Train Timetable Optimization Using Predicted Passenger Demand
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for adjusting train operation schedules to match passenger demand fail to provide uniform quality service, especially in complex railroad line layouts and when resources are limited, leading to inconsistencies in passenger comfort across trains.
Innovation Solution
A timetable creation apparatus that uses predicted passenger demand to optimize arrival and departure times of trains by generating an objective function and constraint conditions, creating a candidate timetable that balances operation headways and adjusts the train schedule to ensure uniform service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of trains is increased to match passenger demand, then passenger comfort is improved, but service cost increases
Solution Approach 1:
The patent implements dynamic adjustment of train operation schedules based on predicted passenger demand. The system calculates appropriate numbers of trains for different time periods and dynamically adjusts turnaround times and operation densities to match actual demand patterns, avoiding both over-provisioning and under-provisioning of train services.
Solution Approach 2:
The system changes operational parameters such as turnaround time and operation density based on predicted demand. By adjusting these parameters dynamically, the system optimizes the match between train supply and passenger demand, improving comfort while controlling costs through efficient resource utilization.
2Reliability
If turnaround time is increased to lower operation density, then train congestion is reduced, but service efficiency decreases
Solution Approach 1:
The system dynamically adjusts turnaround time based on predicted passenger demand for different time periods. During high-demand periods, turnaround time is optimized to maintain adequate headway while maximizing service frequency. During low-demand periods, turnaround time is adjusted to reduce congestion while maintaining acceptable service levels, thereby balancing congestion control with service efficiency.
3Reliability
If the train timetable is adjusted to match peak demand, then passenger comfort during peak hours is improved, but inequality in service quality between trains increases
Solution Approach 1:
The patent applies local quality by adjusting train operation parameters specifically for different time periods and sections based on predicted demand patterns. The system calculates appropriate numbers of trains for representative sections and adjusts schedules locally to match demand, ensuring that each train operates with a reasonable congestion rate appropriate to its time period and route section.
Solution Approach 2:
The system implements dynamic timetable adjustment that considers both peak and off-peak periods. By predicting demand for different time periods and adjusting the number of trains and their schedules accordingly, the system maintains more uniform service quality across all trains while still providing enhanced comfort during peak hours through optimized headways and frequency.
4Quantity of substance
If the number of vehicles is limited, then operational cost is controlled, but ability to match passenger demand is reduced
Solution Approach 1:
The system maximizes the utilization of limited vehicles through dynamic schedule optimization. By predicting passenger demand and calculating appropriate numbers of trains for different time periods, the system adjusts turnaround times and operation densities to ensure that the available fleet is used as efficiently as possible, matching service supply with actual demand patterns throughout the day.
Solution Approach 2:
The system changes operational parameters such as turnaround time, headway, and operation density to optimize the performance of a fixed number of vehicles. By adjusting these parameters based on predicted demand, the system enhances demand-matching capability without requiring additional vehicles, thereby maintaining cost control while improving adaptability.
Data Source
AI summary
A timetable creation apparatus 100 for correcting a target timetable being a train timetable to be used to control a group of trains by using a predicted passenger demand to thereby create a new target timetable, includes: an objective function generation unit 115 that generates an objective function for an operation headway between trains included in the group of trains by using the predicted passenger demand; a constraint condition generation unit 117 that derives constraint conditions which an arrival time and a departure time of each of the trains at each of stations should satisfy for operation of the group of trains; and a candidate timetable creation unit 119 that creates a candidate timetable as an update candidate for a target timetable by using an arrival time and a departure time of each of the trains at each of the stations derived by optimizing the objective function under the constraint conditions.