Decoding City Traffic: A Visual Journey Through Qingdao's Bus Flow

Multi-scale visualization uses techniques such as those implemented with R language, Echarts, and WebGL to transform extensive datasets, like the one from Qingdao Public Transportation Group, into visual representations. In the case of Qingdao, traffic congestion is displayed using statistical pictures and 3D wall maps. By visualizing data across different spatial and temporal scales, the method reveals patterns, trends, and anomalies that might be missed with traditional analysis. While the analysis effectively identifies key problem areas, it may not capture the underlying reasons for congestion, such as road construction or unexpected events.

The analysis of Qingdao's bus flow revealed several key insights. Specifically, Shibei and Shinan areas face more severe delays compared to Licun and Laoshan areas. Several roads, including Hong Kong Middle Road, Shandong Road, and Nanjing Road, experience high congestion. Rush hour congestion is more severe than at other times, with Monday mornings and Friday evenings particularly congested. The implications of these findings are significant for city planning and traffic management, as they highlight specific areas and times that require targeted interventions. However, the analysis focuses primarily on bus traffic and may not fully represent the dynamics of overall traffic congestion involving other types of vehicles.

To address traffic congestion in areas such as Hong Kong Middle Road, Shandong Road, and Nanjing Road, city planners could implement strategies such as optimizing traffic signal timing, adjusting bus routes to better serve demand, or introducing park-and-ride programs in strategic locations. The analysis also suggests that specific interventions are needed during morning and evening rush hours, particularly on Monday mornings and Friday evenings, when congestion is most severe. However, implementing these strategies requires a comprehensive understanding of the transportation network and careful coordination among various stakeholders.

The study used data from approximately 5,000 buses in Qingdao, collected by the Qingdao Public Transportation Group. The data includes records of buses passing through bus stations, totaling about one billion data points collected between September 2014 and September 2015. This extensive dataset provides a solid foundation for understanding bus flow patterns, but its reliance on bus data may limit its ability to capture the full complexity of urban traffic dynamics. Additional data sources, such as data from taxis and private vehicles, could provide a more comprehensive view.

Average travel speed is calculated by dividing the distance between adjacent bus stations by the average travel time taken by all buses passing through that distance. This metric helps identify areas and times of day when buses experience significant delays, indicating congestion. This method provides a quantifiable measure of traffic flow, allowing for comparisons across different locations and time periods. However, this metric does not account for variations in bus routes or passenger loads, which could affect travel times and overall congestion levels.