Transport Reviews最新文献

Car dependency leads to a variety of societal problems and challenges, not least environmental ones. It is thus not only relevant but also critical to better understand the determinants of car ownership and use. Among those contributing factors, the role of subjective factors is often acknowledged to be important, yet not well understood. We conduct a systematic review of the literature regarding the effect of such motives on car ownership and usage. Based on the commonalities found in the reviewed articles, we identify and describe the five most relevant subjective factors in detail: (i) instrumental motives and autonomy, (ii) affective motives, (iii) symbolic motives, (iv) social norms, and (v) environmental motives. We synthesise these findings in a car ownership/use motives model, discuss implications for public policy and outline directions for future research.

Driver fatigue is a major cause of road crashes. However, there is limited knowledge regarding the potential consequences of driver fatigue in taxi (conventional and app-based), ride-hailing, and ridesharing services. Driver fatigue is likely to be significantly exacerbated in this population due to the multi-task characteristics of their jobs; thus, conducting a comprehensive study on driver fatigue in these transportation sectors is of utmost importance. This systematic review summarises the current state of knowledge about the causes and consequences of driver fatigue. We also suggested some potential control mechanisms for driver fatigue in taxi and ride-hailing services along a fatigue risk trajectory. We included studies published prior to September 2022 in three databases (Web of Science, Scopus, and PubMed) using a predefined search strategy. Eligible studies were critically appraised using the Joanna Briggs Institute (JBI) critical appraisal checklists. A total of 18 studies met our eligibility criteria as scoped from the 414 initially identified studies. Eight contributing factors to driver fatigue were revealed including long working hours, short rest breaks, limited driving experience, job demand, poor sleep, algorithmic management, traffic congestion, and additional workload. Furthermore, our review identified risk factors for driver fatigue in taxi and ride-hailing services, including road safety, work pressure and driver’s health, optimism bias, job precariousness, and lack of additional benefits. Findings to date suggest that driver fatigue in taxi and ride-hailing industries is as serious as, or more serious than, in other transportation sectors. Understanding the working conditions of these drivers is critical to establish effective policies and practices for reducing crash-related driver fatigue.

Transport and mobility systems need to be transformed to meet climate change goals and reduce negative environmental and social effects. Despite EU policies having targeted such problems for more than three decades, transitions have been slow and geographically uneven. For effective change to happen, transport and mobility research needs fresh perspectives and better integration of knowledge from the Social Sciences and Humanities. Based on a Horizon Scanning approach, which allowed for a great deal of openness and variety in scholarly viewpoints, this paper presents a novel research agenda consisting of 8 themes and 100 research questions that may contribute to achieving environmentally sustainable mobility transitions within Europe. This research agenda highlights the need to not only support technological solutions for low-carbon mobility, but the importance of transformative policies that include new processes of knowledge production, civic participation and epistemic justice. We contend that the agenda points to the need for further research on the dynamics of science-society interactions.

The view that drivers have from the road can be enjoyable or disturbing, stressful or relaxing, distracting or fatiguing. Road planning guidelines balance aesthetical and safety considerations but are rarely grounded on empirical evidence. This paper reviews evidence on the effects of the roadside visual environment on the wellbeing and behaviour of drivers, focusing on natural and built elements external to the road, i.e. excluding road geometry, design, conditions, and users. Standardised information was extracted from 50 studies. These studies have used experiments involving participants watching videos or driving a simulator or instrumented vehicle, usually with unrepresentative samples (mostly males, young age groups, and students). Most evidence is related to the driving task (e.g. distraction, fatigue), not to wider aspects of driver wellbeing (e.g. stress recovery), and to safety issues, not aesthetical ones. There is increased evidence for monotonous views (linked to fatigue), roadside vegetation (linked mainly to a reduction of stress and risky driving behaviours, but depending on the characteristics of the vegetation) and advertisements (linked to distraction, but depending on advertisement type and other variables). A few studies have looked at other elements of the built environment (memorials, drones, wind turbines, utility poles), with mixed evidence on distraction and safety behaviour. The links between continued exposure to certain types of views and car commuter stress have not been studied. There is little evidence for developing countries or differences by gender, visual impairment, trip purpose, and type of vehicle.

The potential to encourage physical activity through the use of active transport modes, particularly walking and cycling, is of interest to public health and planning professionals alike. The way elements of urban form, such as density, destination accessibility, design, distance and diversity can either promote or discourage active transport use, is a well-developed area of research. While this research body has been examined previously, urban form is often conceptualised in varying ways, resulting in review recommendations that are difficult to operationalise in subsequent urban and transport planning research. This review takes a fresh perspective on the research by undertaking a narrative review of 104 quality assessed articles examining the impact of elements of urban form on walking and cycling. Urban form is conceptualised using the well-known D-variable framework. We found that the impact of urban form on active transport was expressed in 293 measurements, and that 95.5% of these measurements were positively correlated with the use of walking and/or cycling for transport. We conclude that differences in the built environment are related to differences in the use of physically active modes – active travel is practised in environments where distances are shorter, the distribution of uses are mixed, and infrastructure, including transit, is tailored and accessible. Furthermore, while the D variables framework remains effective for conceptualising this link, this review reveals ways that the framework to be used in a more comprehensive, specific and practical way.

Climate hazards have only fairly recently been acknowledged as key risk factors for airports. While there is a growing body of research examining specific climate change impacts, there is only limited work that combines this literature with overall climate risk. This paper seeks to address this gap in the literature by investigating and synthesising findings from studies relating to historical airport sensitivity to climate hazards and offering insights on the overall climate risk for the global airport system. With airports increasingly needing to become more “climate-resilient” due to projected changes in global climate, airport planners and decision-makers face challenges in terms of identifying key priority areas for resilience planning and investment. The findings of the paper provide insights into these challenges by examining best-applied practices and current levels of vulnerability. The paper supports the wider inclusion of climate risks as a key factor in airports’ planning and operational processes. This will require transforming current management cultures to enhance an airport's operational ability to respond to climate events efficiently and recover quickly in the event of a disruption.

Despite the clear theoretical advantages of activity-based models of travel behaviour relative to trip-based models, adoption of such models in planning practice has been slow. This editorial discusses some reasons underlying this fact, including “locking into” outmoded model structures and software and challenges in translating research advances into practice. It argues for more widespread adoption of an activity-scheduling approach to the problem and identifies a number of key areas requiring new research in order to improve the operational capabilities of these models.

Estimation of bicycling volumes is essential for the strategic implementation of infrastructure and related transport elements and policies. Link-level volume estimation models (models that estimate volumes on individual street segments) allow for understanding variation in bicycling volumes across an entire network at higher spatial resolution than area-level models. Such models assist transport planners to efficiently monitor network usage, to identify opportunities to enhance safety and to evaluate the impact of policy and infrastructure interventions. However, given the sparsity and scarcity of bicycling data as compared to its motorised counterparts, link-level bicycling volume estimation literature is relatively limited. This paper conducts a scoping review of link-level bicycling volume estimation methods by implementing systematic search strategies across relevant databases, thereby identifying appropriate studies for the review. The review resulted in some interesting findings. Among all the methods implemented, direct demand modelling was the predominant one. Not a single study implemented multiple modelling approaches in the same study area, thereby not allowing for comparison of these approaches. Most studies were conducted in the United States. It was also observed that there exists a lot of heterogeneity in the reporting of basic study characteristics and validation results, sometimes to the extent of not reporting these at all. The study presents the different types of data used in modelling (count, travel survey, GPS data) along with an array of popular explanatory variables that can inform future studies about data collection and variable selection for modelling. The study discusses the strengths and limitations of different methods and finally presents recommendations for future research.

Predicting traffic states has gained more attention because of its practical significance. However, the existing literature lacks a critical review regarding how to address the spatiotemporal correlation in the ML-based traffic state prediction models from a traffic-oriented perspective. Therefore, this study aims to comprehensively and critically review the spatiotemporal correlation modelling (STCM) approaches adopted for developing ML-based traffic state prediction models and provide future research directions based on traffic-oriented characteristics and ML techniques. Concretely, we investigate the neural network-based traffic state prediction models and characterise the STCM of these models by a proposed systematic review framework including three components: (i) spatial feature representation that demonstrates how the spatial information regarding road network is formulated, (ii) temporal feature representation that illustrates a variety of approaches to extract the temporal features, and (iii) model structure analyses the model layout to address the spatial correlations and temporal correlations simultaneously. Finally, several open challenges regarding incorporating traffic-oriented characteristics such as signal effects with ML techniques are put up with future research directions provided and discussed.