Bus Parking Allocation Using Preference-Based Matching
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Solution Overview
Problem
Existing bus parking allocation methods fail to minimize operational costs and energy waste by ignoring the management requirements of operational branches, relying on manual designation and lacking rational allocation strategies.
Innovation Solution
A bus parking allocation method using a matching theory-based model that considers operational preferences, employing a Gale-Shapley algorithm to optimize parking lots while minimizing total costs, and incorporating visualization tools for decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If buses are assigned to designated parking lots based on operational branch preferences, then management ease is improved, but total parking costs increase due to suboptimal allocation
Solution Approach 1:
The patent transforms the parking allocation problem from a static manual assignment to a dynamic optimization process by changing the parameter of allocation criteria. It introduces a preference-based matching model that quantifies operational preferences and uses algorithms to find optimal allocations, thereby resolving the contradiction between management ease and cost efficiency.
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between operational branch preferences and cost minimization requirements. This system processes preference data, calculates optimal allocations, and produces allocation plans that balance both management ease and energy efficiency, preventing direct conflict between the two objectives.
2Ease of manufacture
If manual designation methods are used for parking allocation, then implementation simplicity is improved, but allocation optimality deteriorates due to reliance on human experience
Solution Approach 1:
The patent replaces the mechanical manual designation process with an automated computational system. It substitutes human experience-based decision-making with algorithmic optimization that processes preference data and calculates optimal allocations, thereby improving allocation precision while maintaining implementation feasibility through systematic procedures.
Solution Approach 2:
The patent enables the system to automatically generate optimal parking allocations by processing operational preference data itself, without requiring manual intervention for each allocation decision. The optimization algorithm serves the system autonomously, finding optimal solutions based on predefined preferences and constraints.
3Loss of energy
If existing parking allocation methods minimize parking costs by assigning buses to closest available spaces, then parking cost efficiency is improved, but operational management requirements deteriorate due to ignoring branch preferences
Solution Approach 1:
The patent segments the parking allocation problem into two distinct components: operational preferences (management requirements) and cost efficiency (energy minimization). By separating these concerns and processing them through a preference-based matching model, the system can simultaneously satisfy both requirements, allowing buses to be allocated to preferred parking lots while minimizing total travel costs.
Solution Approach 2:
The patent transforms the static cost-minimization approach into a dynamic preference-based allocation system. It introduces operational preferences as a variable factor that influences allocation decisions, making the system adaptable to different operational requirements while continuously optimizing for cost efficiency through algorithmic processing.
Data Source
AI summary
Disclosed are a bus parking allocation method and apparatus considering operational preferences. The method includes: obtaining a bus station dataset, a parking lot dataset, a bus information dataset, and a bus departure/return dataset; obtaining a distance dataset containing distances between bus stations and parking lots; building a parking allocation model considering operational preferences; building an objective function of the parking allocation model, where one group of buses are allocated to one group of parking lots, with each bus being allocated to at most one parking lot, and the number of buses parked in each parking lot is less than or equal to a capacity of the parking lot; building bus preference lists and parking lot preference lists, and calculating the objective function while satisfying the preference lists, to minimize total parking costs of all buses, thereby obtaining parking allocation results considering operational preferences; and designing multiple visualization views for display.