Futuristic city bus with data visualizations overlaid, symbolizing passenger flow analysis.

Navigating the Urban Jungle: How Data Can Ease Passenger Crowding on Buses

Kai Mendoza in Tech & Innovation August 2025 • 3 min read.

"Uncover strategies for leveraging data analytics to enhance the commuting experience and improve urban bus networks."

Imagine stepping onto a bus where you can actually find a seat, or at least not be crammed against the window like a sardine. For many city dwellers, this is a distant dream. Public transport networks, especially bus systems, are the lifelines of urban areas, but they often struggle with overcrowding. Understanding and managing passenger flow is crucial for improving the daily commute and making public transport a viable option for more people.

While passenger flow data is invaluable, it is often hard to come by. However, a recent study in Harbin, China, sheds light on how analyzing passenger-crowding characteristics can transform bus transport networks. This study delves into the details of passenger flow, identifies crowded areas, and proposes data-driven solutions to enhance the commuting experience.

The Harbin research offers insights into how cities worldwide can leverage data to optimize their bus systems, making them more efficient, comfortable, and attractive to riders. This isn't just about convenience; it's about creating sustainable, livable cities.

Decoding Passenger Crowding: Key Findings from Harbin

Futuristic city bus with data visualizations overlaid, symbolizing passenger flow analysis.

Researchers in Harbin undertook an extensive investigation of the city's bus transport network (BTN-H). This involved collecting data from 132 bus routes and 993 bus stations, meticulously tracking passenger flow during peak hours. The goal was to understand the patterns of passenger-crowding and identify areas where improvements could be made.

One of the key findings was that crowding isn't uniformly distributed. Some sections of the bus routes experience significantly higher passenger loads than others. By analyzing frequency histograms of passenger numbers, researchers pinpointed the sections most prone to overcrowding. These insights are invaluable for targeted interventions.

The study revealed several key insights:

Crowding varies significantly between different sections of bus routes.
Certain stations act as major hubs, experiencing higher crowding levels.
Passenger-crowding tends to concentrate in the middle sections of routes.

The distribution of degree and crowding degree in space L showed an exponential distribution, this means that the values of the crowding degree are distributed heterogeneously. Furthermore, while over half of the routes aren't crowded, approximately 20%-30% are crowded. Almost 20% of routes are heavily crowded. Those routes need to increase supply to meet deman.

Turning Data into Action: The Future of Bus Transport

The Harbin study offers a blueprint for how cities can use data to tackle passenger-crowding and create more efficient, user-friendly bus systems. By understanding passenger flow patterns, identifying crowded areas, and implementing targeted solutions, cities can transform the daily commute, reduce congestion, and promote sustainable urban mobility. The future of bus transport is data-driven, and the journey has just begun.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.physa.2017.08.004, Alternate LINK

Title: Statistical Analysis Of Passenger-Crowding In Bus Transport Network Of Harbin

Subject: Condensed Matter Physics

Journal: Physica A: Statistical Mechanics and its Applications

Publisher: Elsevier BV

Authors: Baoyu Hu, Shumin Feng, Jinyang Li, Hu Zhao

Published: 2018-01-01

Everything You Need To Know

How did the study in Harbin collect and analyze data to understand passenger-crowding on buses?

The Harbin study meticulously collected data from 132 bus routes and 993 bus stations to understand passenger flow during peak hours. Through the analysis of frequency histograms of passenger numbers, researchers pinpointed specific sections of bus routes and stations prone to overcrowding. This detailed understanding of the BTN-H (Bus Transport Network of Harbin) allowed for targeted interventions to improve the commuting experience.

What were the key findings of the Harbin research regarding passenger-crowding on bus routes and stations?

The Harbin research revealed that passenger-crowding tends to concentrate in the middle sections of bus routes and at specific stations acting as major hubs. The degree and crowding degree distribution in space L shows an exponential distribution, highlighting a heterogeneous distribution of crowding. While most routes aren't crowded, approximately 20%-30% experience crowding, with almost 20% being heavily crowded, indicating a need for increased supply to meet demand.

In what ways can cities leverage data to improve their bus systems, according to the insights from Harbin, and what aspects are not explored?

By understanding passenger flow patterns, identifying crowded areas, and implementing targeted solutions, cities can transform the daily commute, reduce congestion, and promote sustainable urban mobility. This data-driven approach enables cities to optimize the BTN-H (Bus Transport Network of Harbin), making it more efficient, comfortable, and attractive to riders. However, the study doesn't delve into the real-time dynamic adjustments of routes based on live passenger data, which could further optimize bus transport.

What specific data analytics methods were employed in the Harbin study to understand passenger flow and crowding on buses, and what is missing from the description?

The Harbin study used statistical analysis and data collection to uncover passenger flow patterns. Frequency histograms of passenger numbers were analyzed to pinpoint crowded sections and stations. The distribution of degree and crowding degree in space L, showing an exponential distribution, was essential for understanding the heterogeneity of crowding across the BTN-H (Bus Transport Network of Harbin). However, the specific algorithms used to process and analyze the data aren't detailed, nor is the study's use of machine learning techniques mentioned.

What are the broader implications of using data to optimize bus transport systems, and what considerations beyond efficiency should be addressed?

The Harbin research emphasizes the importance of passenger flow data in optimizing bus systems. By understanding how passenger-crowding varies across different sections of bus routes and identifying major hub stations, cities can implement targeted solutions. However, the implications for accessibility and equity are not discussed, such as whether these data-driven solutions disproportionately benefit certain populations or neglect underserved communities.