Digital Location-Based Data Processing for Journey Analysis
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Solution Overview
Problem
Conventional origin-destination matrices fail to capture time-related information effectively, making it difficult to obtain detailed data on journeys between multiple pairs and requiring extensive infrastructure and storage capacity.
Innovation Solution
A method that uses positional data from devices to create digital location-based data, filtering and analyzing this data to generate profiles representing journey counts between origin-destination pairs within different time periods, and then reducing data storage needs by using standard profiles to approximate specific profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road-side traffic counters are used to obtain time-dependent origin-destination data, then measurement precision is improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
The patent uses mobile devices (smartphones, GPS units) as intermediary data collection points instead of fixed road-side counters. These portable devices capture positional data that serves as the intermediary between the travelers and the central processing system, eliminating the need for expensive fixed infrastructure while maintaining data collection capabilities.
Solution Approach 2:
The patent replaces the mechanical/physical road-side traffic counter infrastructure with a software-based solution running on mobile devices. The physical counting mechanism is substituted with digital positional data collection and processing, leveraging existing mobile technology rather than deploying specialized hardware.
2Loss of information
If detailed positional data for multiple origin-destination pairs and time periods is collected, then information completeness is improved, but data storage requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for origin-destination analysis from the raw positional data. Instead of storing complete trajectory data for all devices, it extracts origin locations, destination locations, and time period identifiers, discarding redundant intermediate positional information while preserving the core analytical value.
Solution Approach 2:
The patent merges multiple individual journey records into aggregated origin-destination matrices. By combining data from multiple devices traveling the same or similar routes within the same time period, it creates consolidated records that represent collective travel patterns, significantly reducing the total data volume while maintaining statistical accuracy.
3Device complexity
If conventional origin-destination matrices are used, then data storage is simplified, but time-related information is lost
Solution Approach 1:
The patent extends the conventional two-dimensional origin-destination matrix by adding a time period dimension, creating a three-dimensional data structure. This additional dimension captures when journeys occur without complicating the fundamental matrix structure, allowing time-dependent analysis while maintaining organizational simplicity through the matrix framework.
4Loss of information
If extensive data collection infrastructure is deployed, then data coverage is improved, but cost increases
Solution Approach 1:
The patent leverages mobile devices that serve multiple functions: they are general-purpose communication devices that also perform specialized positional data collection for origin-destination analysis. This universal device approach eliminates the need for dedicated specialized infrastructure, as smartphones and GPS units already possess the necessary sensors, processing capability, and communication functions.
Data Source
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Figure 2
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AI summary
The invention relates to a method of obtaining and storing data relating to a count of journeys made between origin and destination pairs in respect of different predetermined time periods. Positional data is received from vehicles, and filtered to obtain positional data relating to travel between each of a plurality of origin-destination pairs. The filtered positional data is analysed to obtain multiple sets of profiles. Each set of profiles represents a count of journeys made between the origin and destination in a plurality of predetermined time periods for each origin-destination pair. The different sets of profiles relate to different days of the week, and journeys having arrival or departure times in the predetermined time periods. The sets of profiles are used in a clustering operation to provide a reduced set of standard profiles. Location information identifying each origin-destination pair is stored in association with data representing the or each standard profile which can be taken to represent the one or more profiles in respect of the origin-destination pair.