International Journal of Transportation Science and Technology最新文献

{"title":"Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis","authors":"Sonia Mrad ,&nbsp;Rafaa Mraihi ,&nbsp;Aparna S. Murthy","doi":"10.1016/j.ijtst.2024.02.004","DOIUrl":"10.1016/j.ijtst.2024.02.004","url":null,"abstract":"<div><div>The concept of a smart city is emerging to address significant challenges arising from rapid urbanization, economic growth, and climate change. Innovative technologies can be used as a means to promote sustainable and inclusive urban development. These technolgies include the deployment of the internet of things (IoT), artificial intelligence (AI), energy management, and smart transportation. In a smart city, intelligent transportation systems ITSs play a vital role in efficient traffic management. This paper explores the use of hybrid AI techniques for predicting short-term traffic flow data from M25 motorways in the UK. Since volume traffic flow data are non-stationary, wavelet transform (WT), as a powerful signal analyzer, is applied to signal decomposition for the elimination of redundant data from input matrices. The feature selection method based on the Gram-Schmidt (GS) orthogonalization process is used for the selection of more valuable features. The elimination of redundant data can speed up the learning process and improve the generalisation capability of the prediction models. After a pre-processing stage, a wavelet neural network (WNN) with a simple structure is applied as a powerful prediction tool. Two separate structures are considered for the prediction of weekday and weekend traffic volume data. The experiments explore that the debauchies-4 (db4) wavelet function with 7 decomposition levels leads to the best detection accuracy. Moreover, factors such as the range of forecasting, the type of the day, and the level of decomposition all have an impact on prediction stability. Compared with existing prediction methods, the proposed approach produces lower values of root mean square error (RMSE) and mean absolute percentage error (MAPE) for all step-horizons analyzed. These findings provide valuable implications and insights into the development of an efficient and reliable road condition monitoring system for delivering secure and sustainable transportation services.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 21-38"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139829934","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":"Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges","authors":"Ehsan Yahyazadeh Rineh,&nbsp;Ruey Long Cheu","doi":"10.1016/j.ijtst.2024.05.001","DOIUrl":"10.1016/j.ijtst.2024.05.001","url":null,"abstract":"<div><div>The lane changing decision model (LCDM) is a critical component in semi- and fully-automated driving systems. Recent research has found that the fuzzy inference system (FIS) is a promising approach to implementing LCDMs. To improve the FIS’s performance, this research reviewed the challenges in the development an FIS model to make the <span><math><mrow><mfenced><mrow><mi>y</mi><mi>e</mi><mi>s</mi><mo>,</mo><mi>n</mi><mi>o</mi></mrow></mfenced></mrow></math></span> decisions in discretionary lane changes. The FIS model was revised to bring its fuzzy inference rules more consistent with the fuzzy membership functions, and its composition and defuzzification methods more in line with the classical fuzzy logic theory. An equitable test data set with approximately equal number of <span><math><mrow><mfenced><mrow><mi>y</mi><mi>e</mi><mi>s</mi><mo>,</mo><mi>n</mi><mi>o</mi></mrow></mfenced></mrow></math></span> data points was assembled from the same next generation simulation (NGSIM) data used in the past research. The test results proved that: (1) an LCDM’s performance was dependent on how the <span><math><mrow><mfenced><mrow><mi>y</mi><mi>e</mi><mi>s</mi><mo>,</mo><mi>n</mi><mi>o</mi></mrow></mfenced></mrow></math></span> decisions in the test data set were manually labeled; (2) separating the fuzzy inference rules into a <span><math><mrow><mfenced><mrow><mi>y</mi><mi>e</mi><mi>s</mi></mrow></mfenced></mrow></math></span> group and a <span><math><mrow><mfenced><mrow><mi>n</mi><mi>o</mi></mrow></mfenced></mrow></math></span> group and compute the results separately yielded potentially better decision accuracy. Furthermore, The gene expression programming model (GEPM) performed better than the improved FIS-based model. The findings led the authors to suggest two possible research directions: (1) add the subject vehicle’s speed as an input to the LCDM and redesign the decision-making model; (2) construct models for congested and uncongested traffic separately. The authors further suggested the use of instrumented vehicles to collect a set of high-fidelity lane changing data in the naturalistic driving environment.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 312-327"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141048014","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":"Workforce forecasting for state transportation agencies: A machine learning approach","authors":"Adedolapo Ogungbire,&nbsp;Suman Kumar Mitra","doi":"10.1016/j.ijtst.2024.05.004","DOIUrl":"10.1016/j.ijtst.2024.05.004","url":null,"abstract":"<div><div>A decline in the number of construction engineers and inspectors at state transportation agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in these agencies. Forecasting workforce requirements is crucial for effective planning in any industry or agency. This study developed machine learning (ML) models to estimate the person-hour requirements of STAs at the project level. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee and project details data between 2012 and 2021. ML regression models ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. These models were compared based on the accuracy of their predictions, the time taken for training the models and their prediction time. Predictions were tested based on the <em>K</em>-fold cross validation technique. The results indicated a high performance from the random forest regression model, a tree ensemble with bagging, which recorded a mean <em>R</em>-squared value of 0.91. Other ML models such as an ensemble neural network model and the linear models also proved to be fit for the problem, attaining <em>R</em> squared value as high as 0.80 and 0.78, respectively. These findings underscore the capability of ML models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 345-360"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143182","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":"Exploring unobserved heterogeneity in ICT usage and travel pattern changes as the pandemic subsides: A quasi-longitudinal analysis in Florida","authors":"Afsana Zarin Chowdhury,&nbsp;Ibukun Titiloye,&nbsp;Md Al Adib Sarker,&nbsp;Xia Jin","doi":"10.1016/j.ijtst.2024.04.010","DOIUrl":"10.1016/j.ijtst.2024.04.010","url":null,"abstract":"<div><div>This paper presents a study that explored the behavioral heterogeneity of changes in people's information and communications technology (ICT) usage and travel patterns at the end of the pandemic. A quasi-longitudinal approach was employed to collect data from Florida residents, capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021. Utilizing the latent class analysis (LCA) approach to identify subgroups based on the online activity durations and trip frequencies, four distinct classes were identified. A little more than one third (35%) of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies. About 33% of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities. About 16% of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities. Another 16% of the respondents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities. These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities. The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 276-292"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141046920","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":"Investigation of factors affecting crash severity of rear-end crashes with high collision speeds in work zones: A South Carolina case study","authors":"Mahyar Madarshahian ,&nbsp;Jason Hawkins ,&nbsp;Nathan Huynh ,&nbsp;Chowdhury K.A. Siddiqui","doi":"10.1016/j.ijtst.2024.07.003","DOIUrl":"10.1016/j.ijtst.2024.07.003","url":null,"abstract":"<div><div>The aim of this study is to identify factors that affect injury severity levels of work zone rear-end crashes with high collision speeds (<span><math><mrow><mo>⩾</mo></mrow></math></span>35 miles per hour (mph, 1 mph equals about 1.609 344 km/h)). Using statewide crash data provided by the South Carolina Department of Transportation from 2014 to 2020, a mixed binary logit model with heterogeneity in mean and variance is estimated. The model’s outcome variable is injury or non-injury (i.e., property damage only), and the explanatory variables include information related to vehicle, collision, time, occupant, roadway, and environmental characteristics. The estimation results show that the interstate variable is best modeled as a random parameter at a 90% confidence level. Late-night and dawn/dusk conditions influence the mean effect, while driving under the influence affects the variance of the random parameter. Factors positively influencing injury severity include multi-vehicle involvement, airbag deployment, dark conditions, and truck-involved crashes. Conversely, advanced warning area, activity area, lane shift/crossover, young and middle-aged drivers, and dawn/dusk conditions have negative effects on injury severity.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 361-374"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712942","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":"Investigating influential factors on railway passenger flow utilizing multi-source data fusion and flow space theory: A case study of the Yangtze River Delta megalopolis, China","authors":"Yongqi Deng ,&nbsp;Jiaorong Wu ,&nbsp;Chengcheng Yu ,&nbsp;Jihao Deng ,&nbsp;Meiting Tu ,&nbsp;Yuqin Wang","doi":"10.1016/j.ijtst.2024.04.004","DOIUrl":"10.1016/j.ijtst.2024.04.004","url":null,"abstract":"<div><div>Employing flow space theory and multi-source data, this study examines the spatial network structure and factors influencing railway passenger flow, which is crucial for rail planning in densely populated megalopolises. Focusing on China's Yangtze River Delta (YRD) megalopolis, we utilize social network analysis (SNA) to explore the characteristics of various flow networks and their interactions with the railway passenger flow network. Key findings include: (1) a pronounced polarization effect and core-periphery structure exist in the YRD, notably within industry and railway flow networks; (2) industry and corporation flow significantly contributes to rail passenger flow, with corporation networks in commerce, technical services, and finance showing higher similarity to the railway passenger flow network; (3) there is significant heterogeneity in the correlation between rail passenger flow and other flows within sub-networks formed by connections among nodes of different levels; (4) enhancing railway services at lower-level nodes is essential to mitigate the disparity between population mobility and rail passenger flow and to promote rail transportation equity. This research offers valuable insights for policymakers in developing countries to strategically plan railroad networks in megalopolises.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 192-207"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783058","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":"Mechanical response and numerical simulation of liquid soil abutment backfill","authors":"Chongwei Huang ,&nbsp;Chuan Zhao ,&nbsp;Yu Sun ,&nbsp;Shengfei Guan","doi":"10.1016/j.ijtst.2024.02.001","DOIUrl":"10.1016/j.ijtst.2024.02.001","url":null,"abstract":"<div><div>The application of a new liquid soil material and the treatment effect of backfilling an underpass tunnel in an airport are studied. The deformation and mechanical properties of liquid soil and conventional soil under load are comprehensively compared and analyzed via a numerical simulation with finite element software. The effects of the buried depth of overlying fill, tunnel height, and traffic load on the backfilling of liquid soil abutment are analyzed. The research results show that under the action of load, the overall deformation and stress distribution of the liquid soil and conventional soil show similar laws. However, liquid soil backfilling has great advantages over conventional soil backfilling in all aspects. Liquid soil backfilling can reduce the deformation and the compressive stress at the corner of the backfilling area by approximately 13% and 15%, respectively. The overburden buried depth has a great impact on the subgrade deformation. In the actual construction, the overburden buried depth should be 1.5 m. The overburden depth has a greater impact on the vertical deformation of the road, and the self-weight of the overburden will act as an additional load on the overall roadbed, compared with conventional soil backfill. The overburden depth of 2.0 m conventional soil backfill is about equal to the overburden depth of 1.5 m liquid soil backfill. The use of liquid soil backfill is equivalent to the use of the overburden fill in reducing the additional load of 0.5 m. The height of the box culvert has a greater impact on the stress, but this change is not linear. The actual construction in the case of meeting the specific requirements of use should try to control in the vicinity of 8.4 m, and at the same time the use of liquid soil backfill can reduce the compressive stress of about 14%. The compressive stress increases first and then decreases with the increase in the liquid soil modulus. The liquid soil modulus should be controlled to 180 MPa. Moreover, liquid soil backfilling can reduce the compressive stress in the backfilling area by approximately 25%. The trapezoidal slope of the backfill area is proportional to the deformation amount. Although an obvious correlation with compressive stress exists, the regularity is not strong. Thus, the trapezoidal slope should be set to 1:1 during construction. Traffic load slightly affects the overall deformation and compressive stress of the road. However, the distribution trends of deformation and stress change obviously under the action of aircraft load. In the actual design, only one load form of aircraft load should be considered.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 1-20"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768325","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":"An extended intelligent driving model for autonomous and manually driven vehicles in a mixed traffic environment with consideration to roadside crossing","authors":"Yu Bai ,&nbsp;Pengyue Tu ,&nbsp;Ghim Ping Ong","doi":"10.1016/j.ijtst.2024.07.007","DOIUrl":"10.1016/j.ijtst.2024.07.007","url":null,"abstract":"<div><div>While the advantages of Autonomous vehicles (AVs) and their impact on manually-driven vehicles (MVs) have been widely discussed in continuous flow conditions, their performance under mixed traffic, intermitted flow conditions has yet to be properly studied. One of the representative scenarios is that vehicular flow is interrupted by roadside crossing obstacles such as pedestrians or cyclists. Since such interruption makes vehicles stop and go more frequently and creates random and complex traffic conflict, it has become a critical factor that can affect the driving performance of AVs. Therefore, this paper proposes a uniform traffic model (Pre_IDM+) to include roadside crossing impact in traffic flow analysis. The classical intelligent driving model (IDM) is extended into an obstacle-avoiding case, in which a novel pre-reaction workflow is introduced to describe yielding behavior and generate a reasonable braking trajectory. A real mixed traffic data near an un-signalized mid-block crosswalk is used to calibrate Pre_IDM+ and an accordingly microscope mixed traffic simulation platform is constructed. The simulation results show that discreet AVs can greatly avoid hard braking (−83.61%) and slightly improve passing speed (+5.11%) compared with MVs, while competitive AVs can maximize traffic efficiency (+7.03%) but will also deteriorate driving smoothness and comfort (−31.66%). Maintaining a sparse distribution of crossing sites along the road may contribute more to traffic stability and driving continuity compared with gathering all obstacles crossing at one location. This paper may help better understand the impact of AVs on general intermitted flow and give a reference to mixed traffic modeling towards a complex road condition.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 375-391"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847683","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":"The influence of roadway characteristics and built environment on the extent of over-speeding: An exploration using mobile automated traffic camera data","authors":"Boniphace Kutela ,&nbsp;Frank Ngeni ,&nbsp;Cuthbert Ruseruka ,&nbsp;Tumlumbe Juliana Chengula ,&nbsp;Norris Novat ,&nbsp;Hellen Shita ,&nbsp;Abdallah Kinero","doi":"10.1016/j.ijtst.2024.03.003","DOIUrl":"10.1016/j.ijtst.2024.03.003","url":null,"abstract":"<div><div>Over-speeding is a pivotal factor in fatal traffic crashes globally, necessitating robust speed management strategies to augment road safety. In 2021, the National Highway Traffic Safety Administration reported over 12 000 speed-related fatalities in the United States alone. Previous studies aggregated over-speeding tendencies; however, the extent of over-speeding has a significant implication on the crash outcome. This study delves into the prevalence and magnitude of over-speeding in various scenarios, utilizing data from traffic cameras in Edmonton, Canada, and employing a negative binomial statistical model for analysis. The model elucidates the significance and likelihood of over-speeding tendencies by incorporating temporal and built environment variables, i.e., year, month, number of lanes, dwelling unit types, school-related, and open green space. Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of various factors. Notably, the estimated impact of the posted speed limit for the disaggregated models is up to over two times that for the aggregated model. Further, the summer months exhibit a roughly 25% uptick in speed limit violations for aggregated models while about a 40% uptick in the speed limit violations for disaggregated approaches. Conversely, a discernible decline in over-speeding tendencies is observed with camera enforcement, showcasing a 25% reduction over four years. Built environment variables presented mixed results, with one-unit dwellings associated with a 12% increase in over-speeding, while proximity to schools indicated a 10% decrease. These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and countermeasures to curtail speed limit violations and bolster overall road safety conditions.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 120-130"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140275145","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":"Optimizing multimodal timetable synchronization of intercity railway and metro for the first service period during holidays","authors":"Yan Liu ,&nbsp;Quan Zhang ,&nbsp;Xuan Li ,&nbsp;Yang Shi","doi":"10.1016/j.ijtst.2024.04.005","DOIUrl":"10.1016/j.ijtst.2024.04.005","url":null,"abstract":"<div><div>Metro plays a vital role in managing passenger distribution at intercity railway (IR) stations, particularly during holidays when there is a surge in tourist traffic. To efficiently accommodate the high demand for intercity travel, it becomes imperative for metro agencies to optimize holiday timetables. This paper focuses on designing holiday timetables of the first service period for the metro network that connects to an IR station, aiming to enhance multimodal collaboration with IR timetables while ensuring seamless coordination among various metro lines at the network level. A bi-objective model is proposed to maximize the temporal availability of metro network and minimize transfer waiting times for IR passengers traveling in early morning. To solve the model, an improved artificial bee colony algorithm (ABC) is designed, incorporating adaptive neighbour search and simulated annealing techniques. The effectiveness of the model and algorithm is verified using the Shanghai metro network and Hongqiao Railway Station. Results indicate a 9.46% increase in the temporal availability of metro network for IR passengers, coupled with a 9.68% reduction in passenger transfer waiting times. Notably, the study reveals that solely advancing operations of the IR-connected metro lines is inefficient. Instead, optimizing train timetables for the entire metro network proves to be a cost-effective approach to enhancing the overall service level of early-morning operations. Furthermore, the study emphasizes the significance of even-numbered train headways in reducing passenger transfer waiting times.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"17 ","pages":"Pages 208-223"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140778647","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}