Passenger Load Prediction via Wireless Device Detection
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Solution Overview
Problem
Existing methods for predicting passenger load in train cars only consider the current state and fail to account for future changes in passenger distribution, leading to misinformed commuters about available capacity, as they do not accurately estimate the load when passengers alight at the next station.
Innovation Solution
A passenger load prediction system that detects wireless devices carried by passengers, determines their location, predicts their destination, and estimates the load of each train car based on this information, using a combination of onboard sensors, Wi-Fi connectivity data, and historical travel patterns to provide accurate crowd level information to commuters before they board.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current load measurement methods are used to inform commuters, then the system is simple to implement, but the information provided is inaccurate for future stations
Solution Approach 1:
The system performs preliminary actions by detecting wireless devices and predicting destinations before the train arrives at the next station. By identifying passengers early and forecasting their alighting locations, the system prepares accurate load predictions in advance, allowing commuters to make informed decisions about which train cars to board.
Solution Approach 2:
The system introduces wireless device detection as an intermediary mechanism to bridge the gap between current train load and future load predictions. The wireless devices carried by passengers serve as mediators that enable the system to track passenger locations and predict destinations without requiring complex direct observation of future passenger movements.
2Loss of information
If the system only considers current train car load, then the measurement method is simple, but it cannot predict future load state when passengers alight
Solution Approach 1:
The system replaces direct mechanical observation of passengers with wireless device detection. Instead of physically tracking passengers or requiring them to declare destinations, the system uses electronic signals from wireless devices to infer passenger locations and predict destinations, significantly reducing the difficulty of detection and measurement.
Solution Approach 2:
The system creates a digital copy of passenger information through wireless device detection. By detecting and tracking wireless devices, the system generates a virtual representation of passenger locations and movements, which can be used to predict future load states without physically observing or interfering with actual passenger behavior.
3Measurement precision
If uniform passenger distribution is assumed across train cars, then the calculation is simple, but the load prediction becomes inaccurate when destinations are non-uniformly distributed
Solution Approach 1:
The system applies local quality by providing differentiated load predictions for each individual train car rather than treating all cars uniformly. By analyzing wireless device data specific to each car and predicting alighting patterns for that particular car, the system delivers accurate, location-specific information to commuters about which specific train car will be least crowded.
Data Source
AI summary
According to various embodiments, there is provided a passenger load prediction system including: a component configured to detect a wireless device carried by a passenger on a train or a train station platform, the train including a plurality of train cars; a passenger to train car mapper configured to determine a location of the passenger, based on a location of the wireless device; a destination predictor configured to predict a destination of the passenger, based at least in part on an identifier code of the wireless device; and a train car load level estimator configured to predict a respective passenger load of each train car of the plurality of train cars, based on the predicted destination and further based on the determined location of the passenger.