High-capacity Connected and Autonomous Vehicles (CAVs) are expected to be extensively utilized by on-demand services. This paper aims to assess the impacts of large-scale autonomous on-demand mobility services on traffic, environment, and road safety, under various service specifications using microsimulation. To that end, an urban on-demand shuttle service was designed, optimized, based on a variation of the Dial-a-Ride optimization problem (DARP), and implemented in the road network of the city of Athens to serve different portions of demand with various capacity specifications. It was then investigated through forty mobility scenarios, with differences in policy implementation and market penetration rate of CAVs. Findings show that it led to improved network level traffic conditions, as delays decreased, and that traffic impacts evolve with fleet capacity and served demand. Furthermore, the number of conflicts decreased and the environmental conditions significantly improved, with CAVs in the network, while the traveled distance increased.

The present study aims to investigate the benefits of eco-driving in urban areas and on highways through an experiment conducted in a driving simulator. Within a group of 39 participants aged 18–30, multiple driving scenarios were conducted, both without and with eco-driving guides, to assess the impact of eco-driving behavior on environmental sustainability and safety outcomes. Data on pollutant emissions, including carbon dioxide (CO₂), carbon monoxide (CO), and nitrogen oxides (NO_x), as well as crash probability, were collected during the experiment. The relationships between driving behavior and pollutant emissions were estimated using linear regression models, while binary logistic regression models were employed to assess crash probability. The analysis revealed that eco-driving led to a significant reduction in pollutant emissions, with CO₂ emissions decreasing by 1.42%, CO by 98.2%, and NO_x by 20.7% across both urban and highway environments, with a more substantial impact in urban settings due to lower average speeds and smoother driving patterns. Furthermore, eco-driving reduced crash probability by 90.0%, with urban areas exhibiting an 86.8% higher crash likelihood compared to highways due to higher traffic density and more complex driving conditions. These findings highlight the dual benefit of eco-driving in reducing environmental impact and improving road safety. This study supports the integration of eco-driving techniques into transportation policies and driver education programs to foster sustainable and safer driving practices.

Assessing safety using traffic simulation is becoming increasingly feasible with advancements in methodological frameworks and tools, emphasizing the critical importance of accuracy and reliability. This study aims to bridge the gap between simulation models and real-world safety observations, contributing to the advancement of more robust safety assessment methodologies. It presents a comprehensive comparative analysis of traffic safety metrics derived from both simulated and real-world data, employing clustering technique to identify safety patterns. Using Aimsun Next, simulation data were analyzed in the Surrogate Safety Assessment Model (SSAM) to extract traffic conflicts, which were then converted into crash risk levels. Real-world crash data from the Hellenic Statistical Authority (ELSTAT) encompassed various crash types involving at least one slightly injured individual between 2017 and 2019. Specifically, observational data encompassed speed limits, road lengths, injuries, vehicles involved, and crash counts, while simulation metrics included flow, capacity, and crash risk. The analysis of simulation and observational data revealed two distinct clusters: roads with low and high crash risks, clearly distinguished with minimal overlap. Comparison of clustering results demonstrated approximately 87.7% accuracy in predicting road crash risk classifications through traffic simulation, confirming its reliability for safety assessment. The study also highlights the importance of thorough calibration; roads inaccurately predicted lacked sufficient traffic data, underscoring the need for robust calibration to enhance safety assessment. This study validates a framework ready for future research applications in scenarios where direct observation is impractical, enhancing road safety and guiding interventions within evolving traffic conditions and technologies.