Dynamic Vehicle Parking Assignment for Rideshare Efficiency
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Solution Overview
Problem
In ridesharing services, vehicles often face challenges in finding optimal parking places due to limited numbers and distant locations, leading to increased travel times and costs, as well as inefficiencies in reassigning vehicles after completing a run.
Innovation Solution
A method and operation server that dynamically determine vehicle parking places based on demand expectations by generating assignment combinations, calculating total travel times, and assigning vehicles to parking places with the shortest travel times, considering current positions and available parking capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicles are parked at preset parking places, then vehicle management is simplified, but travel distance and time increase significantly
Solution Approach 1:
The patent transforms the static preset parking place system into a dynamic assignment system. The operation server dynamically determines parking places based on real-time factors including vehicle current positions, passenger origins, expected demand, and parking place capacities. This dynamic approach optimizes travel time while maintaining manageable complexity through automated server-based coordination.
Solution Approach 2:
The operation server performs preliminary actions by pre-calculating optimal parking place assignments before vehicles need to park. By anticipating future service needs and pre-determining parking assignments based on expected demand and vehicle positions, the system minimizes subsequent travel time without requiring complex real-time decision-making during peak periods.
2Loss of time
If parking places are located close to vehicle call locations, then travel time is reduced, but the number of available parking places is limited
Solution Approach 1:
The patent applies local quality by assigning different parking places to different vehicles based on their specific contexts. Each vehicle receives a customized parking place assignment considering its current position, the nearest passenger origins, and local parking capacity. This localized optimization approach maximizes the utility of available parking places throughout the service area rather than concentrating all vehicles at a single location.
Solution Approach 2:
The system resolves the spatial limitation by introducing temporal and informational dimensions. Instead of merely optimizing geographic proximity, the server considers time-based factors (expected demand, vehicle availability) and information-based factors (vehicle positions, passenger origins) to assign parking places that may be slightly farther away but provide better overall service efficiency when multiple dimensions are optimized together.
3Productivity
If vehicles are reassigned to nearest parking places, then parking efficiency improves, but vehicles may be assigned to occupied parking places
Solution Approach 1:
The operation server implements a feedback mechanism by continuously monitoring parking place occupancy status and vehicle positions. Before assigning a parking place to a vehicle, the server verifies availability by checking current occupancy data. This feedback loop ensures that parking place assignments are both efficient and reliable, preventing assignments to occupied places while maximizing parking utilization.
Solution Approach 2:
The system enables self-service through automated server-based coordination. The operation server autonomously manages parking place assignments, capacity tracking, and vehicle routing without requiring manual intervention. This automated self-service approach maintains high parking efficiency while ensuring reliability through systematic verification of place availability before each assignment.
Data Source
AI summary
A method for operating a parking place based on demand expectation, may include expecting n quantity of calls corresponding to a current time in a service area, deriving Nc quantity of assignment combinations that assign Nb quantity of vehicles in standby in the service area with respect to Na quantity of parking places positioned in the service area, with respect to each in the Nc quantity of assignment combination, allocating the expected n quantity of calls to Nd quantity of vehicles in the service area including the Nb quantity of vehicles, and deriving Nd quantity of total travel times of the Nd quantity of vehicles, and assigning a corresponding parking place from among the Na quantity of parking places to each in the Nb quantity of vehicles based on the Nd quantity of total travel times with respect to each in the Nc quantity of assignment combination.