Unsupervised Detection of Drivers ’ Behavior Patterns
نویسنده
چکیده
Probes with GPS devices reveal useful information for traffic conditions and patterns. The high level of noises and lack of details make it challenging to mine behavioral patterns from the raw data collected. Behavioral patterns are essential for understanding the underlying structures of data sources and various real-world interests such as traffic planning, vehicle operations and anomalous/popular area detections. This paper proposes an unsupervised approach for mining behavioral patterns from naïve GPS data any devices can collect. The focus on the study is to apply the method for Taxi Drivers’ behavioral analysis, which is essentially different from other road users. Unsupervised clustering algorithm successfully detects the cohesion of points in the 3-D space. Through comparison with other more finegrained data set, the result is evaluated and justified for the key roads across Sydney areas.
منابع مشابه
Traffic Scene Analysis using Hierarchical Sparse Topical Coding
Analyzing motion patterns in traffic videos can be exploited directly to generate high-level descriptions of the video contents. Such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. In this pa...
متن کاملAnalyzing Stop Time Phase Leading to Congestion Based on Drivers’ Behavior Patterns
Traffic oscillation, stop and go traffic, is created by different reasons such as: sudden speed drop of leader vehicle. Stop and go traffic commonly is observed in congested freeways results in traffic oscillation. Many theories had been presented to define congestion traffic based on laws of physics such as: thermodynamics and fluid. But, these theories could not explain the complexity of driv...
متن کاملDeveloping a Model of Heterogeneity in Driver’s Behavior
Intelligent Driver Model (IDM) is a well-known microscopic model of traffic flow within the traffic engineering societies. While it is a powerful technique for modeling traffic flows, the Intelligent Driver Model lacks the potential of accommodating the notion of drivers’ heterogeneous behavior whenever they are on roads. Concerning the above mentioned, this paper takes the lane to recognize th...
متن کاملBotOnus: an online unsupervised method for Botnet detection
Botnets are recognized as one of the most dangerous threats to the Internet infrastructure. They are used for malicious activities such as launching distributed denial of service attacks, sending spam, and leaking personal information. Existing botnet detection methods produce a number of good ideas, but they are far from complete yet, since most of them cannot detect botnets in an early stage ...
متن کاملComparison Between Unsupervised and Supervise Fuzzy Clustering Method in Interactive Mode to Obtain the Best Result for Extract Subtle Patterns from Seismic Facies Maps
Pattern recognition on seismic data is a useful technique for generating seismic facies maps that capture changes in the geological depositional setting. Seismic facies analysis can be performed using the supervised and unsupervised pattern recognition methods. Each of these methods has its own advantages and disadvantages. In this paper, we compared and evaluated the capability of two unsuperv...
متن کامل