Accident; analysis and prevention最新文献

{"title":"Evaluating causes of animal-vehicle collisions through the lens of driver behavior","authors":"Carson J. Pakula ,&nbsp;Olin E. Rhodes Jr. ,&nbsp;Travis L. DeVault","doi":"10.1016/j.aap.2025.108314","DOIUrl":"10.1016/j.aap.2025.108314","url":null,"abstract":"<div><div>Animal-vehicle collisions (AVCs) are ubiquitous in developed regions of the world and pose risks to both wildlife and humans. In the United States, collisions with deer (<em>Odocoileus spp</em>.) cause billions of dollars in economic losses and thousands of human injuries annually. The current AVC literature has largely focused on factors unrelated to driver behavior including AVC hotspots, wildlife movement, and damages caused by AVCs. However, despite being a component in every AVC, few studies have investigated driver behavior during animal-vehicle interactions. Here, we systematically reviewed literature databases to identify factors influencing driver behavior during these interactions and to highlight apparent gaps in the literature. We found that vehicle speed, road attributes, environmental conditions, and vehicle types show inconsistent associations with AVCs and the mechanisms by which they influence driver behavior is not well understood. Many studies focused on mitigation methods to influence driver behavior, including various warning signs; however, the effectiveness of these systems varies considerably. Other topics including wildlife attributes, roadway illumination, and inherent driver attributes directly influence driver behavior, but are understudied. Most studies relied on seemingly logical explanations for results or associations between variables to identify these influences, but few studies directly tested how specific variables influenced driver behavior and detection ability of wildlife. Given that driver behavior influences every potential AVC, future research should directly investigate the behavioral and perceptual mechanisms behind driver detection of wildlife and other factors influencing overall driver behavior during wildlife-vehicle interactions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108314"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145570589","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":"A national randomized field experiment testing the impact of simulated usage-based insurance programs on driver safety","authors":"Jeffrey P. Ebert ,&nbsp;Catherine C. McDonald ,&nbsp;Ruiying A. Xiong ,&nbsp;Dina Abdel-Rahman ,&nbsp;Neda Khan ,&nbsp;Maya Irving ,&nbsp;Athena Lee ,&nbsp;Arjun Patel ,&nbsp;Sadie Friday ,&nbsp;Brian Tefft ,&nbsp;William J. Horrey ,&nbsp;Subhash Aryal ,&nbsp;Michael O. Harhay ,&nbsp;M.Kit Delgado","doi":"10.1016/j.aap.2025.108331","DOIUrl":"10.1016/j.aap.2025.108331","url":null,"abstract":"<div><div>Many motor vehicle crashes are due to risky driving behaviors measurable by smartphone telematics applications. These apps are deployed by usage-based insurance (UBI) programs to estimate crash risk and price policies, incentivizing customers to drive safer. A preregistered randomized controlled trial (<span><span>NCT06101251</span><svg><path></path></svg></span>) tested whether a simulated UBI program improved driver safety, and whether being assigned or choosing focused goals yielded further improvement. Adult drivers (n = 1,449) were recruited nationally with social media advertisements. After a 6-week baseline period, participants were randomly assigned to one of four groups for a 12-week intervention: 1) Observation (control); 2) Standard Feedback (weekly texts about four risky behaviors, $100 incentive); 3) Assigned Goal (weekly texts about algorithmically assigned focus behavior and goal, $100 incentive); 4) Chosen Goal (weekly texts about self-chosen focus behavior and goal, $100 incentive). Post-intervention, participants were monitored for 6 more weeks. The primary outcome was a 0–100 score for overall safety (100 = safest). Standard Feedback (covariate-adjusted difference, 2.0 [95 % CI, 1.2–2.7]; <em>P</em> &lt; 0.001), Assigned Goal (2.7 [95 % CI, 2.0–3.5]; <em>P</em> &lt; 0.001), and Chosen Goal (2.2 [95 % CI, 1.4–3.0]; <em>P</em> &lt; 0.001) had higher safety scores than control during the intervention period and at follow-up. Differences between treatment groups were not significant after adjusting for number of comparisons, <em>P</em>s &gt; 0.10. Treatment groups had significant relative reductions in speeding (11 %–13 %, depending on group), hard braking (16 %–21 %), and rapid acceleration (16 %–25 %), <em>P</em>s &lt; 0.01. The kind of feedback and incentives offered by UBI programs can cause sustained improvements in driver safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108331"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615715","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":"Multi-factor coupled friction coefficient map spatiotemporal modeling and driving risk fusion for rainy roads","authors":"Lintao Yang ,&nbsp;Xing Cui ,&nbsp;Huizhao Tu ,&nbsp;Hao Li ,&nbsp;Yiik Diew Wong","doi":"10.1016/j.aap.2025.108313","DOIUrl":"10.1016/j.aap.2025.108313","url":null,"abstract":"<div><div>The reduction in tire-road friction coefficient (TRFC) during rainy weather is a major cause of traffic accidents. TRFC values result from the spatiotemporal coupling of road, vehicle, and environmental factors, yet existing estimation methods struggle to fully integrate these factors, hindering the accuracy of TRFC-related driving risk assessments. To address this, a framework for spatiotemporal coupling of these factors at the lane level to generate friction coefficient maps is proposed and two fusion methods for multi-driving risks are established for rainy roads. A tire-fluid-road simulation model is developed to output a TRFC dataset for training a surrogate prediction model. Lanes are divided into grids to align TRFC-related factors, which are then input into the surrogate model to estimate TRFC and create friction coefficient maps. Three TRFC-related driving risks (hydroplaning, rear-end collisions, and sideslip) are analyzed and normalized to construct multi-risk maps, evaluated using max-risk and weighted-sum fusion strategies. The proposed method was validated using rainy-day car-following trajectory data on an urban expressway in Shanghai. Results show that heavy rainfall and high speeds reduce TRFC levels and increase its variability between wheel and non-wheel paths, augmenting the hydroplaning and sideslip risks, while reduced TRFC increases safe following distance and rear-end collision risk as vehicle convergence. The risk fusion results evidence that max-risk fusion excels in scenarios with a dominant risk, while weighted-sum fusion suits scenarios with multiple high-risk types. This study offers a lane-level driving risk assessment for rainy roads, providing insights for developing safety measures in wet road conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108313"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145518958","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":"Short-Term driving speed prediction under consecutive Variable speed Limits: An interpretable deep learning approach using Wide-Area trajectory data","authors":"Junhua Wang ,&nbsp;Yiwei Ren ,&nbsp;Ting Fu ,&nbsp;Qiangqiang Shangguan","doi":"10.1016/j.aap.2025.108316","DOIUrl":"10.1016/j.aap.2025.108316","url":null,"abstract":"<div><div>Existing research on driver behavior under Variable Speed Limits (VSLs) primarily relies on simulations and loop detector-based cross-sectional traffic data, with limited studies using real-world microscopic vehicle trajectory data. This study proposes an interpretable deep learning framework for short-term driving speed prediction under consecutive VSLs control. Using wide-area trajectory data from a 2.2 km segment of the Shanxi Wuyu Freeway with two successive VSL signs, driver behavior was quantitatively analyzed. A Convolutional Neural Network − Bidirectional Long Short-Term Memory (CNN-BiLSTM) model enhanced with a Multi-View Spatio-Temporal Attention Mechanism (MSTAM) was developed to predict short-term speeds and assess the influence of spatiotemporal features on driver responses. Results show that heavy vehicles consistently decelerate under all VSL strategies, while light vehicles display minimal adjustment at 100 km/h and 80 km/h limits but respond more significantly at 60 km/h, with greater inter-driver variability. Drivers in the left lane respond more promptly and decisively than those in the right lane, with shorter response distances and greater speed reductions under all VSL conditions. Additionally, the second VSL sign generally exhibited superior regulatory effectiveness compared to the first VSL sign. Compared to the baseline CNN-BiLSTM, the proposed MSTAM model reduces MAE by 17.2 % and RMSE by 23.5 %. The MSTAM model further captures cognitively consistent spatiotemporal attention patterns, focusing on regulatory zones near VSL signs, sustaining elevated attention in the left lane, and selectively recalling past behaviors to simulate adaptive driver responses. These findings offer a scientific foundation for enhanced VSL deployment and lane-specific speed control strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108316"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145547749","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":"Pedaling towards safer streets: evaluating the impact of cycling infrastructure on road safety","authors":"Brice Batomen ,&nbsp;Richard Wen ,&nbsp;Joonsoo Lyeo ,&nbsp;Chaandini Ranganathan ,&nbsp;Omidreza Sadrmanesh ,&nbsp;Andrew Howard ,&nbsp;Brent Hagel ,&nbsp;Meghan Winters ,&nbsp;Moreno Zanotto ,&nbsp;Colin Macarthur ,&nbsp;M. Anne Harris ,&nbsp;Linda Rothman","doi":"10.1016/j.aap.2025.108308","DOIUrl":"10.1016/j.aap.2025.108308","url":null,"abstract":"<div><div>Cycling provides health and environmental benefits but poses safety risks. Over the past decade, Canadian cities have expanded cycling infrastructure, such as cycle tracks (physically separated from motor vehicles) and painted lanes, to support safer active transportation. This study evaluates the impact of cycling infrastructure implemented in Toronto, Calgary, and Vancouver. We used infrastructure data (2011–2022) from municipal reports, validated with street view imagery. The primary outcome was killed or seriously injured (KSI) victims per 10 km of road, based on police-reported data (2009–2023). A difference-in-differences design, adapted for gradual infrastructure implementation, was applied using road segments as the unit of analysis. Subgroup analyses examined cyclist KSI and infrastructure type. Crude data on seasonal ridership from intersection counters on a subset of road segments (9 %) were used to estimate post-installation changes. Results varied by city and infrastructure type. In Toronto, no clear associations were found. In Calgary, new cycle tracks were associated with 2.1 fewer cyclist KSIs per 10 km (95 % CI: −4.5 to 0.3). In Vancouver, painted lanes were associated with 4.7 more KSIs per 10 km (95 % CI: 0 to 9.3), and one additional cyclist KSI per 10 km (95 % CI: 0 to 2.1). After implementation, cycle tracks showed substantial increases in cyclist volumes in Toronto (76 %), Calgary (207 %), and Vancouver (732 %), whereas painted lanes exhibited more modest changes. Although low KSI counts introduced uncertainty, cycle tracks were associated with no change or reductions in KSIs, while painted lanes showed no change or slight increases.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108308"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500141","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":"Have they taken effect as expected? Unpacking the black box of road safety countermeasure effects","authors":"Yingheng Zhang ,&nbsp;Haojie Li","doi":"10.1016/j.aap.2025.108309","DOIUrl":"10.1016/j.aap.2025.108309","url":null,"abstract":"<div><div>Countermeasure evaluation is an important subfield of road traffic safety research. Numerous works have been carried out to establish the cause–effect relationship between countermeasures and crash-related outcomes. However, a single estimate of the countermeasure effect may not always reflect their actual road safety benefits since the underlying causal mechanisms are neglected. To be specific, conventional evaluation frameworks cannot provide a comprehensive view of how a countermeasure influences defined outcomes of interest, e.g., via influencing a specific intermediate variable (mediator). This paper aims to unpack the black box by exploiting the causal inference framework for mediation analysis, which is able to decompose the total causal effect into the indirect effect that relays through the putative mediator and the direct effect. In particular, this framework builds on the potential outcome paradigm, which offers general definitions for the causal estimands of interest and clarifies the core assumptions for nonparametric identification. As a major superiority over traditional mediation analysis, evaluators are permitted to choose from a wide range of statistical methods to estimate the component models (e.g., the mediator model and the outcome model). Simulation experiments are conducted to show the applicability of the causal mediation method to count data analysis with negative binomial models. Also, the method is applied to a speed enforcement camera case study to examine the presence of crash migration phenomena by modelling traffic volume as mediator. Our empirical analysis has found a null indirect effect on crash frequency at enforced road sites, implying that crash migration due to drivers choosing alternative routes to avoid speed cameras is unlikely in this particular case. In summary, by properly applying the causal mediation method to countermeasure evaluation, we could gain a deeper insight into systematic causality.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108309"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145562224","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":"The effect of license plate number-based vehicle restrictions on crash frequency","authors":"Maria C. Valencia-Cardenas ,&nbsp;John Jairo Posada-Henao ,&nbsp;Jhan Kevin Gil-Marin ,&nbsp;Carlos A. Gonzalez-Calderon ,&nbsp;Alexander Paz","doi":"10.1016/j.aap.2025.108326","DOIUrl":"10.1016/j.aap.2025.108326","url":null,"abstract":"<div><div>This study investigates the effects of license plate-based vehicular circulation restrictions, known as “peak-and-plate” policies, on urban traffic crashes in Medellín, Colombia. Although such policies are widely implemented to reduce congestion and emissions, limited research has examined their unintended safety impacts on crash frequency before and during the restriction periods. Considering high-resolution crash data from 2008 to 2022, this study evaluates the temporal effects of these restrictions on crash frequency across different hours, months, and weekdays. Statistical preprocessing and hierarchical clustering techniques were applied to discover crash temporal patterns, and negative binomial, pooled, fixed effects, and random effects models were tested to account for overdispersion and panel structure in the data. Results reveal that crash frequencies increased consistently in the 30 min preceding the enforcement of circulation restrictions, particularly during the morning hours. Surprisingly, crash rates remained elevated during the restriction period, contrary to expectations that reduced vehicle volume would lower crash occurrences. These findings suggest the presence of confounding behavioral, spatial, or enforcement-related factors. Additionally, monthly and weekly crash patterns correlate with seasonal variations, holidays, and changes in traffic volume, emphasizing the importance of context-aware traffic policies. Statistical evidence suggests that current vehicle restriction policies may inadvertently concentrate risk in specific time windows. These insights highlight the need for policymakers to refine urban mobility strategies by considering crash and traffic data simultaneously, implementing targeted public awareness campaigns<em>,</em> targeting speed enforcement, and incorporating enforcement dynamics into the design of vehicle restriction programs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108326"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145562107","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":"State-of-the-art review on sustainable driving behavior: trade-offs between road safety, fuel consumption and emissions","authors":"Virginia Petraki,&nbsp;Apostolos Ziakopoulos,&nbsp;George Yannis","doi":"10.1016/j.aap.2025.108253","DOIUrl":"10.1016/j.aap.2025.108253","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108253"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676070","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":"Collision risk identification and prediction considering heterogeneous braking patterns using large-scale pre-collision trajectories","authors":"Xudong Ren ,&nbsp;Lu Bai ,&nbsp;Pan Liu ,&nbsp;Wai Wong ,&nbsp;Jin Liu ,&nbsp;Shuo Kong ,&nbsp;Bicheng Xu","doi":"10.1016/j.aap.2025.108312","DOIUrl":"10.1016/j.aap.2025.108312","url":null,"abstract":"<div><div>Rear-end collisions often occur in vehicles when successive braking events lead to insufficient deceleration by one or more vehicles. This paper proposed a novel method for collision risk identification and prediction by integrating the braking dynamics of both leading and following vehicles in pre-crash scenarios. Using a piecewise linear model of deceleration profiles, 45 collision risk moments were identified across 10 scenarios based on ten kinematic parameters. A novel critical time-to-collision metric was proposed to integrate both the timing and execution of braking behaviors into collision risk prediction. To account for driver heterogeneity in deceleration, deceleration rate, and reaction time, Gaussian mixture regression was used to perform conditional inference to estimate braking pattern parameters and generate interval-valued crash risk predictions. The performance and optimal threshold were validated using large-scale vehicle trajectories from collision and non-collision events. The results demonstrate that the collision risk moments vary with both the braking timing and execution of the leading and following vehicles. The proposed metric outperformed traditional surrogate safety measures in predicting collision risk, consistently yielding higher accuracy with less variability across pre-crash time intervals. These findings indicate that the estimated critical time-to-collision is a reliable and effective measure for collision avoidance systems and advanced driver assistance technologies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108312"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145601457","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":"The effectiveness of real-time and post-trip interventions within the i-DREAMS naturalistic driving project: A cross-national analysis","authors":"Yanchao Song ,&nbsp;Kris Brijs ,&nbsp;Robert A.C. Ruiter ,&nbsp;Tom Brijs ,&nbsp;Muhammad Adnan ,&nbsp;Muhammad Wisal Khattak ,&nbsp;Yongjun Shen ,&nbsp;Geert Wets ,&nbsp;Veerle Ross","doi":"10.1016/j.aap.2025.108307","DOIUrl":"10.1016/j.aap.2025.108307","url":null,"abstract":"<div><div>The i-DREAMS project set up a platform and system that provides real-time and post-trip interventions (including gamification elements) to keep drivers within safe margins. While the effectiveness of interventions has been widely studied, limited research has explored their interaction. Specifically, it remains unclear how engagement with post-trip interventions influences adherence to real-time interventions and how such engagement and adherence impact individual driving risk. Moreover, the factors contributing to variation in intervention engagement and adherence across drivers remain underexplored. In addition, most existing evaluations of intervention effectiveness have been conducted within a single-country context, with a limited focus on cross-national differences, which are crucial for understanding variation in intervention performance across different national contexts.</div><div>This study aims to assess the impact of real-time and post-trip interventions on drivers’ individual driving risk across European countries, examine cross-national differences, and explore their underlying causes. The results show that the i-DREAMS interventions significantly reduced traffic offense risk and kinematic driving risk, although cross-national differences were observed between Belgium and the UK. The real-time interventions significantly reduced kinematic driving risk among UK drivers, whereas gamified post-trip interventions were more effective for Belgian drivers. Additionally, the real-time interventions effectively reduced traffic offense risk in both countries. A strong negative association was found between adherence to real-time interventions and traffic offense risk, and engagement with post-trip interventions was negatively associated with kinematic driving risk. Gamification elements enhanced engagement with post-trip interventions. The insights gained from this study help enhance the customization of i-DREAMS interventions and application strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108307"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500151","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}