Search Results (389)

Personal mobility (PM) devices have expanded rapidly as an urban transport mode supporting short-distance travel and first- and last-mile access to public transport, while conflicts involving PM users, pedestrians, and other road users have emerged as an important traffic-safety concern. This study reconstructed police-reported PM crash records from Seoul for 2017–2024 into a crash-level dataset of 2398 crashes and examined changes in crash composition and monthly crash-count trajectories around the strengthening of PM safety regulations on 13 May 2021. Grouped-binomial models using monthly outcome and non-outcome counts were treated as the primary analysis. The descriptive PM–pedestrian share increased from 42.9% before the regulation to 49.7% after the regulation. However, the grouped-binomial models did not identify statistically significant PM–pedestrian or severe-or-fatal level or slope changes. The only statistically significant post-regulation composition term in the full-period model was a declining late-night slope; this term was not significant under the conservative month-level HC3 covariance check in the January 2019 sensitivity window. Segmented Poisson models were retained as secondary, descriptive, and exploratory analyses. Observed post-regulation counts were lower than the trajectory obtained by extrapolating the pre-regulation trend; however, the counterfactual became implausibly large within two to three years, so the model could not distinguish a regulatory discontinuity from the natural deceleration or saturation of PM diffusion and other concurrent temporal changes. Among crashes with known helmet-use status, no statistically detectable pre/post change was observed, and license type was not recorded before the regulation. The evidence therefore does not establish either a beneficial or adverse causal effect of the regulation. PM safety policy should combine user-oriented enforcement with multi-indicator monitoring, pedestrian-conflict management, continuous and clearly separated PM travel space, and targeted monitoring of late-night single-vehicle crashes. Full article

Work zones reduce roadway capacity and create unstable merging, queue spillback, and stop-and-go conditions that degrade traffic operations while elevating crash risk. Conventional fixed-time, actuated, and adaptive controllers are poorly suited to these non-stationary conditions, and most reinforcement learning approaches optimize mobility while treating safety only as a post hoc evaluation measure. This study develops a safety-aware Deep Q-Network framework for adaptive signal control at intersections operating near work zone activity areas. Merge conflict risk, upstream spillback propagation, and stop-and-go instability are embedded directly into both the state representation and the reward formulation, alongside operational objectives. A merge-conflict model based on relative spacing, relative speed, and acceleration characterizes unsafe interactions in the merge region, and a Pareto-based procedure samples reward-weight vectors to identify non-dominated policies. The framework was evaluated in a SUMO microscopic simulation of a signalized intersection under lane closure. Relative to default fixed-time control, the selected policy increased throughput by 24.6–37.3% across vehicle classes (p < 0.001; Cohen’s d = 0.53–1.29), with the largest gains for trucks and buses, and reduced maximum queue length by 39.1% and spillback distance by 45.8%. The findings show that a single controller trained with surrogate safety indicators as learning objectives can improve operational performance while reducing safety-critical instability in work zones. Full article

Lane-changing decision-making is a critical component of autonomous driving, as it requires balancing safety, efficiency, and manoeuvre stability under dynamic traffic conditions. This study proposes a hybrid Deep Deterministic Policy Gradient (DDPG) framework with an MPC-inspired predictive safety layer for autonomous lane-changing in a controlled highway overtaking scenario. The DDPG policy generates candidate longitudinal commands and lateral lane-change intentions, while the supervisory layer evaluates the predicted evolution of the target-lane front gap, rear gap, and time-to-collision (TTC) over a short prediction horizon before permitting the lateral manoeuvre. Rather than solving an online MPC optimisation problem, the supervisory layer employs short-horizon state prediction and constraint-based safety assessment to determine whether the candidate lane-change intention meets the predefined safety and overtaking-necessity conditions. The proposed framework is evaluated in a unified Simulation of Urban MObility (SUMO) highway environment and compared with rule-based, MPC-only, and DDPG-only controllers using consistent scenario conditions and performance metrics. The evaluation considers task success, collision occurrence, overtaking time, average speed, safety-related spacing, driving comfort, and lane-change behaviour. The results show that all evaluated controllers completed the overtaking task without collisions under the considered scenario. However, the proposed hybrid controller achieved the shortest mean overtaking time, the highest mean speed, the largest minimum front-gap margin, and a single lane change per episode. These findings indicate that combining learning-based decision-making with lightweight short-horizon predictive safety supervision can improve overtaking efficiency and lane-change consistency while maintaining safe vehicle interactions. Full article

Autonomous vehicles (AVs) will operate alongside human-driven vehicles for an extended transition period, during which aggressive human driving may become a major source of risk. This study proposes an integrated safety-control framework that combines real-world-data-driven behavior modeling with deep reinforcement learning to design longitudinal AV control strategies for mixed traffic. Aggressive, general, and defensive driving patterns are calibrated from the CitySim dataset, and dynamic aggressiveness is incorporated into an improved car-following model. A proximal policy optimization algorithm with a Kullback–Leibler penalty is then used to learn multi-objective strategies balancing safety, efficiency, comfort, and fuel economy in freeway and signalized-intersection scenarios. The results show that the behavior-aware strategies exhibit different strengths across traffic environments. On the freeway, the defensive-threshold strategy maintains a larger time headway, reduces positive acceleration, and lowers system-level fuel consumption, whereas the default, aggressive, and general strategies preserve higher traffic efficiency. At the intersection, signal control narrows the differences among strategies and limits the influence of longitudinal threshold settings on most evaluated indicators. These findings provide a quantitative basis for selecting behavior-aware control thresholds and designing robust AV strategies for mixed-autonomy traffic containing aggressive human drivers. Full article

Due to the absence of traffic signal control and the difficulty in accurately estimating the future movements of surrounding vehicles, autonomous vehicle decision-making faces challenges at unsignalized intersections. This study proposes a trajectory prediction-aided deep reinforcement learning framework. First, a composite prioritized replay mechanism is introduced into the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, jointly considering temporal-difference error and reward-based event severity to enhance critical-experience reuse. Second, a convolutional multi-layer long short-term memory (CM-LSTM) model predicts surrounding-vehicle trajectories through convolutional local-motion encoding and stacked LSTM temporal modeling, and the predicted trajectories are incorporated into the deep reinforcement learning state representation. A multi-objective reward function is designed to balance collision avoidance, passing efficiency, lane keeping, and task completion. In CARLA go-straight and left-turn tests, CLS-TD3 achieves success rates of 93.8% and 90.2%, collision rates of 2.5% and 4.2%, and average passing times of 5.18 s and 5.58 s. Compared with TD3, the success rates increase by 6.3 and 8.6 percentage points, while average passing times decrease by 18.8% and 20.5%. These results demonstrate that the proposed framework improves the safety and crossing efficiency of autonomous vehicle decision-making at unsignalized intersections. Full article

The regulatory disparity in nautical chart carriage requirements between SOLAS and non-SOLAS vessels poses a systemic risk for maritime safety. This study analyzes maritime traffic and grounding accidents in the East Adriatic Sea to examine the empirical patterns associated with this risk and to quantify this risk. Analysis of 2,699,930 vessel arrivals in Croatian ports (2012–2020) reveals that non-SOLAS vessels dominate traffic, outnumbering SOLAS vessels by factors of 3.44 (cargo) and 13.92 (passenger). Correspondingly, over 92% of 170 recorded groundings (2017–2019) and 90.6% of 202 groundings (2020–2025) involved non-SOLAS ships, predominantly during favourable weather conditions. The core problem is identified as the desynchronization in disseminating Maritime Safety Information (MSI) across official and unofficial chart systems, leading to inconsistent situational awareness. This risk is formalized through a conceptual objective function modelling the time delay in MSI updates across different chart production chains. These quantitative metrics demonstrate that the current regulatory gap exposes the majority of maritime users to a reduced safety standard, directly affecting risk management at the operational and regulatory levels. To minimize this delay and enhance navigational safety, the paper proposes a dual-pathway amendment: extending the ECDIS/ENC mandate to all SOLAS ships and establishing a new framework mandating the use of official ENC data within Electronic Chart Systems (ECS) for non-SOLAS vessels. This study is intended as a system-level analysis of regulatory and informational asymmetry rather than as a full accident-causation investigation. The findings should therefore be interpreted as exploratory and policy-relevant, with caution regarding direct causal inference for individual incidents. The proposed measures are essential to synchronize critical maritime safety information across all maritime users. Full article

Motorcyclists are among the most vulnerable road users at roundabouts, particularly in mixed-traffic environments where rider behaviour may be influenced by geometric design. This study investigates the association between roundabout geometry and risky motorcyclist behaviour using the Chi-squared Automatic Interaction Detection (CHAID) method. Video-based observations were conducted at four selected roundabouts in Kuching, Sarawak, Malaysia, generating 15,937 risky-behaviour events from 5400 observed motorcyclists. Six risky-behaviour categories were analysed, covering entry, circulation, and exit manoeuvres. The CHAID results showed that entry radius was the primary geometric factor associated with risky motorcyclist behaviour, while exit radius and exit width acted as secondary variables under specific entry-radius conditions. Four behavioural scenarios were identified. Entry radii of 15–32 m were associated with mixed risky-behaviour patterns, with lane splitting during circulation being the most frequent behaviour. Entry radii of 37–40 m combined with exit radii ≤ 12.43 m were associated with failure to signal before exiting. Entry radii of 43–49 m combined with exit radii ≤ 25.3 m were associated with improper lane positioning when exiting. Larger entry radii of 49–64 m combined with exit widths of 7.38–9.06 m were associated with close stopping or potential blind-zone positioning. The model validation results indicated moderate internal classification performance, supporting the use of CHAID as an interpretable threshold-identification tool rather than a high-precision predictive model. The findings demonstrate that risky motorcyclist behaviour at roundabouts is shaped by non-linear interactions between entry and exit geometric elements. From a sustainability perspective, these results provide preliminary evidence for behaviour-sensitive roundabout design, safety assessment, and policy-oriented geometric improvements that support safer and more inclusive urban transport systems in motorcycle-dominant mixed-traffic contexts. Full article

Growing interest in sustainable urban mobility has intensified research into the factors shaping the adoption of smart bicycle technologies, yet cross-national comparative evidence remains scarce. This study examines user perceptions, mobility habits and willingness to adopt innovative e-bike technologies in Italy and Poland through a structured questionnaire grounded in the Bicycle Smartness Level (BSL) framework. Descriptive statistics and inferential tests were applied to identify exploratory differences between the two survey samples across mobility behavior, perceived barriers, technology utility and willingness to pay. Results reveal that, in these samples, Italian respondents display stronger car dependency and rate safety-related cycling barriers (traffic hazards, lack of infrastructure and road user insecurity) significantly higher than Polish respondents do. In contrast, respondents in the Polish sample attribute significantly greater utility to comfort and performance-oriented smart technologies such as monitoring systems and adaptive gear shifting. In both samples, willingness to pay a price premium converges in the €0–200 range and navigation and safety technologies are universally valued. These descriptive and exploratory findings suggest that smart cycling adoption pathways may differ across contexts: in the Italian sample, perceived road safety and infrastructure gaps appear to be central barriers, whereas in the Polish sample affordability emerges as a particularly relevant condition for adoption, with implications for policy aligned with Sustainable Development Goal (SDG) 11 and the EU Declaration on Cycling. Full article

Advanced Persistent Threats (APTs) pose a serious threat to Internet of Things (IoT) systems because of their stealthiness, persistence, and ability to adapt to defensive responses. Communication-Based Train Control (CBTC) systems, as IoT-enabled railway signaling infrastructures, have evolved from relatively closed operational environments into interconnected cyber-physical networks, exposing train control systems to coupled cyber intrusion and operational-safety risks. To address this challenge, this paper proposes an alert-driven active defense framework for CBTC systems that integrates Bayesian belief updating, hypergame-based cognitive-bias modeling, and Hierarchical Reinforcement Learning (HRL). The framework converts intrusion detection system (IDS) alerts, network traffic observations, and cyber-physical observations into belief-state, transition, and reward inputs. The Bayesian model estimates attacker type and attack stage, the hypergame model represents deception-induced asymmetric cognition between attackers and defenders, and the HRL decouples strategic defense posture selection from tactical defense execution. The scenario-driven simulations in a CBTC APT defense setting show that the proposed model strategy achieves an 87.1% defense success rate against APT attacks while consuming 62.7% of the normalized defense resources, outperforming DQN, PG, and PPO under the same test conditions. These results suggest that explicitly coupling cyber observations, CBTC operational constraints, and hierarchical deception-aware policies can improve cost-aware active defense for railway signaling infrastructures. Full article

Addressing the issues of insufficient adaptability and limited energy efficiency optimization capabilities in traditional tunnel lighting control methods under complex traffic conditions, this paper proposes a dynamic dimming strategy for tunnel lighting based on the Proximal Policy Optimization (PPO) algorithm. First, the tunnel lighting system is modeled as a reinforcement learning environment. A state space integrating multidimensional information—including traffic flow, vehicle speed, external luminance, and tunnel section location—is constructed, and a continuous action space is designed to enable precise dimming control for each functional section. Based on this, a multi-objective reward function is established that integrates luminance tracking error, energy consumption optimization, control stability, and environmental adaptability to guide the agent in learning the optimal dimming strategy. Subsequently, model training and experimental validation were conducted using actual tunnel operation data. Experimental results show that, compared with the conventional $L_{20}$ strategy, the proposed method achieves significant energy savings during the 10:00–17:00 period, with the energy-saving rate remaining above 20% for most of the time from 11:00 to 16:00 and peaking at nearly 24%, while ensuring driving safety and visual comfort. In summary, the PPO-based dynamic dimming strategy demonstrates promising application prospects and engineering value in intelligent tunnel lighting systems. Full article

Background: Four-way shuttle-based storage and retrieval systems (FSS/RSs) have recently emerged as flexible and scalable solutions for high-density warehousing, enabling shuttle movement in four directions and supporting multi-deep dual-access storage configurations. However, these features increase the complexity of vehicle coordination and collision management. This study proposes a multi-agent path-planning methodology for FSS/RSs with multi-deep dual-access lanes hosting homogeneous items. Methods: An A*-based path-planning framework was developed and integrated with a dynamic collision-management strategy comprising collision detection, priority assignment, and collision avoidance. The methodology was evaluated through a multi-scenario analysis considering different fleet sizes, priority-assignment strategies, safety-area extensions, transaction-entry patterns, and collision-management policies. Results: The results show that fleet size is the most influential operational parameter, significantly affecting throughput, waiting times, and collision frequency. Increasing the number of vehicles improves system productivity but also increases traffic interactions and congestion. The analyses further highlight the effects of safety-area size, priority rules, and transaction-entry patterns on operational performance and system robustness. Conclusions: The proposed methodology effectively combines path planning and collision management in four-way shuttle systems, providing a decision-support tool for evaluating operational trade-offs among throughput, congestion control, and system stability in multi-deep dual-access warehouse environments. Full article

Road traffic accidents continue to be a major global cause of fatalities, disproportionately affecting pedestrians and other vulnerable road users. While deep reinforcement learning has proven effective in handling complex navigation tasks, providing formal stability and safety guarantees during both training and deployment remains a significant challenge. This paper introduces a dual-layer safety-aware framework for pedestrian avoidance in occluded urban driving. During training, a first-order Control Lyapunov–Barrier Function is integrated with Proximal Policy Optimization to promote goal-reaching stability and obstacle avoidance: the analytic Lie derivatives of the Lyapunov and barrier functions are embedded as a modifier in the advantage estimate, providing explicit stability and safety signals that accelerate convergence toward safe, goal-reaching behavior without disrupting the standard policy update. At deployment, a higher-order Control Lyapunov–Barrier Function, realized through a quadratic programming safety filter, acts as a safety shield that projects the nominal acceleration onto the intersection of the second-order Lyapunov and barrier feasibility sets; the barrier function is further extended with relative velocity terms to account for dynamic pedestrian motion. Experiments with a four-wheeled vehicle in the Webots simulator show that the framework reliably reaches the goal, avoids an occluded pedestrian across a range of crossing speeds, and improves task success rates and safety-constraint adherence relative to Proximal Policy Optimization and a conventional higher-order safety filter baseline, particularly during emergency braking maneuvers. Full article

As autonomous driving moves toward large-scale deployment, controllable and efficient simulation testing has become a primary means of ensuring system safety. However, in open-world environments, existing scenario catalogs often fail to cover the full spectrum of potential traffic situations, while rare yet high-risk critical scenarios are even harder to obtain. This scarcity renders traditional random sampling and parameter-sweeping strategies ineffective for identifying unknown risks. This study addresses two core challenges: (1) incomplete scenario catalogs hindering unknown critical scenario recognition and (2) insufficient critical samples, where generated scenarios struggle to balance physical realism and edge case coverage. To tackle the first challenge, we propose an open-world recognition method integrating transformers, random forests, and extreme value theory for precise unseen sample detection. Outlier and validity filtering ensure clustering reliability, and random forest activation patterns cluster unknown samples into meaningful groups to expand the scenario catalog. Experiments show the overall F1_macro improved by 2.3 percentage points over SOTA MDENet, with its clustering accuracy surpassing iterative-AutoNovel by 6.2 percentage points. For the second challenge, we introduce a reinforcement-learning-based maneuver-level generation method. It extracts maneuver semantics from trajectories, constructs a low-dimensional parameter space, and models parameter correlations via a multivariate multimodal distribution. A dual-layer LSTM agent with a composite reward iteratively optimizes policies toward high-risk edge scenarios. The results outperformed RLBE; longitudinal and lateral reconstruction errors were reduced by 32.7% and 15.3%, respectively, while high-risk time steps and the collision rate increased by 4.3% and 5.1%, respectively. Finally, we develop a CARLA-based scenario-driven simulation framework, integrating recognized and generated scenarios into closed-loop testing on high-risk road segments. CAS failure cases validate the generated scenarios’ physical feasibility and extreme challenge. Targeted augmentation of scarce critical scenarios enriches the test library and ensures broader coverage of real-world driving conditions. Full article

The entry of JD.com into the food delivery sector and the ensuing subsidy competition have resulted in irrational competition, merchant profit squeezes, and food safety risks in China. This study therefore investigates the collaborative governance mechanisms for food delivery platforms under involutionary competition driven by traffic contestation. A two-agent evolutionary game model between platforms and merchants is developed, and Q-learning simulations are conducted to capture dynamic learning behaviors. The analysis examines the effects of coupon face value, cost-sharing mechanisms, traffic incentives, and government incentive-penalty policies on the strategic choices of both agents. Key findings reveal that merchants are more sensitive than platforms to traffic incentives and government penalties. Traffic-dependent merchants and traffic-independent merchants exhibit significantly different responses to government interventions. The coupon face value demonstrates a threshold effect, where only a reasonable range encourages compliant behavior among both parties. Based on these results, a collaborative governance framework is proposed. For traffic-dependent merchants, the government should focus on regulating platform behaviors and supervising coupon value controls, while platforms should establish a reward-oriented, penalty-supported incentive mechanism. For traffic-independent merchants, the government should strengthen consumer-reporting penalty mechanisms and strictly control collusion risks between platforms and merchants. Platforms should increase inspection frequency and reinforce penalties to prevent, at the source, the decline in product quality and market disorder induced by involutionary competition. This study provides strategic insights for achieving collaborative governance of involutionary competition in platform economies under intense traffic contestation. Full article

Despite growing policy support for active travel, the fatality rate of vulnerable road users has remained persistently high in recent years, while the emergence of autonomous vehicles has further increased the complexity of mixed traffic environments. Interactions between cyclists and motorized vehicles are a major contributor to these fatalities, highlighting the urgent need for effective cyclist protection strategies. As one of the most widely adopted active transport modes, cycling safety cannot be assessed solely through crash statistics; understanding cyclists’ perceived safety is equally critical, as it reflects how infrastructure design and dynamic traffic conditions influence cycling behavior. In this study, we propose a cyclist safety perception framework that combines vision–language models with interpretable machine learning to analyze perceived safety in mixed traffic scenarios. A vision–language model is employed to generate semantic descriptions of traffic scenes, while an Explainable Boosting Machine quantifies both individual and interactive contributions of traffic-related features. By integrating visual information with road attributes extracted from OpenStreetMap, the proposed framework achieves a binary safety classification accuracy of 71% and a mean absolute error of 1.01 on a safety score scale ranging from 1 to 9. The results demonstrate the potential of combining multimodal perception and explainable models to support cyclist-centered safety assessment and inform sustainable and intelligent transportation system design. More specifically, the results show that protected cycling infrastructure is the most significant factor in improving perceived safety, whereas road construction has the opposite effect. Full article