Visual Traffic Jam Analysis Based on Trajectory Data

Wang, Zuchao; Lü, Min; Yuan, Xiaoru; Zhang, Junping; Wetering, Huub van de

doi:10.1109/tvcg.2013.228

Cited by 279 publications

(81 citation statements)

References 40 publications

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“…In our study, since we are interested in the area-wide spatial trend of the traffic condition, traffic congestion is estimated using 1,000 × 1,000 m grids. Our estimated traffic congestion is also consistent with that found in a previous study by Wang, Lu, Yuan, Zhang, and Wetering (2013).…”

Section: Traffic Congestion Estimationsupporting

confidence: 92%

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

Wang

Tong

Liu

et al. 2019

Transactions in GIS

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Traffic congestion represents an ongoing serious issue in many large cities. Many public facilities, such as hospitals, tend to be centrally located to ensure they are most accessible to local residents; as a result, they may contribute significantly to a city's traffic congestion. In this study, a multi‐objective spatial optimization model was provided to help formulate hospital relocation plans, taking into account both traffic congestion and hospital accessibility. Using intra‐urban movement data, we proposed a method to estimate the area‐wide traffic congestion caused by hospital visits and to identify potential hospitals to be relocated. An NSGA‐II (Non‐dominated Sorting Genetic Algorithm II) algorithm was applied to solve the hospital relocation optimization problem; we applied our model to study optimal hospital relocation plans in Beijing. Analysis results provide a tradeoff between traffic congestion relief and hospital accessibility. We discussed plans that significantly reduce traffic congestion while maintaining a high level of hospital accessibility. Our study has significant policy implications and provides insights for future facility planning and transportation planning.

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Section: Traffic Congestion Estimationsupporting

confidence: 92%

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

Wang

Tong

Liu

et al. 2019

Transactions in GIS

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“…In light of the efficacy of circular and columnar pixmaps in mining the space-time and attribute information of trajectory data, Wang et al [85] designed a pixel-based tabular visualization for the traffic speed and road congestion events in Beijing (see Figure 34a). This approach provided a compact view to enable displaying multiple roads side by side for comparison and extracted daily and weekly speed patterns in the visualization.…”

Section: Availabilitymentioning

confidence: 99%

“…Examples of pixmap visualization : (a) tabular pixmaps for speed-pattern exploration[84]; and (b) 24-hour taxi-trajectory patterns[85].…”

mentioning

confidence: 99%

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

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Trajectory big data have significant applications in many areas, such as traffic management, urban planning and military reconnaissance. Traditional visualization methods, which are represented by contour maps, shading maps and hypsometric maps, are mainly based on the spatiotemporal information of trajectories, which can macroscopically study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping. With the improvement of trajectory data quality, these data can generally describe information in the spatial and temporal dimensions and involve many other attributes (e.g., speed, orientation, and elevation) with large data amounts and high dimensions. Additionally, these data have relatively complicated internal relationships and regularities, whose analysis could cause many troubles; the traditional approaches can no longer fully meet the requirements of visualizing trajectory data and mining hidden information. Therefore, diverse visualization methods that present the value of massive trajectory information are currently a hot research topic. This paper summarizes the research status of trajectory data-visualization techniques in recent years and extracts common contemporary trajectory data-visualization methods to comprehensively cognize and understand the fundamental characteristics and diverse achievements of trajectory-data visualization.First, we should fully understand the conceptual distinction between "multidimensional" and "multivariate". Generally, "multidimensional" is used to describe the dimensionality of independent variables in physical space and concentrates on expressing spatial and temporal concepts, while "multivariate" is used for attribute-count description that indicates the attribute amount involved in the data. In fact, trajectory data are typically enormous, high-dimensional, spatiotemporal vector attribute data. We are interested not only in comprehending dimensional information for spatiotemporal-element depictions, but also in the visualization research with attribute-feature expression. Starting in Section 2, we present relevant visualization techniques and methods that function with multisource trajectory data. The chosen examples are for illustration and the list is not exhaustive-we only focus on small but representative choices. Universal Multivariate VisualizationThe trajectory visualization reflects the changing patterns of time-varying geolocations and corresponding attribute information, including basic-dimensional information and the attached thematic variables of space-time objects. However, these variables usually do not exist independently but are combined with each other, and the trajectory data of different sources, scales, or types are plotted in a hybrid manner. We separately list visualization techniques that integrate multiple attributes. As a powerful and portable carrier that encodes abundant information in methods of visual cha...

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“…Summarization techniques present data based on statistical calculations and concerns changes of information in space and time, so that analysts can get an overall understanding of the tendencies and investigate aggregated patterns. This type of techniques includes density map [24], multivariate glyph [25,26], and flow map [27]. Scheepens et al [28] presented a density map of vessel movement data by using color to encode temporal dimensions.…”

Section: Related Workmentioning

confidence: 99%

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media

Teng

Gong

et al. 2016

Sensors

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Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last.

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Visual Traffic Jam Analysis Based on Trajectory Data

Cited by 279 publications

References 40 publications

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media

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