Visual Analysis of Floating Taxi Data based on selection areas (original) (raw)
Related papers
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.
Visualization of traffic congestion based on Floating Taxi Data
Floating Car Data (FCD) are rich data sources collected from GPS-equipped vehicles for analyzing and modelling traffic information and human mobility. Due to the large amount of these movement data, we need visual analysis methods that enable users to inspect traffic situations. This paper investigates two visualization techniques for FCD: (1) three-dimensional representations of both average speed and average density derived from Floating Car Data (FCD) based on road segments and (2) two-dimensional maps representing qualitative traffic congestion inferred from the average speed and density. We propose a selection circle visualization method showing average speeds and average densities within a selected area. This information is represented in a 3-D extrusion projected onto the two-dimensional street network map. A test FCD set from Shanghai is used to evaluate the visualization technique. We can compare the visualization results at different time windows and detect spatio-temporal changes of the traffic situation in Shanghai. This helps to understand the general movement patterns
Processing and Visualizing Floating Car Data for Human-Centered Traffic and Environment Applications
International Journal of Agricultural and Environmental Information Systems, 2017
In the era of the Internet of Things and Big Data modern cars have become mobile electronic systems or computers on wheels. Car sensors record a multitude of car and traffic related data as well as environmental parameters outside the vehicle. The data recorded are spatio-temporal by nature (floating car data) and can thus be classified as geodata. Their geospatial potential is, however, not fully exploited so far. In this paper, we present an approach to collect, process and visualize floating car data for traffic- and environment-related applications. It is demonstrated that cartographic visualization, in particular, is as effective means to make the enormous stocks of machine-recorded data available to human perception, exploration and analysis.
Traffic congestion in urban environments has severe influences on the daily life of people. Due to typical recurrent mobility patterns of commuters and transport fleets, we can detect traffic congestion events on selected hours of the day, so called rush hours. Besides the mentioned recurrent traffic congestion, there are non-recurrent events that may be caused by accidents or newly established building sites. We want to inspect this appearance using a massive Floating Taxi Data (FTD) set of Shanghai from 2007. We introduce a simple method for detecting and extracting congestion events on selected rush hours and for distinguishing between their recurrence and non-recurrence. By preselecting of similar velocity and driving direction values of the nearby situated FTD points, we provide the first part for the Shared Nearest Neighbour (SNN) clustering method, which follows with a density-based clustering. After the definition of our traffic congestion clusters, we try to connect ongoing events by querying individual taxi identifications. The detected events are then represented by polylines that connect density core points of the clusters. By comparing the shapes of congestion propagation polylines of different days, we try to classify recurrent congestion events that follow similar patterns. In the end, we reason on the reasonability of our method and mention further steps of its extension.
Exploratory Study of Urban Flow using Taxi Traces
2011
The analysis of vehicle's GPS traces such as taxis can help better understand urban mobility and flow. In this paper we present a spatiotemporal analysis of taxis GPS traces collected in Lisbon, Portugal during the course of five month. We also show that trip distance can be represented with a Gamma distribution, and discuss the taxi driving strategies and respective income.
BVis: urban traffic visual analysis based on bus sparse trajectories
Journal of Visualization, 2018
Public urban transport network has the characteristic of wide coverage, while buses have the features of stable running routes and static parking places, which is helpful to study urban traffic and bus station congestion patterns. Unlike the common GPS trajectory data, our data includes the pertinent records of the buses arrival and departure from the relevant bus stations due to data compression. In this paper, a visual analysis system called BVis is presented to analyze the urban traffic applying the large-scale real sparse buses dataset. This system covers the four modules of bus data visualization, first, the sparse trajectory data cleaning and mapping, second, the global traffic states and section traffic patterns analysis of roads, third, the bus station congestion patterns analysis using the station parking time, finally, an importance analysis of bus stations in the complex public transport network. Furthermore, an enhanced node importance evaluation algorithm is presented, which combines the dynamic properties of the bus station, such as traffic volume of station and station parking time. Using the real bus GPS dataset, three cases are described to demonstrate the performance and effectiveness of the system.
Research on visual analysis technology and application of urban road data
IOP Conference Series: Materials Science and Engineering, 2019
Many cities use the current wealth of multi-source data to create intelligent transportation systems, which visually process road data so that traffic consumers and traffic managers can better understand urban traffic conditions. Due to the heterogeneity, complexity and mass of these data, it is not easy to conduct effective analysis on them, and it is often necessary to integrate human perception in the analysis process, so as to cause extensive application of visualization. In this paper, we first briefly introduce the pre-processing of urban trajectory data, then systematically analyze the visualization of two data types of road traffic flow and traffic events, and finally briefly summarize the research trend of traffic visualization in recent years and put forward corresponding challenges. The results show that the early research focused on the visualization of road flow, including the main arrow diagram, Mosaic diagram, track wall, etc. With the deepening of visual analysis, th...
Visual analytics for movement behavior in traffic and transportation
IBM Journal of Research and Development, 2015
Understanding movement of vehicles, people, goods, or any type of object is important for making knowledgeable decisions regarding public transportation planning. However, movement is a complex and dynamic phenomenon, and until recently, movement data was difficult to exploit for such planning purposes. The widespread adoption of location-aware devices such as Global Positioning System (GPS) sensors in public transportation systems and the adoption of open data principles have set the stage for new methods and tools for data collection and analysis of movement patterns. This paper illustrates the value and benefit of applying visual analytics techniques to movement data to create valuable insight for public transportation planning using vehicle-mounted devices on buses and trams. The contribution of the paper is three distinct visual analytics solutions that we developed using a real-world open data feed published by the Helsinki Public Transport Authority. The current work addresses encounters between objects, stops that interrupt movement, and flow dynamics of a large number of moving objects. We instantiated the described methods by showing that our findings can be applied in real-world use cases.
Analysing Traffic Flow and Traffic Hotspots from Historic and Real-Time GPS Data
Journal of Communication and Computer, 2015
Traffic congestion is an increasing issue in many road networks. Considering the Maltese Islands, the traffic situation is in bad condition, despite the different attempts by the government to find a solution for this problem. In this report, we investigate how traffic may be analysed, which methods are available for this analysis, and how the traffic flow can be depicted. Many methods for this analysis are presented with a customized method attempted for finding traffic congestions and checking the traffic flow around the towns. The solution implemented in this work enables users to provide data through a data collecting Android app, which displays traffic flow on an interactive map, while allowing the users to view the traffic flow on a web-based interactive map at their chosen town, date and time. The method works by analysing GPS data from vehicles attained from different methods of data collection, namely data collection from Android devices, and historic GPS data from tracking devices installed in commercial fleets. The results of this work demonstrate a method for analysing traffic flow with accurate results, a usable and easy method of depicting traffic, with positive user feedback.
Urban traffic analysis from a large scale floating car data system
The actual knowledge of traffic performances and travel patterns existing in an urban area is crucial to quantify the effects of transportation improvements and of actions that have to be pursued to improve mobility, as well as even an essential input to calibrate land use and traffic simulation models. In this view the use of Floating-Car Data (FCD) is emerging as a reliable and cost-effective way to gather accurate traffic data for a wide-area road network and to improve the analysis of travel conditions. The purpose of this paper is to provide an insight into the potential application of wide scale FCD for improving the traffic analysis process. The reliability of traffic estimates from FCD largely depends not only on the accuracy but also on the size of the input data: until now FCD experiments have been carried out with few equipped vehicles, maximum some dozen; moreover, mostly of them were special vehicles like taxi or bus, with preferential routes, so their behaviour cannot be taken as general reference. The originality of this study lies in the large number of private-owned cars involved corresponding to a penetration rate of about 1.3 percent. A full day of FCD related to the city of Florence is examined in this paper to estimate some travel related measures that cannot be readily replicated on a dayby-day basis using other data sources such as user surveys.