Flood Evacuation Vehicle Scheduling With Dynamic Road Segmentation
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Solution Overview
Problem
Existing flood forecasting models lack real-time guidance for the optimized scheduling of massive transport vehicles during extreme flood disasters, leading to inefficiencies and potential missed evacuation windows due to unbalanced and incomplete traffic diversion strategies.
Innovation Solution
A method utilizing a MIKE model, Muskingum model, and two-dimensional hydrodynamic model to determine flood inundation ranges, marking accessible roads with double truncation, and establishing a time-varying dynamic-planning traffic scheduling model to optimize transport vehicle scheduling, considering road emergencies and time-related variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing flood forecasting models are used, then flood forecasting accuracy is improved, but real-time guidance for transport vehicle scheduling is insufficient
Solution Approach 1:
The system segments the flood-affected area into multiple zones (inundated region, safe transfer region, flood edge transition region) and segments roads into double-truncated segments. This segmentation enables detailed real-time scheduling guidance for transport vehicles by analyzing specific zones and road segments rather than treating the entire area uniformly.
Solution Approach 2:
The patent introduces an intermediary scheduling system that connects flood forecasting data with transport vehicle scheduling. This intermediary processes flood forecast information and translates it into actionable real-time scheduling guidance for vehicles, bridging the gap between forecasting accuracy and practical scheduling needs.
2Ease of operation
If traditional path planning models are used, then point-to-point road planning is achieved, but real-time guided scheduling of massive vehicles is not possible
Solution Approach 1:
The system implements dynamic scheduling by continuously updating road conditions, vehicle locations, and flood status in real-time. The scheduling plan is not static but adapts dynamically to changing conditions, enabling real-time guided scheduling of massive vehicles while maintaining ease of point-to-point route planning.
Solution Approach 2:
The scheduling system serves multiple functions simultaneously: it provides point-to-point route planning, real-time vehicle scheduling for massive fleets, flood risk assessment, and dynamic route optimization. This multi-functionality enables both simple point-to-point planning and complex real-time coordinated scheduling of numerous vehicles.
3Productivity
If comprehensive scheduling schemes are implemented, then evacuation efficiency is improved, but model computation speed decreases
Solution Approach 1:
By segmenting the flood area into zones and roads into double-truncated segments, the system divides the complex scheduling problem into smaller, more manageable sub-problems. This segmentation allows comprehensive scheduling schemes to be implemented efficiently, improving evacuation efficiency while reducing the computational burden compared to treating the entire area as a single complex system.
Solution Approach 2:
The system focuses computational resources on critical areas and time periods during the evacuation process, implementing comprehensive scheduling where it matters most rather than uniformly across all areas and times. This partial action approach maintains high evacuation efficiency while managing computation speed.
4Measurement precision
If real-time updates of road conditions are implemented, then scheduling accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments roads into double-truncated segments and updates conditions for each segment independently. This segmentation allows real-time updates of road conditions to be implemented efficiently, improving scheduling accuracy by capturing local variations in road status while reducing computational complexity compared to updating entire road networks uniformly.
Data Source
AI summary
A method and device for optimized scheduling of massive transport vehicles during flood disasters, relating to the field of transportation resource scheduling is provided. The method includes determining flood inundation ranges during various time periods of a flood based on watershed precipitation and river cross-section structural data and marking accessible roads within a flood-affected area with double truncation. A road information matrix and a resettlement zone information matrix is determined and model information and location information of transport vehicles within the flood-affected area are also determined. A transport vehicle dataset is also determined. With consideration of road emergencies, time-related variations of a road matrix, road collapse incidents, and mud-covered roads, a time-varying dynamic-planning traffic scheduling model is established with a goal of minimizing an arrival time of a last evacuated transport vehicle, and an optimal scheduling plan is determined to perform optimized scheduling on transport vehicles within the flood-affected area.