Door Closing Event Data for Safer Pickup and Drop-Off Routing
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Solution Overview
Problem
Existing dynamic transportation networks lack efficient methods to determine optimal pickup and drop-off locations for transportation services based on vehicle door usage data, leading to potential safety risks and inefficiencies.
Innovation Solution
A dynamic transportation network that utilizes sensor data from inertial measurement units, microphones, and other sensors to detect door closing events, processes this data to determine historical records, and provides routing decisions to transportation providers, ensuring safer and more convenient pickup and drop-off locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If door closing events are detected using sensor data from inertial measurement units and microphones, then the accuracy of pickup and drop-off location determination is improved, but the device complexity increases
Solution Approach 1:
The system segments the door closing detection function into multiple independent sensors (inertial measurement units and microphones), each handling specific aspects of the detection task. The inertial measurement units track motion and orientation changes while microphones capture acoustic signals, and the results are processed separately before being combined for final location determination.
Solution Approach 2:
The sensor system is designed with multi-functionality where the same sensors serve multiple purposes: inertial measurement units not only detect door closing events but also track vehicle movement and orientation throughout the transportation journey, while microphones capture both door closing sounds and can potentially detect other relevant acoustic events.
2Productivity
If routing decisions are made based on historical door closing event records, then transportation network efficiency is improved, but the loss of time in data processing and analysis increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing door closing event data in a historical record during normal operations. This historical database is built up over time without requiring real-time processing, allowing the system to leverage accumulated data for routing decisions without the burden of processing every event instantly.
Solution Approach 2:
The system uses feedback from historical door closing event records to continuously improve routing decisions. By analyzing patterns from past events, the system refines its understanding of optimal pickup and drop-off locations, creating a feedback loop where historical data informs future routing choices, thereby improving efficiency over time.
3Ease of operation
If the system determines pickup and drop-off locations based on door closing events, then requestor satisfaction is improved, but the computing resources required increase
Solution Approach 1:
The system employs self-service principles by having the vehicle's own sensors (inertial measurement units and microphones) automatically detect and record door closing events without requiring external intervention or additional computing infrastructure. The vehicle essentially serves itself by using its built-in sensors to gather the data needed for location determination.
Solution Approach 2:
The system discards unnecessary computational processing by focusing only on the essential door closing event data rather than processing every possible sensor reading. It recovers and stores only the critical information (door closing events) in the historical record, reducing the overall computing burden while maintaining the ability to make accurate routing decisions.
4Reliability
If sensor data is processed to detect door closing events, then safety is improved by minimizing exposure to traffic, but the loss of energy in sensor operation and data processing increases
Solution Approach 1:
The system uses periodic action by having sensors operate in a coordinated manner where inertial measurement units and microphones sample data at appropriate intervals rather than continuously. The sensors activate during relevant events (door closing moments) and maintain baseline monitoring between events, reducing overall energy consumption while maintaining safety monitoring capabilities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves transportation network efficiency by reducing computing resources, enhancing safety by minimizing exposure to traffic, and increasing requestor satisfaction through accurate location determination and fraud detection.
Implementation Method 1
sensor data from inertial measurement units
Implementation Method 2
sensor data from microphones
Data Source
AI summary
The disclosed computer-implemented method may include identifying a transportation task within a dynamic transportation network, accessing a database of door closing event locations, wherein the database is populated from observed door closing events within the dynamic transportation network, determining location information specifying at least one of a pickup location and a drop-off location for the transportation task based at least in part on the database of door closing event locations, and providing the location information to a transportation provider computing device, wherein a transportation provider performs the transportation task that comprises at least one of picking up a transportation requestor at the pickup location and dropping off the transportation requestor at the drop-off location. Other methods, systems, and computer-readable media are disclosed.