01584408

Component

https://doi.org/10.3141/2528-08

Expedient incident detection and understanding are important in traffic management and control. Social media as important information venues have immense value for increasing an awareness of traffic incidents. In this paper, an attempt is made to assess the potential of using harvested social media for traffic incident detection. Twitter in Seattle, Washington, was chosen as a representative sample environment for this work. A hybrid mechanism based on latent Dirichlet allocation and document clustering was proposed to model incident-level semantic information, while spatial point pattern analysis was applied to explore the spatial patterns and to assess the spatial dependence between incident-topic tweets and traffic incidents. A global Monte Carlo K-test indicated that the incident-topic tweets were significantly clustered at different scales up to 600 m. The nearest neighbor clutter removal method was used to separate feature tweet points from clutter; then a density-based algorithm successfully detected the clusters of tweets posted spatially close to traffic incidents. In multivariate spatial point pattern analysis, K-cross functions were investigated with Monte Carlo simulation to characterize and model the spatial dependence, and a positive spatial correlation was inferred between incident-topic tweets and traffic incidents up to 800 m. Finally, the tweet intensity as a function of distance from the nearest traffic incident was estimated, and a log-linear model was summarized. The experiments supported the notion that social media feeds acted as sensors, which allowed enhancing awareness of traffic incidents and their potential disturbances.

01582941

15-5099

English

pp 69-77

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369084

Print

Figures; Maps; References

Data collection; Incident detection; Social media; Spatial analysis; Traffic incidents

Twitter

Seattle (Washington)

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning; I73: Traffic Control

TRIS, TRB, ATRI

Dec 30 2014 1:42PM

• Automated Region-Based Vehicle Conflict Detection Using Computer Vision Techniques
• Behavior Analysis Using a Multilevel Motion Pattern Learning Framework
• Extended Implementation Method for Virtual Sensors: Web-Based Real-Time Transportation Data Collection and Analysis for Incident Management
• Feature-Based Pothole Detection in Two-Dimensional Images
• Integrated Method for Online Calibration of Real-Time Traffic Network Management Systems
• On Missing Traffic Data Imputation Based on Fuzzy C-Means Method by Considering Spatial–Temporal Correlation
• Optimization of Police Facility Locationing
• Rapid Accelerometer Measurements of Vehicle Bumps at Bridge Approaches
• Sample Size Reduction Method Based on Data Fusion for Freeways with Fixed Detectors
• Supervised Intelligence Committee Machine to Evaluate Field Performance of Photocatalytic Asphalt Pavement for Ambient Air Purification
• Transportation Routing Map Abstraction Approach: Algorithm and Numerical Analysis
• Using Unmanned Aerial Vehicles to Conduct Site Inspections of Erosion and Sediment Control Practices and Track Project Progression