Multi-modal Trip Planning Graph Optimization
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Solution Overview
Problem
Current trip planning systems fail to optimize multi-day, multi-destination travel plans that incorporate various transportation modes, user preferences, and environmental factors such as weather and air quality, leading to inefficient mobility solutions in urban areas.
Innovation Solution
A multi-modal trip planning system that uses a planning graph to integrate multiple transportation modes, services, and user preferences, optimizing routes and mobility options by considering factors like travel costs, time, weather, and air quality, and providing personalized and customizable information for users to plan visits to attractions and accommodations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a user manually plans a trip using ad hoc POI search and route construction, then the system is simple to operate, but the trip optimization quality deteriorates (no assistance in planning extensive trip agendas, no help in optimizing best time to travel or visit POIs)
Solution Approach 1:
The trip planning problem is segmented into multiple components: POI search, route construction, multi-day scheduling, and optimization. Each component is handled by a separate module within the system, allowing complex optimization to be achieved through coordinated simple operations.
Solution Approach 2:
The system performs preliminary actions by pre-calculating optimal routes, visiting sequences, and timing for multiple POIs across multiple days before the user actually travels. This advance planning and optimization eliminates the need for manual real-time decision-making during the trip.
2Adaptability or versatility
If traditional route planning is used for single-destination trips, then the routing is simple, but it deteriorates in handling multi-day, multi-destination trips with multiple transportation modes and constraints
Solution Approach 1:
The trip planning system is designed as a universal platform that handles multiple transportation modes (driving, public transit, walking, biking), multiple destinations, multiple days, and various constraints (budget, time, preferences) through a single integrated system, rather than requiring separate tools for each function.
Solution Approach 2:
The system adds temporal dimension (multi-day scheduling) and modal dimension (multiple transportation modes) to traditional route planning, transforming it from a simple point-to-point problem into a comprehensive multi-dimensional optimization problem that considers time, space, and transportation mode simultaneously.
3Manufacturing precision
If comprehensive trip optimization considering weather, air quality, and visiting hours is implemented, then the trip quality is improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary calculations and pre-processing of constraint data (visiting hours, weather forecasts, air quality predictions) before the main optimization. This allows the complex multi-constraint optimization to be solved more efficiently by reducing the search space and pre-identifying feasible options.
Solution Approach 2:
The system dynamically adjusts optimization parameters and constraints based on real-time data such as weather conditions, air quality indices, and attraction visiting hours. By changing parameters adaptively rather than using fixed criteria, the system achieves higher optimization accuracy without requiring exponentially increased computational power.
Data Source
AI summary
A multi-day and multi-person trip planning system comprises a planning graph of nodes interconnected by transit arcs which both have associated time-variable costs and time. A user specifies user objectives within trip destinations and customizes a plurality of user preferences for a multi-day, multi-person, multi-modal trip. An optimizer compiles a plurality of permutations of visiting plans within the planning graph which satisfy the specified user objectives including routing to a plurality of trip destinations. The optimizer compares the permutations using mixed integer programming or constraint programming to identify at least one optimized visiting plan having a lowest aggregate associated cost or shortest travel time that also satisfies a plurality of constraints determined by the customizable user preferences or environment condition. Then the user interface presents at least one optimized visiting plan to a user or route related coupons.