Transit Demand Estimation for Non-Rider Conversion
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Solution Overview
Problem
City planners face challenges in optimally selecting the location of waypoints and frequency of transportation vehicle stops for public transit routes, as current methods fail to accurately account for both rider and non-rider demand, leading to suboptimal ridership.
Innovation Solution
A method is developed to estimate transit demand graphs by collecting and analyzing rider and non-rider data, including conditional information that converts non-riders into riders, and generating normalized demand graphs to optimize stop locations and frequencies based on actual demand patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand estimation methods are used that only consider existing riders, then the implementation is simple, but the accuracy of demand prediction is insufficient and fails to account for potential riders
Solution Approach 1:
The patent segments the population into distinct groups: existing riders and non-riders. By separately analyzing and modeling demand from these two segments, the system achieves more accurate overall demand prediction. Non-riders are further segmented based on their proximity to waypoints and conditional factors that might convert them to riders, allowing for targeted demand estimation that traditional methods miss.
Solution Approach 2:
The patent performs preliminary actions by collecting and analyzing data about non-riders before actual transit usage occurs. By identifying potential riders through conditional information (such as proximity to waypoints, demographic factors, and behavioral patterns) and simulating their conversion to riders under different scenarios, the system prepares accurate demand forecasts in advance, enabling better route planning before implementation.
2Productivity
If waypoint locations and stop frequencies are optimized based on incomplete demand data, then the planning process is faster, but the resulting route configuration is suboptimal and fails to maximize ridership
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring actual rider behavior, non-rider conversion patterns, and demand responses to waypoint configurations. This feedback loop allows the system to refine demand models over time, adjusting waypoint locations and stop frequencies based on real-world performance data. The feedback ensures that route optimization decisions are based on accurate, evolving demand information rather than static or incomplete data.
Solution Approach 2:
The patent utilizes parameter changes by analyzing how variations in waypoint locations, stop frequencies, and service patterns affect demand from both riders and non-riders. By systematically changing these parameters in simulations and observing the impact on ridership conversion, the system identifies optimal configurations that maximize productivity while accounting for the time investment in data collection and analysis.
3Adaptability or versatility
If the system collects and analyzes comprehensive non-rider data with conditional conversion factors, then the ridership optimization improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent introduces intermediaries in the form of computational models and algorithms that bridge the gap between raw non-rider data and actionable route optimization insights. These intermediary processing layers aggregate, filter, and interpret complex conditional information about non-riders, converting it into meaningful demand forecasts that guide waypoint selection and stop frequency decisions without requiring direct complex processing of every individual data point.
Data Source
AI summary
A method of estimating a transit demand graph includes collecting conditional information that includes at least one condition that when satisfied converts at least one non-rider into a rider, generating a non-rider transit demand graph by satisfying one of the conditions, and generating a normalized transit demand graph from the non-rider transit demand graph and a rider transit demand graph. The riders use public transit and the non-riders do not use public transit. The non-rider transit demand graph shows the demand of the non-riders for a public transit route. The rider transit demand graph shows the demand of riders for the same public transit route.