Bus Stop Detection via GPS Mobility Patterns
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Solution Overview
Problem
Determining accurate bus stop and traffic control locations is a time-consuming, complex, and expensive task for transit agencies, requiring manual data collection and annotation, especially when changes occur due to incidents like construction or accidents.
Innovation Solution
A method utilizing machine learning classification strategies based on GPS data from buses to automatically detect bus stops, stop signs, and traffic signals, generating confidence ratings by analyzing mobility patterns and representing them as normalized histograms, allowing for real-time location determination without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and annotation is used to determine bus stop and traffic control locations, then measurement precision can be achieved, but loss of time and increased complexity occur
Solution Approach 1:
The system enables buses to self-report their own location data and operational characteristics (stops, delays, passenger boarding/alighting) without requiring manual surveyors. The bus fleet collectively performs the mapping task through its inherent operational data, eliminating the need for human field workers to manually collect and annotate location information.
Solution Approach 2:
The patent replaces the mechanical/manual field survey process with an automated electronic system that uses GPS data, machine learning algorithms, and pattern recognition. The mechanical action of human surveyors physically measuring and annotating locations is substituted by computational analysis of digital data from existing bus GPS trackers and operational sensors.
2Measurement precision
If manual data collection and annotation is used to determine bus stop and traffic control locations, then measurement precision can be achieved, but device complexity and cost increase
Solution Approach 1:
The system leverages the existing multi-functional data from bus GPS trackers and operational systems to serve multiple purposes: determining bus stop locations, identifying traffic control points, understanding passenger flow patterns, and optimizing routes. This universal use of existing data eliminates the need for separate dedicated surveying equipment and manual annotation processes.
Solution Approach 2:
The complex manual surveying process is replaced by automated computational systems that process electronic data through machine learning models. The complexity shifts from human labor and physical field operations to automated algorithmic processing, reducing overall system complexity while maintaining precision.
3Productivity
If automated detection using machine learning classification is used, then productivity increases, but measurement precision may be compromised
Solution Approach 1:
The machine learning classification system continuously refines its accuracy by analyzing feedback from actual bus operational data. The model learns from patterns in GPS trajectories, stopping behaviors, and passenger movements to improve its location identification accuracy over time, ensuring that automated detection achieves both speed and precision.
Solution Approach 2:
The system changes the parameters of data analysis by transforming raw GPS coordinates and operational data into feature vectors that capture mobility patterns, stop durations, and spatial relationships. These transformed parameters enable the machine learning model to accurately distinguish between bus stops, traffic controls, and other locations while processing data automatically at high speed.
Data Source
AI summary
A method, apparatus and computer program products are provided for automatically detecting specific locations, i.e. bus stops, stop lights, and/or traffic signals, based on received GPS reports. The method can also be adopted to detect the utilization of the specific locations along the route. One example method includes receiving GPS data from a plurality of buses from along a transit route, and utilizes a machine learning classification strategy that captures the mobility patterns of the GPS equipped buses, at specific locations. The method may then generate mini-clusters, each comprised of a first location point from a first route and one or more subsequent location points located within a predetermined distance of the first location point. The mobility patterns of the mini-clusters within larger clusters are represented as a normalized histogram where the bin values become classification features. A machine learning model is then utilized to determine a location of the specific locations.