Traffic - Revision history

Pramod at 17:11, 8 January 2015

2015-01-08T17:11:30Z

Pramod at 17:10, 8 January 2015

2015-01-08T17:10:46Z

Pramod at 17:05, 8 January 2015

2015-01-08T17:05:22Z

Pramod at 17:05, 8 January 2015

2015-01-08T17:05:06Z

Pramod at 16:59, 8 January 2015

2015-01-08T16:59:11Z

Pramod at 16:58, 8 January 2015

2015-01-08T16:58:33Z

Pramod at 16:57, 8 January 2015

2015-01-08T16:57:40Z

Pramod at 16:19, 8 January 2015

2015-01-08T16:19:42Z

Pramod: /* Traffic Management */

2015-01-08T15:53:17Z

‎Traffic Management

Pramod at 14:42, 8 January 2015

2015-01-08T14:42:12Z

@@ Line 22: / Line 22: @@
 We aspire to propose a generic solution framework to the problem of extracting and understanding PCS events.
-==Event Extraction==
+===Event Extraction===
-===Extracting City Traffic Events from Social Streams===
+Extracting City Traffic Events from Social Streams: Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].
 Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].
 We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.
-==Event Understanding==
+===Event Understanding===
 Graphical models have been successfully used to deal with uncertainty, incompleteness, and dynamism within many domains. These models built from data often ignore preexisting declarative knowledge about the domain in the form of ontologies and Linked Open Data (LOD) that is increasingly available on the web. In this paper, we present an approach to leverage such 'top-down' domain knowledge to enhance 'bottom-up' building of graphical models. Specifically, we propose three operations on the graphical model structure to enrich it with nodes, edges, and edge directions. We illustrate the enrichment process using traffic data from 511.org and declarative knowledge from ConceptNet. The resulting enriched graphical model can potentially lead to better predictions of traffic delays [2].
-==Action Recommendation==
+===Action Recommendation===
 Graphical models such as Markov Decision Process (MDP) has been used for planning under uncertainty. While MDP is a well accepted solution for recommending best possible action to the user, it is unclear to integrate the existing declarative knowledge of actions into the MDP framework. We are investigating on algorithms that can transform the declarative knowledge of actions, contextual information, and user information into MDP framework. We aim to provide contextual and personalized action recommendation to the user in a dynamic environment.

@@ Line 32: / Line 32: @@
 ==Action Recommendation==
 ==References==
 *[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit Sheth, 2014. Extracting City Traffic Events from Social Streams. ACM Transactions on Intelligent Systems & Technology, 2014, 26 pages (Accepted).
 *[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.

@@ Line 34: / Line 34: @@
 ==References==
-[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit Sheth, 2014. Extracting City Traffic Events from Social Streams. ACM Transactions on Intelligent Systems & Technology, 2014, 26 pages (Accepted).
+*[1] Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit Sheth, 2014. Extracting City Traffic Events from Social Streams. ACM Transactions on Intelligent Systems & Technology, 2014, 26 pages (Accepted).
-[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.
+*[2] Pramod Anantharam, Krishnaprasad Thirunarayan and Amit Sheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.

@@ Line 17: / Line 17: @@
 Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --> traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.
-==Solution==
+==Solution Components==
 As outlined in challenges, our solution aims to address heterogeneity, complexity, and uncertainty in PCS systems. We propose a three step process to move from multi-modal and multi-sensory observations in a PCS system to actionable information. First, we need to extract events from multi-modal and multi-sensory observations. Since we have both machine sensor (numerical) and citizen sensor (textual) observations, we need deeper understanding to extract events from PCS systems. Second, we need to understand the interactions between various events. There are two levels in understanding the interactions: structure and parameters. Structure qualifies the possible interactions between various events (what variables influence the variable of interest?). Parameters quantifies the interactions between variables (by how much?).
 ==Event Extraction==
 ===Extracting City Traffic Events from Social Streams===
-Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events.
+Cities are composed of complex systems with physical, cyber, and social components. Current work on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected for over four months from San Francisco Bay Area. The evaluation results are promising and provides insights into the utility of social stream for city events [1].
 We use Open Science Framework to share project resources with the research community. [https://osf.io/b4q2t/ Here] is the link for our project on Open Science Framework.
 ==Event Understanding==
 ==Action Recommendation==
 ==References==

@@ Line 2: / Line 2: @@
 Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers.
-==City as a Physical-Cyber-Social (PCS) System==
+==City as a Physical-Cyber-Social ([http://wiki.knoesis.org/index.php/PCS PCS]) System==
-A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects ([http://wiki.knoesis.org/index.php/PCS PCS]).
+A city may have physical sensors monitoring physical processes (Physical) reporting their observations on the Cyber world. There are citizens in a city reporting their observations of various city related activities (Social). Events in a city manifests in physical, cyber, and social modalities. Algorithms for analyzing city events relying only on a single modality may not be able to capture the richness of observations. We require the analytics algorithms to consider observations of all the three aspects (PCS).
 ==Problem Challenges==

@@ Line 1: / Line 1: @@
 ==Traffic Management==
 Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to city policy makers.
 ==Problem Challenges==
@@ Line 9: / Line 12: @@
 ===Complexity of Interactions===
-Traffic events spanning machine and citizen observations may have intricate interactions.
+Traffic events spanning machine and citizen observations may have intricate interactions requiring us to capture these interactions. These interactions may be directed (some of it causal) or undirected (just associations). Representation to understand city events should be able to represent such interactions.
 ===Uncertainty of Interactions===
+Same events in real-world may have different effects e.g., accident during a peak hour may have stronger impact compared to accident during off-peak hours. In special cases, it is possible that an accident may not cause delays depending on the location of accident. Thus, the relationship between accident and traffic delay cannot be certainly stated e.g., accident -- cause --> traffic delays. We can only state that accident may cause or most likely cause traffic delays. Representation of city traffic events should be able to capture such uncertain relationships.
-−
-==City as a Physical-Cyber-Social System==
-A city is not limited to physical sensors
 ==Solution==
 ==Event Extraction==

@@ Line 1: / Line 1: @@
 ==Traffic Management==
-Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward this grand vision of providing actionable information to policy makers in a city.
+Traffic management is a challenging issue in most of the major cities around the world. With increasing number of people moving to cities for economic opportunities, this trend is going to make city resource management a crucial problem. We take this important problem and study its characteristics for creating solutions toward a grand vision of providing actionable information to policy makers in a city.
 ==Problem of Understanding City Traffic Events==