Ridesharing Get-on and Off Place Selection Optimization
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Solution Overview
Problem
In ridesharing services, users often designate get-on and get-off positions based on convenience, which can lead to inconvenience due to prohibited parking areas or long distances from their current position and destination, affecting service efficiency and user experience.
Innovation Solution
A method and operation server that determine optimal get-on and get-off places by calculating total travel time based on passenger moving time, vehicle running time, and expected demand, considering multiple paths and candidate locations within predetermined distances, to minimize overall travel time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users designate get-on and get-off positions based on convenience without restrictions, then user satisfaction improves, but service efficiency deteriorates due to prohibited parking areas and difficult-to-enter locations
Solution Approach 1:
The system pre-establishes a database of valid get-on and get-off positions with attributes including accessibility, parking availability, and safety. Before user input is processed, these positions are pre-validated and stored, enabling the system to quickly filter and suggest appropriate positions without real-time complex calculations, thus maintaining both user convenience and service efficiency
Solution Approach 2:
The operation server acts as an intermediary between users and the ridesharing service. It receives user-designated positions, validates them against the pre-established database, and automatically adjusts to alternative positions if the original designation is inappropriate. This intermediary function resolves the conflict by translating user convenience preferences into service-efficient outcomes without direct user intervention
2Ease of operation
If get-on and get-off places are selected close to user's current position and destination, then user convenience improves, but vehicle routing efficiency deteriorates due to additional detours
Solution Approach 1:
The system dynamically adjusts the weight parameters in the objective function based on service conditions. When demand is high or vehicle capacity is limited, it increases the weight of vehicle routing efficiency, allowing larger deviations from user-designated positions. When demand is low, it prioritizes user convenience with smaller deviations. This parameter adjustment resolves the contradiction by flexibly balancing both requirements
Solution Approach 2:
The system implements dynamic position selection that adapts to real-time service conditions. Instead of fixed rules, it continuously evaluates multiple candidate positions and selects the optimal one based on current vehicle location, demand patterns, and routing constraints. This dynamic approach allows the system to minimize user deviation while preventing excessive vehicle detours
3Measurement precision
If multiple candidate get-on and get-off places are evaluated with comprehensive paths, then position selection accuracy improves, but calculation complexity increases
Solution Approach 1:
The system segments the position selection process into distinct modules: candidate position generation, path calculation, objective function evaluation, and optimal position selection. Each module handles a specific aspect independently, allowing for efficient computation and easier system maintenance. This segmentation reduces overall complexity while maintaining comprehensive evaluation accuracy
Solution Approach 2:
The system implements a two-stage evaluation process where it first performs a coarse filter on candidate positions using basic criteria, then performs detailed path calculations only on the reduced set of promising candidates. This partial action approach maintains high selection accuracy by thoroughly evaluating relevant options while avoiding unnecessary calculations on obviously suboptimal positions, thus reducing overall computational complexity
4Productivity
If the system optimizes for minimum total travel time including passenger moving time and vehicle running time, then overall service efficiency improves, but response time for new service requests increases
Solution Approach 1:
The system pre-calculates and stores optimal paths between major zones and pre-evaluates candidate positions during off-peak periods. When a new service request arrives, it retrieves pre-computed information and performs only incremental adjustments based on current conditions, rather than performing complete path optimization from scratch. This preliminary action significantly reduces response time while maintaining optimization quality
Solution Approach 2:
The system applies different optimization depths to different parts of the routing problem. For well-established routes with historical data, it uses simplified heuristic methods that provide quick results. For new or unusual routes, it performs more comprehensive optimization. This local quality approach allows the system to balance response time and optimization quality based on the specific characteristics of each routing scenario
Data Source
AI summary
A method for determining a vehicle get-on-and-off place includes receiving a first destination and a first origin along with a vehicle call request from a first user terminal, setting first candidate get-on places within a predetermined distance from the first origin and first candidate get-off places within a predetermined distance from the first destination, generating first get-on-and-off pairs by combination of first candidate get-on places and first candidate get-off places, determining a passenger moving time based on the first origin, the first destination, and each entire path, with respect to each in entire paths based on each in first get-on-and-off pairs, determining a vehicle running time based on each entire path, with respect to each in entire paths, and determining a total travel time based on an expected demand, the passenger moving time, and the vehicle running time with respect to each in entire paths.