Urban Infrastructure Deployment Optimization via Graph Models
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Solution Overview
Problem
Current methods for improving urban accessibility, especially for older people and those with mobility issues, are inefficient due to high computational costs and a focus on creating new routes rather than enhancing existing ones, leading to disorientation and accessibility challenges in urban environments.
Innovation Solution
A computer-implemented method using a geo-referenced multi-edge graph model and multi-objective optimization algorithms to determine the deployment of urban infrastructures like ramps, escalators, and barriers, optimizing accessibility and environmental parameters while minimizing costs, by selecting the most suitable type, structural/functional features, and location for existing walkable paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation-based optimization is used to generate models characterizing urban pedestrian mobility, then precision and fidelity of mobility analysis is improved, but computational cost becomes excessively high for supporting strategic decisions
Solution Approach 1:
The patent creates simplified graph-based representations (copies) of the urban environment that capture essential mobility characteristics without requiring full simulation fidelity. These graph models serve as lightweight proxies that enable strategic decision-making with acceptable precision at fraction of the computational cost of detailed simulations.
Solution Approach 2:
The patent transforms the problem from continuous simulation parameters to discrete graph-based parameters (nodes, edges, weights). By changing the parameter representation from detailed physical simulations to abstracted mobility metrics, the system achieves sufficient precision for strategic decisions while dramatically reducing computational requirements.
2Adaptability or versatility
If alternative routes are created for mobility-constrained users, then accessibility requirements are met, but users experience disorientation and security risks due to changes in mobility habits
Solution Approach 1:
The patent enhances accessibility by modifying local properties of existing routes (adding ramps, lifts, barriers at specific locations) rather than creating entirely alternative routes. This allows users to maintain their familiar paths while receiving localized accessibility improvements, thus meeting accessibility requirements without causing disorientation.
Solution Approach 2:
The system performs preliminary analysis of existing routes to identify where accessibility interventions should be placed, allowing users to continue using familiar routes with pre-improved accessibility characteristics rather than forcing users to learn new paths.
3Adaptability or versatility
If traditional heuristic methods are used for infrastructure installation decisions, then expert knowledge is utilized, but the process becomes slow and manual
Solution Approach 1:
The patent implements automated optimization algorithms that independently analyze the graph model and identify optimal infrastructure locations without requiring manual expert intervention at each decision point. This self-service capability dramatically accelerates the decision-making process while incorporating accessibility requirements.
Solution Approach 2:
The system uses optimization algorithms that iteratively evaluate different infrastructure placement scenarios and provide feedback on accessibility improvements, automatically converging on optimal solutions. This feedback loop replaces slow manual heuristic processes with rapid automated optimization while maintaining expertise in accessibility requirements.
4Adaptability or versatility
If new walking routes are created to improve accessibility, then accessibility requirements are satisfied, but existing route infrastructure is left unoptimized and costs increase
Solution Approach 1:
The patent focuses accessibility interventions on specific local locations along existing routes where they are most needed (steep slopes, obstacles, intersections). By targeting local problem areas rather than creating new routes, the system satisfies accessibility requirements while minimizing overall infrastructure investment.
Solution Approach 2:
The optimization algorithm identifies the minimum necessary infrastructure interventions required to meet accessibility standards on existing routes. By implementing only the essential partial actions needed rather than complete route redesigns, the system achieves accessibility compliance with reduced investment.
Data Source
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AI summary
Computer-implemented method for determining installation of urban infrastructures in urban environments, comprising generating a geo-referenced multi-edge graph model representing a portion of an urban area, wherein vertices represent street junctions and edges connecting vertices represent walkable streets within the urban area; assigning a weight to each edge, the weight being associated to parameters of the edge which are related to an accessibility degree of the edge, to environmental conditions in the edge, to a pre-existence of urban infrastructures in the edge. The method selects a departing and an arrival vertex within the graph model, determines by a multi-objective optimization model, a type, structural/functional features and location of the urban infrastructure in a walkable path defined between the departing and arrival vertex, wherein the type, the structural/functional features and the location of the particular urban infrastructure optimize at least two accessibility and/or environmental parameters of the walkable path for a user.