Nowadays, urban centers face the challenge to upgrade life quality by reducing traffic congestion, air pollution emissions and road casualties. Transport charging policies applied in cities at a quick pace are a key tool for sustainable mobility. However, public acceptability is an important precondition to be adopted for such policies. In this context, the scope of this paper is the investigation of the public acceptability of environmentally linked urban charging policies in Greek urban centers. Specifically, the paper’s objective is the investigation of Greek drivers’ acceptability of the implementation of a congestion charging policy and a parking charging policy with the charging being adjusted according to the Euro class and technology of the vehicle in favor of less polluting cars. A structural equation model (SEM) was developed using data from a questionnaire survey which provided a sample encompassing 733 respondent drivers from three main urban centers of Greece: Athens, Thessaloniki and Volos. Several statistical relationships were detected and quantified correlating the two examined urban environmental charging policies with five latent unobserved variables. Based on the results, public acceptability of environmental congestion charging policies and the public acceptability of environmental parking charging policies were found to be positively correlated with each other, meaning that a driver who supports one environmentally linked transport charging policy is more likely to support the other one as well. The environmental sensitivity and high commuting profiles of drivers are influential factors that positively affect the acceptability of the two examined transport charging policies’ implementation in Greek urban centers. Analysis has also shown that younger, higher-educated respondents and females are more likely to accept the environmental charging policies under consideration.

Driving behavior is the most critical factor influencing road transportation sustainability by shaping its three greatest pillars: (i) road safety, (ii) driving economy, and (iii) environmental impact. This paper aims to systematically review the role of driving behavior on these pillars simultaneously, as expressed by road crashes, fuel consumption, and vehicular emissions. Driving behavior factors affecting both road safety and driving efficiency, along with the modeling approaches and methodological issues, are explored, focusing on driving studies under real-world conditions. A two-stage PRISMA approach was developed to shortlist the most relevant studies for systematic review out of 1,120 initial studies, with each stage focused on safety and eco-driving behavior studies. The review extracted 78 metrics covering driver-related and external road-, traffic-, and weather-related parameters, which were found to significantly affect driving efficiency and road crash occurrence. The main driver behavior factors found to affect both road crashes and fuel consumption/emissions are related to average and instantaneous driving speed and acceleration, harsh acceleration and deceleration events, driving volatility, vehicular jerk, and idling during driving. Two main categories of data collection approaches were obtained: controlled experiments and large-scale naturalistic experiments, while the varied modeling techniques were applied across different scales of analysis: microscopic and mesoscopic scales. A discussion of the knowledge gaps in the potential of an integrated methodological framework of driving behavior assessment in the context of road safety, economy, and environmental sustainability is then provided, followed by the relevant conclusions.

The i-DREAMS project introduced the concept of a ‘Safety Tolerance Zone,’ i.e., a context-aware safety envelope designed to assist drivers. Using an ecosystem of sensors, i-DREAMS technology monitors factors that determine driving task complexity and coping capacity and calculates risk levels. Real-time and post-trip interventions are tailored to keep drivers from unsafe driving. Real-time interventions are provided via in-vehicle display, while post-trip interventions are delivered via a smartphone app with gamification provisions. This study focuses on the effectiveness (i.e., outcome evaluation) of real-time and post-trip interventions that involve 4 phases, including the baseline measurement phase. The paper presents a comparative analysis using the data collected from car drivers from three countries: Belgium (n = 48), the UK (n = 49), and Germany (n = 25). Overall, drivers showed a reduction in events per 100 km after exposure to the i-DREAMS technology. So, there was an improved safety outcome. However, differences were found between the countries analyzed. The highest number of events per 100 km was noted for UK drivers, with the reduction pattern consistent across 4 phases. The interventions were more promising for ‘road sharing’ and ‘speeding’ events for Belgian and German drivers. Driver-level analysis revealed that two-thirds of drivers in each country showed a consistent decrease in events/100 km.

This paper aims to provide a detailed overview of driving behaviour indicators during the implementation of the H2020 project i-DREAMS interventions in Greece. To fulfil this aim, a robust methodology utilizing a k-means clustering approach was employed to detect meaningful driving behaviour patterns within a dataset comprising 11,731 trips from 56 Greek car drivers. This exploratory analysis was complemented by an unsupervised pattern recognition algorithm, which aimed at identifying clusters based on safe or dangerous driving behaviour of the users. The assessment of driving behaviour encompassed indicators such as speeding events, harsh braking and accelerating events, and distraction events (phone in hand). This analysis provides valuable insights into the risky driving behaviour among the i-DREAMS naturalistic driving experiment Phases in Greece.

We recommend providing state-supported financial incentives and benefits for vehicle insurance policies using telematics. To achieve this policy recommendation, we propose the following: (1) Provision for financial incentives and benefits by the state for vehicle insurance policies using telematics across the European Union member states; (2) Conduct comprehensive social cost–benefit analysis (CBA) to assess policy feasibility, either at a European Union or at a national level; (3) Advocate for European Union-level policy implementation supported by a centralized fund to promote telematics via insurance policies, aligning with the EU Green Deal and Vision Zero targets; and (4) Showcase benefits of interdisciplinary collaboration involving experts from transportation engineering, economics, psychology, and law for policy design and evaluation.

This study aims to investigate the factors and their complex interrelationships that influence key indicators of driving sustainability, with a focus on both safety and efficiency. A Structural Equation Model is developed using multi-source data to capture how unsustainable driving practices are shaped by the following three latent variables: driving volatility, route characteristics, and weather conditions. These latent variables are modeled using multiple observed indicators derived from smartphone sensors, OpenStreetMap, NASA satellite data, and weather sensors. The dataset consists of approximately 33,000 trips recorded via smartphone sensors in Athens, Greece, between March and May 2024. The model examines fuel consumption as a measure of efficiency, and four Surrogate Safety Measures (SSMs), speeding events, mobile phone use, and harsh acceleration and braking as safety-related variables. SEM enables estimation of both direct and indirect pathways, including how latent factors interact and how safer and more efficient driving behaviors may reinforce each other. This approach supports the generation of targeted insights to inform behavioral interventions for promoting safer and more efficient urban mobility.