Alternative Travel Plan Notification System
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Solution Overview
Problem
Large events can negatively impact users by drawing crowds, increasing traffic, and causing long lines, with users often insufficiently informed of real-time user-specific impacts.
Innovation Solution
A computer-implemented method and system that determines alternative plans for a user by analyzing event status, transactional data, and event data using a trained machine learning model, and transmitting notifications with suggestions for alternative travel plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users continuously check news and websites for event information, then they may obtain event information, but they are insufficiently informed of real-time user-specific impacts
Solution Approach 1:
The system automatically collects and processes event data and transactional data without user intervention, generating personalized impact assessments and alternative plan recommendations that are transmitted to users, eliminating the need for users to manually check multiple information sources
Solution Approach 2:
The system uses machine learning models to analyze transactional data and event data, providing feedback to users about the likelihood of transaction disruptions and recommending alternative plans, creating a closed-loop information delivery system that adapts to user behavior
2Quantity of substance
If large events occur, then event population increases, but traffic increases and long lines form causing user inconvenience
Solution Approach 1:
The system performs preliminary analysis by determining event status and obtaining event data before transactions occur, then proactively transmits notifications to users about potential disruptions and alternative plans, allowing users to take preventive action before encountering traffic or long lines
Solution Approach 2:
The machine learning model acts as an intermediary that processes both event data and transactional data to predict the likelihood of transaction disruptions, translating raw data into actionable insights about traffic and waiting time impacts
3Loss of information
If the system analyzes transactional data and event data using machine learning models, then user-specific impact information is provided, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it determines event status, compares event data with transactional data, calculates likelihood of transaction disruptions, and generates alternative plan recommendations, consolidating multiple analytical tasks into a single multi-functional system
Data Source
AI summary
A computer-implemented method for determining alternative plans for a user may include: determining an event status of an event based on one or more parameters, wherein the event status comprises a target event indicator, wherein the one or more parameters comprise event population; obtaining event data of a target event; obtaining transactional data of the user, wherein the transactional data includes transaction time and transaction location; determining, via one or more processors, a likelihood of a potential transaction of the user associated with the target event by processing the transactional data and the event data using a trained machine learning model; and transmitting, to the user, a notification based on the determined likelihood of the potential transaction, wherein the notification includes a suggestion for alternative travel plans of the user.