Accident; analysis and prevention最新文献

{"title":"Heterogeneous and differential treatment effect analysis of safety improvements on freeways using causal inference","authors":"Syed Zaier Zaidi ,&nbsp;Xuesong Wang ,&nbsp;Yesihati Azati ,&nbsp;Jiaqi Li ,&nbsp;Tianxiang Fan ,&nbsp;Mohammed Quddus","doi":"10.1016/j.aap.2025.108173","DOIUrl":"10.1016/j.aap.2025.108173","url":null,"abstract":"<div><div>Evaluating safety effectiveness of freeway design improvements is crucial for enhancing overall safety and confirming the efficacy of specific measures implemented. Limited research has addressed treatment heterogeneities that influence crash outcomes, and previous studies have often been susceptible to confounding biases, which may distort causal inference results. To mitigate confounding biases and establish reliable causal relationships between crashes and treatment interventions, this study employed a causal forest (CF) model to assess the safety efficacy of freeway exit improvements – including lane control, traffic signs, speed-limit signs, and crash attenuators – on freeways in Suzhou, China. We compared naïve and empirical Bayes before-after methods against the Average Treatment Effect (ATE) estimated by the CF approach. Geometric design and traffic operation characteristics were then considered in measuring the Heterogeneous Treatment Effects (HTE) of these improvements, with the aim of identifying road features where treatment benefits were most pronounced. Additionally, a Differential Treatment Effects (DTE) analysis within a causal framework was employed to estimate treatment effects on the residuals, uncovering more intricate and complex causal relationships. The study demonstrated that CF method provides more stable ATE estimates. An analysis of the distribution of the treatment effects revealed a diverse range of impacts, indicating both positive and negative outcomes. Significant variability in treatment effects was evident from heterogeneous testing results. Noteworthy outcomes from treating freeway exits were observed in areas with an Average Annual Daily Traffic (AADT) ranging from 12,000 to 28,000 vehicles per day, average speeds of 95 km/h and above, two or four lanes on each side, and an exit-only ramp configuration. These findings contribute to valuable technical insights for selecting and evaluating safety enhancement strategies on freeways.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108173"},"PeriodicalIF":5.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of secondary risks induced by defensive braking in autonomous vehicles: a study based on stochastic distribution of drivers","authors":"Tingyu Liu ,&nbsp;Zhenyu Zhao ,&nbsp;Miaomiao Yang ,&nbsp;Tianyuan Han","doi":"10.1016/j.aap.2025.108176","DOIUrl":"10.1016/j.aap.2025.108176","url":null,"abstract":"<div><div>Defensive braking measures in autonomous vehicles effectively enhance driving safety but also raise concerns about the secondary risks they may pose, particularly the potential for rear-end collisions caused by following vehicles. Indeed, being rear-ended by human driven vehicles is already the most common type of accident involving autonomous vehicles. However, the uncertainty in driver following behavior makes it challenging to assess this risk directly. In response, this paper characterizes the stochastic distribution of drivers to simulate and evaluate the impact of defensive braking behavior on the likelihood of rear-end collisions. First, based on Risk Homeostasis Theory and the central limit theorem, we propose the hypothesis that the risk tolerance levels (RTL) of driver populations follow a normal distribution. This hypothesis is validated using the Waymo dataset, leading to the development of a Stochastic Following Model (SFM) that effectively represents the stochastic distribution of drivers. Subsequently, a comparison with the Intelligent Driver Model (IDM) reveals that the SFM not only accurately reflects the stochastic distribution of drivers in mixed traffic flow but also demonstrates its effectiveness in capturing the diversity of driving behaviors. Finally, through the design of simulation experiments across various scenarios using Monte Carlo methods, the results indicate that while brief defensive braking by autonomous vehicles does not significantly affect the collision probability of following vehicles compared to manually driven vehicles, continuous defensive braking behavior substantially increases the likelihood of being rear-ended. The proposed SFM captures the extensive diversity of drivers and the stochasticity of the following process, illustrating the uncertainties inherent in mixed traffic flow. This model may serve as a valuable reference for future studies on the safety characteristics of mixed traffic flows.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108176"},"PeriodicalIF":5.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable multi-variable transformer network for regional-level short-term bicycle crash risk prediction","authors":"Ling Deng,&nbsp;Chengcheng Xu,&nbsp;Pan Liu,&nbsp;Yuxuan Wang,&nbsp;Kequan Chen","doi":"10.1016/j.aap.2025.108169","DOIUrl":"10.1016/j.aap.2025.108169","url":null,"abstract":"<div><div>Effective short-term prediction of bicycle crashes at the urban regional level is critical for proactive infrastructure safety interventions and data-driven traffic management. However, three key challenges persist: (1) inadequate modeling of complex spatiotemporal dependencies in multi-source heterogeneous data; (2) poor handling of extreme class imbalance and lack of interpretability in deep learning-based short-term predictions; and (3) limited exploration of bicycle infrastructure’s role in regional crash risk assessment. In response to these challenges, we propose an Interpretable Multi-variable Transformer Network (IMTN) that employs four specialized Transformer encoder blocks to extract spatial and temporal dependencies from heterogeneous inputs. To mitigate the severe class imbalance, our approach uses a single, shared model to predict crash risk for one region at a time, rather than all regions simultaneously. This reformulation avoids data sparsity while retaining multi-region inputs, and a spatial weighting mechanism is used to preserve inter-regional dependencies. Meanwhile, an improved Layer-wise Relevance Propagation (LRP) framework is employed to enhance the interpretability of IMTN. We conduct our experiments on a four-year dataset from London, which includes crash records, public bicycle trips, time, weather, road networks, land use, and a rich set of 48 bicycle infrastructure features. The model comparison demonstrates that IMTN consistently outperforms competitive baselines, reducing false positive rate (FPR) by up to 9.08%, improving the area under the curve (AUC) by up to 3.49%, and increasing the G-mean by up to 5.39%. Our model achieves the best performance at the finest temporal resolution (1-hour aggregation), contrary to common expectations. This suggests that the proposed class imbalance handling method may enhance model performance in high-resolution settings. In addition, interpretability analysis identifies segregated cycle lanes, Sheffield stands, and colored path markings as high-impact infrastructure variables, providing data-driven insights that can help inform urban safety planning.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108169"},"PeriodicalIF":5.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of autonomous vehicle buyer’s decisions: Balancing ethics with Innovation in the trolley dilemma","authors":"Youngjae Yoo ,&nbsp;Hun Kim ,&nbsp;Jiseob Park","doi":"10.1016/j.aap.2025.108175","DOIUrl":"10.1016/j.aap.2025.108175","url":null,"abstract":"<div><div>This study explores complex decision-making processes in autonomous driving, focusing on the ethical challenges presented by the trolley dilemma and the regulatory focus theory. When faced with significant choices in autonomous driving scenarios, such as the trolley dilemma, passengers prefer to make their own decisions rather than rely on a system’s automated choices. This preference of having a choice significantly increases their trust in the technology and their willingness to purchase autonomous vehicles. No notable difference was found in the moral judgment between decisions made by participants and those made by the autonomous system. Moreover, this research highlights the influence of the regulatory focus theory, demonstrating that participants placed greater trust in the system and made safer decisions when presented with prevention-focused messages, emphasizing avoiding adverse outcomes rather than promotion-focused messages highlighting positive outcomes and aspirations. The findings suggest that the presentation of the information and decisions by autonomous vehicles can profoundly influence passengers’ ethical choices, affecting their trust in and acceptance of autonomous technology. This study contributes to the understanding of consumer behavior and ethical decision-making in the rapidly advancing domain of autonomous vehicle technology, offering valuable insight into policy-making and the future design of these systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108175"},"PeriodicalIF":5.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the efficacy of pre-trained large language models in analyzing autonomous vehicle field test disengagements","authors":"Melika Ansarinejad ,&nbsp;Sherif M. Gaweesh ,&nbsp;Mohamed M. Ahmed","doi":"10.1016/j.aap.2025.108178","DOIUrl":"10.1016/j.aap.2025.108178","url":null,"abstract":"<div><div>This study evaluates the efficacy of pre-trained large language models (LLMs) in analyzing disengagement reports of Levels 2–3 autonomous vehicle (AV) field tests, utilizing data provided from California Department of Motor Vehicles. Disengagement reports document instances where autonomous vehicles, tested under the Autonomous Vehicle Tester (AVT) and AVT Driverless Programs, transition from autonomous to manual control. These disengagements occur when human intervention is required due to incidents or limitations in the operational design domain that prevent AVs from functioning properly. Understanding factors leading to disengagements is pivotal for assessing AV performance and guiding infrastructure owners and operators (IOOs) about modifications needed. Manual approaches for analysis of the disengagement data are labor-intensive and prone to human error. Our research investigates the capability of LLMs to automate this analysis, focusing on identifying patterns, categorizing disengagement causes, and extracting meaningful insights from extensive datasets. GPT-4o as an LLM was employed to analyze the disengagement reports. The study aims to measure the accuracy, efficiency, and reliability of these models in comparison to traditional techniques. The application of LLMs demonstrated significant potential in identifying insights from the disengagement dataset, while effectively processing the textual data, achieving an accuracy of 87%. Several data limitations were encountered, including inconsistencies in disengagement descriptions from different manufacturers, which posed challenges to standardizing the analysis. Additionally, the disengagement reports offered limited details on the specific causes of disengagements and the surrounding conditions, restricting the depth of insights that could be drawn. Despite these challenges, our findings indicate that LLMs can substantially enhance the speed and precision of analyzing AV disengagement reports, offering valuable insights, while being cost-effective, that can inform further research and development in AV technology and safety protocols.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108178"},"PeriodicalIF":5.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety contributing factors analysis of older vulnerable road users: General and local perspectives","authors":"Xueyu Zhang ,&nbsp;Xuesong Wang ,&nbsp;Mohamed Abdel-Aty ,&nbsp;George Yannis ,&nbsp;Guangzhu Luo","doi":"10.1016/j.aap.2025.108166","DOIUrl":"10.1016/j.aap.2025.108166","url":null,"abstract":"<div><div>Increasing attention to older people’s traffic safety is necessary to understand the relationship between their traffic safety and contributing factors on a spatial scale. However, zero crashes exist at the analysis unit for some specific types of crashes, and few studies have considered the spatial heterogeneity between older people’s crash frequency and the influencing variables. To fill these gaps, this study developed an analytic approach to explore the effects of contributing factors for older vulnerable road users’ (VRUs) crashes, with particular attention to the integration of general and local analysis. Socio-economic, road network, public facility, traffic enforcement and older VRU crashes were collected in the grids. The gradient tree-boosted Tweedie compound Poisson models (TDboost) were employed to address zero-inflated crash data from the general aspect. Geographically weighted random forests (GWRF) models were employed to reveal the spatial heterogeneity from the local aspect. The results showed that population and healthcare played an important role in predicting older VRU crashes. Major influencing factors showed nonlinear effects on older VRU crashes. They had a positive correlation with both older pedestrian crashes and non-motorized vehicle (NMV) crashes. This study demonstrated that the TDboost excelled in dealing with zero-inflated crash data and the complex effects of safety contributing factors, compared with conventional statistical models (e.g., negative binomial model and zero-inflated negative binomial model) in both prediction accuracy and parameter interpretation. The local variable importance of major contributing factors for VRU crashes showed a spatial clustering tendency and a block distribution tendency. The findings provided important insights into reducing older VRU crashes. For example, the concentration areas for older people, including healthcare facilities, markets, and bus stops, could be targeted to make safety improvements. The analysis sheds light on the nonlinear effects and spatial heterogeneity of safety contributing factors on older VRU crashes, which are usually disregarded in the older traffic safety. The proposed approach emphasizes that the countermeasures for improvement should be formulated based on the spatial distribution of the variable importance, that is, “adapt to local conditions”.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108166"},"PeriodicalIF":5.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System reliability analysis of CRTS III track slab considering multiple failure modes","authors":"Wengao Liu ,&nbsp;Juanjuan Ren ,&nbsp;Shijie Deng ,&nbsp;Jiayu Liang ,&nbsp;Huan Xu ,&nbsp;Wenlong Ye","doi":"10.1016/j.aap.2025.108162","DOIUrl":"10.1016/j.aap.2025.108162","url":null,"abstract":"<div><div>Under train loads, temperature loads, and subgrade deformation, CRTS III track slabs may experience various failure modes such as longitudinal bending, transverse bending, steel yielding, and fatigue damage. To explore the system reliability of CRTS III track slabs under various failure modes, this study is based on analytical expressions and finite-element techniques. The load effects on track slabs were calculated, and the limit state functions corresponding to different failure modes of track slabs were analyzed. To perform series system analysis of various failure modes, and establish dimensionless limit state functions, eight different load combination forms are generated. Reliability considering individual failure modes and system reliability of track slabs are analyzed using the method of moments. Results show that combined load effects in the same direction are more likely to cause track slab failure. For instance, under the combined action of negative temperature gradient and subgrade settlement, the reliability of longitudinal bending is <em>β</em> = −0.2536, which is seriously unsatisfactory for meeting safety requirements. Conducting an independent analysis of individual failure modes would overestimate the reliability of track slabs, thus emphasizing the necessity of analyzing the system reliability of the track slabs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108162"},"PeriodicalIF":5.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Classification of driving simulators validation: A case study using an immersive driving simulator\" [Acc. Anal. Prevent. 213 (2025) 107944].","authors":"Cesar Andriola, Gustavo Rubén Di Rado, Daniel Sergio Presta García, Christine Tessele Nodari","doi":"10.1016/j.aap.2025.108160","DOIUrl":"https://doi.org/10.1016/j.aap.2025.108160","url":null,"abstract":"","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":" ","pages":"108160"},"PeriodicalIF":5.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144625249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CoRTSG: A general and effective framework of risky testing scenario generation for cooperative perception in mixed traffic","authors":"Rongsong Li ,&nbsp;Xin Pei ,&nbsp;Lu Xing","doi":"10.1016/j.aap.2025.108163","DOIUrl":"10.1016/j.aap.2025.108163","url":null,"abstract":"<div><div>The individual perception capabilities of autonomous vehicles face significant challenges in overcoming occlusions and achieving long-distance visibility. Consequently, cooperative or collaborative perception (COOP), which can effectively expand the perception field and help to detect the human-driven vehicles or vulnerable road users by leveraging vehicle-to-everything (V2X) communication among connected and automated vehicles (CAVs) and roadside units (RSUs), has garnered increasing academic attention in recent years. Despite notable advancements in datasets, simulation platforms, and algorithms, there remains a dearth of research focused on the evaluation and testing methodologies for COOP systems, particularly concerning driving safety. This study proposes a general and effective framework for <strong>R</strong>isky <strong>T</strong>esting <strong>S</strong>cenarios <strong>G</strong>eneration for <strong>Co</strong>operative Perception (CoRTSG), which can integrate traffic data and prior knowledge to sequentially produce risky functional, logical, and concrete scenarios. Specific functional scenarios pertinent to COOP are extracted from the traffic crashes due to vision occlusion, thereby defining its operational design domain. Subsequently, by selecting appropriate sites on an OpenDRIVE map, risky logical scenarios are determined. A fast occlusion judgment algorithm is also developed, assigning roles to objects within a logical scenario and employing autoregressive sampling to derive risky concrete scenarios. Accordingly, a comprehensive large-scale library of risky testing scenarios encompassing 11 functional and 17,490 concrete scenarios for COOP in a mixed traffic environment with CAVs, non-CAVs, and vulnerable road users has been created for the first time in literatures. All concrete scenarios have been simulated in the CARLA environment, facilitating thorough testing of representative COOP algorithms in terms of detection accuracy, driving safety, and communication efficiency. The results highlight that COOP significantly enhances driving safety and detection accuracy compared to individual perception, however, further optimization is needed to balance performance with bandwidth requirements and to ensure stable safety improvements. Data and code are released at <span><span>https://github.com/RadetzkyLi/CoRTSG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108163"},"PeriodicalIF":5.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pedestrian crash causation analysis near bus stops: Insights from random parameters Negative Binomial–Lindley model","authors":"Mohammad Anis ,&nbsp;Srinivas R. Geedipally ,&nbsp;Dominique Lord","doi":"10.1016/j.aap.2025.108137","DOIUrl":"10.1016/j.aap.2025.108137","url":null,"abstract":"<div><div>Pedestrian safety remains a pressing concern near bus stops along urban transit, where frequent pedestrian–vehicle interactions occur. While prior research has primarily focused on intersections and midblock locations, bus stops have often been treated as secondary contributors rather than as distinct sites requiring targeted safety assessments. This has left a critical gap in understanding how traffic exposure, roadway characteristics, and bus stop design features specifically influence pedestrian crash risks around bus stop locations. To address these gaps, this study develops a comprehensive framework focused on pedestrian safety in the vicinity of bus stops. The proposed approach employs a Random Parameters Negative Binomial–Lindley (RPNB–L) model to account for unobserved heterogeneity and site-specific variability. Using data from 596 bus stops in Fort Worth, Texas (2018–2022), the model identifies that higher pedestrian crash frequencies are significantly associated with increased AADT, elevated boarding activity, and the absence of key safety elements such as crosswalks, medians, and lighting. Conversely, far-side bus stop placement, signalized intersections, sidewalks, and mixed-use development are associated with lower crash risks. Roads near schools and those with speed limits of <span><math><mo>≤</mo></math></span>35 mph show elevated crash risk. To support proactive safety management, the study integrates a Full Bayes-based Potential for Safety Improvement (PSI) metric, enabling the identification of hazardous stops and high-risk corridors. By unifying advanced count-based modeling with strategic risk prioritization, this research offers actionable, data-driven insights for improving pedestrian safety near bus stops.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108137"},"PeriodicalIF":5.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}