Revealing travel patterns and city structure with taxi trip data (1310.6592v2)

Abstract: Detecting regional spatial structures based on spatial interactions is crucial in applications ranging from urban planning to traffic control. In the big data era, various movement trajectories are available for studying spatial structures. This research uses large scale Shanghai taxi trip data extracted from GPS-enabled taxi trajectories to reveal traffic flow patterns and urban structure of the city. Using the network science methods, 15 temporally stable regions reflecting the scope of people's daily travels are found using community detection method on the network built from short trips, which represent residents' daily intra-urban travels and exhibit a clear pattern. In each region, taxi traffic flows are dominated by a few 'hubs' and 'hubs' in suburbs impact more trips than 'hubs' in urban areas. Land use conditions in urban regions are different from those in suburban areas. Additionally, 'hubs' in urban area associate with office buildings and commercial areas more, whereas residential land use is more common in suburban hubs. The taxi flow structures and land uses reveal the polycentric and layered concentric structure of Shanghai. Finally, according to the temporal variations of taxi flows and the diversity levels of taxi trip lengths, we explore the total taxi traffic properties of each region and proved the city structure we find. External trips across regions also take large proportion of the total traffic in each region, especially in suburbs. The results could help transportation policy making and shed light on the way to reveal urban structures with big data.

• The paper reveals a two-level hierarchical polycentric city structure by applying community detection methods on taxi trip data.
• It constructs a directed, weighted grid network from over 860,000 taxi trips to model intra-city flows in Shanghai.
• The findings inform urban planning and transportation policy by challenging traditional boundaries with dynamic, data-driven spatial insights.

Insights into Urban Travel Patterns and City Structure via GPS-enabled Taxi Data

This paper, authored by Xi Liu, Li Gong, Yongxi Gong, and Yu Liu, explores the intricate connections between urban travel patterns and city structures through the lens of taxi trip data from Shanghai. The paper adopts a network science approach, utilizing community detection methods to uncover a rich, hierarchical, polycentric city structure, which provides new insights into urban and transportation planning.

The main objective of this research is to move beyond traditional urban analysis dominated by morphological characteristics and administrative boundaries, and instead highlight the hidden spatial interactions manifest in travel behaviors. By focusing on intra-city flows and leveraging GPS-enabled taxi trajectory data, the research explores the dynamics of travel that underpin the functional structure of the city.

Methodology and Data

1. Data Source and Preparation: The researchers utilize GPS data from over 6,600 taxis in Shanghai, recorded over four weekdays in June 2009, to reflect regular travel patterns. This dataset was curated to include 860,905 trips after filtering out erroneous data.
2. Network Construction: The city was divided into 1 km x 1 km grid cells, forming the nodes of a directed, weighted network based on taxi trips. The construction process involved identifying spatial interactions between these cells.
3. Community Detection: The Infomap algorithm was employed for community detection, which is particularly effective for analyzing weighted and directed networks. This approach identified sub-regions (communities) with intense internal interactions.

Key Findings

The paper identifies a two-level hierarchical polycentric city structure in Shanghai:

• Level One Zones (L1Zs) comprise smaller regions dominated by short-distance spatial interactions. These zones highlight local resident travel demand and routine mobility, often extending over steady, contiguous areas.
• Level Two Zones (L2Zs) encompass larger areas and integrate longer-distance trips, reflecting broader urban interactions. They act as connectors between multiple L1Zs.

The paper also underscores that community detection results from short-trip datasets tend to stabilize, capturing regular spatial structures, while long-trip data primarily link these stable structures without significant alteration to sub-region boundaries.

Analysis of Sub-Regions

By examining the properties of sub-regions, the paper establishes that urban L1Zs exhibit broader spatial interactions, while suburban L1Zs have centers with pronounced influence on local traffic. Urban centers are typically associated with commerce and business, while suburban centers are mixed-use, including residential and transit-oriented areas.

Implications and Future Directions

This research has significant implications for urban and transportation policy-making:

• Urban Planning and Policy: The sub-regional divisions derived from mobility data challenge traditional administrative boundaries that might not accurately represent functional urban areas, particularly for dynamic cities like Shanghai. They propose alternative planning units that could better address urban interactions.
• Transport Infrastructure: By identifying centers of high mobility demand, this paper suggests enhancements to public transport infrastructures, such as optimizing bus and metro routes, to alleviate congestion in areas where taxi demand indicates insufficient service.
• Land-use Policy: Adjusting land use in key centers could potentially reduce travel distances and improve local accessibility.

This analysis demonstrates the potential of leveraging big data in understanding urban dynamics, calling for further research incorporating additional data sources such as private car and public transport data over extended temporal scales. This future work can offer comprehensive views of urban mobility and inform more robust city planning frameworks.