Intersection Traffic Control Using Vehicle Energy Consumption Priority
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Solution Overview
Problem
Existing traffic control systems fail to optimize energy consumption efficiently, particularly in urban and rural areas, leading to unnecessary energy loss and inefficiencies in vehicle movements at intersections.
Innovation Solution
A computer-implemented system using machine learning to calculate and compare the energy consumption of vehicles approaching an intersection, determining which vehicles to prioritize based on their energy expenditure, and adjusting traffic signals accordingly to minimize energy loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional traffic control systems are used, then traffic flow is maintained, but energy consumption is not optimized efficiently
Solution Approach 1:
The system changes the control parameter from simple traffic flow management to energy consumption optimization. By calculating and comparing energy consumption values for different vehicles based on their status messages, the traffic control system dynamically adjusts signal timing to minimize total energy consumption while managing traffic flow.
Solution Approach 2:
The system performs preliminary calculations of energy consumption for each vehicle before making traffic control decisions. By receiving vehicle status messages in advance and computing energy consumption values beforehand, the system can optimize traffic signal timing to reduce energy waste from unnecessary stops and accelerations.
2Loss of energy
If traffic signals are adjusted to optimize energy consumption, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The system implements feedback by receiving real-time vehicle status messages, calculating energy consumption values, and using this information to adjust traffic signal timing. The continuous loop of monitoring vehicle status, computing energy metrics, and modifying control signals creates a responsive system that minimizes energy loss while managing complexity through automated calculations.
Solution Approach 2:
The system replaces traditional mechanical traffic control with an intelligent computing approach. Instead of fixed timing mechanisms, the system uses machine learning models and calculations to dynamically determine optimal signal timing based on vehicle energy consumption characteristics, substituting physical complexity with computational intelligence.
3Productivity
If vehicles are prioritized based on energy consumption calculations, then overall energy usage is reduced, but processing requirements increase
Solution Approach 1:
The system applies partial action by focusing energy consumption calculations only on vehicles approaching intersections, rather than monitoring all vehicles citywide. By concentrating computational resources on relevant vehicles and using targeted energy consumption models, the system achieves traffic flow optimization without requiring excessive computational power across the entire vehicle fleet.
Data Source
AI summary
Provided are techniques for real time traffic controls based on energy consumption of vehicles. A first traffic control message is broadcast, via wireless communication technology, to a first vehicle and a second vehicle. A first vehicle status message from the first vehicle and a second vehicle status message from the second vehicle are received. A first energy consumption of the first vehicle to stop and accelerate to a speed based on the first vehicle status message is calculated. A second energy consumption of the second vehicle to stop and accelerate to the speed based on the second vehicle status message is calculated. It is determined that the first energy consumption is greater than the second energy consumption. A second traffic control message is sent to the first vehicle to proceed through an intersection. A third traffic control message is sent to the second vehicle to stop at the intersection.