Passenger Forecasting Model Using Weather Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current passenger forecasting models in regional transit are inadequate due to insufficient parameters, leading to significant variations in occupancy forecasts, especially when weather conditions change, affecting the accuracy of vehicle fleet deployment and test run planning.
Innovation Solution
Incorporating weather-specific parameters, such as precipitation type, precipitation strength, duration, road conditions, and outside temperature into the forecasting model, using sensors like rain sensors and temperature gauges, and transmitting data to a central computer for improved capacity planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical forecasting models use only basic parameters (passenger count, date, time, position), then the model structure remains simple, but the forecast accuracy deteriorates significantly when weather conditions change
Solution Approach 1:
The patent applies parameter changes by expanding the set of input parameters from basic temporal and spatial parameters to include weather-specific parameters (precipitation type, precipitation strength, duration, road conditions, outside temperature). This transformation of the parameter set allows the forecasting model to capture weather-dependent variations in passenger behavior, thereby improving forecast accuracy without fundamentally changing the model structure.
2Reliability
If weather-specific parameters are added to the forecasting model, then forecast accuracy improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent introduces weather data as an intermediary factor that mediates between environmental conditions and passenger behavior. By incorporating weather-specific parameters (precipitation, temperature, road conditions) as intermediate variables, the model can systematically account for weather influences on passenger behavior, improving reliability while maintaining structured data processing through defined parameter categories.
3Ease of manufacture
If statistical models are used to approximate actual conditions, then the modeling approach remains straightforward, but the forecasts deviate from real situations when unforeseeable weather changes occur
Solution Approach 1:
The patent applies preliminary action by incorporating weather forecast data and historical weather patterns into the modeling process before actual passenger behavior occurs. By pre-incorporating weather-specific parameters and their temporal patterns, the model can anticipate weather-related variations in passenger behavior, improving forecast precision while maintaining statistical modeling approaches.
Data Source
AI summary
A method for planning trips to transport passengers on the basis of a preferably statistical predictive model, in particular to plan capacities, optimize the utilization of vehicle fleets, determine the routes to be taken, etc., preferably in short-distance passenger traffic, wherein the predictive model takes into account, using parameters, characteristic passenger patterns over the course of a day, week, and/or year, is characterized in that the predictive model additionally uses weather-specific parameters.