Jackeline Rios-Torres, Andreas A. Malikopoulos, P. Pisu
This paper addresses the problem of coordinating online connected vehicles at merging roads to achieve a smooth traffic flow without stop-and-go driving. We present a framework and a closed-form solution that optimize the acceleration profile of each vehicle in terms of fuel economy while avoiding collision with other vehicles at the merging zone. The proposed solution is validated through simulation and it is shown that coordination of connected vehicles can reduce significantly fuel consumption and travel time at merging roads.
{"title":"Online Optimal Control of Connected Vehicles for Efficient Traffic Flow at Merging Roads","authors":"Jackeline Rios-Torres, Andreas A. Malikopoulos, P. Pisu","doi":"10.1109/ITSC.2015.392","DOIUrl":"https://doi.org/10.1109/ITSC.2015.392","url":null,"abstract":"This paper addresses the problem of coordinating online connected vehicles at merging roads to achieve a smooth traffic flow without stop-and-go driving. We present a framework and a closed-form solution that optimize the acceleration profile of each vehicle in terms of fuel economy while avoiding collision with other vehicles at the merging zone. The proposed solution is validated through simulation and it is shown that coordination of connected vehicles can reduce significantly fuel consumption and travel time at merging roads.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"6 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":"128756649","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}
This research explores how to compute the minimum number of runs (MNR) required to achieve a specified confidence level for multiple measures of performance (MOP) of a simulated traffic network. Traditional methods to calculate MNR consider the confidence intervals of multiple MOPs separately and hence are not able to control the overall confidence level. A new method to calculate MNR is proposed, which sequentially runs the model and recalculates sample standard deviations and means whenever an additional run is made until a stopping condition based on the Bonferroni inequality is satisfied. The overall confidence level is controlled by the Bonferroni inequality. The proposed method is computationally practical since it can be implemented automatically in most traffic micro-simulation packages. The proposed method is evaluated using a case study with multiple simulation-based surrogate safety measures, including time to collision (TTC) or deceleration rate required to avoid a crash (DRAC), and an empirical confidence level analysis based on a very large number of runs. Evaluation results indicate the effectiveness of the proposed method as it enables all MOPs at the same time to be estimated accurately at the desired confidence level whereas traditional methods do not. In addition, the proposed method is not conservative since it does not require significantly more runs compared to traditional methods.
{"title":"How Many Simulation Runs are Required to Achieve Statistically Confident Results: A Case Study of Simulation-Based Surrogate Safety Measures","authors":"L. Truong, M. Sarvi, G. Currie, T. Garoni","doi":"10.1109/ITSC.2015.54","DOIUrl":"https://doi.org/10.1109/ITSC.2015.54","url":null,"abstract":"This research explores how to compute the minimum number of runs (MNR) required to achieve a specified confidence level for multiple measures of performance (MOP) of a simulated traffic network. Traditional methods to calculate MNR consider the confidence intervals of multiple MOPs separately and hence are not able to control the overall confidence level. A new method to calculate MNR is proposed, which sequentially runs the model and recalculates sample standard deviations and means whenever an additional run is made until a stopping condition based on the Bonferroni inequality is satisfied. The overall confidence level is controlled by the Bonferroni inequality. The proposed method is computationally practical since it can be implemented automatically in most traffic micro-simulation packages. The proposed method is evaluated using a case study with multiple simulation-based surrogate safety measures, including time to collision (TTC) or deceleration rate required to avoid a crash (DRAC), and an empirical confidence level analysis based on a very large number of runs. Evaluation results indicate the effectiveness of the proposed method as it enables all MOPs at the same time to be estimated accurately at the desired confidence level whereas traditional methods do not. In addition, the proposed method is not conservative since it does not require significantly more runs compared to traditional methods.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"48 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":"129262022","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}
Qingwen Han, Yingxiang Zhu, Lingqiu Zeng, L. Ye, Xueying He, Xiaoying Liu, Haotian Wu, Qingsheng Zhu
During the last decade, the concept of cluster, has become a popular practice in the field of road safety, mainly for the identification of worst performing areas or time slots also known as hotspots. However, current clustering methods used to identify road accident hotspots suffer from various deficiencies at both theoretical and operational level, these include parameter sensitivity, identify difficultly on arbitrary shape, and cluster number's rationality. The objective of this study is to contribute to the ongoing research effort on hotspots identification. Employing the concept of natural neighbor, a new algorithm, named distance threshold based on natural nearest neighbor (DTH3N), is proposed in this paper, striving to minimize the aforementioned deficiencies of the current approaches. Experiment results show that, comparing with existing methods, proposed algorithm presents a better performance on cluster division. Furthermore, this new method can be viewed as an intelligent decision support basis for road safety performance evaluation, in order to prioritize interventions for road safety improvement.
{"title":"A Road Hotspots Identification Method Based on Natural Nearest Neighbor Clustering","authors":"Qingwen Han, Yingxiang Zhu, Lingqiu Zeng, L. Ye, Xueying He, Xiaoying Liu, Haotian Wu, Qingsheng Zhu","doi":"10.1109/ITSC.2015.97","DOIUrl":"https://doi.org/10.1109/ITSC.2015.97","url":null,"abstract":"During the last decade, the concept of cluster, has become a popular practice in the field of road safety, mainly for the identification of worst performing areas or time slots also known as hotspots. However, current clustering methods used to identify road accident hotspots suffer from various deficiencies at both theoretical and operational level, these include parameter sensitivity, identify difficultly on arbitrary shape, and cluster number's rationality. The objective of this study is to contribute to the ongoing research effort on hotspots identification. Employing the concept of natural neighbor, a new algorithm, named distance threshold based on natural nearest neighbor (DTH3N), is proposed in this paper, striving to minimize the aforementioned deficiencies of the current approaches. Experiment results show that, comparing with existing methods, proposed algorithm presents a better performance on cluster division. Furthermore, this new method can be viewed as an intelligent decision support basis for road safety performance evaluation, in order to prioritize interventions for road safety improvement.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th 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":"130531036","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}
This papers studies the kind of control that is needed to efficiently coordinate multiple automated vehicles. An intersection is chosen in order to present the main concept but consequences of this work also hold for other areas of cooperation, such as lane changes or maneuvers in parking lots. We chose the classical framework for multi-robots systems: the coordination space i.e. we assume the future paths are known and fixed. The problem is to coordinate the speeds of the vehicles. We first prove a theorem stating that a smooth feedback control cannot always avoid gridlocks: for more than 2 vehicles, there are always starting states ending into gridlocks. The paper then proposes some ways to avoid this drawback, leading to a better conceptual way to take decision in such a cooperative system, in order to have provable efficient decision and control.
{"title":"Coordination of Automated Vehicles at Intersections: Decision, Efficiency and Control","authors":"A. D. L. Fortelle","doi":"10.1109/ITSC.2015.277","DOIUrl":"https://doi.org/10.1109/ITSC.2015.277","url":null,"abstract":"This papers studies the kind of control that is needed to efficiently coordinate multiple automated vehicles. An intersection is chosen in order to present the main concept but consequences of this work also hold for other areas of cooperation, such as lane changes or maneuvers in parking lots. We chose the classical framework for multi-robots systems: the coordination space i.e. we assume the future paths are known and fixed. The problem is to coordinate the speeds of the vehicles. We first prove a theorem stating that a smooth feedback control cannot always avoid gridlocks: for more than 2 vehicles, there are always starting states ending into gridlocks. The paper then proposes some ways to avoid this drawback, leading to a better conceptual way to take decision in such a cooperative system, in order to have provable efficient decision and control.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"79 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":"123662083","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}
In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction algorithms, each of which is characterized by a small number of adjustable parameters. Adaptability means that parameters of the constructed combination depend on a number of control parameters of the model, which includes the following factors: weather conditions, traffic density, driving dynamics, prediction horizon, and others. Adaptability is achieved by the use of a hierarchical regression (similar to a regression tree). The proposed arrival prediction algorithm has been tested with the data of all the public transport routes in Samara, Russia.
{"title":"An Adaptive Algorithm for Public Transport Arrival Time Prediction Based on Hierarhical Regression","authors":"A. Agafonov, V. Myasnikov","doi":"10.1109/ITSC.2015.446","DOIUrl":"https://doi.org/10.1109/ITSC.2015.446","url":null,"abstract":"In this paper we consider a problem of public transport arrival time prediction for a large city in real time. We propose a new prediction algorithm based on a model of an adaptive combination of elementary prediction algorithms, each of which is characterized by a small number of adjustable parameters. Adaptability means that parameters of the constructed combination depend on a number of control parameters of the model, which includes the following factors: weather conditions, traffic density, driving dynamics, prediction horizon, and others. Adaptability is achieved by the use of a hierarchical regression (similar to a regression tree). The proposed arrival prediction algorithm has been tested with the data of all the public transport routes in Samara, Russia.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"56 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":"123669326","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}
P. Lima, M. Trincavelli, J. Mårtensson, B. Wahlberg
This paper presents a method for optimal speed profile generation in specified clothoid-based paths with known semantic - maximum speed and longitudinal and lateral acceleration - and geometric information. A clothoid can be described using only its kink-points information, i.e. the points defining the start and end of a clothoid. Using the clothoid-based path representation, we formulate the speed profile generation as a convex optimization problem where the objective is to produce a smooth speed that is close to the maximum allowed speed. The vehicle and the road profile define the constraints of the problem. Furthermore, we develop a longitudinal controller by using the speed profiler in a receding-horizon fashion. Thus, we only consider a finite horizon when computing the optimal inputs every sampling time and, in addition, the longitudinal controller also takes into account the newest prediction available from measurements and from the lateral controller. We present simulations that demonstrate the ability of the method to generate safe and feasible speed profiles and the tracking of those by the longitudinal controller. We also study the influence of the clothoid-based path representation in the optimality of the speed profile obtained. We show that we can get a very good suboptimal speed profile approximation with few more points than the kink-points. In addition, we analyze the influence of an acceleration penalization factor in the smoothness of the speed profiler. The higher the acceleration penalization the smoother and the further from the maximum allowed speed is the speed profile.
{"title":"Clothoid-Based Speed Profiler and Control for Autonomous Driving","authors":"P. Lima, M. Trincavelli, J. Mårtensson, B. Wahlberg","doi":"10.1109/ITSC.2015.354","DOIUrl":"https://doi.org/10.1109/ITSC.2015.354","url":null,"abstract":"This paper presents a method for optimal speed profile generation in specified clothoid-based paths with known semantic - maximum speed and longitudinal and lateral acceleration - and geometric information. A clothoid can be described using only its kink-points information, i.e. the points defining the start and end of a clothoid. Using the clothoid-based path representation, we formulate the speed profile generation as a convex optimization problem where the objective is to produce a smooth speed that is close to the maximum allowed speed. The vehicle and the road profile define the constraints of the problem. Furthermore, we develop a longitudinal controller by using the speed profiler in a receding-horizon fashion. Thus, we only consider a finite horizon when computing the optimal inputs every sampling time and, in addition, the longitudinal controller also takes into account the newest prediction available from measurements and from the lateral controller. We present simulations that demonstrate the ability of the method to generate safe and feasible speed profiles and the tracking of those by the longitudinal controller. We also study the influence of the clothoid-based path representation in the optimality of the speed profile obtained. We show that we can get a very good suboptimal speed profile approximation with few more points than the kink-points. In addition, we analyze the influence of an acceleration penalization factor in the smoothness of the speed profiler. The higher the acceleration penalization the smoother and the further from the maximum allowed speed is the speed profile.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"58 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120920314","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}
Cameras are often controlled by algorithms adapting the image capturing process parameters (like exposure or gain) to the present scene. In most cases these algorithms have to be parametrized by a parameter set in order to define the behavior of the control. The issue of selecting the best parameter set for a specific application or environment arises. The parameter selection is not a simple task since the internal structure of the control algorithm is often not sufficiently known by the user. Only the inputs (parameter sets) can be specified and the outputs (images) can be analyzed. This paper presents a generic workflow for the determination of a parameter set which achieves good image quality for a chosen application with specific light conditions. The developed workflow is able to deal with any control algorithm and any chosen application. In general, the four main steps of the developed workflow are: 1. Build a database of images with their related parameter sets, 2. Evaluate which image criteria are best to assess the image quality for the particular application, 3. Choose an optimization method, 4. Optimize the parameter sets. The presented workflow is developed and examined based on the example of real-world automotive scenarios. At the end of the paper experimental results confirm that the optimized camera parameters achieve a meaningful and useful optimization result regarding the images captured by the camera.
{"title":"A Generic Parameter Optimization Workflow for Camera Control Algorithms","authors":"Jens Westerhoff, M. Meuter, A. Kummert","doi":"10.1109/ITSC.2015.158","DOIUrl":"https://doi.org/10.1109/ITSC.2015.158","url":null,"abstract":"Cameras are often controlled by algorithms adapting the image capturing process parameters (like exposure or gain) to the present scene. In most cases these algorithms have to be parametrized by a parameter set in order to define the behavior of the control. The issue of selecting the best parameter set for a specific application or environment arises. The parameter selection is not a simple task since the internal structure of the control algorithm is often not sufficiently known by the user. Only the inputs (parameter sets) can be specified and the outputs (images) can be analyzed. This paper presents a generic workflow for the determination of a parameter set which achieves good image quality for a chosen application with specific light conditions. The developed workflow is able to deal with any control algorithm and any chosen application. In general, the four main steps of the developed workflow are: 1. Build a database of images with their related parameter sets, 2. Evaluate which image criteria are best to assess the image quality for the particular application, 3. Choose an optimization method, 4. Optimize the parameter sets. The presented workflow is developed and examined based on the example of real-world automotive scenarios. At the end of the paper experimental results confirm that the optimized camera parameters achieve a meaningful and useful optimization result regarding the images captured by the camera.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"43 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":"114067331","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}
A. Voronov, Cristofer Englund, Hoai Hoang Bengtsson, Lei Chen, J. Ploeg, Jan de Jonhg, Jacco van de Sluis
This paper presents the architecture of an Interactive Test Tool (ITT) for interoperability testing of Cooperative Intelligent Transport Systems (C-ITS). Cooperative systems are developed by different manufacturers at different locations, which makes interoperability testing a tedious task. Up until now, interoperability testing is performed during physical meetings where the C-ITS devices are placed within range of wireless communication, and messages are exchanged. The ITT allows distributed (e.g. over Internet) interoperability testing starting from the network Transport Layer and all the way up to the Application Layer, e.g. to platooning. ITT clients can be implemented as Hardware-in-the-Loop, thus allowing to combine physical and virtual vehicles. Since the ITT considers each client as a black box, manufacturers can test together without revealing internal implementations to each other. The architecture of the ITT allows users to easily switch between physical wireless networking and virtual ITT networking. Therefore, only one implementation of the ITS communication stack is required for both development and testing, which reduces the work overhead and ensures that the stack that is used during the testing is the one deployed in the real world.
{"title":"Interactive Test Tool for Interoperable C-ITS Development","authors":"A. Voronov, Cristofer Englund, Hoai Hoang Bengtsson, Lei Chen, J. Ploeg, Jan de Jonhg, Jacco van de Sluis","doi":"10.1109/ITSC.2015.278","DOIUrl":"https://doi.org/10.1109/ITSC.2015.278","url":null,"abstract":"This paper presents the architecture of an Interactive Test Tool (ITT) for interoperability testing of Cooperative Intelligent Transport Systems (C-ITS). Cooperative systems are developed by different manufacturers at different locations, which makes interoperability testing a tedious task. Up until now, interoperability testing is performed during physical meetings where the C-ITS devices are placed within range of wireless communication, and messages are exchanged. The ITT allows distributed (e.g. over Internet) interoperability testing starting from the network Transport Layer and all the way up to the Application Layer, e.g. to platooning. ITT clients can be implemented as Hardware-in-the-Loop, thus allowing to combine physical and virtual vehicles. Since the ITT considers each client as a black box, manufacturers can test together without revealing internal implementations to each other. The architecture of the ITT allows users to easily switch between physical wireless networking and virtual ITT networking. Therefore, only one implementation of the ITS communication stack is required for both development and testing, which reduces the work overhead and ensures that the stack that is used during the testing is the one deployed in the real world.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"82 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":"114239731","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}
Traffic flow prediction is a fundamental component in Intelligent Transportation Systems (ITS). Nearest neighbor based nonparametric regression method is a classic data-driven method for traffic flow prediction. Modern data collection technologies provide the opportunity to represent various features of the nonlinear complex system which also bring challenges to fuse the multiple sources of data. Firstly, the classic Euclidean distance metric based models for traffic flow prediction that treat each feature with equal weight is not effective in multi-source high-dimension feature space. Secondly, traditional handcrafting feature engineering by experts is tedious and error-prone. Thirdly, the traffic conditions in real-life situation are too complex to measure with only one distance metric. In this paper, we propose a hybrid multi-metric based k-nearest neighbor method (HMMKNN) for traffic flow prediction which can seize the intrinsic features in data and reduce the semantic gap between domain knowledge and handcrafted feature engineering. Experimental results demonstrate multi-source data fusion helps to improve the performance of traffic parameter prediction and HMMKNN outperforms the traditional Euclidean-based k-NN under various configurations. Furthermore, visualization of feature transformation clustering results implies the learned metrics are more reasonable.
{"title":"Hybrid Multi-metric K-Nearest Neighbor Regression for Traffic Flow Prediction","authors":"Haikun Hong, Wenhao Huang, Xingxing Xing, Xiabing Zhou, Hongyu Lu, Kaigui Bian, Kunqing Xie","doi":"10.1109/ITSC.2015.365","DOIUrl":"https://doi.org/10.1109/ITSC.2015.365","url":null,"abstract":"Traffic flow prediction is a fundamental component in Intelligent Transportation Systems (ITS). Nearest neighbor based nonparametric regression method is a classic data-driven method for traffic flow prediction. Modern data collection technologies provide the opportunity to represent various features of the nonlinear complex system which also bring challenges to fuse the multiple sources of data. Firstly, the classic Euclidean distance metric based models for traffic flow prediction that treat each feature with equal weight is not effective in multi-source high-dimension feature space. Secondly, traditional handcrafting feature engineering by experts is tedious and error-prone. Thirdly, the traffic conditions in real-life situation are too complex to measure with only one distance metric. In this paper, we propose a hybrid multi-metric based k-nearest neighbor method (HMMKNN) for traffic flow prediction which can seize the intrinsic features in data and reduce the semantic gap between domain knowledge and handcrafted feature engineering. Experimental results demonstrate multi-source data fusion helps to improve the performance of traffic parameter prediction and HMMKNN outperforms the traditional Euclidean-based k-NN under various configurations. Furthermore, visualization of feature transformation clustering results implies the learned metrics are more reasonable.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"193 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":"121543559","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}
L. Nack, Roman Roor, Michael Karg, Alexandra Kirsch, Olga Birth, Sebastian Leibe, M. Strassberger
With large parts of human population increasingly living in big cities, the mobility behavior of humans is about to change faster than ever before. Not only convenience and increasing ecological awareness lead to more intermodal mobility behavior, also the rise of new mobility options like car-or bike sharing are becoming more and more common. Wide distribution of smartphones and the on-trip availability of high-speed Internet let users inform themselves about a vast variety of mobility options. This information overload can overburden users who often have the simple wish to conveniently travel from A to B. Digital Mobility Assistants ease the burden of selecting the best mobility option for a particular user by incorporating the users' habits and preferences and providing relevant information at just the right time. To enable such intelligent assistance, we propose to create personalized mobility models that include not only information about habitual trips and destinations, but also allow for the detection of preferred travel modes. Our system is specifically designed to use sparse sensor data from mobile devices, such as smartphones, to offer an adequate balance between battery-life and data quality.
{"title":"Acquisition and Use of Mobility Habits for Personal Assistants","authors":"L. Nack, Roman Roor, Michael Karg, Alexandra Kirsch, Olga Birth, Sebastian Leibe, M. Strassberger","doi":"10.1109/ITSC.2015.245","DOIUrl":"https://doi.org/10.1109/ITSC.2015.245","url":null,"abstract":"With large parts of human population increasingly living in big cities, the mobility behavior of humans is about to change faster than ever before. Not only convenience and increasing ecological awareness lead to more intermodal mobility behavior, also the rise of new mobility options like car-or bike sharing are becoming more and more common. Wide distribution of smartphones and the on-trip availability of high-speed Internet let users inform themselves about a vast variety of mobility options. This information overload can overburden users who often have the simple wish to conveniently travel from A to B. Digital Mobility Assistants ease the burden of selecting the best mobility option for a particular user by incorporating the users' habits and preferences and providing relevant information at just the right time. To enable such intelligent assistance, we propose to create personalized mobility models that include not only information about habitual trips and destinations, but also allow for the detection of preferred travel modes. Our system is specifically designed to use sparse sensor data from mobile devices, such as smartphones, to offer an adequate balance between battery-life and data quality.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"AES-20 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":"126552360","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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