Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492621
Ning Zhang, Rengkui Liu, Futian Wang, Shiyi Li
The research on the variation law of track vertical profile irregularity index structure plays a guiding role in the rational compilation of the tamping operation plan and the scientific evaluation of tamping operation quality. This paper presented a modeling method for predicting the variation law of track vertical profile irregularity index structure based on the grey compositional data modeling theory. In order to verify the effectiveness and reliability of the modeling method, a total of 7 times track inspection car historical data concerning four consecutive track segments (K58+600 to K59+200) of Shenmu-Shuozhou railway up line between two tamping operations were used, and the results showed that these models had good fitting and predicting effects on the track vertical profile irregularity index structure.
{"title":"Prediction for Track Vertical Profile Irregularity Index Structure of Shenmu-Shuozhou Railway","authors":"Ning Zhang, Rengkui Liu, Futian Wang, Shiyi Li","doi":"10.1109/ICITE.2018.8492621","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492621","url":null,"abstract":"The research on the variation law of track vertical profile irregularity index structure plays a guiding role in the rational compilation of the tamping operation plan and the scientific evaluation of tamping operation quality. This paper presented a modeling method for predicting the variation law of track vertical profile irregularity index structure based on the grey compositional data modeling theory. In order to verify the effectiveness and reliability of the modeling method, a total of 7 times track inspection car historical data concerning four consecutive track segments (K58+600 to K59+200) of Shenmu-Shuozhou railway up line between two tamping operations were used, and the results showed that these models had good fitting and predicting effects on the track vertical profile irregularity index structure.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134089069","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492619
Xiaohan Zhang, Yixiong He, Ruhong Tang, J. Mou, Shuai Gong
Viewed from AIS (Automatic Identification System) data, ship trajectories comprise a non-continuous series of spatiotemporal positions. Subject to the quality of the raw data, e.g. error, anomaly, it is challenging to reconstruct an original and continuous trajectory for further safety and efficiency analysis. This paper presents a novel method to detect data anomaly, identify line type, and restore ship trajectory based on vector analysis. Ship trajectory is segmented into underway and mooring sub-trajectories by analyzing characteristics of AIS data. A base vector, which represents the trend of the trajectory, is established on the basis of position vectors. With comparison of the vectors, Anomaly data is detected and filtered. A sparse sampling technique is employed to identify the linetsype of the rest sub-trajectory. Linear interpolation and cubic spline interpolation are finally applied for straight and curve sub-trajectories respectively to reconstruct a new smooth trajectory. A case study is performed and the results indicate that the reconstructed trajectory meets the layout of fairway well, with mean errors of 2.86×10−4 degrees in longitude, 2.30×10−4 degrees latitude and 2.35×10−2 nautical miles distance. This algorithm can effectively detect abnormal data points, and approximate the original movement of the ship.
{"title":"A Novel Method for Reconstruct Ship Trajectory Using Raw AIS Data","authors":"Xiaohan Zhang, Yixiong He, Ruhong Tang, J. Mou, Shuai Gong","doi":"10.1109/ICITE.2018.8492619","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492619","url":null,"abstract":"Viewed from AIS (Automatic Identification System) data, ship trajectories comprise a non-continuous series of spatiotemporal positions. Subject to the quality of the raw data, e.g. error, anomaly, it is challenging to reconstruct an original and continuous trajectory for further safety and efficiency analysis. This paper presents a novel method to detect data anomaly, identify line type, and restore ship trajectory based on vector analysis. Ship trajectory is segmented into underway and mooring sub-trajectories by analyzing characteristics of AIS data. A base vector, which represents the trend of the trajectory, is established on the basis of position vectors. With comparison of the vectors, Anomaly data is detected and filtered. A sparse sampling technique is employed to identify the linetsype of the rest sub-trajectory. Linear interpolation and cubic spline interpolation are finally applied for straight and curve sub-trajectories respectively to reconstruct a new smooth trajectory. A case study is performed and the results indicate that the reconstructed trajectory meets the layout of fairway well, with mean errors of 2.86×10−4 degrees in longitude, 2.30×10−4 degrees latitude and 2.35×10−2 nautical miles distance. This algorithm can effectively detect abnormal data points, and approximate the original movement of the ship.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130963282","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492710
Dongdong Liu, Guoyou Shi, Weifeng Li
In order to solve the problem of collision avoidance decision-making by using the ship domain that only can be applied to certain waters, and if's connected parameters cannot match the evaluation parameters of collision risk (CR), the optimal collision risk model based on Fuzzy Quaternion Ship Domains (FQSD) was been proposed. In order to solve the problem when making decision to avoid collision by using the shortest distance that does not consider cross track error (XTE) and time deviation (TDEV), and it also cannot let the overall voyage(OV) to be the shortest throughout the voyage, the objective function combines with XTE, TDEV and OV was been proposed. Considering with the ship domain, The International Regulations for Preventing Collisions at Sea 1972 (COLREGS) (IMO 1972) and the watch officer's subjective consciousness, the optimal collision avoidance path was been obtained by using the particle swarm optimization (PSO) algorithm. The simulation results show that the above optimal method can quickly obtain the optimal collision avoidance path and improve the safety and energy efficiency of transportation.
{"title":"Decision Support Based on Optimal Collision Avoidance Path and Collision Risk","authors":"Dongdong Liu, Guoyou Shi, Weifeng Li","doi":"10.1109/ICITE.2018.8492710","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492710","url":null,"abstract":"In order to solve the problem of collision avoidance decision-making by using the ship domain that only can be applied to certain waters, and if's connected parameters cannot match the evaluation parameters of collision risk (CR), the optimal collision risk model based on Fuzzy Quaternion Ship Domains (FQSD) was been proposed. In order to solve the problem when making decision to avoid collision by using the shortest distance that does not consider cross track error (XTE) and time deviation (TDEV), and it also cannot let the overall voyage(OV) to be the shortest throughout the voyage, the objective function combines with XTE, TDEV and OV was been proposed. Considering with the ship domain, The International Regulations for Preventing Collisions at Sea 1972 (COLREGS) (IMO 1972) and the watch officer's subjective consciousness, the optimal collision avoidance path was been obtained by using the particle swarm optimization (PSO) algorithm. The simulation results show that the above optimal method can quickly obtain the optimal collision avoidance path and improve the safety and energy efficiency of transportation.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133986752","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492565
Wei Li, Q. Luo, Jingnan Zhou, Xiongfei Zhang
Urban rail transit network is composed of static network physical structure and dynamic train working diagram, whose accessibility evaluation should include both spatial and temporal characteristics. This paper proposed a comprehensive dynamic accessibility evaluation model of urban rail transit network. Its spatial characteristics were determined by station passenger flow, path impedance etc., while its temporal characteristics were defined by train departure intervals, train carrying passenger flow etc. And the dynamic accessibility index can be calculated through these factors, OD path accessible set and passenger route preference. Finally, Shanghai metro network was used as a case study to show the calculation process and analysis result of the proposed model. Result showed that the model could remedy the shortcoming that some traditional accessibility index models did not take into account temporal characteristics (metro service frequency, service level et al), and it could also give a reasonable allocation for urban rail transport capacity by analyzing the whole day dynamic accessibility index.
{"title":"Quantitative Modeling and Comprehensive Evaluation of Urban Rail Transit Network Dynamic Accessibility","authors":"Wei Li, Q. Luo, Jingnan Zhou, Xiongfei Zhang","doi":"10.1109/ICITE.2018.8492565","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492565","url":null,"abstract":"Urban rail transit network is composed of static network physical structure and dynamic train working diagram, whose accessibility evaluation should include both spatial and temporal characteristics. This paper proposed a comprehensive dynamic accessibility evaluation model of urban rail transit network. Its spatial characteristics were determined by station passenger flow, path impedance etc., while its temporal characteristics were defined by train departure intervals, train carrying passenger flow etc. And the dynamic accessibility index can be calculated through these factors, OD path accessible set and passenger route preference. Finally, Shanghai metro network was used as a case study to show the calculation process and analysis result of the proposed model. Result showed that the model could remedy the shortcoming that some traditional accessibility index models did not take into account temporal characteristics (metro service frequency, service level et al), and it could also give a reasonable allocation for urban rail transport capacity by analyzing the whole day dynamic accessibility index.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128737183","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492676
Riccardo Iacobucci, B. McLellan, T. Tezuka
Shared autonomous electric vehicles (SAEV s), also known as autonomous mobility on demand systems, are expected to soon be commercially available. This work proposes a methodology for the optimization of SAEV charging taking into account optimized vehicles routing and rebalancing. The methodology presented is based on previous work expanded to include charge scheduling optimization. Our model deals with the different time frames at which transport service and charging have to be optimized with a model-predictive control optimization routine which is run in parallel at two different time scales. Vehicle charging is optimized over longer time scales to minimize waiting times for passengers and electricity costs. Routing and rebalancing is optimized at shorter time-scales to minimize waiting times for passengers, taking as charging constraints the results of the long-time-scale optimization. This approach allows the efficient optimization of both aspects of SAEV operation. The problem is solved as a mixed-integer linear program. A case study using real transport data for Tokyo is used to test the model, showing that the system can substantially cut charging costs while keeping passenger wait times low.
{"title":"Model Predictive Control of a Shared Autonomous Electric Vehicles System with Charge Scheduling and Electricity Price Response","authors":"Riccardo Iacobucci, B. McLellan, T. Tezuka","doi":"10.1109/ICITE.2018.8492676","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492676","url":null,"abstract":"Shared autonomous electric vehicles (SAEV s), also known as autonomous mobility on demand systems, are expected to soon be commercially available. This work proposes a methodology for the optimization of SAEV charging taking into account optimized vehicles routing and rebalancing. The methodology presented is based on previous work expanded to include charge scheduling optimization. Our model deals with the different time frames at which transport service and charging have to be optimized with a model-predictive control optimization routine which is run in parallel at two different time scales. Vehicle charging is optimized over longer time scales to minimize waiting times for passengers and electricity costs. Routing and rebalancing is optimized at shorter time-scales to minimize waiting times for passengers, taking as charging constraints the results of the long-time-scale optimization. This approach allows the efficient optimization of both aspects of SAEV operation. The problem is solved as a mixed-integer linear program. A case study using real transport data for Tokyo is used to test the model, showing that the system can substantially cut charging costs while keeping passenger wait times low.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114252888","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492545
Laura Garcia Cuenca, Enrique Puertas, N. Aliane, Javier Fernández Andres
Traffic accidents constitutes the first cause of death and injury in many developed countries. However, traffic accidents information and data provided by public organisms can be exploited to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. This article presents a case study of traffic accidents classification and severity prediction in Spain. Raw data are from Spanish traffic agency covering a period of six years ranging from 2011 to 2015. To this end, are compared three different machine learning classification techniques, such as Gradient Boosting Trees, Deep Learning and Naïve Bayes.
{"title":"Traffic Accidents Classification and Injury Severity Prediction","authors":"Laura Garcia Cuenca, Enrique Puertas, N. Aliane, Javier Fernández Andres","doi":"10.1109/ICITE.2018.8492545","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492545","url":null,"abstract":"Traffic accidents constitutes the first cause of death and injury in many developed countries. However, traffic accidents information and data provided by public organisms can be exploited to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. This article presents a case study of traffic accidents classification and severity prediction in Spain. Raw data are from Spanish traffic agency covering a period of six years ranging from 2011 to 2015. To this end, are compared three different machine learning classification techniques, such as Gradient Boosting Trees, Deep Learning and Naïve Bayes.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121691103","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492686
J. Hua, Ren Zhang, D. Liu, Yun-xia Wang, Chen Qian
To evaluate and analyze the spatial dimension feature of transportation network, Travel Time Ratio Inequality Index is proposed in this paper. The proposed index utilizes the Lorenz Curve to evaluate the traffic load variations among road regions of transportation network, and the inputs of Lorenz Curve is redefined to match the needs of spatial analysis. Based on Internet traffic data, the mathematical model of Travel Time Ratio Inequality Index is deducted, following with the solution method. The numerical results from comparing the index performance of ten large-scale cities justify the validness and usefulness of the proposed index and the related mathematical model.
{"title":"Spatial Inequality Analysis of Urban Road Network based on Internet Traffic Data","authors":"J. Hua, Ren Zhang, D. Liu, Yun-xia Wang, Chen Qian","doi":"10.1109/ICITE.2018.8492686","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492686","url":null,"abstract":"To evaluate and analyze the spatial dimension feature of transportation network, Travel Time Ratio Inequality Index is proposed in this paper. The proposed index utilizes the Lorenz Curve to evaluate the traffic load variations among road regions of transportation network, and the inputs of Lorenz Curve is redefined to match the needs of spatial analysis. Based on Internet traffic data, the mathematical model of Travel Time Ratio Inequality Index is deducted, following with the solution method. The numerical results from comparing the index performance of ten large-scale cities justify the validness and usefulness of the proposed index and the related mathematical model.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131950053","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492575
Mei Bin, S. Licheng, Shi Guoyou, Zhang Yuanqiang
Previous artificial intelligence methods to system identification modeling for ship motion requires a mass of training data, modeling workload is vast. Aiming at these defects, an identification modeling method based on the reference model structure and Bayesian regularization network is proposed. For a start, an existed and public model is selected as the reference model. Secondly, With BR network, the reference model improves the generalization ability and reduces the training data. Finally, the method is verified with benchmark called KVLCC2. The illustrative example demonstrates the effectiveness and generalization ability of the proposed method.
{"title":"Improved Model Structure for Ship Motion Identification Based on Reference Model and Bayesian Regularization Network","authors":"Mei Bin, S. Licheng, Shi Guoyou, Zhang Yuanqiang","doi":"10.1109/ICITE.2018.8492575","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492575","url":null,"abstract":"Previous artificial intelligence methods to system identification modeling for ship motion requires a mass of training data, modeling workload is vast. Aiming at these defects, an identification modeling method based on the reference model structure and Bayesian regularization network is proposed. For a start, an existed and public model is selected as the reference model. Secondly, With BR network, the reference model improves the generalization ability and reduces the training data. Finally, the method is verified with benchmark called KVLCC2. The illustrative example demonstrates the effectiveness and generalization ability of the proposed method.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116489362","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}
Pub Date : 2018-09-01DOI: 10.1109/ICITE.2018.8492607
N. E. Chelbi, D. Gingras, Claude Sauvageau
Metropolis-Hastings algorithm (MH) is the most popular Markov Chain Monte Carlo (MCMC) method. Essentially, the MH algorithm generates a sample, accepts or rejects the sample based on an acceptance probability that is related to the continuous target probability distribution. In this work, we propose a modified Metropolis-Hastings algorithm (MMH-DPD) that can draw samples from discrete probability distributions. For starters, the discrete probability distribution is replaced with a multimodal distribution and a new step after the rejection and acceptation step is added to the original algorithm. To reduce the error caused by the tail of the multimodal distribution, we used a mixture of Generalized Gaussians instead. Numerical results and a generalization of the proposed algorithm are provided. Our simulations show that the proposed sampler reliably creates a Markov chain that generates a sequence of values, in such a way that as the number of samples goes to infinity, we can guarantee that they reflect samples from the target discrete distribution.
{"title":"Modified Metropolis-Hastings Algorithm for Efficient Sampling from Discrete Probability Distributions (MMH-DPD) Applied to Field Operational Tests database (SPMD)","authors":"N. E. Chelbi, D. Gingras, Claude Sauvageau","doi":"10.1109/ICITE.2018.8492607","DOIUrl":"https://doi.org/10.1109/ICITE.2018.8492607","url":null,"abstract":"Metropolis-Hastings algorithm (MH) is the most popular Markov Chain Monte Carlo (MCMC) method. Essentially, the MH algorithm generates a sample, accepts or rejects the sample based on an acceptance probability that is related to the continuous target probability distribution. In this work, we propose a modified Metropolis-Hastings algorithm (MMH-DPD) that can draw samples from discrete probability distributions. For starters, the discrete probability distribution is replaced with a multimodal distribution and a new step after the rejection and acceptation step is added to the original algorithm. To reduce the error caused by the tail of the multimodal distribution, we used a mixture of Generalized Gaussians instead. Numerical results and a generalization of the proposed algorithm are provided. Our simulations show that the proposed sampler reliably creates a Markov chain that generates a sequence of values, in such a way that as the number of samples goes to infinity, we can guarantee that they reflect samples from the target discrete distribution.","PeriodicalId":336951,"journal":{"name":"2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133377724","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}
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