Steffen Müller

This study examined infrastructure-related casualty involvement in police-reported crashes and assessed how record quality and crash context affect interpretation. The cohort comprised 6190 Eslamshahr crashes recorded from March 2022 to June 2024, including 1805 injury or fatal crashes. Models developed on 5299 records were assessed on 891 spring-1403 records. The reference CatBoost achieved ROC-AUC 0.857, average precision 0.725 and Brier score 0.120; development-only sigmoid recalibration reduced Brier to 0.112. A 39-configuration comparison did not improve CatBoost and yielded stronger average-precision and Brier results for random forest on the same existing test. Adjusted associations for inadequate nighttime lighting (OR 2.78, 95% CI 1.66–4.64) and visibility obstacles (OR 1.89, 1.05–3.38) remained elevated across width and influence checks. Exactly 12 m accounted for 64.3% of widths and materially affected geometric estimates. Removing crash type and at-fault vehicle information reduced CatBoost ROC-AUC to 0.532. The findings support retrospective examination of recorded lighting and visibility conditions and show why documentation, predictor availability and calibration matter when interpreting severity models.

Road traffic crashes remain one of the leading causes of injuries and fatalities worldwide, resulting in approximately 1.35 million deaths and 20–50 million non-fatal injuries annually (Organizatio...

We present the coupling of the microscopic traffic simulation package SUMO with the microscale building-resolving large-eddy simulation model PALM to calculate the effect of anthropogenic heat from vehicles on the near-surface meteorological conditions. The spatially and temporally resolved anthropogenic heat flux is calculated from the fuel consumption of each simulated vehicle in SUMO. This coupled model is applied to one summer and one winter day for a central district of Berlin, Germany, using the Berlin SUMO Traffic dataset. The resulting traffic heat flux density follows the simulated diurnal cycle of vehicle activity and fuel consumption, with street-average values above 10 Wm-2 between 10:00 and 21:00, a peak-hour street-average value of about 26 Wm-2 and localized maxima up to 2100 Wm-2 near traffic lights. The domain-mean effect on the 2m air temperature is small, with daily mean increases of about 0.02 K over the non-building area. The strongest spatially averaged temperature responses occur during weak-wind periods, reaching about 0.14 K in summer and 0.18 K in winter, while individual hotspot grid cells show much larger responses of up to 2.6 K. These results show that dynamically derived traffic heat has only a small effect on district-scale mean near-surface temperature under the investigated conditions, but can produce localized warming along traffic corridors and near intersections. The coupled SUMO–PALM framework, therefore, provides a process-based method for representing traffic-related anthropogenic heat in microscale urban climate simulations.

Automated vehicles (AVs) may improve urban road safety, yet aggressive calibration and mixed-traffic interactions may produce non-linear safety effects. This study examines how AV penetration, fleet composition, and behavioral calibration shape traffic safety outcomes. Using SUMO microsimulation for Charlottenburg, Berlin, with TTC-based surrogate safety analysis, LightGBM modeling, and spatial hotspot mapping, we evaluate 12 scenarios varying AV penetration (0–100%) and fleet composition (small AV12, and larger AV46). Each scenario is tested under three AV time-headway settings, τ = 0.6, 0.8, and 1.0 s, yielding 36 configurations; human-driven (HDV) vehicle headway is fixed at 1.0 s. Results reveal a non-linear safety transition. Total conflict frequency declines with increasing AV penetration, reaching reductions of approximately 45% under full automation. However, severe conflicts increase during the mixed-traffic phase under aggressive calibration, τ = 0.6 s, indicating a transition-related safety paradox. Sensitivity analysis shows that τ = 0.8 s provides the most balanced performance, improving both total and severe-conflict outcomes. Spatially, conflicts shift from network-wide dispersion under HDV conditions to localized concentration at critical junctions under AV-dominant traffic. Fleet composition further moderates safety outcomes, with AV46-dominant scenarios showing higher safety improvements and more stable kinematic patterns. LightGBM identifies relative speed difference as the dominant risk driver. Although higher AV penetration and AV46 shares reduce average severity, they may increase tail risk under short headways. AV conflicts occur at lower speeds and lower ΔV, suggesting lower crash severity despite shorter temporal margins. The findings highlight context-dependent AV operation, particularly headway regulation and fleet composition.

The field of Connected and Autonomous Vehicles (CAVs) promises to improve road safety through advanced communication technologies and data sharing. This study addresses the need for a clearer understanding of the relationship between CAV Market Penetration Rates (MPRs) and Safety Improvement Rates (SIRs) by conducting an updated meta-analysis that includes 49 studies published between 2015 and 2024. Across these studies, the primary focus of safety surrogate measures (SSMs) was on Time-to-Collision (TTC) and Post Encroachment Time (PET), reflecting their dominant role in evaluating traffic safety impacts of CAVs. We applied sensitivity analysis to refine the data and mitigate the effects of variability across simulation parameters and scenarios. A power function was identified as the most accurate model to describe the MPR-SIR relationship, capturing how safety benefits scale with increasing adoption of CAVs. At low MPRs (10%–20%), safety gains are modest, with SIRs of 2.8% and 4.9%, respectively. Mid-range MPRs (30%–60%), while frequently studied, show overestimated safety benefits in raw findings; our corrected estimates yield SIRs of 8.0% at 30% MPR and 9.4% at 40% MPR, highlighting the complexity of mixed traffic environments. At high MPRs (70%–90%), safety benefits become substantial and more consistent, with an SIR of 39.4% at 90% MPR. These findings inform transportation planners and policymakers on the safety potential of CAVs, emphasizing the importance of high market penetration to realize meaningful safety improvements.

Autonomous vehicle controllers are responsible for handling the vehicle at all times in any situation, including emergency conditions. An emergency might arise from, e.g., adverse weather conditions, short-time detection, and system faults. In this paper, we develop a fault-tolerant controller to handle actuator faults for an over-actuated autonomous vehicle based on reinforcement learning. A worst-case scenario is selected for the controller development, involving short-time detection of the preceding objects, high velocity, and dry to wet road conditions. The design of the vehicle controller is performed in three steps. First, a robust controller based on sliding mode control with lateral and longitudinal coupled strategy was built to ensure stability in emergency scenarios. Secondly, a strategy was proposed to identify the most critical vehicle faults that might lead to a crash. Building on these foundations, this study extends the vehicle controller to handle vehicle faults with a reinforcement learning strategy, enabling adaptive and robust fault handling in complex fault scenarios. The vehicle controller is designed and optimized in IPG-Carmaker®, and proof of concept is carried out in a scaled 1:3.33 test vehicle. The results demonstrate the robustness of the proposed controller in an emergency single-lane change with a velocity of up to 130 km/h. Tests in the scaled vehicle demonstrate the vehicle controller’s accuracy against simulation, with the application of reinforcement learning strategy in real-case scenarios.