utilitarian travel. Existing literature reviews on motorcycles only focused on the adverse impacts of motorcycle transport. A total of 45 articles in English published up to 2022 from quantitative and qualitative studies were reviewed to identify socioeconomic and built environment predictors of motorcycle travel which can be affected by planning or policy interventions. Motorcycle ownership level of a country is explained by average income, population density, and urbanisation level; that of a province or city by average income; and that of a household by the numbers of adults and workers, car ownership level, income as well as the population density and road density at the residential location. The choice of individuals to use a motorcycle rather than other modes is predicted by income and age as well as the advantages of time and cost savings of motorcycles over other modes. The amount of motorcycle use of households or individuals is associated with the number of household members, car ownership level, the age of the principal user, and income. Supply of public transport should focus on areas prone to widespread motorcycle ownership to both slow down the growth in motorcycle ownership and pre-empt that in car ownership as both types of motorised vehicles bring environmental and public health harms. More research is needed for further understanding of the relationships between motorcycle travel, the built environment, and public transport supply.

Road users experience mobility impacts when a train occupies a highway-rail grade (level) crossing. Research has shown that the cost of reduced mobility exceeds safety costs, yet there is little consistency in the integration of mobility-related criteria into approaches for prioritising crossings for grade separation. A synthesis of findings from a review of literature and practice demonstrated the importance of mobility impacts at blocked crossings, identified and compared mobility-related decision criteria and actionable thresholds used within prioritisation approaches to rank crossings for grade separation, and revealed methods to quantify and monetise delay at blocked crossings. The review identified the need for the joint application of traffic microsimulation and intelligent transportation systems to quantify road user delay at blocked crossings. Such work should consider network-level effects, account for the severe consequences of delay for certain road users (e.g. emergency responders), and develop methods for monetising delay impacts associated with different road users. Moreover, a knowledge gap persists in establishing the interrelationship between road user delay at blocked crossings, risky behaviour, and safety impacts. Finally, further work is required to establish and calibrate thresholds for mobility-related criteria within prioritisation approaches used to rank crossings for all types of improvements, including grade separation.

The promotion of urban mobility by integrating people-and-goods transportation has attracted increasing attention in recent years. Within this framework, diversified forms such as co-modality, freight on transit, and crowdshipping have been proposed, piloted or implemented. The success of the implementation and market penetration depends on not only the novelties of the concept but also the planning and operational efficiency. Thus, a comprehensive review focusing on the operation of integrated people-and-goods transportation systems and associated critical decisions and subproblems is performed. Different practical forms in which people and goods are transported in an integrated manner are identified. The critical decisions associated with each form and subproblem are discussed, along with corresponding models and solution approaches. Notably, because integrated transportation systems are in the early exploration stage at present, new forms are expected to emerge. Therefore, this paper proposes a general framework to realise the planning and operation of new forms in the future. The decisions and subproblems identified from existing forms are fed to the proposed general framework to identify two key research opportunities: to improve or extend existing research and to conduct pioneering research to fill the gaps in the frameworks for operating potential forms of integrated people-and-goods transportation.

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.

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.

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.

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.