ABSTRACT The ability to travel freely and independently to participate in society is essential for an individual’s wellbeing and quality of life. People with disabilities are often unable to access public transport due to barriers in the urban environment and public transport systems. This review provides a comprehensive examination of the barriers faced by people with disabilities who are independent public transport riders. It includes: (a) barriers encountered from the perspective of the whole journey chain, from origin to destination for public transport trips; (b) investigates the commonalities and differences in the perceived barriers for different disability types; (c) discusses the impacts and shortcomings of universal design; and (d) limitations to data collection procedures. Evidence from the review of the existing literature has shown that there is limited research on travel behaviour relating to the whole public transport journey chain for people with disabilities. The consequences of not being able to make a journey due to the barriers in public transport inhibit participation in society that can have a range of negative impacts on the wellbeing of people with disabilities. This paper highlights the importance of the “bottom-up” approach for policy implementation and engagement with the disability community. It concludes with directions for further work and recommendations for practitioners.

An archaic principle continues to dominate the design and use of streets in the U.S.: by prioritising the amount and flow of car traffic streets can handle, cities across the country accept the dir...

ABSTRACT The conventional form of traffic interaction undergoes a notable change with the integration of automated driving systems as a new road user, into the public roads. This may be more challenging during the transition phase, while manual-driven vehicles are still on the road, and the road infrastructure is not fully ready for merging such vehicles into the traffic patterns. Therefore, developing a robust interaction method is crucial to ensure the safety of those users interacting with automated driving systems and to ensure the efficiency of these systems on the road. For this purpose, the interaction of automated driving systems with pedestrians, as one of the most vulnerable road user groups, is investigated in this paper. Previous studies have shown the necessity for a comprehensive understanding of pedestrian behaviours and intentions, their responses to different stimuli on the road, the factors influencing their decisions during the interaction, and various external communication techniques among road users. As a result, a wide range of factors related to the communication environment, pedestrian characteristics, and existing communication methods have been found to be significant in the decision-making process of pedestrians.

ABSTRACT Flight delays are a significant problem for society as they evenly impair airlines, transport companies, air traffic controllers, facility managers, and passengers. Studying prior flight data is an essential activity for every player involved in the air transportation system. Besides, developing accurate prediction models for flight delays is a crucial component of the decision-making process. Prescribing actions to solve on-going delays is an even challenging task due to the air transportation system complexity. In this regard, this paper presents a thorough literature review of data science techniques used for investigating flight delays. This work proposes a taxonomy and compiles the initiatives used to address the flight delay studies. It also offers a systematic literature review that describes the trends of the field and methods to analyse the applicability of newly proposed methods.

ABSTRACT With the evolving demand for sustainable mobility, adequate charging infrastructure for electric vehicles (EVs) has been growing steadily and wireless power transfer (WPT) technology has been seen as an efficient alternative for EV charging while maintaining seamless traffic flow. This paper reviews the modelling challenges in terms of both static (plug-in) charging (SC) and wireless charging (WC) facilities in a transportation network in terms of system integration, focusing on the evolution of the WPT technology. The first part of the paper provides an overview of all the major progress and achievements made by different research organisations in the area of WPT technology for EV charging. These technologies are ranked based on two indices, namely technological readiness level and system readiness level. The optimal location of WC facilities comes with more design and operational issues than conventional static charging facilities. However, they are similar in terms of the infrastructure modelling approach to locate these charging facilities, as the overall goal is to maximise the network flow and minimise the overall system cost. The second part of the paper assesses different modelling approaches used to analyse the network and locate the charging infrastructure for static and WC facilities. The economic feasibility of the technology is an important consideration for successful system integration as well as the overall performance of the system. As such, this paper also provides a synopsis of different socio-economic studies related to the WC infrastructure allocation problem. Finally, future research directions in this field are discussed based on the knowledge gaps identified from the existing literature.

ABSTRACT Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.

ABSTRACT Despite their growing application and worldwide diffusion, the transformative potential of experiments aimed at achieving “streets for people” rather than “streets for traffic” remains largely under researched. There is little to no comparative assessment of already existing experiments, and no critical reflection on their specific added value for systemic change. Building from a literature review and discussion, this paper aims to fill this gap by addressing the following questions: Which types of city street experiments have been undertaken in the pursuit of the vision of “streets for people” instead of “streets for traffic”? What are their backgrounds, main characteristics, and reported impacts? And perhaps most importantly: How can these city street experiments trigger systemic change in urban mobility? These elements are detailed per experiment type, in order of ascending functional complexity: the re-marking of streets, the re-purposing of car parking, the re-purposing of sections of streets, and the re-purposing of entire streets. Illustrative examples from practice include intersection repairs, parklets, the pavement to plazas programme, play streets, ciclovias and open streets. The reviewed literature documents positive impacts on physical activity, active transportation, safety and social interaction and capital, and more mixed impacts on business activity. While street experiments aim to create fundamentally different arrangements of urban mobility, their potential as triggers of a greater systemic change is unclear. This paper uses the defining characteristics of “transition experiments” – a concept derived from the field of transition studies – to develop and illustrate a framework to assess this transformative potential. In the conclusions, the review and assessment framework are used to sketch a research and policy agenda for this increasingly topical phenomenon.

ABSTRACTThis study estimated the external cost of air pollution from shipping by means of a meta-regression analysis, which has not been made before. Three pollutants, which were included in most of the primary studies, were considered: nitrogen oxides (NOx), sulphur dioxides (SO2) and particulate matters with a diameter of max 2.5 micrometres (PM2.5). All primary studies included damages of health and a majority added impacts on agriculture and estimated the cost of air pollutants by transferring cost estimates from studies on costs of air emissions from transports in Europe. Different regression models and estimators were used and robust results were found of statistically significant emission elasticities of below one, i.e. total external costs increase by less than 1% when emissions increase by 1%. There was a small variation between the pollutants, with the highest elasticity for PM2.5 and lowest for NOx. Calculations of the marginal external cost of the pollutants showed the same pattern, with this cost being approximately six times higher for PM2.5 than for the other pollutants. Common to all pollutants was that the marginal external cost decreases when emission increases. Another robust result was a significant increase in the cost of studies published in journals compared with other publication outlets. These findings point out some caution when transferring constant external unit cost of air pollutant from shipping, which is much applied in the literature, and the cost functions estimated in this study could thus provide a complementary transfer mechanism.

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.