Multi-Modal Transport Scheduling Integrating Fixed and On-Demand Vehicles
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Solution Overview
Problem
Current multi-modal transportation networks struggle to integrate private and public transportation systems effectively, leading to inefficient route planning and increased environmental impact, as conventional methods fail to optimize joint scheduling across different transportation modes.
Innovation Solution
A system and method that utilize autonomous vehicles for last-mile transportation, integrating fixed schedule vehicles with flexibly scheduled commuter vehicles, employing optimization algorithms to assign passengers and vehicles based on itinerary requests, arrival time windows, and route optimization to minimize travel time and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional routing methods are used separately for private and public transportation networks, then each network can be optimized independently, but integration between the two networks is poor and true multi-modal route planning cannot be achieved
Solution Approach 1:
The patent merges private transportation network routing with public transportation network routing into a unified multi-modal routing system. The system integrates both networks by allowing route planning that seamlessly transitions between private vehicles and public transport, optimizing the combined journey rather than treating them as separate entities.
Solution Approach 2:
The routing system is designed to be universal, handling multiple transportation modes (private vehicles, public transport, walking, cycling) within a single platform. The system can plan routes using any combination of these modes, making it adaptable to various user needs and transportation infrastructures.
2Productivity
If fixed schedule vehicles are used for mass transportation, then operational efficiency is improved, but flexibility in passenger scheduling and routing is reduced
Solution Approach 1:
The system dynamically adjusts routing plans based on real-time conditions, passenger requests, and vehicle availability. While fixed schedule vehicles maintain their schedules for operational efficiency, the system provides flexible routing options by allowing passengers to combine fixed schedule segments with on-demand private transport segments, creating adaptable multi-modal journeys.
Solution Approach 2:
The journey is segmented into multiple legs, where some segments use fixed schedule vehicles and others use flexible private transport. This segmentation allows the system to maintain the operational efficiency of fixed schedules while providing flexibility through alternative segments, enabling passengers to optimize their overall travel experience.
3Adaptability or versatility
If autonomous vehicles are deployed for last-mile transportation, then service coverage and accessibility are improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
Autonomous vehicles serve as intermediaries between public transport hubs and final destinations, bridging the gap in the last-mile connectivity. The system integrates these vehicles into the existing public transport network, allowing passengers to transfer from fixed schedule vehicles to autonomous vehicles for the final leg of their journey, thereby extending service coverage without requiring complete system replacement.
4Loss of time
If optimization algorithms are used to assign passengers to vehicles, then travel time and energy consumption are reduced, but computational complexity increases
Solution Approach 1:
The system applies optimization algorithms selectively to critical decision points in the routing process, such as determining the optimal combination of transportation modes and assigning passengers to specific vehicles. Rather than optimizing every aspect of the system, the focus is placed on key areas that have the greatest impact on travel time and efficiency, balancing computational requirements with performance benefits.
Data Source
AI summary
A system for assigning commuter vehicles (CVs) in a multi-modal transportation network having the CVs and fixed schedule vehicles to passengers is disclosed. The system receives itinerary requests from the passengers, wherein the itinerary requests of the passengers include initial locations, target locations, departure times from the initial locations, and arrival time windows including deadlines at the target locations. The system includes a memory to store computer executable programs including a grouping program, a route-search program, an operation route map program of the CVs, and a commuter assigning program, and a processor to perform steps of the programs in connection with the memory, wherein the steps include grouping the passengers into a set of groups, assigning the CVs to the groups by performing the commuter assigning program and generating assignment information of assigned CVs among the available CVs.