Traffic volume
Overview
Traffic volume (Korean: 교통량; 交通量, traffic volume) refers to the number of travel subjects—such as vehicles, pedestrians, and bicycles—passing through a given road segment or point during a unit of time. It is used as basic data for road planning, signal operations, safety measures, and environmental impact assessments, and in recent years it has been collected and utilized in real time through ITS (Intelligent Transport Systems) and AI image analysis.
Definition and units
Traffic volume is generally expressed as vehicles per hour (vph), and daily volumes are aggregated as AADT (annual average daily traffic). Traffic volume is distinguished from "travel volume": travel volume refers to the number of trips between origins and destinations, while traffic volume refers to the cross-sectional flow passing a specific point. To correct for temporal variation, indicators such as the peak hour factor (PHF) and design hourly volume (DHV) are used together.
Measurement methods
Traditionally, point detection methods such as loop detectors, pressure and piezoelectric sensors, and ultrasonic and radar detectors were used, along with field surveys in which surveyors counted manually. In recent years, object detection and tracking models have been applied to CCTV footage to automatically aggregate traffic volume by vehicle type, and wide-area traffic volume is estimated by combining mobile carrier base station signals, navigation GPS trajectories, and private traffic data.
Relationship with traffic flow theory
Traffic volume (q), density (k), and speed (v) have the relationship q = k × v. As traffic volume increases, density rises and speed falls; when capacity is reached, traffic flow breaks down and congestion occurs. The fundamental diagram visualizes this relationship and shows the congested state in which traffic volume drops sharply after the critical density.
Key indicators and applications
AADT is used in road design, pavement thickness, and determination of the number of lanes, while design hourly volume is used for signal cycles and lane allocation. Traffic volume data is widely used for real-time signal control, congestion pricing and transportation demand management (TDM), air quality and noise prediction, logistics route planning, and autonomous driving infrastructure design.
Traffic volume and policy
Traffic volume serves as a basis for road expansion policy, but it is pointed out that due to induced demand, congestion can recur even when lanes are added. Accordingly, efficiency-oriented policies such as prioritizing public transportation, managing demand in congested sections, and smart signals are emphasized.
Recent trends
In 2024–2025, AI-based traffic volume forecasting and digital twins are spreading. Deep learning time-series models such as LSTM and graph neural networks have improved the accuracy of short-term traffic volume forecasting, and city-level "traffic digital twins" are used for policy simulation. Korea has expanded real-time traffic volume disclosure linked with the National Traffic Information Center (국가교통정보센터) and local government big data, and demand for precise traffic volume data needed for autonomous driving and robotaxi operations is growing. In addition, integrated management of traffic volume and emissions linked to carbon neutrality policy, and automation of pedestrian and bicycle traffic volume surveys, are drawing attention. Compliance with regulations that anonymize and aggregate trajectory data for privacy protection has also emerged as an important issue.
Related topics
- [[Intelligent Transport Systems]]
- [[Traffic congestion]]
- [[Transportation demand management]]
- [[Digital twin]]
- [[Autonomous driving]]