Anger while driving negatively affects performance in both manual and supervised driving scenarios. While numerous studies have focused on detecting anger behind the wheel, research on its regulation remains limited and does not extend to automated driving. Moreover, previous work used alone emotion regulation strategies while combined methods work best in emotion modulation. This study explores a multimodal approach to regulate anger in autonomous driving by integrating auditory and visual modalities. Specifically, we combined low-arousal music, unconscious biofeedback via green ambient lighting, and participants’ photos. In our experiment, 36 participants were split into three groups. Anger was induced in two groups with only one benefiting from the regulation. The third is a control group with neutral induction and no regulation. Our findings highlight that the multimodal intervention effectively improves subjective and physiological emotional responses without specific impact on driving performances. Additionally, participants reported high acceptance and perceived relaxation, highlighting the potential of this approach for enhancing driver well-being in autonomous vehicles.

Despite being a niche market, cargo bikes have significant potential to revolutionize urban mobility. Nevertheless, their adoption for personal use remains limited, with usage patterns concentrated among highly educated urban men aged 30–45 in German-speaking countries. This study aimed to explore potential user groups for cargo bikes and respective mobility services by applying an explorative segmentation approach. The research focused on understanding potential cargo bike users’ socio-demographic, socio-economic, and socio-spatial characteristics, along with their current mobility behaviors and attitudes. Therefore, an original survey of 1,511 participants was conducted across Austria, capturing various variables: travel behavior, mobility preferences, attitudes, and perceived barriers to cargo bike usage. The methodological approach involved CHAID and TwoStep cluster analysis. It resulted in the identification of six clusters with moderate quality, of which three can be interpreted as potential cargo bike user groups: (1) cargo bike-affine urban dwellers, (2) bike-affine best agers, and (3) younger multimodal urban dwellers with neutral-to-positive attitudes, offering potential to prevent car dependency. These groups show favourable conditions based on current mobility behavior and attitudes for cargo bike uptake, each group in different stages of the Stage Model of Self-Regulated Behavioural Change [Bamberg, Journal of Environmental Psychology 34, 2013]. Following behavioral change theories, encouraging cargo bike use will require a multi-level strategy addressing the specific needs of each stage, combining general awareness campaigns with targeted interventions. This study enhances the understanding of user segmentation and supports effective promotion of sustainable mobility]. Following behavioral change theories, encouraging cargo bike use will require a multi-level strategy addressing the specific needs of each stage, combining general awareness campaigns with targeted interventions. This study enhances the understanding of user segmentation and supports effective promotion of sustainable mobility.

Abstract The limitations of field testing capabilities present a significant challenge in evaluating the performance level of autonomous vehicle (AV) functionalities. Mixed reality (MR) with digital twin can facilitate performing tests because of reduction in costs and safety issues. However, one of the recent problems in the application of MR for evaluation purposes is the achievable low immersion level. This paper proposes a solution to the problem, and presents a use case through the implementation of a speed planner. The speed planner is implemented in a real test AV, together with an MR-based environment, and field tests are performed in urban context. This paper presents the effectiveness of the speed planner through simulations and AV tests, specifically in regard to the safe adaptation of the AV motion to the surrounding vehicles. Moreover, the test environment and the implementation process are presented in details, such as the test AV setup, the software structure, and the prediction process for achieving immersive MR visualization.

This study presents a comprehensive evaluation of the real challenges related to the calibration of Network Macroscopic Fundamental Diagrams (NMFDs), with a focus on two key aspects: observability and stability. Observability addresses the estimation of NMFDs using multimodal traffic data, including loop detectors (LDD), floating car data (FCD), and public transport data (PTD), and examines how data quality, spatial coverage, and aggregation intervals influence NMFD characteristics. Stability explores the temporal consistency of NMFDs through longitudinal analysis, investigating their sensitivity to loading and unloading phases, weekday versus weekend dynamics, and recurring demand patterns. Using case studies in Athens and Lyon demonstrates that fusing data sources improves the accuracy of multimodal NMFD estimation, while separating network loading/unloading phases and different days enables reliable NMFD estimation. Building on these insights, we propose a structured NMFD calibration pipeline that integrates bias correction, data fusion, and temporal clustering. This framework supports reproducible, data-driven NMFD estimation and offers practical guidance for urban traffic monitoring and multimodal network management.

As the adoption of active safety systems in road vehicles increases, there is a need for quantitative tools to evaluate the performance of Advanced Driver Assistance Systems (ADAS) aimed at collision mitigation. This paper introduces a Virtual Forward Simulation (VFS) tool that assesses ADAS performance by estimating Injury Risk for occupants involved in conflict situations. Established Injury Risk curves for impacts involving passenger cars are derived as a function of the vehicle’s velocity change during the impact (ΔV). The paper provides a thorough validation of the proposed tool by comparing ΔV from real-world accidents (or crash tests) with those obtained by simulations using a reduced-order impact model that underpins the VFS. Additionally, the ΔV from simulations are compared with another VFS assessment tool available from the literature. The validation of the proposed VFS tool is further supported by comparing real and simulated deformed shapes of the vehicles. Results indicate that the proposed tool reliably replicates real accidents or crash tests, both quantitatively (in terms of ΔV) and qualitatively (in terms of deformations). The impact simulation algorithms implemented in the validated VFS tool provide an effective trade-off between high accuracy typical of finite element analysis and low computational costs typical of momentum-based models.

The penetration of shared mobility services is spreading worldwide, leading to the restructuring of the existing transportation system. Mobility as a Service (MaaS) is presumed to answer the challenge of utilizing several transport modes including emerging services, where travel planning, booking options, payment methods, and ticketing solutions are integrated into one single application. MaaS promises several benefits, but studies on motivational mechanism behind the technology adoption are still lacking and usually rely on structural equation modelling approach. In this study, we apply latent class cluster analysis using attitudinal variables as indicators to examine the motivational factors in adopting MaaS, revealing the heterogeneity in preferences among college students. Based on the results, five clusters are identified, namely MaaS neutral, MaaS enthusiast, MaaS opponent, MaaS avoider, MaaS lover. College students with high environmental consciousness and seeking variety are more enthusiastic about MaaS, while others coming from higher-income households are likely to adopt MaaS. The negative precautions regarding MaaS should be decreased by respecting data privacy and providing easy-to-use MaaS applications, while the perceived safety risks should be eliminated by providing emergency features. Finally, some policy recommendations are drawn based on the identified clusters of university students.

This study investigates the impact of double-parking during pickup and delivery activities in urban areas for an innovative extended cargo bike concept consisting of a bicycle and up to five trailers. Microscopic traffic flow simulations in VISSIM explore various scenarios, considering different stopping positions, stopping durations, traffic volumes, and vehicle configurations. To evaluate their impact on urban traffic, we compare the extended cargo bike to a conventional delivery van. The results show that one-way operation leads to lower delay times while oncoming traffic significantly increases delay times. The extended cargo bike outperforms conventional delivery vans when stopping on the bicycle lane which is possible because of its smaller width. Stopping times exceeding 10 min lead to deteriorating levels of service of road traffic and reduced travel speeds. We validated the simulation results with real world observations, finding similar results. This study emphasizes the need for efficient urban delivery solutions to minimize traffic disruptions and create more livable cities. With its adaptability and potential for multiple trailers, the extended cargo bike holds a promise for last-mile delivery improvements, contributing to sustainable and congestion-free urban logistics.

It seems hard now to ignore uncertainty over what the future holds. Such uncertainty poses significant challenges for assessing and appraising policy and investment decisions. Correspondingly scenario planning has become the subject of increasing interest internationally as a means to explore uncertainty and in turn support more robust decision making. In this paper we bring together scenario planning alongside more traditional national planning to support more robust planning for sustainable mobility for small and medium-sized Norwegian cities. The paper describes the mapping of key uncertainties influencing mobility, the development of four explorative plausible future scenarios—and in turn how these scenarios can be used as part of transport analysis and appraisal to inform more robust decision making. The purpose of the paper is twofold. First, it provides case study insight into scenario development and planning. Second, it takes the reader on a ‘learning by doing’ journey through the process of what was done and why, with a sharing of the lessons learned by the study team in the interest of helping others who wish to embark on their own journeys into this important developmental area of transport planning.

Digital Twins (DTs) are gaining interest among urban planners and city authorities, but a notable gap exists in fully understanding and effectively applying them to complex and multifaceted transport planning processes. This review is an in-depth exploration of the promising concept of DTs and its implications for transport planning—its current applications and conceptualisations within the domain, focusing on their distinction from traditional transport models and identifying critical areas for further DT development. The review was conducted per PRISMA guidelines to identify 136 relevant studies from 2000 to 2024. Over three-quarters of the articles were published after 2021, indicating a growing focus on digital twins in transport planning. A classical five-component definition of DT, consisting of physical, digital, data model, service and connection components, was adapted and used to synthesise key components of the DTs presented in the studies. Simulations were the most common “data model,” and 31% of studies do not present insight into what “services” could be derived from proposed DTs, indicating a top-down approach to DT development. A prevalent misapplication of the DT concept was observed where most studies conflated basic digital models or shadows as DTs without fully meeting the criteria of real-time data integration or bi-directional interaction with physical systems. Interestingly, mature DTs with real-time data and bidirectional interaction were associated with smaller geographical systems and shorter time-range predictions. This suggested that maturity in transport planning DTs depends not only on technological capabilities but also on the spatiotemporal dimension of the system. The review concludes by suggesting directions towards a revised DT maturity, highlighting the level of maturity needed for “usable DTs”, and advocating the need for a bottom-up approach to DT development rather than the top-down one currently adopted by most studies.

Shared on-demand mobility services, also known as microtransit, have become a major mobility provider around the world, yet this has predominantly taken place within urban areas. In areas with lower population density and poor quality public transport, such services could substantially improve accessibility. In early 2023, a regional microtransit pilot was carried out in the Ljubljana Urban Region in Slovenia. To assess the preferences towards such a service, a stated preference experiment is carried out among pilot participants, comparing car, public transport and microtransit for their daily commute. The obtained data is modelled using a Panel mixed logit model, with random parameters modelled as normally or log-normally distributed. Additionally, we also model for potential nesting effects among the alternatives. The results show participants perceive microtransit as a viable alternative, with public transport commuters finding it particularly attractive, whereas car commuters see it on par with the car. Parking price and a guaranteed parking spot tended to be key factors for decision-making. Simulating different policies, we conclude that combining subsidising microtransit and higher parking prices is the most effective strategy for achieving a modal shift primarily from car to microtransit while not affecting public transport as much.