Traffic Signal Phase Estimation Using Vehicle Probe Data
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Solution Overview
Problem
Current systems face challenges in accurately estimating traffic signal phase and timing (SPaT) information due to uncertainties from clock drift, varying timing plans, and limited update rates of probe data, which hinders fuel efficiency and safety applications in connected vehicles.
Innovation Solution
The method involves estimating average acceleration and deceleration of vehicles at intersections using positioning system data, filtering out outliers, and calculating stop durations to determine red phase durations, cycle times, and future green phases, even with low-frequency probe data updates, by aggregating data from multiple vehicles and applying statistical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct access to signal timing plans and real-time state is obtained, then measurement precision of SPaT information is improved, but device complexity and infrastructure investment requirements increase significantly
Solution Approach 1:
The patent uses vehicle probe data as an intermediary to indirectly estimate SPaT information. Instead of directly accessing signal timing plans through complex infrastructure, the system observes vehicle behavior (speed, position, acceleration) around intersections and infers traffic signal states from these measurements, thereby achieving SPaT estimation without direct infrastructure access
Solution Approach 2:
The patent replaces the mechanical/physical infrastructure-based approach (direct access to signal controllers) with a data-processing-based approach. By substituting physical infrastructure access with algorithmic analysis of vehicle probe data, the system achieves SPaT information extraction without requiring complex infrastructure modifications or direct hardware access
2Measurement precision
If high update rates of probe data are used, then measurement precision of SPaT information is improved, but data availability and penetration level requirements increase
Solution Approach 1:
The patent accepts that individual probe data updates may be infrequent and potentially inaccurate, but by aggregating data from multiple vehicles and multiple time points, the system achieves sufficient measurement precision. Rather than requiring every probe to provide high-precision data, the system uses partial contributions from many sources to achieve the desired accuracy
Solution Approach 2:
The patent merges data from multiple vehicles, multiple sensors, and multiple time points to compensate for low individual update rates. By combining probe data from many vehicles passing through or near an intersection, the system reconstructs SPaT information even when individual vehicles provide infrequent updates, thereby reducing the burden on any single data source
3Ease of manufacture
If vehicle probe data with low update frequency is used, then infrastructure investment is reduced, but measurement precision of SPaT information deteriorates
Solution Approach 1:
The patent enables the system to be self-sufficient by using existing, readily available probe data from vehicles' own sensors and communication systems. Rather than requiring expensive dedicated infrastructure for data collection, the system leverages data that vehicles already generate and transmit for other purposes, thereby achieving low-cost deployment without sacrificing measurement capability
Solution Approach 2:
The patent makes the probe data serve multiple functions: it is used for navigation, traffic management, and SPaT estimation simultaneously. By extracting multiple values from the same data source, the system achieves accurate SPaT estimation without requiring dedicated infrastructure, thereby reducing deployment costs while maintaining measurement precision
4Reliability
If statistical patterns from multiple vehicles are aggregated, then reliability of SPaT estimation is improved, but loss of time in data processing increases
Solution Approach 1:
The patent performs preliminary filtering and validation of probe data before aggregation, establishing quality criteria in advance to ensure only reliable data contributes to SPaT estimation. By pre-defining acceptance criteria for vehicle trajectories, speed patterns, and positioning accuracy, the system reduces the need for extensive post-processing and validation, thereby maintaining reliability while reducing processing time
Data Source
AI summary
Traffic signal information is estimated based on positioning system data obtained from a plurality of vehicles. Each data set includes the position and the velocity of a vehicle as functions of time. For an intersection having a traffic signal, an average acceleration of the vehicles when leaving the intersection is estimated, and an average deceleration of the vehicles when approaching the intersection is estimated. For each of a subset of the vehicles, a stop duration at the intersection is estimated based on the average acceleration, the average deceleration, and the positioning system data for the respective vehicle. A duration of a red phase of the traffic signal is estimated based on the stop duration of each of the subset of the vehicles.