International Journal of Transportation Science and Technology最新文献

{"title":"Evaluating the impacts of major transportation disruptions – San Francisco Bay Area case study","authors":"","doi":"10.1016/j.ijtst.2023.08.003","DOIUrl":"10.1016/j.ijtst.2023.08.003","url":null,"abstract":"<div><div>The constraints of transportation networks are fundamental to disaster planning. Having the capability of evaluating the emergent dynamics of such networks in the context of large traffic incidents can inform the design of traffic management strategies. On February 7, 2019, the Richmond-San Rafael Bridge in the San Francisco Bay Area, connecting multiple cities and carrying over 100 000 vehicles daily, had to be suddenly closed for over 9 hours due to a structural failure of its upper deck. This incident caused major disruptions in the region as the typical traffic was interrupted and detoured as travelers found alternate routes. In this study, we demonstrate the capability of large-scale traffic impact assessments of major network disruptions using the Richmond-San Rafael Bridge closure as a case study. Using a high-performance, parallel-discrete event traffic simulation, we assess the traffic impacts resulting from the bridge closure at both the regional system and city levels. Our model estimates that the region incurred an additional 14 000 vehicle hours of delay and 600 000 vehicle miles in distance due to the bridge closure. The incident affected over 55 000 trips; certain trips experienced an increase of 46 min in delay and 26 miles in travel distance. The median traffic volume on neighborhood streets in San Francisco, Vallejo, and San Rafael increased by 30%, 22%, and 13%, respectively. The results suggest that the cities’ local roads provided the additional adaptive capacity to disperse the traffic. With large-scale modeling of a critical network disruption using dynamic rerouting capability, complete road network, and full demand, we provide valuable insights into the response dynamics of this specific event. In doing so, the value of such regional analyses to incident and disaster planning is demonstrated.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 155-169"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43217649","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":"Geotechnical properties of cohesive soils used in the construction of subgrade for the development of the railways in the Azov-Black Sea region","authors":"","doi":"10.1016/j.ijtst.2023.05.003","DOIUrl":"10.1016/j.ijtst.2023.05.003","url":null,"abstract":"<div><p>This work is devoted to the determination and systematization of the properties of clay soils used in the construction of new railway tracks in order to develop the railway network in the Azov-Black Sea region of Russia. To this end, classification characteristics are determined by traditional laboratory methods, and the possibility of soil swelling under excessive moisture is estimated. In addition, the compressibility of soils is studied as the main factor ensuring the trouble-free operation of the subgrade of railways during their long-term operation. Soil samples for measurements were taken from open pits located near construction sites at an extended length of construction of 530 km. The new regression relations proposed in the work provide in some cases the accuracy of determining the soil characteristics close to the accuracy of laboratory tests. They may be in demand when monitoring the accuracy of laboratory tests of soil properties of other open pits and increasing the speed of pre-design surveys during further development of the railroad network in this region.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 237-257"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000485/pdfft?md5=7c46e6e1d1411da71c0b462757a1c7e5&pid=1-s2.0-S2046043023000485-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46685176","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":"Enabling edge computing ability in view-independent vehicle model recognition","authors":"","doi":"10.1016/j.ijtst.2023.03.007","DOIUrl":"10.1016/j.ijtst.2023.03.007","url":null,"abstract":"<div><p>Vehicle model recognition (VMR) benefits the parking, surveillance, and tolling system by automatically identifying the exact make and model of the passing vehicles. Edge computing technology enables the roadside facilities and mobile cameras to achcieve VMR in real-time. Current work generally relies on a specific view of the vehicle or requires huge calculation capability to deploy the end-to-end deep learning network. This paper proposes a lightweight two-stage identification method based on object detection and image retrieval techniques, which empowers us the ability of recognizing the vehicle model from an arbitrary view. The first-stage model estimates the vehicle posture using object detection and similarity matching, which is cost-efficient and suitable to be programmed in the edge computing devices; the second-stage model retrieves the vehicle’s label from the dataset based on gradient boosting decision tree (GBDT) algorithm and VGGNet, which is flexible to the changing dataset. More than 8 000 vehicle images are labeled with their components’ information, such as headlights, windows, wheels, and logos. The YOLO network is employed to detect and localize the typical components of a vehicle. The vehicle postures are estimated by the spatial relationship between different segmented components. Due to the variety of the perspectives, a 7-dimensional vector is defined to represent the relative posture of the vehicle and screen out the images with a similar photographic perspective. Two algorithms are used to extract the features from each image patch: (1) the scale invariant feature transform (SIFT) combined with the bag-of-features (BoF) and (2) pre-trained deep neural network. The GBDT is applied to evaluate the weight of each component regarding its impact on VMR. The descriptors of each component are then aggregated to retrieve the best matching image from the database. The results showed its advantages in terms of accuracy (89.2%) and efficiency, demonstrating the vast potential of applying this method to large-scale vehicle model recognition.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 73-86"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S204604302300028X/pdfft?md5=ce6f5579d9069f7f5e9ff520676a8fd5&pid=1-s2.0-S204604302300028X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47810036","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":"Meta-analysis of driving behavior studies and assessment of factors using structural equation modeling","authors":"","doi":"10.1016/j.ijtst.2023.05.002","DOIUrl":"10.1016/j.ijtst.2023.05.002","url":null,"abstract":"<div><p>The aim of this paper is to understand the factors that influence unsafe driving practices by examining published studies that utilized the theory of planned behavior (TPB) to predict driving behavior. To this end, 42 studies published up to the end of 2021 are reviewed to evaluate the predictive utility of TPB by employing a meta-analysis and structural equation model. The results indicate that these studies sought to predict 20 distinct driving behaviors (e.g., drink-driving, use of cellphone while driving, aggressive driving) using the original TPB constructs and 43 additional variables. The TPB model with the three original constructs is found to account for 32% intentional variance and 34% behavioral variance. Among the 43 variables researchers have examined in TPB studies related to driving behavior, this study identified the six that are commonly used to enhance the TPB model’s predictive power. These variables are past behavior, self-identity, descriptive norm, anticipated regret, risk perception, and moral norm. When past behavior is added to the original TPB model, it increases the explained variance in intention to 52%. When all six factors are added to the original TPB model, the best model has only four variables (perceived risk, self-identity, descriptive norm, and moral norm); and increases the explained variance to 48%. The influence of the TPB constructs on intention is modified by behavior category and traffic category. The findings of this paper validate the application of TPB to predicting driving behavior. It is the first study to do this through the use of meta-analysis and structural equation modeling.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 219-236"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000382/pdfft?md5=ccbb2909db79056082a96852fad3d28e&pid=1-s2.0-S2046043023000382-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49356997","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":"Traffic flow modelling of long and short trucks using a hybrid artificial neural network optimized by particle swarm optimization","authors":"","doi":"10.1016/j.ijtst.2023.04.004","DOIUrl":"10.1016/j.ijtst.2023.04.004","url":null,"abstract":"<div><p>The significance of intelligent transportation systems and artificial intelligence in road transportation networks has made the prediction of traffic flow a subject of discussion among transportation engineers, urban planners, and researchers in the last decade. However, limited research has been done on traffic flow modelling of long and short trucks considering that they are among the major causes of traffic congestions and traffic-related accidents on freeways, especially freeway collisions between them and passengers’ vehicles. This study focused on the traffic flow of long and short trucks on the <span><math><mrow><mi>N</mi><mn>1</mn><mspace></mspace><mi>freeway</mi></mrow></math></span> in South Africa due to its high traffic volume and persistent traffic congestions caused by trucks. We obtained traffic data from this freeway using inductive loop detectors and video cameras. Traffic flow variables such as speed, time, traffic density, and traffic volume were identified, and the traffic datasets comprising 920 datasets were divided into 70% for training and 30% for testing. A hybrid <span><math><mrow><mi>ANN</mi><mo>-</mo><mi>PSO</mi></mrow></math></span> model was used in modelling the truck traffic flow due to its ability to converge to optimization quickly. The PSO's features (accelerating factors and number of neurons) assist in evaluating traffic flow conditions (traffic flow, traffic density, and vehicular speed). Also,<!--> <!-->PSO algorithms are simple and require few adjustment parameters. The results suggest that the ANN-PSO model can model long and short trucks traffic flow with a <span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></mrow></math></span> training and testing of <span><math><mrow><mn>0.999 0</mn><mspace></mspace><mi>and</mi><mspace></mspace><mn>0.993 0</mn></mrow></math></span>. This is the first study to undertake a longitudinal analysis of traffic flow modelling of long and short trucks on a freeway using a metaheuristic algorithm (ANN-PSO). The results of this study will provide knowledgeable insights (division of traffic flow variables and analysing of traffic flow data) to transportation planners and researchers when it comes to minimizing truck-related accidents and traffic congestions on freeways.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 137-155"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000333/pdfft?md5=6e62e57cd7e4fcf0c803c216bf7ac91e&pid=1-s2.0-S2046043023000333-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43285859","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":"Tire-pavement friction modeling considering pavement texture and water film","authors":"","doi":"10.1016/j.ijtst.2023.04.001","DOIUrl":"10.1016/j.ijtst.2023.04.001","url":null,"abstract":"<div><p>The accurate estimation of tire-pavement friction, especially under wet conditions, is critical to ensure pavement safety. For this purpose, this paper develops a modified tire-pavement friction model which takes the effect of pavement texture and water film into consideration. The influence of pavement texture is quantified by a newly proposed parameter called texture influence coefficient, which is related to the real contact patch of tire-pavement. The water effect is calculated from two parts, namely lubrication effect and hydrodynamic effect. Based on these two steps, a modified average lumped LuGre (ALL) model is developed. The proposed model is calibrated and verified by GripTester data collected under different vehicle velocities and water film thicknesses. The root mean square error between the calculated value of the model and the measured value is 0.023. In addition, the effects of vehicle velocity, slip rate, water film thickness, and pavement type on the friction coefficient are analyzed by numerical calculation. The results show that the friction coefficient reaches the maximum when the slip rate is in the range of [0.15, 0.20]. The increases in the vehicle speed and water film thickness will lead to the decrease in the friction coefficient. Besides, in thin water film (&lt;1 millimeter) conditions, the deterioration effect of water film thickness on the friction coefficient is more remarkable. The results prove that the modified tire-pavement friction model provides a precise and reliable way to estimate the friction coefficient of pavement, which can assist the pavement management systems in risk warning and safety guarantee.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 99-109"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S204604302300031X/pdfft?md5=7af3bbd8e7f3c373c7350a55fd211356&pid=1-s2.0-S204604302300031X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45188226","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":"Application of machine learning models and SHAP to examine crashes involving young drivers in New Jersey","authors":"","doi":"10.1016/j.ijtst.2023.04.005","DOIUrl":"10.1016/j.ijtst.2023.04.005","url":null,"abstract":"<div><p>Motor vehicle crashes are the leading cause of the death of teenagers in the United States. Young drivers have shown a higher propensity to get involved in crashes due to using a cellphone while driving, breaking the speed limit, and reckless driving. This study analyzed motor vehicle crashes involving young drivers using New Jersey crash data. Specifically, four years of crash data (2016–2019) were gathered and analyzed. Different machine learning (ML) methods, such as Random Forest, Light GBM, Catboost, and XGBoost, were used to predict the injury severity. The performance of the models was evaluated using accuracy, precision, and recall scores. In addition, interpretable ML techniques like sensitivity analysis and Shapley values were conducted to assess the most influential factors' impacts on young driver-related crashes. The results revealed that XGBoost performed better than Random Forest, CatBoost, and LightGBM models in crash severity prediction. Results from the sensitivity analysis showed that multi-vehicle crashes, angular crashes, crashes at intersections, and dark-not-lit conditions had increased crash severity. A partial dependence plot of SHAP values revealed that speeding in clear weather had a higher likelihood of injury crashes, and multi-vehicle crashes at the intersection had more injury crashes. We expect that the results obtained from this study will help policymakers and practitioners take appropriate countermeasures to improve the safety of young drivers in New Jersey.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 156-170"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000345/pdfft?md5=00851751ca9a7d9ae4b65b6eb418a6fe&pid=1-s2.0-S2046043023000345-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45687277","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":"Carbon-emission calculation method during operation period based on asphalt pavement performance","authors":"","doi":"10.1016/j.ijtst.2023.03.002","DOIUrl":"10.1016/j.ijtst.2023.03.002","url":null,"abstract":"<div><p>Current vehicle carbon emission models tend to ignore the influence of road roughness on driving speed selection, which may damage the carbon emission evaluation accuracy. In this study, first, based on the results obtained with a portable emissions measurement system (PEMS), an explicit model for user vehicle carbon emissions, driving speed, and pavement roughness is established. Second, the influence of road roughness on driver behavior choice is investigated, and an interrelationship model between roughness and driving speed choice is developed. Finally, a more realistic carbon emission calculation model during the operation period is proposed based on the pavement performance model, and the accuracy is verified in comparison with the traditional vehicle operating cost (VOC) model. It is found that there exists a carbon emission minimization point under free-flow conditions, and the corresponding driving speed is the optimal speed point of user vehicles, i.e. 63 km/h. In addition, a great linear correlation exists between the roughness and driving speed selection, which should be considered in the final calculation model. The vehicle carbon emission model developed in this research provides solid references for evaluating the life-cycle emission of asphalt pavement and guiding the selection of maintenance strategies for the pavement to lower carbon emissions.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 1-11"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000229/pdfft?md5=105694b2ac867b26e92ac024aeec60a8&pid=1-s2.0-S2046043023000229-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48719122","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 the effects of work zone on vehicular flow on ring freeways with a tunnel using a three-lane continuum model","authors":"","doi":"10.1016/j.ijtst.2023.03.004","DOIUrl":"10.1016/j.ijtst.2023.03.004","url":null,"abstract":"<div><p>Freeway work zone forms as a result of traffic crash or road rehabilitation. To ascertain the effects of work zone with lane II completely blocked on vehicular flow on ring freeways with a tunnel, a three-lane continuum model is put forward. The mandatory net lane-changing rate from lane II to lane I or III just upstream of the work zone is described by a random number model, with the random number being produced within a small range around a median based on a golden section analysis. The net-changing rate between adjacent lanes is described using a lane-changing time on the basis of an assumption: the time ratio to relaxation time equals infinity when the absolute value of traffic densities between the two adjacent lanes is less than 1 veh/km, implying that the net-changing rate is zero; otherwise, the time ratio is inversely proportional to the vehicular spatial headway, which is equal to unity for traffic flow at saturation state, but infinity when the traffic flow is completely jammed. It is assumed that the freeway is a three lane ring with a total length of 100 km, and has a tunnel with a speed limit of 60 km/h and a length of 1.6 km located downstream the work zone with a length of 0.16 km. The free flow speeds on lanes I, II, and III are 120 km/h, 100 km/h, and 85 km/h, respectively. For the vehicular flow on the ring freeway with a tunnel, numerical simulations based on the three-lane continuum model are carried out with a reliable numerical method of high accuracy. It is found that the vehicular flow has two thresholds of traffic jam formation, one depending upon the tunnel and the other upon the work zone. The tunnel triggers a traffic jam when the initial density normalized by jam density is equal to the first threshold 0.15, and the work zone originates another traffic jam when the normalized initial density equals the second threshold 0.19. The freeway tunnel plays a dominant role in the prediction of mean travel time as soon as the tunnel has generated a traffic jam at the tunnel entrance. For the vehicular flow at unsaturated state, the average speed through the tunnel is about 26.67 km/h. When the normalized initial density exceeds the second threshold 0.19, the mean travel time through every lane increases with the initial density linearly. Vehicle fuel consumption can be estimated by interpolation with the time averaged grid traffic speed and an assumed vehicle performance curve. It is found that the vehicle fuel consumption is lane number dependent, and distributes with the initial density concavely, as well as has a value in the range of 6.5 to 8.3 l.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 27-41"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000254/pdfft?md5=0c045aab4a96cce7e15d52d26ff9464b&pid=1-s2.0-S2046043023000254-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48321367","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":"Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes","authors":"","doi":"10.1016/j.ijtst.2023.03.003","DOIUrl":"10.1016/j.ijtst.2023.03.003","url":null,"abstract":"<div><p>Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure. Although conventional algorithms for periodic frequent pattern detection have numerous applications, there is still little research on periodic frequent pattern detection of individual passengers in the metro. The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network, which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data. This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes (PFPTS). This discovered pattern can automatically capture the features of the temporal dimension (morning and evening peak hours, week) and the spatial dimension (entering and leaving stations). The corresponding complete mining algorithm with the PFPTS-tree structure has been developed. To evaluate the performance of PFPTS-tree, several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network. The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.</p></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"14 ","pages":"Pages 12-26"},"PeriodicalIF":4.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2046043023000242/pdfft?md5=d19c7c58f05bff921a0a74d2418d33c8&pid=1-s2.0-S2046043023000242-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54865077","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}