Adaptive Railway Vehicle Movement Modeling System
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Solution Overview
Problem
Current railway network management systems face challenges in handling operational disruptions due to manual, time-consuming, and error-prone processes for predicting train arrival and departure times, lacking real-time adaptability and considering ground realities and external conditions.
Innovation Solution
A system and method for adaptive rescheduling of vehicle movements in railway networks, utilizing intelligent nodes for data acquisition, processing, and dissemination to create conflict-free plans, allocate resources optimally, and generate visual layouts of past, present, and future movements, minimizing deviations from timetables and maximizing throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual train graph methods are used to predict train arrival and departure times, then controllers can manage train operations, but the process becomes time-consuming, error-prone, and suboptimal
Solution Approach 1:
The patent replaces manual mechanical graph-based prediction methods with an automated computerized system that uses processors to acquire data, generate movement plans, and predict train arrival and departure times algorithmically, eliminating human error and time consumption
Solution Approach 2:
The system enables self-service by allowing the computerized processing system to automatically acquire vehicle data, generate conflict-free movement plans, and update schedules without requiring manual controller intervention for each prediction task
2Adaptability or versatility
If manual handling of operational disruptions is implemented, then controllers can address issues, but the handling becomes time-consuming and suboptimal
Solution Approach 1:
The patent implements dynamic adaptability by enabling the system to continuously acquire updated vehicle data and automatically regenerate movement plans in response to operational disruptions, allowing real-time adaptation without manual intervention
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring vehicle positions and schedule deviations, then using this information to automatically adjust and regenerate movement plans, creating a closed-loop control system that improves response efficiency
3Extent of automation
If existing automated systems are deployed for conflict resolution, then some operational issues are addressed, but ground realities and external conditions are not considered
Solution Approach 1:
The patent achieves universality by designing a comprehensive system that handles multiple types of vehicles, various disruption scenarios, and diverse operational conditions through a single integrated processing framework that considers ground realities and external conditions
4Productivity
If resource allocation is optimized for complete voyages, then throughput is maximized, but conflicts in resource usage must be avoided
Solution Approach 1:
The patent applies preliminary action by generating complete conflict-free movement plans for all vehicles before execution, allocating resources in advance while ensuring no conflicts arise during implementation, thus maximizing throughput reliably
Data Source
AI summary
The present invention relates to a system and a method for vehicle movement modeling in a network. The modeling is characterized by vehicle related intelligence gathering, processing and dissemination thereof for an adaptive rescheduling of the vehicle movement in the railway network. Predefined data associated with the vehicle in the railway network is acquired and is further processed to resolve one or more conflicts associated with the vehicle movement. The processing comprises of allocating resources, developing plans for voyages, and continuously gathering deviation data. The vehicle movement modeling also comprises of generating detailed layouts of vehicle movements for particular time-periods over the railway network.