Railway Timetable Simulation for Gridlock-Free Demand Control
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Solution Overview
Problem
Existing methods for timetabling trains in railway networks face challenges in efficiently generating feasible timetables that avoid deadlocks and gridlocks, especially for large networks with many trains, where computational demands can result in unfeasibly long computer times.
Innovation Solution
A method involving establishing communications with vehicles and network control apparatus over a data communications network, receiving a state report of the network, and running multiple computer simulations to generate feasible timetables. The simulations use vehicle agents to move over a graph of the network, avoiding gridlocking and optimizing journeys based on demand, with varied weightings to produce different timetables. An optimal timetable is identified and controls are issued to vehicles and network control apparatus to implement the timetable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple computer simulations are run to generate feasible timetables, then the quality and feasibility of timetables improve, but the computational time increases
Solution Approach 1:
The system performs preliminary actions by running multiple computer simulations with varied weightings before final timetable implementation. These simulations pre-identify feasible timetables and optimal routing strategies, allowing the system to select from pre-computed options rather than computing in real-time, thus improving reliability while managing computational time
Solution Approach 2:
The system dynamically adjusts weightings in the simulations to explore different routing scenarios and optimize timetables. By varying parameters across multiple simulations, the system adapts to find optimal solutions without requiring exhaustive computation of all possible scenarios, balancing feasibility quality with computational efficiency
2Productivity
If optimal destination selection is implemented to meet demand, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The system implements self-service through automated destination selection algorithms that independently optimize vehicle routing based on demand data. The simulations autonomously determine optimal destinations and timetables without requiring complex manual intervention, improving operational efficiency while the automation manages the inherent complexity
Solution Approach 2:
The system changes parameters such as weightings in the simulation models to optimize destination selection. By adjusting these parameters across multiple simulations, the system efficiently explores different routing scenarios and identifies optimal solutions without requiring fundamentally more complex system architecture
Data Source
AI summary
A method is provided for operating a transport network, for example a rail freight network including receiving a state report (or “snapshot”) of the transport network including positions of the vehicles and states of the network control apparatuses via the data communications network. A plurality of computer simulations of the transport network are run with reference to the state report. The computer simulations generate a number of feasible timetables and subsequently one of them is selected as an optimal feasible timetable, being a timetable that best satisfies demands for load at terminals of the network. Each of the plurality of computer simulations involves moving vehicle agents corresponding to the vehicles over a graph of the transport network according to a vehicle movement procedure that is configured to avoid gridlocking. The computer simulations are also run according to an optimal destination selection method which is configured to optimise vehicle agent journeys to meet the demand for the loads at locations in the transport network. Each of the plurality of computer simulations is made with varied weightings in respect of optimisation factors in the optimal destination selection method to thereby vary each of the number of feasible timetables. The method includes identifying an optimal timetable from the number of feasible timetables and issuing controls to the vehicles and network control apparatus via the data communications network to control movement of the vehicles across the transport network in accordance with the optimal timetable.