IATSS Research最新文献

{"title":"Empirical evaluation of vehicle safety envelope across vehicle type and speed in disordered traffic condition","authors":"Shahana Avathkattil ,&nbsp;Vedagiri Perumal","doi":"10.1016/j.iatssr.2025.12.006","DOIUrl":"10.1016/j.iatssr.2025.12.006","url":null,"abstract":"<div><div>Real-time safety assessments face unique challenges in developing economies due to the disordered and high heterogeneity in traffic. This study proposes a dynamic two-dimensional surrogate safety model, known as the Vehicle Safety Envelope (VSE), that represents the minimum space required around a vehicle for safe and comfortable manoeuvring in traffic. The VSE is mathematically modelled and calibrated using trajectory data collected from five signalized intersections in India. The proposed elliptical shape of the VSE was found to be analogous to the field observed safety space maintained by a vehicle from its neighbouring vehicles for safe and comfortable manoeuvres. Empirical results indicate that both lateral and longitudinal clearance thresholds, the key parameters defining the VSE, exhibit a positive linear dependency on vehicle type and speed. As speed increased from 5 to 65 km/h, the lateral clearance threshold increased from 0.2 m to 0.8 m for two-wheelers and from 0.6 m to 1.2 m for heavy commercial vehicles. Similarly, the average longitudinal clearance threshold increased from 1 m to 14 m. These thresholds closely matched safe stopping distances calculated from field data, validating the VSE's capability to capture unsafe vehicle proximities. Integrating VSE into advanced driver-assistance systems could enhance proactive safety decision-making.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 1","pages":"Pages 633-652"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing evacuation behaviors using virtual simulation and Levenshtein similarity: A case study of railway stations","authors":"C.Y. LAM ,&nbsp;S. AN ,&nbsp;A.M. CRUZ","doi":"10.1016/j.iatssr.2025.12.003","DOIUrl":"10.1016/j.iatssr.2025.12.003","url":null,"abstract":"<div><div>Effective analysis of evacuation behavior is essential for improving safety management in complex public spaces such as railway stations. This study presents a methodological framework that integrates a virtual simulation environment with the Levenshtein Similarity method to quantitatively examine behavioral sequences during emergency evacuation scenarios. A controlled experiment was conducted using a repeated measures design to observe participants' route choices under both normal and simulated emergency conditions. Behavioral trajectories were compared using Levenshtein Similarity to identify patterns and deviations in decision-making processes. The results demonstrate that this combined approach captures variations in individual responses to environmental cues, such as exit signs and spatial configurations. By focusing on low-congestion scenarios, the method provides a robust and reproducible way to assess evacuation strategies and decision-making processes. The study highlights the potential of integrating simulation and similarity based analysis as a scalable tool for evaluating human behavior in safety-critical environments.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"50 1","pages":"Pages 669-679"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Framing automation of driving tasks: Effects on risk perception and consumers’ understanding of different levels of vehicle automation across four European countries","authors":"Ilse M. Harms ,&nbsp;Wouter Van den Berghe","doi":"10.1016/j.iatssr.2025.10.005","DOIUrl":"10.1016/j.iatssr.2025.10.005","url":null,"abstract":"<div><div>Framing is the process whereby people's perception is influenced by how information is presented. In the automotive sector, well-known frames concern the names for advanced driver assistance systems (ADAS) which suggest higher levels of automation (e.g., by including the word ‘pilot’). The current study focusses on the framing of messages about the system's capabilities and how they impact on drivers' mental model regarding the need to control their car.</div><div>A total of 3000 licensed drivers across four European countries participated in a survey with an embedded video, to provide insight in whether the framing of ADAS would affect consumers' willingness to take more risks in traffic and to compare their understanding of vehicle automation. Three levels of automation were distinguished: (1) assisted driving, a level of automation which supports the human driver without taking over control of the vehicle; (2) automated driving, at which level a vehicle takes over control of the vehicle within the limits of its operational design domain; (3) autonomous driving, when a vehicle is capable of driving itself under all circumstances. The effect of framing was studied by dividing respondents into two groups. The questions in the survey were the same for both groups, but the video was not. One group was shown a video stressing the increased comfort resulting from ADAS, i.e. the ‘Comfort condition’. The other group saw a video with the same factual content to explain the system's capabilities and limitations, but in which the importance of remaining responsible for and in control of driving was emphasised, i.e. the ‘Responsible condition’.</div><div>This study has shown that framing of messages about ADAS' system capabilities indeed impacts drivers' mental model of these systems. Participants exposed to the ‘Comfort’ frame indicated a tendency for higher risk-taking behaviour compared to participants in the ‘Responsible’ frame. Moreover, the study also showed that the ‘Comfort’ frame shifted people's mental model about their role as a driver towards that for a higher level of automation. A well-calibrated mental model is a prerequisite for the safe use of these systems on the road.</div><div>Given the findings of this study, the authors also propose a modification of the labels for the SAE levels of automation, distinguishing more clearly assisted, automated and autonomous driving.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 493-501"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145418738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-cultural perspectives of vulnerable road user safety performance based on evidence from 39 countries","authors":"George Yannis ,&nbsp;Apostolos Ziakopoulos ,&nbsp;Dimitrios Nikolaou ,&nbsp;Konstantinos Kaselouris ,&nbsp;Mette Møller ,&nbsp;Dagmara Jankowska-Karpa ,&nbsp;Marie-Axelle Granié","doi":"10.1016/j.iatssr.2025.11.001","DOIUrl":"10.1016/j.iatssr.2025.11.001","url":null,"abstract":"<div><div>Pedestrians, cyclists, moped riders and motorcyclists are regarded as vulnerable road users (VRUs), as they face a heightened risk of injury in the event of a collision with a vehicle. The present study aims to provide a quantified update on VRUs safety performance by analyzing data from a broad-country sample. For this study, data was utilized from the third edition of the E-Survey on Road Users' Attitudes (ESRA3) survey, which was conducted in 2023, covering 39 countries from 5 continents. Specifically, the paper investigates the attitudes and opinions of pedestrians, cyclists, and moped riders and motorcyclists regarding (i) their safety perceptions of specific transport modes and (ii) various types of unsafe behavior (for instance, speeding, alcohol/drug consumption, helmet use, red light violations and others). Furthermore, a statistical analysis based on Self-Organizing Maps (SOMs) and clustering was conducted to meaningfully categorize VRU groups, enabling the quantification of each category and providing scientific documentation for more informed policymaking. Key findings include the fact that VRU safety perceptions and behaviors differ significantly across regions, with Europeans feeling safer overall, with older VRUs displaying fewer risky habits than younger ones. Cyclists often neglect helmet use in particular countries (Thailand, Bosnia), while drug and alcohol consumption while riding is notably high in specific countries as well (Ireland, Thailand, the Netherlands). SOM analysis reveals that most VRUs fall into low-risk behavior clusters, however, it is worth noting that respondents that engaged in a number of unsafe individual behaviors to smaller extents were classified as unsafe along with the more frequent offenders. For all VRU modes, statistical tests revealed that there is a statistically significant association between younger individuals and higher-risk cluster categorization. Finally, the paper provides recommendations for road safety stakeholders operating at different levels, which could be implemented in efforts to enhance VRUs road safety.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 502-527"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of traditional traffic control devices on wrong-way driving incidents at interchange off-ramps","authors":"Qing Chang ,&nbsp;Huaguo Zhou ,&nbsp;Yukun Song ,&nbsp;Md Roknuzzaman","doi":"10.1016/j.iatssr.2025.09.008","DOIUrl":"10.1016/j.iatssr.2025.09.008","url":null,"abstract":"<div><div>Wrong-Way Driving (WWD) poses a significant threat to road safety, as drivers travel in the opposite direction of legal traffic flow, often resulting in severe injuries and fatalities. This study evaluated and summarized traditional low-cost countermeasures to prevent WWD incidents. Data from 406 WWD incidents were analyzed to assess the effectiveness of various countermeasures. The results revealed that the average WWD distance can be reduced by implementing more supplemental Wrong-Way (WW) signs on off-ramps, highlighting the importance of additional visual cues. At least two sets of Traffic Control Devices (TCDs) were found to be necessary to achieve optimal performance in reducing WWD distance and also accommodating 97 % of self-corrected turnaround events. Moreover, the study emphasized the significance of TCD placement, as the average WWD distance increased when TCDs were located farther from off-ramp terminals. Every 10 ft increase in the distance of 1st TCD is expected to increase WWD distance by 3.4 ft. Based on the findings, it is recommended to place the first set of WW-related TCDs within 75 ft of the stop line to enhance their early warning capabilities. The second set of TCDs should be placed 50 ft from the first set, and a gap of 150 ft should be maintained between the second and third sets of TCDs. Additionally, the installation of supplemental WW-related TCDs at high-risk locations was advocated to effectively target areas prone to WWD incidents. By strategically deploying these countermeasures, transportation agencies can take substantial steps toward enhancing roadway safety and reducing the occurrence of WWD.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 448-458"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145278256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive traffic signal control using deep reinforcement learning: A multi-objective approach for single and multi-intersection scenarios","authors":"Marwa Elharoun,&nbsp;Sherif M. El-Badawy,&nbsp;Elsayed Abd-Elazem Shwaly,&nbsp;Usama Elrawy Shahdah","doi":"10.1016/j.iatssr.2025.10.004","DOIUrl":"10.1016/j.iatssr.2025.10.004","url":null,"abstract":"<div><div>Traffic congestion in urban networks necessitates adaptive signal control systems that balance efficiency, safety, and environmental impact. While Deep Reinforcement Learning (DRL) has shown promise for traffic signal control (TSC), existing approaches often optimize single objectives or lack scalability for multi-intersection corridors. This study proposes a multi-objective DRL framework that simultaneously minimizes vehicle delays, traffic conflicts (using Time-to-Collision metrics), and CO₂ emissions. Six reward functions were designed and tested with three DRL algorithms (PPO, A2C, DQN) in synthetic and real-world intersections simulated in SUMO. Key results demonstrate that: (1) PPO outperformed A2C and DQN, achieving up to 21 % fewer conflicts and 7 % lower delays in high-traffic scenarios; (2) A safety-focused reward (Reward 3) reduced conflicts by 4–20 % compared to fixed-time controls in high-traffic scenarios, while multi-objective rewards (Rewards 5–6) balanced all targets effectively; and (3) Decentralized control for multi-intersection corridor reduced delays, conflicts, and emissions by 26.4 %, 26.9 %, and 12 %, respectively, surpassing centralized approaches. Real-world validation in Hangzhou, China, and Cologne, Germany, confirmed robustness, with 10–15 % delay reductions and 2.2–6.4 % lower emissions versus prior DRL models. This work advances adaptive TSC by integrating safety and sustainability into DRL optimization, offering scalable solutions for heterogeneous traffic networks.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 481-492"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crash risk assessment at unsignalized intersections using vehicle trajectory data","authors":"Debashis Ray Sarkar ,&nbsp;K. Ramachandra Rao ,&nbsp;Niladri Chatterjee","doi":"10.1016/j.iatssr.2025.09.001","DOIUrl":"10.1016/j.iatssr.2025.09.001","url":null,"abstract":"<div><div>Crash prediction models (CPMs) typically use statistical or data-driven approaches derived from observed crash data, but these can be limited by unreliable historical data. Near-crash-based CPMs provide a proactive alternative, predicting crash frequencies before actual crashes occur. Surrogate Safety Measures (SSMs) examine potentially hazardous traffic events to improve the understanding of traffic safety dynamics. These events serve as proxies for crashes, enabling proactive and timely safety assessments. This study proposes a methodological framework for evaluating crash risk at unsignalized intersections using UAV-acquired vehicle trajectory data and applies Extreme Value Theory (EVT) to statistically model the tail behavior of a time-based SSM—Post Encroachment Time (PET). High-resolution (4 K) video data were acquired at six different unsignalized intersections to capture morning rush hour traffic (8 to 9 a.m.). Vehicle trajectories and surrogate measures such as Post Encroachment Time (PET) were extracted using advanced AI-driven video analysis via the DataFromSky (DFS) platform. The analysis employed the Peak Over Threshold (POT) method. The threshold was determined to be −1.25 s using the Mean Residual Life (MRL) plot, as well as the scale and shape parameter stability plots of the Generalized Pareto Distribution (GPD). The results show that traffic volume and crash frequency have a significant impact on collision risk. As traffic volume increases, PET decreases, leading to a higher likelihood of conflicts and crashes. Additionally, mean speed shows an inverse relationship with both crash frequency and collision risk. Overall, traffic volume and conflict frequency emerge as key predictors of crash risk occurrences. This study establishes a foundation for leveraging UAV-based vehicle trajectory data in conducting proactive safety assessments at unsignalized intersections.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 459-469"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145326258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conflict-based crash risk estimation of heterogeneous lane-changing traffic at the Panipat Toll Plaza (NH-44, India) using surrogate safety measures and UAV-based trajectory data","authors":"Parveen Kumar ,&nbsp;Debashis Ray Sarkar","doi":"10.1016/j.iatssr.2025.11.005","DOIUrl":"10.1016/j.iatssr.2025.11.005","url":null,"abstract":"<div><div>A gradual increase in the number of lanes and frequent lane-changing behaviour characterizes the approach of the toll plaza. These characteristics significantly increase the propensity for conflicts and collisions. This study aims to estimate the crash risk of heterogeneous lane-changing traffic at the approaching section of the toll plaza by analyzing vehicle trajectories. In this study, traffic data was collected from a toll plaza located on National Highway-44 in Haryana, India, using an Unmanned Aerial Vehicle (UAV). The vehicle trajectory data was retrieved using Data from Sky (DFS), a fully automated image processing software. The traffic crash risk was assessed using Extreme Value Theory (EVT) in conjunction with Lane Changing Time to Collision, a Surrogate Safety Measure (SSM) indicator. A comprehensive assessment of crash risk across vehicle categories indicates a negative relationship between vehicle size and conflict involvement, with larger vehicles such as trucks, buses, and Light Commercial Vehicles (LCVs) exhibiting a reduced likelihood of conflicts compared to two-wheelers and cars. Moreover, vehicle speed demonstrated a positive correlation with crash risk, indicating that higher average speeds are associated with an increased likelihood of crashes. The study is limited to a single morning peak-hour dataset and primarily covers motorized vehicles, as non-motorized traffic is prohibited on access-controlled highways. Additionally, the current video-classification technique could not differentiate between electric and conventional fuel-powered two-wheelers. These limitations should be considered while determining the scope and generalizability of the findings. The study findings are expected to assist engineers and toll plaza operators in selecting suitable traffic control measures to improve safety at the toll plaza.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 580-592"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal stability analysis of crash injury severity in school zones: A mixed logit modeling approach","authors":"Monire Jafari ,&nbsp;Michael Starewich ,&nbsp;Subasish Das ,&nbsp;Swastika Barua ,&nbsp;Reuben Tamakloe","doi":"10.1016/j.iatssr.2025.11.002","DOIUrl":"10.1016/j.iatssr.2025.11.002","url":null,"abstract":"<div><div>School zones present critical challenges where vehicular traffic intersects with vulnerable pedestrians, especially children. While previous studies have examined school zone crash risk and countermeasures, few have investigated how injury severity risk factors change over time especially before and during the COVID-19 pandemic leaving a gap in understanding the temporal stability of these influences. This study analyzes 3638 police-reported crashes in Louisianna school zones from 2018 to 2021 to identify factors influencing injury severity. Using a Random Parameters Logit Model with Heterogeneity in Means and Variances, the analysis accounts for unobserved heterogeneity and temporal changes, comparing pre-pandemic (2018–2019) and pandemic (2020−2021) periods. Results show that crashes near state highways significantly increase the likelihood of fatal, severe, or moderate injuries. In contrast, lower speed limits (≤ 25 mph), improved lighting conditions, and seasons such as summer and autumn correlate with reduced injury severity. Driver behaviors like failure to yield and weekday crashes increase possible injury risks. Temporal instability in risk factors highlights the need for regionally and temporally calibrated interventions. These findings support targeted safety measures such as traffic calming near high-speed roads, enhanced enforcement, and educational campaigns to reduce injury severity and improve safety for all road users in school zones.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 528-543"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting higher-risk crash factors and patterns using machine learning-association rule mining","authors":"Emmanel Kofi Gbey ,&nbsp;Charles Atombo ,&nbsp;Emmanuel Kofi Adanu ,&nbsp;William Agyemang","doi":"10.1016/j.iatssr.2025.11.006","DOIUrl":"10.1016/j.iatssr.2025.11.006","url":null,"abstract":"<div><div>Road traffic crashes remain a significant global public health concern, particularly in low- and middle-income countries, where fatalities and injuries disproportionately affect vulnerable road users. Despite efforts to improve road safety, crash severity levels are conventionally classified based on observable injury and fatality outcomes. This outcome-based approach oversimplifies the complexity of crash risk by ignoring latent hazards embedded in severity-based categories for non-fatal or minor crashes. This study addresses this gap by integrating Machine Learning (ML) and Association Rule Mining (ARM) to predict high-risk crashes and identify crash patterns respectively. Using nine years of historical crash data (2013−2021) from Ghana, the study employed Logistic Regression, Random Forest (RF), and XGBoost for high-risk crash prediction, followed by ARM to identify hidden patterns. RF outperformed the other models, achieving 71.4 % accuracy and a 90.3 % ROC AUC. The identified high-risk crashes depicted a mismatch between crashes classified as high severity and crashes identified as high-risk crashes. Crashes termed low severity dominated the set of crashes classified by the RF model as high-risk crashes. ARM revealed significant hidden patterns, such as rear-end collisions at signalized intersections and road width and non-impaired driving co-occur crashes under optimal environmental and behavioural driving conditions. The study demonstrates the value of combining ML and ARM for actionable insights. The findings emphasize that infrastructural design and driver behaviour both play important roles in high-risk crash outcomes, suggesting a need for holistic road safety strategies, including infrastructure redesign, enhanced traffic control measures, and public awareness campaigns to mitigate complacency in ideal driving conditions. Policymakers and traffic engineers are urged to adopt context-sensitive designs and prioritize non-junction segments, where road width significantly impacts crash risk.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 565-579"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}