Artificial Intelligence (AI) is widely recognised as the revolution of this era; however, its adoption is uneven across industries and countries, with a wide gap between Europe and non-EU countries. The paper analyses the state of AI adoption, focusing on transport workforce, and aims at proposing common actions, research, and policies to create cooperation rather than competition. To this end, a literature review was conducted, followed by two field surveys on the needs and actions of stakeholders and students to manage the transition to increasing digitalisation and automation accelerated by AI. The field surveys involved focus groups, interviews and archaeological ethnography and were based on a worldwide selection of stakeholders from different transport sectors, and a sample of undergraduate, master's, and PhD students. Textual analysis was used for data analysis. Over 900 stakeholders from 45 countries, including all levels of workforce, and nearly 600 students were involved. Needs, issues and concrete actions emerged, proposing specific measures to reduce the risks generated by AI. The main recommendations refer to: a) labour market regulation that requires broader inclusion of social dialogue; b) integration of the educational approach at different school and work levels, to prepare people to think independently and be creative in order to prevent shocks in adapting to AI evolution, fostering collaboration instead of competition. Field experiments are proposed to test policies, making the transition to AI use effective. The EU-US comparison in light of the proposed policies highlights the barriers created by different economic contexts, values and market regulations.

The transportation industry is experiencing a significant transformation due to the increasing adoption of automation and digitalization. As a result, professionals in the industry are increasingly concerned about how these changes will impact the workforce, and they are seeking effective strategies to address any potential challenges. This paper presents the findings of a study that sought to identify barriers and opportunities related to the impact of automation and digitalization on the transportation industry workforce. The study involved a collective intelligence and consensus-building process through structured discussions among stakeholders and partners/experts during a sequence of thematic area group convocations and focus groups, as part of the WE-TRANSFORM project. Over 70 suggestions were recorded during 25 meetings, which were then transcribed and reported through rapporteurs of thematic groups and focus groups run within thematic areas. The process included a two-round Delphi-type of survey to formulate a narrower list of the most significant actions. The study's results highlight the importance of soft skill development, such as communication, collaboration, and problem-solving, in the face of automation and digitalization in the transport industry. The findings suggest that a more significant focus on developing these skills can help address potential challenges.

Within transport systems, train stations cover a primary role as places where access to different modes of transport must be realised effectively, providing a valuable opportunity to make rail services, public transport, and soft mobility more attractive. This research seeks to shed some light on how Italian travellers perceive the quality of train stations, and to identify priorities for action in relation to design, building, and operation that might help revitalise their attractiveness. The methodology involved designing a questionnaire capable of identifying significant correlations between attitudinal and behavioural variables via an exploratory factor analysis, reaching around 400 respondents through a snowball sampling plan. The factor “sociality and daily life” showed the importance that people place on the vitality of urban places. Travellers also consider other factors, like the overall service quality, the cleanliness and safety of a train station, the walkability of connections within the node, and the possibility of reaching the station by bicycle. The profiling of respondents using a cluster analysis based on latent factors points to specific policies, showing how actions targeting stations can have positive effects on the use of rail transport and on the propensity towards intermodality and sustainable mobility. A safe, “living” place can mitigate the risk of social degradation, while promoting walking and cycling.

Digitalisation and automation are producing and will produce profound changes for transport workforce. This research aims to develop a policy agenda addressing challenges, impacts, and effects that digitalisation and automation in transport may have on the labour force. Policies are defined through a participatory approach, using collective intelligence to co-create an evidence-based and action-oriented agenda that provides for a just technology transition in the transport sector.The methodology involves four stages: 1. defining the framework of the ‘living hub’ and selecting the stakeholders involved; 2. selecting the methods adopted within the ‘living hub’ to co-create knowledge; 3. policy agenda creation; and 4. policy agenda validation.The ‘living hub’—where the co-creation process takes place—gathered more than 600 stakeholders from 450 organisations coming from 40 countries. Out of them, 322 stakeholders from all the transport modes (social partners, companies, and representatives of ministries and political institutions) and 73 workers have been involved in focus groups and creative thinking. Overall, 395 persons have participated to the creation of 11 policies eventually validated by a panel of 43 stakeholders coming from 10 EU countries, UK, USA, and South Korea (companies, labour lawyer and economists, psychologists, R&D, training agencies, trade unions, and politicians). The policies are divided in four thematic areas: 1. Public Governance and Regulation (4 policies); 2: Industrial Governance (2 policies); 3. Training and Reskilling (1 integrated policy); 4. Minimisation of Workforce Exclusion and Exploitation (4 policies). A comparison between EU versus US implementation is eventually discussed.

Automatic passenger counting (APC) systems are an important asset for public transport operators, allowing them to optimise networks by monitoring lines’ utilisation. However, the cost of these systems is high and the development of alternative devices, cheaper than the most widely used optical systems, seems promising. This paper aims at understanding the influence of local factors on the accuracy of a Wi-Fi APC system by analysing error patterns in a real-world scenario. The APC system was installed on a bus operating regularly within the public transport network and, in the meantime, ground truth data were collected through manual counting. The collected data were then analysed to calculate accuracy and, finally, multilevel modelling was used to identify error patterns due to local factors. This study challenges traditional assumptions, revealing that factors like high pedestrian traffic or intense vehicular movement around the bus have minimal impact on accuracy, if effective received signal strength indicator filters are used. Instead, the number of passengers within the bus affects Wi-Fi systems significantly, especially when the bus is carrying more than 10 passengers, which leads to undercounting due to signal obstruction. This research lays the foundation for strategic error correction to improve accuracy in real-world scenarios.

Urban transport planning and the integration of various mobility options have become increasingly complex, necessitating a thorough understanding of user mobility patterns and their diverse needs. This paper focuses on benchmarking different Automatic Passenger Counting (APC) technologies, which play a key role in Mobility as a Service (MaaS) systems. APC systems provide valuable data for analysing mobility patterns and informing decisions about resource allocation. Our study presents a comprehensive data collection and benchmark analysis of APC solutions. The literature review emphasises the significance of passenger counting for transport companies and discusses various existing APC technologies, such as pressure sensors, wireless sensors, optical infrared sensors (IR), and video image technology. Real-world applications of APC systems are examined, highlighting experimental results and their potential for improving accuracy. The methodology outlines the data collection process, which involved identifying APC companies, conducting interviews with companies and customers, and administering an ad hoc survey to gather specific information about APC systems. The collected data were used to establish criteria and key performance indicators (KPIs) for the benchmarking analysis. The benchmarking analysis compares APC devices and companies based on ten criteria: technology, accuracy, environment, coverage, interface, interference, robustness (for devices), price, pricing model, and system integration (for companies). KPIs were developed to measure performance and make comparison easier. The results of the benchmarking analysis offer insights into the costs and accuracy of different APC systems, enabling informed decision making regarding system selection and implementation. The findings fill a research gap and provide valuable information for transport companies and policy makers, and we offer a comprehensive analysis of APC systems, highlighting their strengths, weaknesses, and business strategies. The paper concludes by discussing limitations and suggesting future research directions for APC technologies.

Counting people is an important part of people-centric applications, and the increase in the number of IoT devices has allowed the collection of huge amounts of data to facilitate people counting. The present study seeks to provide a novel, low-cost, automatic people-counting system for the use at bus stops, featuring a sniffing device that can capture Wi-Fi probe requests, and overcoming the problem of Media Access Control (MAC) randomization using deep learning. To make manual data collection considerably easier, a “People Counter” app was designed to collect ground truth data in order to train the model with higher accuracy. A user-friendly, operating system-independent dashboard was created to display the most relevant metrics. A two-step methodological approach was followed comprising device choice and data collection; data analysis and algorithm development. For the data analysis, three different approaches were tested, and among these a deep-learning approach using Convolutional Recurrent Neural Network (CRNN) with Long Short-term Memory (LSTM) architecture produced the best results. The optimal deep learning model predicted the number of people at the stop with a mean absolute error of ~ 1.2 persons, which can be considered a good preliminary result, considering that the experiment was done in a very complex open environment. People-counting systems at bus stops can support better bus scheduling, improve the boarding and alighting time of passengers, and aid the planning of integrated multi-modal transport system networks.

Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems.

Automation in transport and digitalization will affect both transport users and its workforce. Focusing on the latter, this paper aims at analyzing barriers, gaps, opportunities, and success and failure factors of transport automation on the labor force, through the perceptions and contributions of employees and employers, as well as of stakeholders from the private, public, and private–public sectors. In a nutshell, the study aims to understand workforce-related barriers and facilitators associated with the implementation of automation. This has been achieved through input derived from the organization of five focus groups, one poll and one extensive questionnaire survey administered to the participants of the 2nd WE-TRANSFORM EU H2020 funded project Workshop, and to project partners’ stakeholder contacts. The analysis of the results indicated that the transport sector’s automation has been evolving at a different pace per sector. An interesting conclusion is that the challenges do not concern all categories among the workforce in the same way. Challenges related to loss of jobs and related repercussions are bound to affect groups within the workforce, which may be constrained by regulatory age limits, or vulnerable, if in part-time employment without access to retraining, which may be the case of workforce members near retirement age or of women limited due to family obligations to part-time employment. The study’s limitations are related to the size of the sample and how representative it is of all stakeholders in the transport sector, including policymakers, regulators, and unions. Future directions should focus on exploring the long-term impacts of automation on the labor force and identify strategies to mitigate the negative effects on vulnerable groups.

This paper aims at assessing the operational strategy of FlixBus during the COVID-19, in relation to its main competitors and to customers’ perceptions. We analysed FlixBus strategy based on its supply, and designed an online survey administered to European residents to understand their preferences towards leisure travelling during COVID-19. A sample of 437 respondents was collected and the Exploratory Factor Analysis followed by clustering was used to segment the customers’ perceptions. Results show that willingness to travel, change in modal choice and destination are the factors featuring most the five clusters. An analysis of the Flixbus routes shows an overall dynamic demand-response strategy and a flexible approach closely related to their competitors’ operations at the same time. FlixBus saw the pandemic as a new opportunity, entering in new markets and offering FlixDeal.

Ecological behaviour and its impact on the environment are subjects of public concern and understanding individual behavioural measures to induce sustainable lifestyles is of extreme importance for policy makers to assess and promote sustainable mobility. To this end, a questionnaire with highly reliable items, evaluations of determinants and accurate measurements of ecological behaviour is a precondition for understanding the levers of behavioural change. This paper aims at an understanding of whether the dichotomous Rasch model provides a legitimate measurement of General Ecological Behaviour (GEB) using a 26-item questionnaire as a valid tool to assess the pro-environment behaviour of a large sample of users. A web questionnaire was administered using the snowball sampling plan in the Piedmont region (Italy), with a sample of 4473 respondents. The results suggest that using the dichotomous Rasch model, the proposed questionnaire is able to effectively measure the pro-environment behaviour of travellers. Unidimensionality, the perfect level of item reliability of 1, the very high item separation of 34.22, the absence of larger differential item functions, and the local independence are all good indicators of a valid model. This research shows how a good, validated, and reliable measurement of ecological behaviour would support public bodies in planning environment-focused transport policies thanks to the knowledge of which variables determine pro-environment behaviour. In addition, the proposed approach also allows us to measure the efficacy of the adopted policies.

The introduction of shared autonomous vehicles into the transport system is suggested to bring significant impacts on traffic conditions, road safety and emissions, as well as overall reshaping travel behaviour. Compared with a private autonomous vehicle, a shared automated vehicle (SAV) is associated with different willingness-to-adopt and willingness-to-pay characteristics. An important aspect of future SAV adoption is the presence of other passengers in the SAV—often people unknown to the cotravellers. This study presents a cross-country exploration of user preferences and WTP calculations regarding mode choice between a private non-autonomous vehicle, and private and shared autonomous vehicles. To explore user preferences, the study launched a survey in seven European countries, including a stated-preference experiment of user choices. To model and quantify the effect of travel mode attributes and socio-demographic characteristics, the study employs a mixed logit model. The model results were the basis for calculating willingness-to-pay values for all countries and travel modes, and provide insight into the significant heterogeneous, gender-wise effect of cotravellers in the choice to use an SAV. The study results highlight the importance of analysis of the effect of SAV attributes and shared-ride conditions on the future acceptance and adoption rates of such services.

Travel surveys and other traditional methods have been used for collecting mobility data since 1930s. Those surveys have been so far the most reliable approaches to understand people mobility patterns, but their high costs do not allow a high frequency collection to obtain continuously updated data. To overcome these limitations, digitalization opens the gate for renewed travel data collection and analysis methods. To this extent, this paper aims to present a review of the various smartphone applications, classifying them according to three different purposes: 1) Travel Data Collection and Analysis; 2) Travel Surveys; and 3) Promotion of Sustainable Mobility. 81 apps were retrieved and analysed in detail and evaluated according to their features and the methods used for data collection. A subsequent SWOT analysis has then been performed to understand the strengths, weaknesses, opportunities and threats of using the smartphone applications to understand mobility patterns. Finally, recommendations for future research are put forward.

Autonomous vehicles are anticipated to play an important role on future mobility offering encouraging solutions to today's transport problems. However, concerns of the public, which can affect the AVs' uptake, are yet to be addressed. This study presents relevant findings of an online survey in eight European countries. First, 1639 responses were collected in Spring 2020 on people's commute, preferred transport mode, willingness to use AVs and demographic details. Data was analyzed for the entire dataset and for vulnerable road users in particular. Results re-confirm the long-lasting discourse on the importance of safety on the acceptance of AVs. Spearman correlations show that age, gender, education level and number of household members have an impact on how people may be using or allowing their children to use the technology, e.g., with or without the presence of a human supervisor in the vehicle. Results on vulnerable road users show the same trend. The elderly would travel in AVs with the presence of a human supervisor. People with disabilities have the same proclivity, however their reactions were more conservative. Next to safety, reliability, affordability, cost, driving pleasure and household size may also impact the uptake of AVs and shall be considered when designing relevant policies.

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.

The paper aims to define the new operational requirements and procedures to allow the Gruppo Torinese Trasporti (Torino public transport company) to implement mandatory validation without negative impacts on both the company and the users. To this end, a four-step methodology has been put forward: a) choice of the reference route; b) sampling plan and data collection; c) data analysis design and model specification and d) definition and analysis of future scenarios. Attained results show an increase of commercial speed from 1.5% to 14.5%, and an increase of the proportion of total dwell time on total trip time from 1 to 13 points. The most unfavourable situation for the company would be banning people from boarding the bus/tram through any door (the case today). Indeed, it would require an increase of trips in the morning peak hour in order to maintain the same time interval at bus stops. However, the impact on passengers' travel time is non negligible since total vehicle trip time shows a rise of up to 10 minutes during weekends shifts (from 62 minutes in the current situation to 72 minutes for the worst case scenario). Thus, the present system limits the outcomes negatively for the users in terms of waiting time. However, a change could lead to such positive consequences as fuller passenger cooperation to validate tickets/passes and a more ordered boarding, thus reducing fraud and improving the image of the company.

Transport systems are undergoing a change of paradigm that focuses on resource-sharing and collaboration of multiple and diverse stakeholders. This paper aims to present a state-of-the-art on the main research issues of multi-stakeholder collaboration in urban transport and address the main contributions of the Special Issue on Collaboration and Urban Transport to the field. To that end, it seems necessary to identify and address the complexity of the relations of the stakeholders in the field, beyond the traditional classification of private and public stakeholders. A functional classification of urban stakeholders related to the different land uses is proposed a refer to space users and space organizers, each with several sub-categories. Furthermore, the collaboration among those stakeholders can take different forms and can be developed at different levels: transactional, informational and decisional. Thus, the main research topics regarding multi-stakeholders’ collaboration are defined as: partnerships, resource sharing, resource pooling and Mobility-as-a-Service (MaaS) systems. A set of papers in this special issue focus on Urban Consolidation Centres (UCCs), partnerships in transport under a general perspective, multi-stakeholder cooperation and its barriers, collaborative decision-making, traffic prediction and urban congestion. In the papers, which deal with the field of multi-stakeholder collaboration in urban transport, there is a predominance on the use of surveys, but also a focus on data-driven techniques. As a result, this special issue contributes not only to the theoretical aspects, but adds value to technical and methodological issues.

1Saint-Etienne School of Mines, France 2Sorbonne University, France 3Polytechnic University of Turin, Italy 4Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece 5Dept of Automobile Engineering, Vilnius Gediminas Technical University, Lithuania #Guest Editor of the Special Issue on Collaboration and Urban Transport of the Research Journal TRANSPORT ##Managing Editor of the Research Journal TRANSPORT

This paper aims to review variables and behavioural theories originating from social and environmental psychology as applied to transport research, to better understand decision-making mechanisms, information processing and modal choice. The first section provides an overview of the main psycho-social variables which explain behaviour and, notably, pro-environment behaviour. The analysis shows the relations among variables, highlighting some potential cause-effect mechanism or, at least, the influence that such variables can have on behaviour. Furthermore, the strengths and weaknesses of using psycho-social variables to predict travel behaviour are discussed. Such analysis feeds the section related to the behavioural theories. These are reviewed with a focus on potential application to transport sector, showing the would-be added value of introducing a socio-psychological approach in the current vision, focused on stochastic models based on maximisation of personal utility. To this end, attention is paid to the data collection and analysis, basic for any models and even more challenging to collect when they deal with personal characteristics of individuals. Finally, the concept of attitude and intention is discussed, opening the doors between disciplines to overcome the attitude-behaviour gap.