Clustered Vehicle Indicators on Maps
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Solution Overview
Problem
Existing transportation systems face challenges in efficiently displaying visual indicators for multiple types of vehicles within limited screen space, leading to cluttered and difficult-to-parse maps.
Innovation Solution
A system and method that utilize a processor and memory to transmit a user device's location, receive nearby vehicle locations, identify clusters of vehicles, and generate a map with indicators for clusters and selected vehicles, optimizing display based on zoom level and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If individual vehicle indicators are displayed for all nearby vehicles, then complete information is provided, but screen space is excessive and map becomes cluttered
Solution Approach 1:
Multiple individual vehicle indicators of the same type are merged into a single cluster indicator that represents the entire group. The cluster indicator displays aggregated information (number of vehicles, type of vehicles) instead of showing each vehicle separately, thus reducing screen space while preserving essential information.
Solution Approach 2:
The display is segmented into different levels of detail: cluster indicators for groups of vehicles and individual indicators for selected vehicles. This segmentation allows the system to present summarized information for most vehicles while providing detailed individual information only where necessary, optimizing the balance between information completeness and screen space utilization.
2Loss of information
If individual vehicle indicators are displayed for all nearby vehicles, then complete vehicle details are shown, but map readability deteriorates
Solution Approach 1:
Multiple vehicle indicators are merged into cluster indicators that provide a simplified visual representation. This merging reduces visual clutter and improves map readability while still conveying essential information about vehicle availability and type through the cluster indicator design.
Solution Approach 2:
Different display qualities are applied to different regions: cluster indicators with aggregated information for most areas, and individual vehicle indicators with full details for selected or important vehicles. This local differentiation maintains readability while preserving necessary detail where needed.
3Measurement precision
If cluster location represents closest vehicle, then nearest vehicle is highlighted, but other vehicle locations are obscured
Solution Approach 1:
The cluster indicator consolidates information about multiple vehicle locations into a single visual element positioned at the cluster center (representing the closest vehicle). This merging approach highlights the nearest vehicle for quick identification while the cluster itself implies the presence of other vehicles in the surrounding area, balancing precise nearest-vehicle identification with awareness of overall location distribution.
Data Source
AI summary
Methods and systems for identifying and selectively displaying indications of nearby vehicles are presented. In one example, a method is provided that includes receiving locations of vehicles located near a location of a user device. The vehicles may include vehicles of a first type and vehicles of a second type. A cluster including at least a first subset of the vehicles of the first type may be identified and a location of the cluster may be identified. The location of the cluster may represent the location of at least one of the first subset of the vehicles. A second subset of the vehicles of the second type may then be identified including at most a predetermined quantity of vehicles. A map may be generated that includes a first indication at the location of the cluster and second indications at the location of the second subset of the vehicles.