Niels A. H. Agatz, A. Bazzan, Ronny J. Kutadinata, D. Mattfeld, Monika Sester, S. Winter, O. Wolfson
Since in many cities transport infrastructure is operating at or beyond capacity, novel approaches to organize urban mobility are gaining attraction. However, assessing the benefits of a measure that has disruptive capacity in a complex system requires a carefully designed research. This paper takes a recent idea for urban mobility - flexible road trains - and illustrates the computational and research challenges of realizing its full potential and describing its social, ecological and economical impact.
{"title":"Autonomous car and ride sharing: flexible road trains: (vision paper)","authors":"Niels A. H. Agatz, A. Bazzan, Ronny J. Kutadinata, D. Mattfeld, Monika Sester, S. Winter, O. Wolfson","doi":"10.1145/2996913.2996947","DOIUrl":"https://doi.org/10.1145/2996913.2996947","url":null,"abstract":"Since in many cities transport infrastructure is operating at or beyond capacity, novel approaches to organize urban mobility are gaining attraction. However, assessing the benefits of a measure that has disruptive capacity in a complex system requires a carefully designed research. This paper takes a recent idea for urban mobility - flexible road trains - and illustrates the computational and research challenges of realizing its full potential and describing its social, ecological and economical impact.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84286633","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 investigate a novel query type for road networks, namely the k-Optimal Meeting Points based on Preferred paths (k-OMP3) query. Consider a group of friends currently at different places, e.g., their offices. Before going to their respective homes, using their own preferred paths in the underlying road network, the group is willing to meet at a restaurant for dinner. A k-OMP3 query would return the k restaurants that would minimize the group detour distance. In this short paper we present a provably correct approach which exploits the geometric properties of the problem in order to reduce the query's processing time. Our experiments, using real and synthetic data sets, confirm the effectiveness and efficiency of such approach.
{"title":"k-Optimal meeting points based on preferred paths","authors":"E. Ahmadi, M. Nascimento","doi":"10.1145/2996913.2996994","DOIUrl":"https://doi.org/10.1145/2996913.2996994","url":null,"abstract":"In this paper, we investigate a novel query type for road networks, namely the k-Optimal Meeting Points based on Preferred paths (k-OMP3) query. Consider a group of friends currently at different places, e.g., their offices. Before going to their respective homes, using their own preferred paths in the underlying road network, the group is willing to meet at a restaurant for dinner. A k-OMP3 query would return the k restaurants that would minimize the group detour distance. In this short paper we present a provably correct approach which exploits the geometric properties of the problem in order to reduce the query's processing time. Our experiments, using real and synthetic data sets, confirm the effectiveness and efficiency of such approach.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81917993","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}
Given a large collection of urban datasets, how can we find their hidden correlations? For example, New York City (NYC) provides open access to taxi data from year 2012 to 2015 with about half million taxi trips generated per day. In the meantime, we have a rich set of urban data in NYC including points-of-interest (POIs), geo-tagged tweets, weather, vehicle collisions, etc. Is it possible that these ubiquitous datasets can be used to explain the city traffic? Understanding the hidden correlation between external data and traffic data would allow us to answer many important questions in urban computing such as: If we observe a high traffic volume at Madison Square Garden (MSG) in NYC, is it because of the regular peak hour or a big event being held at MSG? If a disaster weather such as a hurricane or a snow storm hits the city, how would the traffic be affected? Most of existing studies on traffic dynamics focus only on traffic data itself and do not seek for external datasets to explain traffic. In this paper, we present our results in attempts to understand taxi traffic dynamics in NYC from multiple external data sources. We use four real-world ubiquitous urban datasets, including POIs, weather, geo-tagged tweets, and collision records. To address the heterogeneity of ubiquitous urban data, we present carefully-designed feature representations for these datasets. Our analysis suggests that POIs can well describe the regular traffic patterns. In addition, geo-tagged tweets can be used to explain irregular traffic caused by big events, and weather may account for abnormal traffic drops.
{"title":"Interpreting traffic dynamics using ubiquitous urban data","authors":"Fei Wu, Hongjian Wang, Z. Li","doi":"10.1145/2996913.2996962","DOIUrl":"https://doi.org/10.1145/2996913.2996962","url":null,"abstract":"Given a large collection of urban datasets, how can we find their hidden correlations? For example, New York City (NYC) provides open access to taxi data from year 2012 to 2015 with about half million taxi trips generated per day. In the meantime, we have a rich set of urban data in NYC including points-of-interest (POIs), geo-tagged tweets, weather, vehicle collisions, etc. Is it possible that these ubiquitous datasets can be used to explain the city traffic? Understanding the hidden correlation between external data and traffic data would allow us to answer many important questions in urban computing such as: If we observe a high traffic volume at Madison Square Garden (MSG) in NYC, is it because of the regular peak hour or a big event being held at MSG? If a disaster weather such as a hurricane or a snow storm hits the city, how would the traffic be affected? Most of existing studies on traffic dynamics focus only on traffic data itself and do not seek for external datasets to explain traffic. In this paper, we present our results in attempts to understand taxi traffic dynamics in NYC from multiple external data sources. We use four real-world ubiquitous urban datasets, including POIs, weather, geo-tagged tweets, and collision records. To address the heterogeneity of ubiquitous urban data, we present carefully-designed feature representations for these datasets. Our analysis suggests that POIs can well describe the regular traffic patterns. In addition, geo-tagged tweets can be used to explain irregular traffic caused by big events, and weather may account for abnormal traffic drops.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84588730","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}
With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory data. To exploit the main-memory setting SPNET exploits efficient shortest-path compression of trajectories to achieve a compact index structure. SPNET is capable of exploiting the parallel computing capabilities of modern machines and supports both intra- and inter-query parallelism. The former improves response time, and the latter improves throughput. By design, SPNET supports a wider range of query types than any single existing index. An experimental study in a real-world setting with 1.94 billion GPS records and nearly 4 million trajectories in a road network with 1.8 million edges indicates that SPNET typically offers performance improvements over the best existing indexes of 1.5 to 2 orders of magnitude.
{"title":"Efficient in-memory indexing of network-constrained trajectories","authors":"Benjamin B. Krogh, Christian S. Jensen, K. Torp","doi":"10.1145/2996913.2996972","DOIUrl":"https://doi.org/10.1145/2996913.2996972","url":null,"abstract":"With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory data. To exploit the main-memory setting SPNET exploits efficient shortest-path compression of trajectories to achieve a compact index structure. SPNET is capable of exploiting the parallel computing capabilities of modern machines and supports both intra- and inter-query parallelism. The former improves response time, and the latter improves throughput. By design, SPNET supports a wider range of query types than any single existing index. An experimental study in a real-world setting with 1.94 billion GPS records and nearly 4 million trajectories in a road network with 1.8 million edges indicates that SPNET typically offers performance improvements over the best existing indexes of 1.5 to 2 orders of magnitude.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75604319","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}
Scene parsing has attracted significant attention for its practical and theoretical value in computer vision. A typical scene parsing algorithm seeks to densely label pixels or 3-dimensional points from a scene. Traditionally, this procedure relies on a pre-trained classifier to identify the label information, and a smoothing step via Markov Random Field to enhance the consistency. LabelTranfer is a category of scene parsing algorithms to enhance traditional scene parsing framework, by finding dense correspondence and transferring labels across scenes. In this paper, we present a novel scene parsing algorithm which matches maximal similar structures between scenes via efficient low-rank graph matching. The inputs of the algorithm are images, and well- aligned point clouds if available. The images and the point clouds are processed in separate pipelines. The pipeline of images is to learn a reliable classifier and to match local structures via graph matching. The pipeline of point clouds is to conduct preliminary segmentation and to generate feasible label sets. The two pipelines are merged at inference step, in which we elaborate effective and efficient potential functions. We propose a new graph matching model incorporating low-rank and Frobenius regularization, which not only guarantees an accurate solution, but also provides high optimization efficiency via an eigen-decomposition strategy. Several challenging experiments are conducted, showing competitive performance of the proposed method compared to state-of-the-art LabelTransfer algorithm. Further, with point clouds, the performance can be significantly enhanced.
{"title":"Enhancing scene parsing by transferring structures via efficient low-rank graph matching","authors":"Tianshu Yu, Ruisheng Wang","doi":"10.1145/2996913.2996956","DOIUrl":"https://doi.org/10.1145/2996913.2996956","url":null,"abstract":"Scene parsing has attracted significant attention for its practical and theoretical value in computer vision. A typical scene parsing algorithm seeks to densely label pixels or 3-dimensional points from a scene. Traditionally, this procedure relies on a pre-trained classifier to identify the label information, and a smoothing step via Markov Random Field to enhance the consistency. LabelTranfer is a category of scene parsing algorithms to enhance traditional scene parsing framework, by finding dense correspondence and transferring labels across scenes. In this paper, we present a novel scene parsing algorithm which matches maximal similar structures between scenes via efficient low-rank graph matching. The inputs of the algorithm are images, and well- aligned point clouds if available. The images and the point clouds are processed in separate pipelines. The pipeline of images is to learn a reliable classifier and to match local structures via graph matching. The pipeline of point clouds is to conduct preliminary segmentation and to generate feasible label sets. The two pipelines are merged at inference step, in which we elaborate effective and efficient potential functions. We propose a new graph matching model incorporating low-rank and Frobenius regularization, which not only guarantees an accurate solution, but also provides high optimization efficiency via an eigen-decomposition strategy. Several challenging experiments are conducted, showing competitive performance of the proposed method compared to state-of-the-art LabelTransfer algorithm. Further, with point clouds, the performance can be significantly enhanced.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77102413","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}
Abdeltawab M. Hendawi, Mohamed E. Khalefa, Harry Liu, Mohamed H. Ali, J. Stankovic
A few decades ago, the Internet was created. Since then, searching for information and services has increased exponentially. With the introduction of GPS-enabled devices, a special type of search appeared offering location-aware services. These services customize search results based on users' location. This includes, but not limited to, (1) service finding, e.g., "find the nearest pizza restaurant", (2) routing, e.g., "obtain the shortest path from a user's home to the airport", (3) transportation, e.g., "what are the bus links to get a user from downtown to the mall", and (4) monitoring, e.g., "alert a parent if their child school-bus deviates from its regular route". Though new hardware and software technologies such as smart watches, voice search, and big-data platforms have been introduced and widely used, each single type of the above services has benefited very little from these technologies. On the local level of each service (the micro level), a full-fledged view is still missing. On the global level of all service types (the macro level), all Location-aware services are still acting as isolated islands and a global optimized service is not available. This paper presents our vision of how to provide an integrated macro location-aware service that acts harmoniously, and how each micro service can be further improved by better incorporation of novel technologies. We also overview the key challenges associated with these suggested improvements. Then, we highlight the potential value-added by the application of our vision.
{"title":"A vision for micro and macro location aware services","authors":"Abdeltawab M. Hendawi, Mohamed E. Khalefa, Harry Liu, Mohamed H. Ali, J. Stankovic","doi":"10.1145/2996913.2996977","DOIUrl":"https://doi.org/10.1145/2996913.2996977","url":null,"abstract":"A few decades ago, the Internet was created. Since then, searching for information and services has increased exponentially. With the introduction of GPS-enabled devices, a special type of search appeared offering location-aware services. These services customize search results based on users' location. This includes, but not limited to, (1) service finding, e.g., \"find the nearest pizza restaurant\", (2) routing, e.g., \"obtain the shortest path from a user's home to the airport\", (3) transportation, e.g., \"what are the bus links to get a user from downtown to the mall\", and (4) monitoring, e.g., \"alert a parent if their child school-bus deviates from its regular route\". Though new hardware and software technologies such as smart watches, voice search, and big-data platforms have been introduced and widely used, each single type of the above services has benefited very little from these technologies. On the local level of each service (the micro level), a full-fledged view is still missing. On the global level of all service types (the macro level), all Location-aware services are still acting as isolated islands and a global optimized service is not available. This paper presents our vision of how to provide an integrated macro location-aware service that acts harmoniously, and how each micro service can be further improved by better incorporation of novel technologies. We also overview the key challenges associated with these suggested improvements. Then, we highlight the potential value-added by the application of our vision.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87770610","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}
Gautam Thakur, P. Kuruganti, M. Bobrek, S. Killough, J. Nutaro, Cheng Liu, W. Lu
It is estimated that 50% of the global population lives in urban areas occupying just 0.4% of the Earth's surface. Understanding urban activity constitutes monitoring population density and its changes over time, in urban environments. Currently, there are limited mechanisms to non-intrusively monitor population density in real-time. The pervasive use of cellular phones in urban areas is one such mechanism that provides a unique opportunity to study population density by monitoring the mobility patterns in near real-time. Cellular carriers such as AT&T harvest such data through their cell towers; however, this data is proprietary and the carriers restrict access, due to privacy concerns. In this work, we propose a system that passively senses the population density and infers mobility patterns in an urban area by monitoring power spectral density in cellular frequency bands using periodic beacons from each cellphone without knowing who and where they are located. A wireless sensor network platform is being developed to perform spectral monitoring along with environmental measurements. Algorithms are developed to generate real-time fine-resolution population estimates.
{"title":"Real-time urban population monitoring using pervasive sensor network","authors":"Gautam Thakur, P. Kuruganti, M. Bobrek, S. Killough, J. Nutaro, Cheng Liu, W. Lu","doi":"10.1145/2996913.2996937","DOIUrl":"https://doi.org/10.1145/2996913.2996937","url":null,"abstract":"It is estimated that 50% of the global population lives in urban areas occupying just 0.4% of the Earth's surface. Understanding urban activity constitutes monitoring population density and its changes over time, in urban environments. Currently, there are limited mechanisms to non-intrusively monitor population density in real-time. The pervasive use of cellular phones in urban areas is one such mechanism that provides a unique opportunity to study population density by monitoring the mobility patterns in near real-time. Cellular carriers such as AT&T harvest such data through their cell towers; however, this data is proprietary and the carriers restrict access, due to privacy concerns. In this work, we propose a system that passively senses the population density and infers mobility patterns in an urban area by monitoring power spectral density in cellular frequency bands using periodic beacons from each cellphone without knowing who and where they are located. A wireless sensor network platform is being developed to perform spectral monitoring along with environmental measurements. Algorithms are developed to generate real-time fine-resolution population estimates.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86912023","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 significant challenge we face in assessing spatio-temporal data quality is a lack of ground-truth data. Error is by definition the deviation of observation from ground truth. In the absence of ground truth, we depend on our own or provider quality assessment to evaluate our methods. The focus of this paper is the development of a representative, weather-like spatio- temporal dataset and the use of this dataset to develop and evaluate a robust, interpolation-based method for assessment of data quality. We call our method the SMART method, short for Simple Mappings for the Approximation and Regression of Time series. We present this method as a representative approach to demonstrate and overcome the challenges of spatio- temporal data quality assessment. Our results bring into question the validity of provider-based quality control indicators.
{"title":"A SMART approach to quality assessment of site-based spatio-temporal data","authors":"D. Galarus, R. Angryk","doi":"10.1145/2996913.2996932","DOIUrl":"https://doi.org/10.1145/2996913.2996932","url":null,"abstract":"A significant challenge we face in assessing spatio-temporal data quality is a lack of ground-truth data. Error is by definition the deviation of observation from ground truth. In the absence of ground truth, we depend on our own or provider quality assessment to evaluate our methods. The focus of this paper is the development of a representative, weather-like spatio- temporal dataset and the use of this dataset to develop and evaluate a robust, interpolation-based method for assessment of data quality. We call our method the SMART method, short for Simple Mappings for the Approximation and Regression of Time series. We present this method as a representative approach to demonstrate and overcome the challenges of spatio- temporal data quality assessment. Our results bring into question the validity of provider-based quality control indicators.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89806126","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}
Predictive range queries retrieve objects in a certain spatial region at a (future) prediction time. Processing predictive range queries on large moving object databases is expensive. Thus effective pruning is important, especially for long-term predictive queries since accurately predicting long-term future behaviors of moving objects is challenging and expensive. In this work, we propose a pruning method that effectively reduces the candidate set for predictive range queries based on (high-order) Markov chain models learned from historical trajectories. The key to our method is to devise compressed representations for sparse multi-dimensional matrices, and leverage efficient algorithms for matrix computations. Experimental evaluations show that our approach significantly outperforms other pruning methods in terms of efficiency and precision.
{"title":"A Markov chain based pruning method for predictive range queries","authors":"Xiaofeng Xu, Li Xiong, V. Sunderam, Yonghui Xiao","doi":"10.1145/2996913.2996922","DOIUrl":"https://doi.org/10.1145/2996913.2996922","url":null,"abstract":"Predictive range queries retrieve objects in a certain spatial region at a (future) prediction time. Processing predictive range queries on large moving object databases is expensive. Thus effective pruning is important, especially for long-term predictive queries since accurately predicting long-term future behaviors of moving objects is challenging and expensive. In this work, we propose a pruning method that effectively reduces the candidate set for predictive range queries based on (high-order) Markov chain models learned from historical trajectories. The key to our method is to devise compressed representations for sparse multi-dimensional matrices, and leverage efficient algorithms for matrix computations. Experimental evaluations show that our approach significantly outperforms other pruning methods in terms of efficiency and precision.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79698192","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}
Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this paper, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China. The results show that MultiCalib outperforms state-of-the- art calibration by 25% on average with same input data.
{"title":"MultiCalib: national-scale traffic model calibration in real time with multi-source incomplete data","authors":"Desheng Zhang, Fan Zhang, T. He","doi":"10.1145/2996913.2996918","DOIUrl":"https://doi.org/10.1145/2996913.2996918","url":null,"abstract":"Real-time traffic modeling at national scale is essential to many applications, but its calibration is extremely challenging due to its large spatial and fine temporal coverage. The existing work mostly is focused on urban-scale calibration with complete field data from single data sources (e.g., loop sensors or taxis), which cannot be generalized to national scale, because complete single-source field data at national scale are almost impossible to obtain. To address this challenge, in this paper, we design MultiCalib, a model calibration framework to optimize traffic models based on multiple incomplete data sources at national scale in real time. Instead of naively combining multi-source data, we theoretically formulate a multi-source model calibration problem based on real-world contexts and multi-view learning. More importantly, we implement and evaluate MultiCalib with two heterogeneous nationwide vehicle networks with 340,000 vehicles to infer traffic conditions on 36 expressways and 119 highways, along with 4 cities across China. The results show that MultiCalib outperforms state-of-the- art calibration by 25% on average with same input data.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83036057","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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