Intercity railway passenger ticket time-sharing pricing method based on generalized cost function
A technology of intercity railway and generalized cost, which is applied in data processing applications, instruments, commerce, etc., and can solve problems such as low passenger occupancy rate, large passenger flow, and loss of passenger flow
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment
[0074] Such as figure 1 As shown, the embodiment of the present invention provides a time-sharing pricing method for intercity railway tickets based on a generalized cost function, the method includes the following process steps:
[0075] Step S110: Set the initial ticket price, aim at the minimum generalized cost, build a balanced passenger flow distribution model, and determine the passenger travel time and travel mode selection;
[0076] Step S120: constructing a time-of-use pricing model corresponding to the passenger travel period and travel mode with the goal of maximizing railway revenue;
[0077] Step S130: Solve the passenger flow balance distribution model and the time-sharing pricing model by using the combination of the particle swarm optimization algorithm with inertia and the Frank-wolfe algorithm, and obtain the ticket pricing result.
[0078] The step S110 specifically includes:
[0079] The change of the fare in each time period of passenger transport leads ...
Embodiment 2
[0133] Embodiment 2 of the present invention provides a kind of time-sharing pricing method of intercity railway passenger ticket based on generalized cost function, and the method comprises the following process steps:
[0134] Step 1): Model assumptions.
[0135] Step 2): Establish the upper-level planning model.
[0136] Step 3): Establish the lower-level planning model, including two parts: the lower-level user equilibrium model and the construction of the generalized cost function.
[0137] Step 4): Improve the two-tier model of time-of-use pricing.
[0138] Step 5): Solve the bi-level programming model by combining the particle swarm algorithm with inertia weight and the Frank-wolfe algorithm.
[0139] The assumptions for model building in step 1) include:
[0140] 1) The distribution of passenger flow is uneven at different times of the day, with obvious peaks and valleys.
[0141] 2) The total passenger flow in the intercity corridor remains unchanged and passenger...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com