Dynamic Space-Time Diagram for Transit Schedule Adherence
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Solution Overview
Problem
Current visualization techniques for public transit on-time performance primarily focus on static representations and do not facilitate exploration or analysis of aggregate data, making it difficult for transit operators to identify causes of unreliability and its impact on passenger travel behavior.
Innovation Solution
A dynamic space-time diagram is generated using collected transit schedule data, allowing users to interactively select and display specific data sets, with automatic generation of aggregated statistics and inferences, and suggestions for improving schedule adherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static Marey graph visualization is used, then the system maintains simplicity in visualization method, but the ability to explore and analyze aggregate data is limited
Solution Approach 1:
The patent transforms the static Marey graph into a dynamic visualization system that allows users to interact with and explore transit schedule data. The system enables users to filter, zoom, and analyze different time periods and routes, converting a fixed image into an interactive analytical tool that preserves simplicity while enabling deep data exploration.
Solution Approach 2:
The patent adds temporal and interactive dimensions to the traditional space-time diagram. By incorporating time-based filtering, route selection, and hierarchical drilling capabilities, the system transforms a two-dimensional static graph into a multi-dimensional interactive analysis environment that reveals aggregate patterns without sacrificing visual clarity.
2Quantity of substance
If detailed individual incident data is displayed, then the system provides comprehensive information about specific events, but the overall trend analysis becomes difficult
Solution Approach 1:
The patent implements a hierarchical segmentation approach where the visualization system can display aggregated trend data at one level and drill down into individual incident details at another level. This segmentation allows users to analyze overall patterns across the fleet while being able to examine specific incidents when needed, without the detailed data obscuring the broader trends.
Solution Approach 2:
The system displays only the necessary level of detail based on user needs and context. Rather than showing all individual incidents simultaneously, the system presents aggregated trends by default and allows users to access detailed incident information on-demand, preventing information overload while maintaining comprehensive analysis capability.
3Measurement precision
If real-time GPS tracking points are used for visualization, then the system shows current vehicle movement accurately, but insight into aggregate performance properties is reduced
Solution Approach 1:
The patent merges real-time GPS tracking data with historical schedule adherence data in a unified visualization framework. The system overlays current vehicle positions on the space-time diagram while simultaneously displaying aggregate performance metrics, allowing users to see both precise real-time locations and broader performance patterns in the same visual context.
Data Source
AI summary
A method for generating a visual representation of transport schedule adherence in a transit system including collecting transport schedule data received by a plurality of sensors deployed in a plurality of transit vehicles of the transit system, organizing and saving the received transport schedule data to a data storage facility, and receiving a user selection for the generation of a visual representation of transport schedule adherence in the transit system. The method may further include, by a transportation system analysis module, extracting a portion of the transport schedule data from the data storage facility and using the extracted transport schedule data to generate and display a dynamic space time diagram of transport schedule adherence in response to the user selection, automatically generating aggregated statistics and inference based on the dynamic space time diagram, and automatically generating a suggestion for improving transport schedule adherence based on the aggregated statistics and inferences.