Dynamic Timing Intervals for High Capacity Transit Corridors
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Solution Overview
Problem
High capacity transit systems with fixed routes and schedules fail to efficiently utilize data from on-demand services and mobile devices, leading to suboptimal resource allocation and increased traffic congestion.
Innovation Solution
A computing system that parses road networks into corridors for high capacity vehicles (HCVs) to dynamically route them based on real-time transport demand, using historical data to establish optimal pick-up and drop-off locations and adjust timing intervals to maintain supply flow equilibrium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed routes and fixed schedules are used for high capacity transit, then operational simplicity and ease of management are improved, but adaptability to real-time transport demand and resource allocation efficiency deteriorate
Solution Approach 1:
The patent implements dynamic routing and scheduling for high capacity vehicles, transitioning from fixed static routes to adaptive dynamic corridors. The system continuously adjusts vehicle routes, timing intervals, and pick-up/drop-off locations based on real-time demand data, traffic conditions, and supply flow equilibrium requirements, enabling the transit system to adapt flexibly to changing conditions while maintaining operational control
Solution Approach 2:
The system incorporates real-time feedback mechanisms by collecting and processing data from mobile devices, traffic sensors, and vehicle telematics to monitor transport demand and supply conditions. This feedback loop enables the computing system to continuously optimize routing decisions, adjust timing intervals, and rebalance supply flow across corridors, resolving the contradiction between operational simplicity and adaptability
2Productivity
If dynamic routing with real-time data utilization is implemented, then resource allocation efficiency and adaptability to transport demand are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the service region into multiple directional corridors with defined start and end locations. Each corridor operates semi-independently with its own supply flow schedule and timing intervals. This segmentation allows the complex dynamic routing problem to be divided into manageable sub-problems, reducing overall system complexity while maintaining high resource allocation efficiency through localized optimization
Solution Approach 2:
The system dynamically adjusts key parameters including timing intervals between vehicles, corridor capacity allocations, and pick-up/drop-off location selections based on real-time conditions. By changing these parameters adaptively rather than reconfiguring entire routes, the system achieves high productivity while controlling complexity through parameter-based optimization
3Productivity
If timing intervals are adjusted to maintain supply flow equilibrium, then transit service efficiency and demand fulfillment are improved, but computational overhead and scheduling complexity increase
Solution Approach 1:
The system implements periodic timing intervals for vehicle dispatch along each corridor, creating regular predictable service patterns. By using periodic rather than completely adaptive random scheduling, the system maintains supply flow equilibrium and service efficiency while reducing computational complexity through pattern-based planning that is easier to manage and predict
4Ease of operation
If fixed pick-up and drop-off stations are used, then operational simplicity is improved, but service coverage flexibility and user convenience deteriorate
Solution Approach 1:
The patent implements dynamic pick-up and drop-off location selection within each corridor, replacing fixed stations with flexible geographic zones. The system determines optimal pick-up/drop-off locations based on real-time demand patterns, traffic conditions, and vehicle positioning, allowing users to be served at convenient locations along the corridor rather than at predetermined fixed stations, thereby improving service coverage flexibility while maintaining operational control
Data Source
AI summary
A system computes a timing interval between high-capacity vehicles (HCVs) for each of a plurality of HCV corridors within a geographic region, each respective HCV corridor of the plurality of HCV corridors including a start area. For each respective HCV corridor, the system transmits, via a network communication interface, (i) first data to a first computing device associated with a first HCV, the first data indicating the start area of the respective HCV corridor, and a first start time for the first HCV, and (ii) second data to a second computing device associated with a second HCV, the second data indicating the start area of the respective HCV corridor and a second start time for the second HCV, wherein the first start time for the first HCV and the second start time for the second HCV are based on the computed timing interval for the respective HCV corridor.