Pub Date : 2016-11-01DOI: 10.1109/ITSC.2016.7795782
Emerson Luiz Chiesse da Silva, M. Rosa, K. Fonseca, R. Lüders, N. P. Kozievitch
Complex networks have been used to model public transportation systems (PTS) considering the relationship between bus lines and bus stops. Previous works focused on statistically characterize either the whole network or their individual bus stops and lines. The present work focused on statistically characterize different regions of a city (Curitiba, Brazil) assuming that a passenger could easily access different unconnected bus stops in a geographic area. K-means algorithm was used to partition the bus stops in (K =) 2 to 40 clusters with similar geographic area. Results showed strong inverse relationship (p < 2 × 10−16 and R2 = 0.74 for K = 40 in a log model) between the degree and the average path length of clustered bus stops. Regarding Curitiba, it revealed well and badly served regions (downtown area, and few suburbs in Southern and Western Curitiba, respectively). Some of these well served regions showed quantitative indication of potential bus congestion. By varying K, city planners could obtained zoomed view of the behavior of their PTS in terms of complex networks metrics.
{"title":"Combining K-means method and complex network analysis to evaluate city mobility","authors":"Emerson Luiz Chiesse da Silva, M. Rosa, K. Fonseca, R. Lüders, N. P. Kozievitch","doi":"10.1109/ITSC.2016.7795782","DOIUrl":"https://doi.org/10.1109/ITSC.2016.7795782","url":null,"abstract":"Complex networks have been used to model public transportation systems (PTS) considering the relationship between bus lines and bus stops. Previous works focused on statistically characterize either the whole network or their individual bus stops and lines. The present work focused on statistically characterize different regions of a city (Curitiba, Brazil) assuming that a passenger could easily access different unconnected bus stops in a geographic area. K-means algorithm was used to partition the bus stops in (K =) 2 to 40 clusters with similar geographic area. Results showed strong inverse relationship (p < 2 × 10−16 and R2 = 0.74 for K = 40 in a log model) between the degree and the average path length of clustered bus stops. Regarding Curitiba, it revealed well and badly served regions (downtown area, and few suburbs in Southern and Western Curitiba, respectively). Some of these well served regions showed quantitative indication of potential bus congestion. By varying K, city planners could obtained zoomed view of the behavior of their PTS in terms of complex networks metrics.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127179449","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}
N. Narendra, Karthikeyan Ponnalagu, A. Ghose, Srikanth G. Tamilselvam
One of the crucial research issues in an IoT-based system is how to manage the huge amount of data transmitted by the potentially large number of sensors that form the system. Prior research has focused on centralized cloud-based "Big Data" architectures for collecting, collating and analyzing the data. However, most of these scenarios accumulate thousands of petabytes in a short period of time, increasing the demand for more storage, and also slowing down speed of data analysis. Hence for real-time scenarios, e.g., agricultural crop tracking, traffic management, etc., such an approach would be impractical. Moreover, depending on the context in which the data is generated and is to be used, only a fraction of the data would be needed for analysis. Therefore, the challenges are to determine which data to keep and which to discard for both short term and long term usage, and define the contextual parameters along which this filtering is to be done. Hence one key problem addressed in this paper is how to define what data the user needs so that filtering algorithms can be defined to extract the data needed. To that end, in this paper, we present a goal driven, context-aware data filtering, transforming and integration approach for IoT-based systems. We propose a data warehouse-based data model for specifying the data needed at particular levels of granularity and frequency, that drive data storage and representation (aligned with the Semantic Sensor Network ontology). Throughout our paper, we illustrate our ideas via a realistic running example in the smart city domain, with emphasis on traffic management, and also present a proof of concept prototype.
{"title":"Goal-Driven Context-Aware Data Filtering in IoT-Based Systems","authors":"N. Narendra, Karthikeyan Ponnalagu, A. Ghose, Srikanth G. Tamilselvam","doi":"10.1109/ITSC.2015.351","DOIUrl":"https://doi.org/10.1109/ITSC.2015.351","url":null,"abstract":"One of the crucial research issues in an IoT-based system is how to manage the huge amount of data transmitted by the potentially large number of sensors that form the system. Prior research has focused on centralized cloud-based \"Big Data\" architectures for collecting, collating and analyzing the data. However, most of these scenarios accumulate thousands of petabytes in a short period of time, increasing the demand for more storage, and also slowing down speed of data analysis. Hence for real-time scenarios, e.g., agricultural crop tracking, traffic management, etc., such an approach would be impractical. Moreover, depending on the context in which the data is generated and is to be used, only a fraction of the data would be needed for analysis. Therefore, the challenges are to determine which data to keep and which to discard for both short term and long term usage, and define the contextual parameters along which this filtering is to be done. Hence one key problem addressed in this paper is how to define what data the user needs so that filtering algorithms can be defined to extract the data needed. To that end, in this paper, we present a goal driven, context-aware data filtering, transforming and integration approach for IoT-based systems. We propose a data warehouse-based data model for specifying the data needed at particular levels of granularity and frequency, that drive data storage and representation (aligned with the Semantic Sensor Network ontology). Throughout our paper, we illustrate our ideas via a realistic running example in the smart city domain, with emphasis on traffic management, and also present a proof of concept prototype.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117303239","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}
J. Horgan, Ciarán Hughes, J. McDonald, S. Yogamani
Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving. It is a cross disciplinary area encompassing specialised fields like computer vision, machine learning, robotic navigation, embedded systems, automotive electronics and safety critical software. In this paper, we survey the list of vision based advanced driver assistance systems with a consistent terminology and propose a taxonomy. We also propose an abstract model in an attempt to formalize a top-down view of application development to scale towards autonomous driving system.
{"title":"Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances","authors":"J. Horgan, Ciarán Hughes, J. McDonald, S. Yogamani","doi":"10.1109/ITSC.2015.329","DOIUrl":"https://doi.org/10.1109/ITSC.2015.329","url":null,"abstract":"Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving. It is a cross disciplinary area encompassing specialised fields like computer vision, machine learning, robotic navigation, embedded systems, automotive electronics and safety critical software. In this paper, we survey the list of vision based advanced driver assistance systems with a consistent terminology and propose a taxonomy. We also propose an abstract model in an attempt to formalize a top-down view of application development to scale towards autonomous driving system.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165720","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}
Fast simultaneous localization and mapping (FastSLAM), a popular algorithm based on the Rao-Blackwellized Particle Filter, has been used to solve the large-scale simultaneous localization and mapping (SLAM) problem for autonomous vehicle, but it suffers from two serious shortcomings: one is the calculation of Jacobian matrices and the linear approximations of the nonlinear vehicle kinematics model and the nonlinear environment measurement model, the other is particle set degeneracy due to inaccurate proposal distribution of particle filter. Hence an improved FastSLAM algorithm based on the strong tracking square root central difference Kalman filter (STSRCDKF) is proposed in this paper to overcome these problems. In the proposed algorithm, STSRCDKF is based on the combination of a strong tracking filter (STF) and a square root central difference Kalman filter (SRCDKF), STSRCDKF is used to design an adaptive adjustment proposal distribution of the particle filter and to estimate the Gaussian densities of the feature landmarks. The performance of the proposed algorithm is compared with that of UFastSLAM and FastSLAM2.0 in simulations and experimental tests, the results verify that the proposed algorithm has better adaptability and robustness. Furthermore, it reduces computational cost and improves state estimation accuracy and consistency.
{"title":"An Improved FastSLAM Algorithm for Autonomous Vehicle Based on the Strong Tracking Square Root Central Difference Kalman Filter","authors":"Jianmin Duan, Dan Liu, Hongxiao Yu, Hui Shi","doi":"10.1109/ITSC.2015.118","DOIUrl":"https://doi.org/10.1109/ITSC.2015.118","url":null,"abstract":"Fast simultaneous localization and mapping (FastSLAM), a popular algorithm based on the Rao-Blackwellized Particle Filter, has been used to solve the large-scale simultaneous localization and mapping (SLAM) problem for autonomous vehicle, but it suffers from two serious shortcomings: one is the calculation of Jacobian matrices and the linear approximations of the nonlinear vehicle kinematics model and the nonlinear environment measurement model, the other is particle set degeneracy due to inaccurate proposal distribution of particle filter. Hence an improved FastSLAM algorithm based on the strong tracking square root central difference Kalman filter (STSRCDKF) is proposed in this paper to overcome these problems. In the proposed algorithm, STSRCDKF is based on the combination of a strong tracking filter (STF) and a square root central difference Kalman filter (SRCDKF), STSRCDKF is used to design an adaptive adjustment proposal distribution of the particle filter and to estimate the Gaussian densities of the feature landmarks. The performance of the proposed algorithm is compared with that of UFastSLAM and FastSLAM2.0 in simulations and experimental tests, the results verify that the proposed algorithm has better adaptability and robustness. Furthermore, it reduces computational cost and improves state estimation accuracy and consistency.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131267896","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}
R. Kohlhaas, Daniel Hammann, T. Schamm, Johann Marius Zöllner
Highly automated driving is addressed more and more by research and also by vehicle manufacturers. In the past few years several demonstrations of automated vehicles driving on highways and even in urban scenarios were performed. In this context several challenges arose. One challenge is the understanding of complex situations and behavior generation within these especially in urban areas. Trajectory planning in these scenarios can be complex and expensive. Semantic scene modeling and planning can provide vital information to generate reliable and safe trajectories for automated vehicles. In this work we present a novel approach for high-level maneuver planning. It is based on a semantic state space that describes possible actions of a vehicle with respect to other scene elements like lane segments and traffic participants. The semantic characteristic of this state space allow for generalized planning even in complex situations. Concepts like heuristics and homotopies are utilized to optimize planning. Therefore, it is possible to efficiently generate high-level maneuver sequences for automated driving. The approach is tested on synthetic data as well as sensor data of a real test drive. and homotopies are utilized to optimize planning. Therefore, it is possible to efficiently generate high-level maneuver sequences for automated driving. The approach is tested on synthetic data as well as sensor data of a real test drive.
{"title":"Planning of High-Level Maneuver Sequences on Semantic State Spaces","authors":"R. Kohlhaas, Daniel Hammann, T. Schamm, Johann Marius Zöllner","doi":"10.1109/ITSC.2015.338","DOIUrl":"https://doi.org/10.1109/ITSC.2015.338","url":null,"abstract":"Highly automated driving is addressed more and more by research and also by vehicle manufacturers. In the past few years several demonstrations of automated vehicles driving on highways and even in urban scenarios were performed. In this context several challenges arose. One challenge is the understanding of complex situations and behavior generation within these especially in urban areas. Trajectory planning in these scenarios can be complex and expensive. Semantic scene modeling and planning can provide vital information to generate reliable and safe trajectories for automated vehicles. In this work we present a novel approach for high-level maneuver planning. It is based on a semantic state space that describes possible actions of a vehicle with respect to other scene elements like lane segments and traffic participants. The semantic characteristic of this state space allow for generalized planning even in complex situations. Concepts like heuristics and homotopies are utilized to optimize planning. Therefore, it is possible to efficiently generate high-level maneuver sequences for automated driving. The approach is tested on synthetic data as well as sensor data of a real test drive. and homotopies are utilized to optimize planning. Therefore, it is possible to efficiently generate high-level maneuver sequences for automated driving. The approach is tested on synthetic data as well as sensor data of a real test drive.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124182929","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 : 2014-11-20DOI: 10.1109/ITSC.2014.6957985
Shu-yun Niu, Jian-Ping Liu, Liang-you Li, H. Sha, Ji-Sheng Zhang
Based on geographic information system (GIS) technology, a spatial data analysis method for blocked road information data is presented in this paper. First, blocked road information data is introduced. Second, based on GIS technology, the blocked road information data analysis process is proposed. In which, simplify the processing of spatial data, isometric transformation based GIS platform, segment split processing, stake assignment, space matching of blocked road information data, and so on, are included. Finally, the method proposed in the paper is validated by blocked road information data of a province in eastern China from 2010 to 2013. The results show that the method is feasible and effectively, the analysis results can provide support for choosing highway network monitoring sites position, emergency material reserves and management.
{"title":"Data analysis of blocked road information based on GIS","authors":"Shu-yun Niu, Jian-Ping Liu, Liang-you Li, H. Sha, Ji-Sheng Zhang","doi":"10.1109/ITSC.2014.6957985","DOIUrl":"https://doi.org/10.1109/ITSC.2014.6957985","url":null,"abstract":"Based on geographic information system (GIS) technology, a spatial data analysis method for blocked road information data is presented in this paper. First, blocked road information data is introduced. Second, based on GIS technology, the blocked road information data analysis process is proposed. In which, simplify the processing of spatial data, isometric transformation based GIS platform, segment split processing, stake assignment, space matching of blocked road information data, and so on, are included. Finally, the method proposed in the paper is validated by blocked road information data of a province in eastern China from 2010 to 2013. The results show that the method is feasible and effectively, the analysis results can provide support for choosing highway network monitoring sites position, emergency material reserves and management.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124693689","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 : 2014-11-20DOI: 10.1109/ITSC.2014.6957986
Michel B. W. De Oliveira, A. A. Neto
This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-ANN Controller (Environment Observation Method based on Artificial Neural Networks Controller). EOM is a very interesting mathematical method for determining traffic lights timing. However, this method has some implications which artificial neural networks were proposed to improve such problems. To evaluate the proposed traffic control system, an isolated intersection was built in simulation software named SUMO (Simulation of Urban Mobility).
本文提出了一种基于神经网络的城市交通路口红绿灯控制器，称为EOM-ANN控制器(Environment Observation Method based on Artificial neural networks controller)。EOM是一种非常有趣的确定交通灯定时的数学方法。然而，这种方法对人工神经网络的提出也有一定的启示。为了评估所提出的交通控制系统，在仿真软件SUMO (simulation of Urban Mobility)中建立了一个孤立的交叉口。
{"title":"Optimization of traffic lights timing based on Artificial Neural Networks","authors":"Michel B. W. De Oliveira, A. A. Neto","doi":"10.1109/ITSC.2014.6957986","DOIUrl":"https://doi.org/10.1109/ITSC.2014.6957986","url":null,"abstract":"This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-ANN Controller (Environment Observation Method based on Artificial Neural Networks Controller). EOM is a very interesting mathematical method for determining traffic lights timing. However, this method has some implications which artificial neural networks were proposed to improve such problems. To evaluate the proposed traffic control system, an isolated intersection was built in simulation software named SUMO (Simulation of Urban Mobility).","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122595517","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 : 2014-10-01DOI: 10.1109/ITSC.2014.6957945
Xiaolin Zhu, Teng Li, Kaicheng Li, J. Lv
As the high-speed train control system is a typical real-time system, it should not only guaranty its functional logic correctness, but also satisfy certain time delay constraints. The traditional offline testing method, which used to be widely used in train control system's functional conformance testing, however, is no longer suitable. Especially with the increasing system complexity and more information interaction to its outer environment, the offline testing is apparently insufficient to describe the non-deterministic latency restrictions. In this paper, the authors proposed an online testing method, which is suitable for generating and executing test case together and solves the problem of real-time performance testing. Firstly, the authors used timed automata theory to model a typical scenario of Radio Block Center (RBC) handover process. Secondly, the above-mentioned TA network is divided by observable message channels into two parts, the environment model part and the equipment model part, which both work as the testing specifications of real implement. Thirdly, the authors used black-box conformance testing tool UPPAAL-TRON to generate and execute “online” test cases automatically. Specifically, this paper studied the case of RBC handover scenario, and concentrated on non-deterministic time delay performance of crossing interlock messages and wireless messages. Finally we analyzed the inconsistencies between the actual system design and its requirement specification, which could be provided as a reference for CTCS-3 train control norm-setting and system development.
{"title":"Online testing of real-time performance in high-speed train control system","authors":"Xiaolin Zhu, Teng Li, Kaicheng Li, J. Lv","doi":"10.1109/ITSC.2014.6957945","DOIUrl":"https://doi.org/10.1109/ITSC.2014.6957945","url":null,"abstract":"As the high-speed train control system is a typical real-time system, it should not only guaranty its functional logic correctness, but also satisfy certain time delay constraints. The traditional offline testing method, which used to be widely used in train control system's functional conformance testing, however, is no longer suitable. Especially with the increasing system complexity and more information interaction to its outer environment, the offline testing is apparently insufficient to describe the non-deterministic latency restrictions. In this paper, the authors proposed an online testing method, which is suitable for generating and executing test case together and solves the problem of real-time performance testing. Firstly, the authors used timed automata theory to model a typical scenario of Radio Block Center (RBC) handover process. Secondly, the above-mentioned TA network is divided by observable message channels into two parts, the environment model part and the equipment model part, which both work as the testing specifications of real implement. Thirdly, the authors used black-box conformance testing tool UPPAAL-TRON to generate and execute “online” test cases automatically. Specifically, this paper studied the case of RBC handover scenario, and concentrated on non-deterministic time delay performance of crossing interlock messages and wireless messages. Finally we analyzed the inconsistencies between the actual system design and its requirement specification, which could be provided as a reference for CTCS-3 train control norm-setting and system development.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127818282","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 : 2013-10-06DOI: 10.1109/ITSC.2013.6728523
A. P. V. D. Beukel, M. V. Voort
When applying automated driving as a means for congestion assistance, developers are challenged how to accommodate the transitions between automated and manually driving, especially because these transitions might occur regularly and suddenly. During automated driving, the ability to take over control is also aggravated due to the driver being placed out of the control-loop. To assess then the ability to retrieve human control, we tested within a driver simulator experiment the influence of criticality (available time) on Situation Awareness (SA) gained during time-critical take-overs within a scenario of congested driving. Though one of the applied measurement methods did not show the expected effect of SA on successfulness of taking back control, the results show that drivers are able to successfully retrieve control, also within time-critical situations. Furthermore, the results show that the ability to retrieve control is positively influenced if drivers gain increased levels of SA.
{"title":"The influence of time-criticality on Situation Awareness when retrieving human control after automated driving","authors":"A. P. V. D. Beukel, M. V. Voort","doi":"10.1109/ITSC.2013.6728523","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728523","url":null,"abstract":"When applying automated driving as a means for congestion assistance, developers are challenged how to accommodate the transitions between automated and manually driving, especially because these transitions might occur regularly and suddenly. During automated driving, the ability to take over control is also aggravated due to the driver being placed out of the control-loop. To assess then the ability to retrieve human control, we tested within a driver simulator experiment the influence of criticality (available time) on Situation Awareness (SA) gained during time-critical take-overs within a scenario of congested driving. Though one of the applied measurement methods did not show the expected effect of SA on successfulness of taking back control, the results show that drivers are able to successfully retrieve control, also within time-critical situations. Furthermore, the results show that the ability to retrieve control is positively influenced if drivers gain increased levels of SA.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123147399","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 : 2013-10-01DOI: 10.1109/ITSC.2013.6728284
A. G. Leal, A. Santos, M. Y. Miyake, C. Marte
Control Objectives for Information and related Technology (CobIT) establish maturity models, the assessment of process capability is an essential part of IT governance implementation. In an analogous manner, the efficacy of operations in Toll Collection Ecosystem could be evaluated using the same approach from CobIT. Maturity models enable managers to identify gaps in key processes and controls. It describes a tool to assess the maturity and effectiveness of all processes associated with the Toll Collection Ecosystem. The Toll Collection processes constitute an ecosystem that involves the quality and maturity of operations, business processes, institutional aspects, equipment maintenance and infrastructure management. The creation of a methodology for effectiveness and maturity analysis of the full Toll Collection Ecosystem allows the establishment of quantitative and qualitative parameters, which may be assessed and monitored, therefore, enabling a useful tool for the operators' and Government Regulatory Agencies' decision-making processes.
{"title":"Assessment of maturity and efficacy of Toll Collection Ecosystems","authors":"A. G. Leal, A. Santos, M. Y. Miyake, C. Marte","doi":"10.1109/ITSC.2013.6728284","DOIUrl":"https://doi.org/10.1109/ITSC.2013.6728284","url":null,"abstract":"Control Objectives for Information and related Technology (CobIT) establish maturity models, the assessment of process capability is an essential part of IT governance implementation. In an analogous manner, the efficacy of operations in Toll Collection Ecosystem could be evaluated using the same approach from CobIT. Maturity models enable managers to identify gaps in key processes and controls. It describes a tool to assess the maturity and effectiveness of all processes associated with the Toll Collection Ecosystem. The Toll Collection processes constitute an ecosystem that involves the quality and maturity of operations, business processes, institutional aspects, equipment maintenance and infrastructure management. The creation of a methodology for effectiveness and maturity analysis of the full Toll Collection Ecosystem allows the establishment of quantitative and qualitative parameters, which may be assessed and monitored, therefore, enabling a useful tool for the operators' and Government Regulatory Agencies' decision-making processes.","PeriodicalId":184458,"journal":{"name":"International Conference on Intelligent Transportation Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121183415","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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