Difference between revisions of "Traffic"

Revision as of 16:57, 8 January 2015

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.

City as a Physical-Cyber-Social (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 (PCS).

Problem Challenges

A city is a complex system of systems with many heterogeneous components. With decreasing cost of sensors to monitor our environment, there are efforts to deploy sensors to monitor vehicular traffic. 511.org is an exemplar of such an effort which monitors traffic flow in San Francisco Bay Area. These sensors monitor vehicular speed and volume though various road links. These observations are then utilized to derive travel time for each links. The idea of deploying these sensors is to capture the traffic flow variations resulting from real-world events in a city such as accidents, breakdowns, bad weather, etc.

Heterogeneity

Traffic related observations are not limited to sensor observations (e.g., speed, volume). Citizens report traffic events on social streams such as twitter. Such a report of traffic events from citizens are often complementary to machine sensors e.g., accident report by citizen observation complements slow moving traffic detected by sensors. We need algorithms that can extract traffic events from such heterogeneous streams.

Complexity of 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.

Solution

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.

We use Open Science Framework to share project resources with the research community. Here is the link for our project on Open Science Framework.

Event Understanding

Action Recommendation

References