Report on Public Transportation Analysis in Macau and its Alignment with Sustainable Development Goals

Executive Summary

This report presents an analysis of public bus transportation data in Macau, comparing operational dynamics during normal periods against the high-demand season of the Macau Grand Prix. By leveraging real-time data from the Transport database, the study performs a comparative analysis of key metrics, including passenger flow, arrival times, and traffic conditions. The findings highlight significant increases in passenger volume and traffic congestion during the Grand Prix, underscoring the profound impact of large-scale events on urban public transport.

The analysis utilizes Principal Component Analysis (PCA) and correlation heatmaps to illustrate distinct transportation patterns, providing critical insights for enhancing service delivery. This research directly supports the achievement of several Sustainable Development Goals (SDGs), particularly:

• SDG 9 (Industry, Innovation, and Infrastructure): By advocating for the use of data analytics to build resilient and efficient transport infrastructure capable of withstanding the pressures of major events.
• SDG 11 (Sustainable Cities and Communities): By providing an evidence-based framework for optimizing public transport, making urban mobility safer, more accessible, and sustainable for all residents and visitors.

The dataset and its analysis are intended to serve as a vital resource for future transport policy development and smart city initiatives. The ultimate goal is to optimize resource allocation and improve overall urban mobility, contributing to the creation of more resilient and sustainable urban environments in line with global development agendas.

Introduction: Urban Mobility and the Sustainable Development Agenda

The Challenge of Urban Data Utilization for Sustainable Cities

Efficient public transportation is a cornerstone of sustainable urban development, directly addressing SDG 11 (Sustainable Cities and Communities) by providing an alternative to private vehicles, thereby reducing congestion and emissions. Modern urban environments generate vast quantities of data through smart city technologies; however, this data is often underutilized. Public Transport Authorities (PTAs) are significant producers of this underutilized big data, which includes:

• Vehicle locations and real-time conditions
• Passenger counts and travel behaviors
• Service disruptions and arrival times

Harnessing this data is crucial for advancing SDG 9 (Industry, Innovation, and Infrastructure), as it allows for data-driven innovation in infrastructure management. Effective data utilization can transform service delivery, enhance network efficiency, and optimize resource allocation, leading to more livable and sustainable cities.

Case Study Context: Macau’s Urban and Economic Landscape

Macau is one of the world’s most densely populated tourist cities, with its public bus service playing a critical role in mitigating traffic congestion. This intense demand places significant pressure on the transport system, making efficiency and resilience paramount for achieving SDG 11. The city hosts the annual Macau Grand Prix, a major international event that significantly disrupts normal traffic flow and tests the resilience of its infrastructure. Analyzing transport dynamics during this event provides a unique opportunity to develop strategies that support sustainable tourism and economic growth (SDG 8: Decent Work and Economic Growth) while ensuring the transport system remains functional and efficient.

Methodology for Data-Driven Transport Analysis

Data Collection and Processing

The dataset was compiled by crawling real-time data from Macau’s Regional Government Transport Bureau (DSAT) database. The data, reflecting bus operations during both normal and Grand Prix periods, was structured into .csv format for analysis. To ensure data quality, outliers in arrival duration were removed. This systematic data collection and processing provides the empirical foundation for evidence-based policymaking, a critical component for achieving the targets set forth in SDG 9 and SDG 11.

Analytical Framework

To derive meaningful insights, key metrics not directly available in the raw data were constructed.

1. Stop Duration Calculation: The time a bus remains stationary at a stop was calculated by subtracting the travel time (arrival duration) from the total time difference between consecutive arrival timestamps.
2. Full Bus Route Reconstruction: Complete journey paths for individual buses were reconstructed by filtering the data based on unique bus plate numbers and ordering the entries by arrival time.

This innovative approach, aligned with the principles of SDG 9, transforms raw data into a structured format suitable for detailed analysis of transport efficiency and performance.

Analysis of Findings: Transport Dynamics and SDG Implications

Comparative Analysis: Normal vs. Grand Prix Seasons

A comparative analysis using violin plots revealed significant differences in transportation dynamics between the two seasons, offering insights crucial for resilient infrastructure planning (SDG 9).

• Passenger Flow: During the normal season, passenger activity showed typical daily fluctuations with peaks around midday and evening. In contrast, the Grand Prix season exhibited a more uniform and compressed distribution, likely due to event-specific traffic management.
• Arrival Duration: The normal season displayed greater variability in arrival times, with more frequent long durations indicating occasional delays. The Grand Prix season showed a tighter concentration of shorter arrival durations, reflecting more controlled and consistent, albeit congested, traffic flow.
• Traffic Conditions: The Grand Prix season showed more uniform traffic conditions with less variability compared to the normal season, highlighting the impact of event-specific regulations and traffic management measures.

Dataset Characteristics and Contribution to SDG 11

The dataset, comprising over 217,000 samples from five key bus routes, provides a granular view of public transport operations. The data from the DSAT includes several unique features essential for sustainable transport planning:

1. Passenger Flow: A numerical scale indicating the level of crowding inside the bus, offering insights into service demand and passenger comfort.
2. Traffic Condition: A categorical scale representing the level of traffic congestion on a road segment, allowing for an understanding of external impacts on bus travel times.

This comprehensive dataset is a critical resource for urban planners to model, predict, and manage public transport systems. Its application directly supports SDG 11.2, which aims to provide access to safe, affordable, accessible, and sustainable transport systems for all.

Validation, Limitations, and Future Directions

Technical Validation

The accuracy of the collected data was validated through manual, on-site observation of bus arrival times at a selected station. Predicted arrival times from the official Macao bus application were compared against actual arrival times. The observed average arrival duration was found to be consistent with the collected dataset, confirming the reliability of the data for analysis despite resource constraints on the validation process.

Dataset Limitations and Opportunities for Enhancement

The study acknowledges several limitations that present opportunities for future work to further support sustainable urban planning.

• Limited Scope: Data was collected for only five of Macau’s 74 bus routes and over specific, limited time frames.
• Data Imbalance: A significant disparity exists in the volume of data between the normal and Grand Prix seasons.
• Outlier Removal: The methodology for removing outliers, while standard, might have inadvertently excluded some valid data points reflecting extreme conditions.

Future research should aim to expand data collection across more routes and over longer periods. Building a more comprehensive and balanced dataset will enable the development of more robust predictive models, strengthening efforts to build resilient and intelligent transport systems in line with SDG 9 and SDG 11.

Conclusion: Advancing Sustainable Urban Mobility Through Data

This report demonstrates the profound value of applying data analytics to urban transportation challenges. By analyzing and comparing public bus data from normal and event-specific periods, the study provides actionable insights for improving operational efficiency and service resilience. The findings and the resulting dataset are directly applicable to advancing Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities). The insights gained are essential for optimizing resource allocation, enhancing infrastructure resilience during major events, and ultimately fostering the development of public transport systems that are efficient, accessible, and sustainable.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 11: Sustainable Cities and Communities

   • The article directly addresses the core themes of SDG 11 by focusing on urban public transportation in Macau, a densely populated city. It investigates issues of traffic congestion, passenger volume, and the overall efficiency of the public bus system. The study’s aim to use data for “future transport policy development and smart city initiatives” and to “improve overall urban mobility” aligns perfectly with making cities more sustainable, inclusive, and resilient.

2. SDG 9: Industry, Innovation and Infrastructure

   • This goal is relevant as the article examines public transport infrastructure and its resilience, particularly during a large-scale event like the Macau Grand Prix. The study’s use of “real-time data,” “Principal Component Analysis (PCA),” and “smart city applications” showcases innovation in infrastructure management. The objective to “optimize resource allocation” and enhance “service delivery and operational planning” contributes to developing reliable and sustainable infrastructure.

3. SDG 8: Decent Work and Economic Growth

   • The article connects to SDG 8 through its focus on tourism, a key economic driver for Macau. It notes that Macau is the “World Center of Tourism and Leisure” and attracts “over 35 million visitors annually.” The analysis of the transport system’s performance during the Grand Prix, a major tourist event, is crucial for supporting sustainable tourism. An efficient transport system is fundamental to the city’s tourism industry and, by extension, its economic growth.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. SDG 11: Sustainable Cities and Communities

   • Target 11.2: “By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all…” The entire article is centered on analyzing and improving Macau’s public bus system. It discusses accessibility (74 bus routes), affordability (MOP6.00 fare), and sustainability by aiming to optimize the system and alleviate traffic congestion. The study of passenger flow and arrival times directly relates to the quality and accessibility of the transport system.
   • Target 11.a: “Support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning.” The research explicitly aims to provide “insights essential for enhancing service delivery and operational planning” and to serve as a “critical resource for future transport policy development.” This is a direct example of using data analysis to strengthen regional transport planning.

2. SDG 9: Industry, Innovation and Infrastructure

   • Target 9.1: “Develop quality, reliable, sustainable and resilient infrastructure… to support economic development and human well-being, with a focus on affordable and equitable access for all.” The study analyzes the reliability and resilience of the bus network by comparing its performance during normal and high-stress (Grand Prix) periods. The findings on “significant spikes in passenger volume and increased traffic congestion” highlight the need for resilient infrastructure, and the goal is to use data to improve it.

3. SDG 8: Decent Work and Economic Growth

   • Target 8.9: “By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products.” The article’s analysis of the transport system’s challenges during the Macau Grand Prix directly informs policies for managing large-scale tourist events. Improving urban mobility during such events is essential for enhancing the visitor experience and ensuring the long-term sustainability of Macau’s tourism-driven economy.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. Indicators for Target 11.2 (Access to sustainable transport)

   • Passenger Flow: The article introduces “passenger flow” as a key feature, measured on a scale from 0 to 5, to indicate crowding levels. This directly measures the usage and capacity of the public transport system.
   • Traffic Condition: A categorical scale (1 to 3) is used to measure traffic congestion. This serves as an indicator of the transport system’s efficiency and the challenges it faces.
   • Arrival Duration & Stop Duration: The analysis of “arrivalDuration” and the calculation of “stop duration” are direct measures of the system’s punctuality, reliability, and operational efficiency.
   • Bus Fare: The article mentions the bus fare is “set at MOP6.00,” which is a direct indicator of the system’s affordability.

2. Indicators for Target 9.1 (Quality and resilient infrastructure)

   • Comparative Analysis of Operational Metrics: The study’s comparison of metrics like passenger volume, arrival times, and traffic conditions between “normal and Grand Prix seasons” serves as an indicator of the infrastructure’s resilience under stress.
   • Data Utilization Rate: The article implies an indicator by stating that “the majority [of organizations] utilizing less than half of the data they collect.” The study’s purpose is to increase the utilization of data from Public Transport Authorities (PTAs) for planning, which can be measured.

3. Indicators for Target 8.9 (Sustainable tourism)

   • Number of Visitors: The article states that Macau “attracts over 35 million visitors annually,” which is a direct indicator of the scale of tourism the city’s infrastructure must support.
   • Passenger Volume during Events: The finding of “significant spikes in passenger volume” during the Grand Prix is a specific indicator of the impact of tourism events on public transport demand.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article. In this table, list the Sustainable Development Goals (SDGs), their corresponding targets, and the specific indicators identified in the article.

Source: nature.com