Predictive Zone-to-Zone Demand Control for Citywide Traffic Flow
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Intelligent Transportation Systems (ITS) lack the ability to apply proactive distribution of traffic on complex urban networks and effective demand and predictive parking control, leading to inefficient and costly solutions for citywide traffic management.
Innovation Solution
A system utilizing GNSS tolling-based incentivized predictively controlled navigation with multi-agent predictive control and deep learning methods for zone-to-zone demand control optimization, combined with predictive parking management, to enhance traffic flow and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional ITS solutions are implemented, then infrastructure coverage is provided, but the system lacks proactive traffic distribution capability and effective demand control
Solution Approach 1:
The system performs preliminary actions by predicting future traffic demand patterns and pre-planning optimal route assignments before congestion occurs. The predictive model analyzes historical data and forecasts to proactively distribute traffic, rather than reacting to current conditions. This enables the system to prepare route assignments and demand management strategies in advance, achieving proactive traffic distribution without requiring complex real-time intervention mechanisms.
Solution Approach 2:
The system segments the urban road network into multiple zones and divides traffic management into discrete demand control units. Each zone can be independently managed with its own demand control strategies, allowing the complex citywide traffic distribution problem to be broken down into manageable segments. This segmentation enables effective demand control while reducing the complexity of coordinating citywide interventions.
2Productivity
If citywide traffic control is implemented, then traffic flow optimization is achieved, but privacy of travelers must be preserved
Solution Approach 1:
The system introduces an intermediary layer of aggregation and anonymization between individual traveler data and traffic control decisions. Personal travel information is aggregated into zone-level demand patterns, and control decisions are made based on these anonymized patterns rather than individual data. This intermediary processing enables traffic flow optimization while preserving traveler privacy by never directly accessing or storing personal travel information.
Solution Approach 2:
The system applies different levels of data processing and control strategies to different zones based on their specific characteristics and demand patterns. Rather than uniformly processing all traveler data, each zone receives customized demand control based on its local patterns, allowing traffic optimization while maintaining privacy through localized, anonymized management.
3Productivity
If predictive parking management is added, then traffic interference from parking search is reduced, but system complexity increases
Solution Approach 1:
The system merges parking management functions with the existing demand control and route assignment framework. Parking availability predictions are integrated into the zone-to-zone demand control calculations, allowing the same predictive model to handle both traffic distribution and parking guidance. This consolidation reduces overall system complexity compared to implementing separate parking management systems, while still achieving the benefit of reduced traffic interference from parking search.
Data Source
AI summary
Some demonstrative embodiments include an apparatus, system and/or method, which may be related, for example, to a system and/or a method, which may be configured, for example, to optimize citywide traffic flow, for example, by privacy preserving scalable predictive citywide traffic load-balancing supporting, and/or being supported by, optimal zone to zone demand-control planning and/or predictive parking management.