Origin-Destination Estimation via Sensor Network Trajectory Analysis
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Solution Overview
Problem
Conventional origin-destination (OD) analysis methods, such as surveys and GPS-equipped floating vehicles, are expensive, time-consuming, and generate biased estimates of traffic patterns due to their limited scope and sampling fraction in urban areas.
Innovation Solution
A framework utilizing a sensor network to collect and analyze vehicle trajectory data, calculating probability distributions of travel time and stop probabilities between sensor pairs to determine accurate OD patterns, facilitating efficient and accurate traffic pattern analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If surveys of sampled sub-population are used for OD analysis, then cost and time consumption are reduced compared to complete data collection, but measurement precision and reliability of OD patterns deteriorate due to sampling bias
Solution Approach 1:
The sensor network serves multiple functions: it collects trajectory data for OD analysis, provides real-time traffic monitoring, and supports various transportation planning applications. This universal data collection approach replaces multiple separate studies (surveys, floating car data) with a single comprehensive system, achieving both efficiency and accuracy.
Solution Approach 2:
Sensor stations act as intermediaries between vehicles and the central analysis system. These stationary sensors capture trajectory data from passing vehicles without requiring GPS equipment in the vehicles themselves, thereby obtaining comprehensive data without the biases of voluntary GPS participation.
2Quantity of substance
If GPS-equipped floating vehicles are used for OD analysis, then data collection cost is reduced, but measurement precision deteriorates due to limited scope and biased sampling of only a small fraction of daily traffic volume
Solution Approach 1:
Instead of equipping vehicles with GPS (mobile sensors), the patent inverts the approach by placing sensors at fixed locations along the road network. This stationary sensor network captures all passing vehicles uniformly, eliminating the selection bias inherent in voluntary GPS participation and achieving comprehensive traffic coverage.
3Measurement precision
If sensor network with distributed sensor stations is deployed, then measurement precision and data coverage are improved, but device complexity and infrastructure cost increase
Solution Approach 1:
The sensor network is segmented into multiple independent sensor stations distributed along the road network. Each station operates autonomously to collect local trajectory data, and the central system aggregates these segmented data streams. This segmentation allows scalable deployment without requiring a fully connected complex system.
Data Source
AI summary
A framework for origin-destination (OD) analysis of vehicle trajectory data is described herein. In accordance with one aspect, a vehicle trajectory dataset is provided to an OD analyzer. The vehicle trajectory dataset includes vehicle trajectory data collected from a sensor network having a plurality of sensor stations for detecting vehicles. The sensor stations of the sensor network are distributed in a geographical area of interest, where the vehicle trajectory data include trajectories of vehicles captured by the sensor network. The vehicle trajectory dataset may be analyzed by the OD analyzer to determine an origin and a destination of trips for trajectories of the vehicles in the vehicle trajectory dataset. The analysis includes calculating a probability distribution of travel time between sensor pairs of the sensor network of a number of (dropped-out) intermediate stations, and determining a stop probability between a station pair in the trajectories of the vehicles, where a stop is a destination of a previous trip and an origin of a next trip in the trajectories.