City skyline with glowing bus routes representing traffic analysis

Decoding City Traffic: How Qingdao's Bus Flow Analysis Can Help You Navigate Like a Pro

Rhea Morgan in Tech & Innovation September 2025 • 4 min read.

"Multi-scale Visualization Techniques for Smart Urban Mobility"

Urban traffic is a beast of its own, an intricate dance of vehicles, pedestrians, and roads. In Qingdao, like many cities, the rise of private cars has intensified traffic congestion and environmental pollution, making daily commutes a stressful ordeal. But what if we could transform this chaos into clarity? That's where data visualization comes in, turning complex information into understandable insights.

Imagine being able to see where and when congestion builds up in your city. Multi-scale visualization makes this possible by converting raw data into recognizable images, revealing the hidden patterns of traffic flow. This approach helps urban planners and commuters alike understand the dynamics of congestion, leading to smarter solutions for urban mobility.

In a pioneering study, researchers in Qingdao developed a multi-scale visualization of average travel speeds using floating bus data. By employing techniques like R language, Echarts, and WebGL, they created statistical pictures and 3D maps that illustrate congestion across space and time. The results? A clearer understanding of Qingdao's traffic challenges and potential avenues for improvement.

Unveiling Qingdao's Traffic Secrets: A Multi-Scale Visualization Approach

City skyline with glowing bus routes representing traffic analysis

The study, titled 'Multi-Scale Visualization Analysis of Bus Flow Average Travel Speed in Qingdao,' delves into the intricacies of urban traffic using data from Qingdao's Public Transportation Group, which operates approximately 5,000 buses. The data, recorded each time a bus passes through a station, spans from September 2014 to September 2015, comprising a staggering 1 billion records.

The core of the analysis lies in calculating the average travel speed between adjacent bus stations. This is achieved by dividing the distance between stations by the average travel time taken by all buses traversing that route. The process involves several critical steps:

Data Collection and Classification: Gathering GPS data from buses and categorizing it by bus routes and travel direction.
Outlier Elimination: Identifying and removing any unusual data points that could skew the analysis.
Clustering: Applying the K-means algorithm to cluster travel times, determining the optimal number of clusters (K) and their centers.
Data Fusion: Computing clustering borders and statistically analyzing the number of data points in each range.
Weighting and Averaging: Computing the weight of each class using a weighted average method to determine the average travel speed.

By visualizing this data across different scales—from the main districts to specific arterial roads—the researchers uncovered valuable insights into Qingdao's traffic patterns. This multi-faceted approach provides a comprehensive understanding of urban congestion, paving the way for targeted interventions.

Turning Insights into Action: The Future of Urban Mobility

This study underscores the power of data visualization in understanding and addressing urban traffic challenges. By transforming raw data into actionable insights, cities like Qingdao can make informed decisions to improve public transportation, reduce congestion, and enhance the overall quality of life for their residents. As technology advances, expect even more innovative approaches to revolutionize urban mobility and create smarter, more livable cities.

About this Article -

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Everything You Need To Know

How does Qingdao use data visualization to understand traffic congestion?

Qingdao employs multi-scale visualization techniques to analyze bus flow data and understand traffic congestion. Researchers used data from Qingdao's Public Transportation Group, focusing on the average travel speeds of buses. This data, collected from approximately 5,000 buses between September 2014 and September 2015, was processed using tools like R language, Echarts, and WebGL. This created statistical pictures and 3D maps to illustrate congestion across space and time. The visualization allows urban planners and commuters to understand the dynamics of congestion, helping to develop smarter solutions for urban mobility.

What specific data is used to analyze traffic flow in Qingdao?

The analysis in Qingdao uses GPS data from the city's buses. The data, collected from roughly 5,000 buses operated by Qingdao's Public Transportation Group, includes information recorded each time a bus passes through a station. This data, which covers the period from September 2014 to September 2015, is used to calculate the average travel speed between adjacent bus stations. The process involves data collection, outlier elimination, clustering using the K-means algorithm, data fusion, and weighting to determine the average travel speed, which is then visualized to identify traffic patterns.

What are the main steps involved in visualizing bus flow data?

The main steps include data collection and classification of GPS data by bus routes and travel direction. It also includes outlier elimination to remove unusual data points. Clustering using the K-means algorithm is applied to group travel times, while data fusion computes clustering borders. Finally, weighting and averaging are employed to determine the average travel speed. These steps transform raw data into a visual format, revealing patterns of congestion across different scales, such as main districts and arterial roads.

How does multi-scale visualization help improve urban mobility?

Multi-scale visualization helps improve urban mobility by providing a comprehensive understanding of traffic patterns. By visualizing data across different scales—from main districts to specific roads—researchers and urban planners can pinpoint where and when congestion occurs. This detailed insight allows for targeted interventions, such as optimizing bus routes, adjusting traffic signals, and planning new infrastructure. The ultimate goal is to reduce congestion, improve public transportation, and enhance the overall quality of life for Qingdao residents.

What tools and technologies were utilized in Qingdao's traffic analysis?

In the study of Qingdao's traffic, researchers utilized several key tools and technologies to analyze bus flow data. These include the use of the R language for statistical analysis and data processing, Echarts for creating interactive visualizations, and WebGL for generating 3D maps. By combining these tools, researchers were able to convert raw data into recognizable images, revealing the hidden patterns of traffic flow and congestion. This approach allows for a clearer understanding of the city's traffic challenges and facilitates the development of smarter solutions for urban mobility.