Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips

Abstract

Fig. 1. Comparison of taxi trips from Lower Manhattan to JFK andLGA airports in May 2011. The query on the left selects trips that occurred on Sundays, while the one on the right selects trips that occurred on Mondays. Users specify these queries by visually selecting regions on the map and connecting them. In addition to inspecting the results depicted on the map, i.e., the dots corresponding to pickups (blue) and dropoffs (orange) of the selected trips, they can also explore the results through other visual representations. The scatter plots below the maps show the relationship between hour of the day and trip duration. Points in the plots are colored according to the spatial constraint represented by the arrows between the regions: trips to JFK in blue, and trips to LGA in red. The plots show that many of the trips on Monday between 3PM and 5PM take much longer than trips on Sundays.Abstract-As increasing volumes of urban data are captured and become available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based decision making and policies. In this paper, we focus on a particularly important urban data set: taxi trips. Taxis are valuable sensors and information associated with taxi trips can provide unprecedented insight into many different aspects of city life, from economic activity and human behavior to mobility patterns. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses (e.g., compare different regions over time). This problem is compounded due to the size of the data-there are on average 500,000 taxi trips each day in NYC. We propose a new model that allows users to visually query taxi trips. Besides standard analytics queries, the model supports origin-destination queries that enable the study of mobility across the city. We show that this model is able to express a wide range of spatio-temporal queries, and it is also flexible in that not only can queries be composed but also different aggregations and visual representations can be applied, allowing users to explore and compare results. We have built a scalable system that implements this model which supports interactive response times; makes use of an adaptive level-of-detail rendering strategy to generate clutter-free visualization for large results; and shows hidden details to the users in a summary through the use of overlay heat maps. We present a series of case studies motivated by traffic engineers and economists that show how our model and system enable domain experts to perform tasks that were previously unattainable for them.

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

“…These trajectory patterns of location can be usually described by OD [2][3][4]. In this paper, the GPS data were gathered from taxi GPS points in Aracaju (Brazil), Chongqing (China), Roma (Italy), and San Francisco (USA) [35] (Figure 2a-d).…”

Trực Quan Hoá Dữ Liệu Trạng Thái Giao Thông Trên Nền Web

“…Để truyền tải thông tin thời gian, chúng ta có thể sử dụng mô hình biểu đồ thời gian tuyến tính như minh hoạ ở hình sau, đã được trình bày trong bài báo [13]. Trong công trình này, thời gian được thể hiện một cách tuyến tính ở trục X, trục còn lại Y để biểu diễn tham số khác.…”

“…Biểu đồ đường thể hiện thời gian tuyến tính [13] Một kỹ thuật khác để trực quan thời gian với những bài toán có tính lặp lại theo chu kỳ. Đó là kỹ thuật thể hiện theo kiểu bố trí tròn như minh hoạ ở hình dưới đây [14].…”

The Quantified Community and Neighborhood Labs: A Framework for Computational Urban Science and Civic Technology Innovation

“…Using data from QC sensors, this research can address common, but persistent, questions of locational access, propensity of residents/ visitors to use different modes of transport, and the relationship between land use and transportation (Frank, 2000). In addition, such a mobility baseline provides an important reference for measuring perturbations and responses to disasters and other unexpected events; research in taxi trip visualization provides an early example of this (Ferreira et al, 2013). Moreover, the quantification of mobility patterns can be used to assess physical activity and significant social science, engineering, and public health questions of how the built environment shapes and influences health-related outcomes.…”