IATSS Research最新文献

{"title":"Motorcycle injury severity prediction: Bridging statistical inference and explainable machine learning","authors":"Amirhassan Kermanshah,&nbsp;Romina Pandamooz,&nbsp;Mohammad Tork Ali Askari Rizi","doi":"10.1016/j.iatssr.2026.08.002","DOIUrl":"10.1016/j.iatssr.2026.08.002","url":null,"abstract":"<div><div>Motorcycle crashes represent a major road safety challenge, particularly in developing countries. This study analyzes motorcycle crash injury severity on intercity roads in Iran using an integrated framework combining classical statistical models and machine learning techniques. Police-reported crash data from eight provinces (2018–2021) were examined, with injury severity modeled as a binary outcome distinguishing property-damage-only crashes from injury or fatal crashes. Binary logit and probit models were applied as interpretable benchmarks, while multiple machine learning classifiers were used to capture nonlinear effects, with SHapley Additive exPlanations (SHAP) employed for model interpretability. Results show strong consistency between statistical inference and explainable machine learning outcomes. Collision partner type, human-related factors, licensing status, rider age, and temporal conditions emerged as key determinants of injury severity. Ensemble-based models, particularly XGBoost, achieved superior predictive performance. Overall, the study demonstrates that combining interpretable statistical models with explainable machine learning offers a robust and transparent approach for motorcycle injury severity analysis and supports data-driven road safety policies.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1154-1165"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and classification of run-off-road conflict using surrogate safety metrics in mixed traffic environments","authors":"Kommoju Prathyusha,&nbsp;Indrajit Ghosh","doi":"10.1016/j.iatssr.2026.08.003","DOIUrl":"10.1016/j.iatssr.2026.08.003","url":null,"abstract":"<div><div>Run-off-road crashes, though relatively infrequent, often result in severe or fatal outcomes, making them a critical focus in road safety research. Due to the limited availability of crash data, this study proposes a data-driven framework to identify, characterise, and classify run-off-road conflicts using surrogate safety measures. Two enhanced Time-to-Collision measures are proposed: Lateral TTC (LTTC) and Road Edge TTC (RETTC), which capture lateral drift and forward boundary-approach risks, respectively. Using the Peak-Over-Threshold method of Extreme Value Theory, conflict thresholds are identified as 0.6 s for LTTC and 0.8 s and 1.5 s for RETTC at the curved and straight segments, respectively. Conflict events are further characterised using Time Exposure TTC (TET), Time Integrated TTC (TIT), and lateral jerk, and classified into Low, Moderate, and High risk categories via bilevel K-means clustering. The labelled conflicts are then trained ordinal classification models incorporating behavioural, geometric, and contextual variables. Among the models evaluated, the ordinal Gradient Boosting Machine performed best, with low MAE, high Kappa, high within-one accuracy, and a high consistency index. SHAP analysis highlights vehicle speed, lateral acceleration, and distance to road edge as key predictors, providing critical insights for proactive road safety management and intervention design, particularly relevant in heterogeneous traffic environments typical of developing countries.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1088-1104"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving anxiety related cognitions as mediators: Bridging driver skills and behavioral outcomes among Turkish drivers","authors":"Derya Azık Özkan,&nbsp;Bahar Öz","doi":"10.1016/j.iatssr.2026.08.001","DOIUrl":"10.1016/j.iatssr.2026.08.001","url":null,"abstract":"<div><div>Drivers' cognitive appraisals of their performance are significantly associated with their actual driving behaviors, often in ways that remain insufficiently explored. While individuals naturally strive to optimize their driving performance, intrusive cognitions related to performance anxiety, skill uncertainty, accident potential, or causing harm can substantially impair driving acts. Although previous research has predominantly focused on clinical populations with driving phobias or anxiety, these anxiety-laden cognitions affect the general driving population with varying frequency and intensity, shaping everyday driver behavior. This study investigates the mediating role of driving-related cognitions, specifically accident-panic-related concerns and social concerns, in the relationship between self-perceived driving skills and both anxious and aberrant driving behaviors in a sample of 484 Turkish drivers. Results reveal that these concerns significantly mediate the relationships between both perceptual-motor and safety skills and subsequent anxiety-based performance deficits, lapses, and errors. These findings illuminate the complex cognitive mechanisms through which drivers' skill self-appraisals relate to subsequent driving behavior patterns through the mediation of anxiety-related cognitions. The study provides critical insights into the formation of driving errors and lapses, advancing our theoretical understanding of aberrant driving behaviors. More broadly, these results highlight the significant role of cognitive-emotional processes in relation to driver actions, providing insight for interventions aimed at promoting safer driving through cognitive restructuring approaches. These findings underscore the relevance of driving-related cognitions beyond clinical contexts, highlighting their role in shaping everyday driving behavior across the general population.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1116-1126"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149153518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychosocial determinants of helmet use among young motorcyclists","authors":"Dewi Maulina,&nbsp;Muthia Rahmah,&nbsp;Almira Rahma Safira,&nbsp;Dimitri van der Linden,&nbsp;Guritnaningsih A. Santoso,&nbsp;Revina Putri,&nbsp;Fadly Zirman,&nbsp;Adela Putri Anggraini","doi":"10.1016/j.iatssr.2026.09.001","DOIUrl":"10.1016/j.iatssr.2026.09.001","url":null,"abstract":"<div><div>The use of helmets among motorcyclists is important as protection against fatalities in road crashes. Although previous studies have identified psychosocial factors that influence helmet-use behaviour, they have not considered how these factors apply within different riding contexts that may encourage (pro-helmet condition) or discourage helmet use (anti-helmet condition). Accordingly, the current study examined the combined and unique roles of attitude, self-efficacy, descriptive norm, threat perception, past helmet use behaviour, and intentions on helmet use in pro and anti-helmet conditions. A total of 628 licensed motorcyclists aged 18–24 years from the Greater Jakarta responded to helmet use questions in different scenarios. Results showed that in pro-helmet conditions, females were more likely to wear helmets than males. Meanwhile, in anti-helmet conditions, helmet use was predicted by descriptive norm, helmet use intention, perceived susceptibility, gender, and past behaviour. These findings suggest that when external incentives for helmet use are limited, it is important to examine how individuals recognize potential risks and health implications, and how social norms influence helmet use. This insight can contribute to designing effective interventions aimed at increasing helmet use in different contexts, thereby improving road safety.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1208-1216"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149153519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variable speed limits (VSL) and road safety: A systematic review and guidance for VSL design and implementation","authors":"Mahyar Hosseini,&nbsp;Kayvan Aghabayk,&nbsp;Nirajan Shiwakoti","doi":"10.1016/j.iatssr.2026.10.001","DOIUrl":"10.1016/j.iatssr.2026.10.001","url":null,"abstract":"<div><div>Variable speed limit (VSL) systems are increasingly deployed as part of active traffic management, yet their safety impacts remain heterogeneous and context dependent. This paper presents a structured review of empirical and modeling studies evaluating the safety effects of VSL, with explicit distinction between crash-based and non-crash-based evidence. This systematic review used PRISMA-guided procedures to identify and synthesize 51 eligible studies that span a wide range of methodological approaches, from traditional before–after crash evaluations and simulation-based analyses to more recent data-driven and reinforcement learning–based VSL control frameworks. Crash-based evaluations generally report meaningful safety gains, with VSL associated with reductions in total crashes and in specific crash types, particularly rear-end, weather-related, and injury crashes, with the magnitude of these reductions varying depending on roadway, traffic, and environmental conditions. Complementary non-crash-based evidence shows that these reductions are plausibly mediated by lower speed variance, smoother traffic flow, and mitigation of congestion-induced shockwaves, although many studies rely on assumed compliance and are limited to simulated environments. At the same time, several evaluations highlight that poorly chosen activation conditions, aggressive speed steps, and low driver compliance can erode or even reverse safety benefits. Drawing on this mixed evidence base, the review synthesized practice-oriented guidance on where, when, and how VSL should be implemented. The findings indicate that VSL is most effective on corridors with recurrent congestion, adverse weather, or geometric bottlenecks that produce unstable traffic conditions, and that incremental, harmonization-oriented control strategies with short update intervals are preferable to large step-down speed reductions. The paper proposes indicative decision thresholds for VSL activation based on traffic-flow, weather, and safety triggers, and outlines agency readiness requirements. Overall, the review clarifies the conditions under which VSL is most likely to deliver robust safety benefits and provides a set of evidence-based recommendations to inform design and deployment.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1231-1244"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of motorcycle riders' gap acceptance at unsignalized mid-block crosswalks","authors":"Tanawan Sutantaviboon,&nbsp;Kunnawee Kanitpong","doi":"10.1016/j.iatssr.2026.08.004","DOIUrl":"10.1016/j.iatssr.2026.08.004","url":null,"abstract":"<div><div>Unsignalized mid-block crosswalks can often lead to conflicts between motorcyclists and pedestrians, especially in Thailand which has a high number of motorcycles using the road system. Generally, motorcyclists must yield to crossing pedestrians. However, many pedestrian-motorcycle crashes occur due to motorcyclists failing to stop or yield. This risky decision on the part of motorcyclists is caused by too short gap acceptance, which can cause injury or death to both the motorcyclist and pedestrian. This study aims to investigate motorcyclists' critical gap when faced with pedestrians crossing at unsignalized mid-block crosswalks and to determine the significant factors that influence motorcyclists' decision to pass unsignalized mid-block crosswalks. The critical gap was estimated using Raff's Method, Wu's Method, and binary logistic regression, while the influencing factors for motorcyclists' decision were analyzed using the binary logistic regression model. From the results of this study, the critical gaps of motorcyclists when faced with a single movement of a pedestrian are very short (0.54–1.07 s). Factors that influence motorcyclists' decision to stop at crosswalks were found to include gap time, direction of pedestrians' movement, presence of vehicles nearby, pedestrian crossing locations, number of crossing pedestrians, and crosswalk colors. The interaction effect between crosswalk color and median type was significant, indicating that the influence of red-white crosswalks on motorcyclists' stopping behavior varied depending on the presence of a raised median. Specifically, red-white crosswalks were associated with a greater increase in stopping likelihood on roads without a raised median than on roads with a raised median. The short critical gap reflects the risky behavior of motorcyclists in Thailand. To improve the safety of motorcyclists and pedestrians, the findings of the present study are very important to improve law enforcement and the geometric design of unsignalized mid-block crosswalks.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1105-1115"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel traffic accident modeling framework by integrating fractal dynamics and machine learning","authors":"Rana Ibrahim Abid,&nbsp;Duha Wael Alsarayreh,&nbsp;Hassan Musaed Al-Ahmadi,&nbsp;Syed Masiur Rahman","doi":"10.1016/j.iatssr.2026.09.003","DOIUrl":"10.1016/j.iatssr.2026.09.003","url":null,"abstract":"<div><div>This study investigates the temporal and fractal dynamics of daily traffic accidents in Amman, Jordan, from 2013 to 2022, with particular focus on changes during the COVID-19 pandemic. Fractal analysis using the Hurst exponent (H) and Rescaled Range (R/S) dimension was applied to assess long-term dependence and complexity in accident patterns. Results show that H ranged from 0.379 to 0.895, with a strong inverse relationship between H and R/S (<em>r</em> = −1.0, <em>p</em> &lt; 0.001), indicating that increased complexity corresponds to reduced persistence. During the 2020 lockdown, accident dynamics became more persistent, and less complex compared to pre- and post-lockdown periods. Higher accident frequencies were associated with lower H and higher R/S, reflecting more unstable behavior. The Seasonal-Trend decomposition using Losses (STL) with the Gated Recurrent Unit (GRU) network was applied for traffic accident prediction. The model achieved a high R² value by utilizing and selecting statistical features based on their importance and correlations. A combination of fractal features slightly increased the R² prediction value. Benchmark machine learning models (Random Forest, Ridge Regression, and Elastic Net) demonstrated inadequate predictive ability in traffic accident forecasting. Random Forest, optimized with the Snake Optimizer, achieved the lowest Mean Absolute Error of about 55. The SHAPE analysis findings indicated that the seasonal and trend components generated from STL were the main predictors, although the R/S fractal dimension had more significance than other statistical metrics. The results demonstrate that integrating fractal analysis with machine learning enhances the understanding and prediction of traffic accident dynamics. The findings support adaptive traffic management and resilience-oriented safety strategies during disruptive events.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1217-1230"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Route-level safety assessment of candidate intercity evacuation routes for a Sakurajima volcanic eruption using historical crash data","authors":"Andreas Keler,&nbsp;Masato Iguchi","doi":"10.1016/j.iatssr.2026.09.005","DOIUrl":"10.1016/j.iatssr.2026.09.005","url":null,"abstract":"<div><div>A future large-scale eruption of Sakurajima may require rapid, area-based, long-distance evacuation from Kagoshima City under an east-wind scenario with massive tephra fall. This study assesses the traffic safety of 39 candidate evacuation routes (and 6 alternative routes) proposed in Kagoshima City's Plan for Responding to Massive Tephra Fall by linking evacuation demand with historical crash occurrence. The routes are digitized from the plan maps using automated image segmentation and data conversion, then matched to geocoded crash locations by 1-nearest-neighbor search to the nearest route edge within a 10 m tolerance. For each route, we compute crash frequency (crashes per year) and crash density (crashes per km per year) from multi-year crash counts, and relate these safety indicators to assigned households. Assuming one passenger car per household and a six-hour evacuation window, we also estimate the expected traffic flow rate q (veh/h) as a demand proxy. Results indicate substantial heterogeneity across routes: crash frequency and density vary with route length and district household concentration, and two relatively short routes in Zone D show the highest crash counts while serving the most populous districts. Historical road crash occurrence is also associated with the number of junctions along evacuation routes. By using Negative Binomial (NB) regression, we show that a one-junction-per-km increase corresponds to about a 24.3% higher expected crash rate per km-year. These findings point to safety-demand trade-offs in pre-eruption route guidance and motivate next steps such as segment-level risk profiling, intersection classification, directionality and turning-restriction constraints, exposure normalization and identifying safer alternative routes to the same shelters.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1166-1181"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149153517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive analysis of accident severity determinants in Bangladesh using machine learning","authors":"Ahnaf Tahmid,&nbsp;Nafisa Mahbub,&nbsp;Shahnewaz Hasanat-E-Rabbi","doi":"10.1016/j.iatssr.2026.08.007","DOIUrl":"10.1016/j.iatssr.2026.08.007","url":null,"abstract":"<div><div>Road traffic accidents are a major cause of fatalities in developing countries like Bangladesh, with the country's accident fatality rate significantly exceeding that of neighboring countries. By leveraging police reported accident data from the Accident Research Institute (ARI) at BUET, this study conducts a comprehensive analysis of the determinants of accident severity (AS) in Bangladesh using machine learning (ML) techniques. However, the dataset has been segmented based on predefined contextual criteria such as area (urban/rural), vehicle involvement (single/two vehicles) and road class (Highways, other roads). Previous studies analyzing AS primarily use traditional statistical models, which are limited by assumptions about data distribution and linear relationships. These studies rarely employ explainable AI (XAI) methods or stratified analysis to identify significant factors within each subgroup. To address these limitations, this study employed Explainable Artificial Intelligence approaches: permutation importance, and SHapley Additive exPlanations (SHAP) method across subgroups, using tree-based Random Forest (RF), Extreme Gradient Boosting (XGBoost); classification-based K-Nearest Neighbor (KNN); and hybrid Stack model ML approaches. Wilcoxon signed-rank test depicts that, stack model most effectively captures the complex structure of data for all. The result of the study indicates that, vehicle type, collision type, district, divider, surface quality, location type, time and driver age are the key variables for predicting AS. The findings are derived from 2006 to 2015 police-reported crash data and should therefore be interpreted in the context of the road infrastructures of that period, while future studies should validate the results using more recent datasets. Based on partial dependency plot (PDP) and descriptive analysis this research concludes that common collision scenarios on Bangladeshi roads include hit pedestrian, head on collision, collision between heavy and light vehicles, and incidents involving drivers aged between 31 and 45 years. Based on the analysis, this study provides valuable insights for key organizations in Bangladesh, including the Bangladesh Road Transport Authority (BRTA), Roads and Highway department (RHD), Bangladesh Police (BP), and Local Government Engineering Department (LGED).</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1127-1145"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surrogate safety evaluation of emerging alternative intersection designs using safe system and microsimulation-based methods","authors":"Gaurav Aryal,&nbsp;Stephen Osafo-Gyamfi,&nbsp;Amirarsalan Mehrara Molan,&nbsp;William Rasdorf,&nbsp;Ali Hajbabaie,&nbsp;Hayden Edwards","doi":"10.1016/j.iatssr.2026.09.004","DOIUrl":"10.1016/j.iatssr.2026.09.004","url":null,"abstract":"<div><div>Transportation agencies have shown high interest in implementation of newer designs such as thru-cut, and alternative intersections with a three-critical-phase traffic signal. However, due to the limited number of installations and insufficient crash data, traditional crash-based evaluations and the development of reliable Crash Modification Factors (CMFs) for these designs remain impossible. To address this gap, in the absence of crash data, the present study applies three surrogate safety analysis methods: the FHWA-recommended Surrogate Safety Assessment Model, the Safe System Intersection (SSI) framework, and a newly developed Conflict Point Analysis (CPA) tool, to assess and compare the safety performance of thirteen (13) alternative intersections, including the conventional configuration, ten (10) emerging alternative designs with three-phase traffic signals, and two (2) well-adopted two-phase alternative intersections. As part of this study, the research team developed two spreadsheet-based evaluation tools: the SSI tool, based on FHWA's Safe System framework, and the CPA, a new concept that assesses conflict severity based on conflict type, location, traffic volume, and vehicle speed. While the results varied to some extent across the three methods, a general trend emerged indicating that most alternative designs demonstrate improved safety performance compared to the conventional design. Reverse reduced conflict intersection (Reverse RCI) and RCI emerged as the safest among all evaluated configurations.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 3","pages":"Pages 1193-1207"},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}