A visual analytics system for metropolitan transportation (original) (raw)
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A Visual Analytics Framework for Large Transportation Datasets
The advancement of sensor technologies makes it possible to collect large amounts of dynamic urban data. On the other hand, how to store, process, and analyze collected urban data to make them useful becomes a new challenge. To address this issue, this paper proposes a visual analytics framework, which is applied to transportation data to manage and extract information for urban studies. More specifically, the proposed framework has three components: (1) a geographic information system (GIS) based pipeline providing basic data processing functions; (2) a spatial network analysis that is integrated into the pipeline for extracting spatial structure of urban movement; (3) interactive operations allowing the user to explore and view the output data sets at different levels of details. Taking Singapore as a case study area, we use a sample data set from the automatic smart card fare collection system as an input to our prototype tool. The result shows the feasibility of proposed framework and analysis method. To summarize, our work shows the potential of geospatial based visual analytics tools in using 'big' data for urban analysis.
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.
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.
XStar: a software system for handling taxi trajectory big data
Computational Urban Science, 2021
Advances in positioning and communicating technologies make it possible to collect large volumes of taxi trajectory data, quickly providing a complete picture of the ground traffic systems and thus being applied to different fields. However, there are still challenges for data users to handle such big data. In view of this, we have developed a software system named XStar to deal with trajectory big data. Its core is a scalable index and storage structure. Based on it, raw data can be saved in a more compact scheme and accessed more efficiently. A real taxi trajectory dataset is employed to demonstrate its performance. In general, XStar facilitates processing and analyzing trajectory data affordably and straightforwardly. Since its release in Jan. 2019, it has received downloads of over 4000 by May 2021. More analytical functions are being developed.
Visual Exploration of Urban Data: A Study of Riyadh Taxi Data
Social Computing and Social Media. Applications and Analytics
In this paper, we describe one approach of land classification through linking taxi drop-off cost to traffic analysis zones (TAZs). We visually explore the number and costs of taxi drop-off points in the city of Riyadh, Saudi Arabia, to identify social dynamics and urban behavioral patterns. After analyzing the data with regard to gender, we identify some expected gender biases in the data set for taxi traces of trips since female mobility is constrained in Saudi Arabia and public transportation options are limited. We present a series of case studies of gendered mobility analysis that show how our model enables domain experts to visually explore data sets that were previously unattainable for them. Finally, we visualize the number and cost of drop-offs per TAZ for males and females and identify potential areas for future research in visual analytics for taxi traces.
Riding from Urban Data to Insight Using New York City Taxis
IEEE Data Eng. Bull., 2014
About half of humanity lives in urban environments today and that number will grow to 80% by the middle of this century. Cities are thus the loci of resource consumption, of economic activity, and of innovation. Given our increasing ability to collect, transmit, store, and analyze data, there is a great opportunity to better understand cities, and enable them to deliver services efficiently and sustainably while keeping their citizens safe, healthy, prosperous, and well-informed. But making sense of all the data available is hard. Currently, urban data exploration is often limited to confirmatory analyses consisting of batch-oriented queries and the exploration of well-defined questions over specific regions. The lack of interactivity makes this process both time-consuming and cumbersome. This problem is compounded in the presence of big, multivariate spatio-temporal data, which is ubiquitous in urban environments. Another challenge comes from the need to empower social scientists, ...
Challenges and Opportunities in Transportation Data
Proceedings of the 1st ACM/EIGSCC Symposium on Smart Cities and Communities - SCC '18, 2018
From the time and money lost sitting in congestion and waiting for traffic signals to change, to the many people injured and killed in traffic crashes each year, to the emissions and energy consumption from our vehicles, the effects of transportation on our daily lives are immense. A wealth of transportation data is available to help address these problems; from data from sensors installed to monitor and operate the roadways and traffic signals to data from cell phone apps and-just over the horizon-data from connected vehicles and infrastructure. However, this wealth of data has yet to be effectively leveraged, thus providing opportunities in areas such as improving traffic safety, reducing congestion, improving traffic signal timing, personalizing routing, coordinating across transportation agencies and more. This paper presents opportunities and challenges in applying data management technology to the transportation domain.
2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), 2017
Forecasting and analyzing urban car traffic is an actual but still very complex problem. The modern car fleet handling IT systems designed for taxi and delivery service companies allows GPS coordinate data acquisition from large amount of vehicles for optimizing the ride and freight allocation. Since the database of these companies contains movement patterns belonging to multitude of vehicles, arise the question if these data, belonging to vehicles with special purpose, is suitable for representing the whole car traffic. To make the first step to answer this question, our case study utilizes the time-resolved GPS coordinate database of one of the largest taxi company in Budapest from year 2014. Index terms-Data mining, Taxi cab database, Data visualization, Urban traffic modeling Gy. Eigner was supported by theÚNKP-17-4/IV. New National Excellence Program of the Ministry of Human Capacities.
Visualising Data for Smart Cities
Handbook of Research on Social, Economic, and Environmental Sustainability in the Development of Smart Cities
This chapter introduces a range of analytics being used to understand the smart city, which depends on data that can primarily be understood using new kinds of scientific visualisation. We focus on short term routine functions that take place in cities which are being rapidly automated through various kinds of sensors, embedded into the physical fabric of the city itself or being accessed from mobile devices. We first outline a concept of the smart city, arguing that there is a major distinction between the ways in which technologies are being used to look at the short and long terms structure of cities, and we then focus on the shorter term, first examining the immediate visualisation of data through dashboards, then examining data infrastructures such as map portals, and finally introducing new ways of visualising social media which enable us to elicit the power of the crowd in providing and supplying data. We conclude with a brief focus on how new urban analytics is emerging to m...