V2I Speed Advisory With Queue Prediction for Stop-and-Go Traffic
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Solution Overview
Problem
Stop-and-go traffic patterns on urban roads lead to excessive energy consumption due to unnecessary vehicle braking, idling, and accelerations, causing negative impacts such as delayed travel time, air pollution, and increased carbon emissions.
Innovation Solution
A mobile edge computing framework that integrates real-time vehicle-to-infrastructure (V2I) communication and intelligent speed optimization algorithms into a mobile application, predicting queue lengths and generating optimal speed advisories for vehicles to reduce stop-and-go behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If vehicles maintain steady speed through optimized signal timing and queue prediction, then energy consumption is reduced, but traffic flow control complexity increases
Solution Approach 1:
The system performs queue length prediction at downstream intersections before vehicles arrive, using historical traffic data and current detector readings. This preliminary action allows the traffic control system to pre-calculate optimal signal timing and provide speed advisories to vehicles in advance, enabling smooth deceleration or maintenance of speed without sudden braking, thereby reducing energy consumption while managing system complexity through proactive rather than reactive control
Solution Approach 2:
The system continuously monitors actual queue lengths at downstream intersections and compares them with predicted values. This feedback mechanism allows the traffic control system to adjust signal timing and speed advisories in real-time, optimizing energy consumption by preventing both excessive braking and unnecessary accelerations. The feedback loop manages complexity by using standardized detection and communication protocols between detectors, mobile devices, and traffic controllers
2Productivity
If real-time queue prediction and speed advisory systems are deployed, then stop-and-go traffic is reduced, but system implementation complexity increases
Solution Approach 1:
The system uses universal communication protocols and standardized data formats that allow different components (detectors, mobile devices, traffic controllers) to interact seamlessly. The queue prediction algorithm and speed advisory mechanism can be applied across multiple intersections and vehicle types, reducing implementation complexity through reusability and standardization while improving overall traffic flow efficiency
Solution Approach 2:
The mobile device serves as an intermediary between the traffic infrastructure (detectors and controllers) and the vehicle. It receives queue length predictions and signal timing data, processes speed advisory calculations, and provides guidance to drivers. This intermediary approach simplifies the overall system architecture by centralizing the computational burden on readily available mobile devices rather than requiring complex modifications to vehicle systems or traffic infrastructure
3Loss of time
If vehicles receive speed advisories based on predicted queue lengths, then travel time is reduced, but measurement precision requirements increase
Solution Approach 1:
The system provides speed advisories based on predicted queue lengths rather than requiring perfectly accurate real-time measurements. By using predicted values derived from historical data and current detector readings, the system achieves sufficient accuracy to reduce travel time without demanding extreme measurement precision. The advisory nature of the speed guidance allows for some uncertainty while still delivering tangible travel time benefits
Data Source
AI summary
Systems and methods for controlling the speed of a vehicle traveling over a terrain. The methods comprise: predicting, by a mobile device (MD), a queue length defined by the number of vehicles in a queue at a downstream intersection based on a traffic volume and vehicle speeds that were detected during a past period of time by a detector located at or near an upstream intersection; sensing, by MD's sensor(s), the vehicle's velocity and location; computing a distance to a downstream intersection from the vehicle's location; generating a desired speed for the vehicle based on the traffic volume and vehicle speeds, signal phase and timing data for at least the next two cycles, the predicted queue length at the downstream intersection, the vehicle's velocity and location, the distance to the downstream intersection, and a current time; and using the desired speed to facilitate control of the speed of the vehicle.