Modeling and visualizing the spatial uncertainty of moving transport hubs in urban spaces - a case study in NYC with taxi and boro taxi trip data (original) (raw)
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Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips
. Comparison of taxi trips from Lower Manhattan to JFK and LGA 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.
Urban Mobility Study using Taxi Traces
In this work, we analyze taxi-GPS traces collected in Lisbon, Portugal. We perform an exploratory analysis to visualize the spatiotemporal variation of taxi services; explore the relationships between pickup and drop-off locations; and analyze the behavior in downtime (between the previous drop-off and the following pickup). We also carry out the analysis of predictability of taxi trips for the next pickup area type given history of taxi flow in time and space.
Understanding Taxi Driving Behaviors from Movement Data
Lecture Notes in Geoinformation and Cartography, 2015
Understanding taxi mobility has significant social and economic impacts on the urban areas. The goal of this paper is to visualize and analyze the spatiotemporal driving patterns for two income-level groups, i.e. high-income and low-income taxis, when they are not occupied. Specifically, we differentiate the cruising and stationary states of non-occupied taxis and focus on the analysis of the mobility patterns of these two states. This work introduces an approach to detect the stationary spots from a large amount of non-occupied trajectory data. The visualization and analysis procedure comprises of mainly the visual analysis of the cruising trips and the stationary spots by integrating data mining and visualization techniques. Temporal patterns of the cruising trips and stationary spots of the two groups are compared based on the line charts and time graphs. A density-based spatial clustering approach is applied to cluster and aggregate the stationary spots. A variety of visualization methods, e.g. map, pie charts, and space-time cube views, are used to show the spatial and temporal distribution of the cruising centers and the clustered and aggregated stationary spots. The floating car data collected from about 2000 taxis in 47 days in Shanghai, China, is taken as the test dataset. The visual analytic results demonstrate that there are distinctive cruising and stationary driving behaviors between the high-income and low-income taxis.
Spatiotemporal Structure of Taxi Services in Shanghai:Using Exploratory Spatial Data Analysis
Admitting the stochastic nature of taxi movement at the individual level, this paper explored the spatiotemporal structure of taxi services in Shanghai from a macro perspective. A GPS-based taxi service dataset was first processed to derive a total of 9921 taxies’ OD,which were then tallied by individual street districts. An index known as Traffic Ratio Density was computed to characterize the level of taxi services for each street district and facilitate the mapping of its spatiotemporal variation. In the end, the method of Exploratory Spatial Data Analysis was used to identify spatial clusters of taxi services over time. Both global and local Moran′s I values were computed for Shanghai as a whole and for individual street districts. The positive values of the global index strongly suggested high and stable concentration pattern across all the time-periods. The local index showed that the taxi OD pattern has a high-high cluster in the CBD area, versus the low-low cluster in the suburban regions, and between them was a stochastic distribution. There was no noticeable temporal variation at eitherglobal or local level, indicating arather stable spatial and temporal structure of taxi service distribution.
Visualization of Urban Mobility Data from Intelligent Transportation Systems
Sensors, 2019
Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings.
IEEE Access
There has been a recent push towards using opportunistic sensing data collected from sources like Automatic Vehicle Location (AVL) systems, mobile phone networks, and Global Positioning System (GPS) tracking to construct Origin-Destination (O-D) matrices, which are an effective alternative to expensive and time-consuming traditional travel surveys. These data have numerous drawbacks: they may have inadequate detail about the journey, may lack spatial and temporal granularity, or may be limited due to privacy regulations. Taxi trajectory data is an opportunistic sensing data type that can be effectively used for O-D matrix construction because it addresses the issues that plague other data sources. This paper presents a new approach for using taxi trajectory data to construct a taxi O-D matrix that is dynamic in both space and time. The model's origin and destination zone sizes and locations are not fixed, allowing the dimensions to vary from one matrix to another. Comparisons between these spatiotemporal-varying O-D matrices cannot be made using a traditional method like matrix subtraction. Therefore, this paper introduces a new measure of similarity. Our proposed approaches are applied to the taxi trajectory data collected from Lisbon, Portugal as a case study. The results reveal the periods in which taxi travel demand is the highest and lowest, as well as the periods in which the highest and lowest regular taxi travel demand patterns take shape. This information about taxi travel demand patterns is essential for informed taxi service operations management. INDEX TERMS dynamic origin-destination matrix, adaptive zoning scheme, origin-destination matrix similarity measure, taxi trajectory data, taxi travel demand WERABHAT MUNGTHANYA received B.Sc. in statistics from Chiang Mai University, Thailand. He is currently a graduate student in the Department of Computer Engineering at Chiang Mai University. His research interests include data science, intelligent transportation systems, and visual analytics.
Visual analytics of taxi trajectory data via topical sub-trajectories
Visual Informatics, 2019
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Revealing travel patterns and city structure with taxi trip data
Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long-and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban subregions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate subregion formation and the interaction patterns of center-local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.
Visual Analysis of Floating Taxi Data Based on Interconnected and Timestamped Area Selections
Lecture Notes in Geoinformation and Cartography, 2016
Floating Car Data (FCD) is GNSS-tracked vehicle movement, includes often large data size and is difficult to handle, especially in terms of visualization. Recently, FCD is often the base for interactive traffic maps for navigation and traffic forecasting. Handling FCD includes problems of large computational efforts, especially in case of connecting tracked vehicle positions to digitized road networks and subsequent traffic state derivations. Established interactive traffic maps show one possible visual representation for FCD. We propose a user-adapted map for the visual analysis of massive vehicle movement data. In our visual analysis approach we distinguish between a global and a local view on the data. Global views show the distribution of user-defined selection areas, in the way of focus maps. Local views show user-defined polygons with 2-D and 3-D traffic parameter visualizations and additional diagrams. Each area selection is timestamped with the time of its creation by the user. After defining a number of area selections it is possible to calculate weekday-dependent travel times based on historical taxi FCD. There are 3 different types of defined connections in global views. This has the aim to provide personalization for specific commuters by delivering only traffic and travel time information for and between user-selected areas. In a case study we inspect traffic parameters based on taxi FCD from Shanghai observed within 15 days in 2007. We introduce test selection areas, calculate their average traffic parameters and compare them with recent (2015) and typical traffic states coming from the Google traffic layer.
Visual Analytics for Characterizing Mobility Aspects of Urban Context
Springer eBooks, 2021
Visual analytics science develops principles and methods for efficient human-computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.