1,500 research outputs found
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
Intelligent Development Research on Job-Housing Space in Chinese Metropolitan Area under the Background of Rapid Urbanization
Under the impact of regional integration and rapid urbanization, Chinese metropolitan area is confronted with the pressure brought by further massiveness, high density and continuous development. The existing layout of job-housing space balance in cities has been further spread and aggravated, which leads to a series of problems including traffic jams and air pollution, etc. This thesis excavates, analyzes and integrates the city residents’ action trajectory data in various heterogeneous cities through the intelligent transportation data platform of metropolitan area. Furthermore, the research also extracts the intelligent knowledge on the aspect of urban job-housing space, identifies and analyzes its characteristics effectively.
This thesis takes Beijing-Tianjin-Hebei metropolitan area as the research object to carry out intelligent analysis on working and residential space in main cities. We can identify residents' commuting behaviors with multi-source location perception data. Firstly, the GPS trajectory data of large-scale taxi will be utilized, and the transportation behaviors and characteristics of taxi will be assumed as the urban residents’ trip behaviors. Then the research of urban space-time structure and residents’ activities hot spots will be carried out from the macro perspective. Secondly, a residents’ trip survey method combining mobile phone location and internet feedback will be put forward. Aiming at the location Microblog data, the characteristics of residents’ workplaces and residences could be identified with fuzzy mathematical method. During the identification process, the individual behavior patterns obtained from the resident trip survey data will be used as the recognition feature.
Through the analysis, We discovered that the data mining method of the residents’ action trajectory is feasible for the study of job-housing space. The study shows that the key factor influencing the job-housing balance in metropolitan area is the improvement of disperse urbanization life-style which takes family as a single unit. It also puts forwards the future ternary development mode of “employment-residence-public service” of job-housing balance in Chinese metropolitan area. The research also discovers a measurement method of excess commuting to develop the commuting efficiency in job-housing space. Furthermore, through the research on excess commuting degree of main cities in Beijing-Tianjin-Hebei metropolitan area by utilizing the commuting behaviors extraction result of Microsoft data, the correlation factor of characteristic attributes and job-housing separation phenomenon in urban community could be found. Finally, the intelligent development characteristics of job-housing space in metropolitan area will be discussed by combining the geographical visualization method and taxi trajectory mining result
Reimagining City Configuration: Automated Urban Planning via Adversarial Learning
Urban planning refers to the efforts of designing land-use configurations.
Effective urban planning can help to mitigate the operational and social
vulnerability of a urban system, such as high tax, crimes, traffic congestion
and accidents, pollution, depression, and anxiety. Due to the high complexity
of urban systems, such tasks are mostly completed by professional planners.
But, human planners take longer time. The recent advance of deep learning
motivates us to ask: can machines learn at a human capability to automatically
and quickly calculate land-use configuration, so human planners can finally
adjust machine-generated plans for specific needs? To this end, we formulate
the automated urban planning problem into a task of learning to configure
land-uses, given the surrounding spatial contexts. To set up the task, we
define a land-use configuration as a longitude-latitude-channel tensor, where
each channel is a category of POIs and the value of an entry is the number of
POIs. The objective is then to propose an adversarial learning framework that
can automatically generate such tensor for an unplanned area. In particular, we
first characterize the contexts of surrounding areas of an unplanned area by
learning representations from spatial graphs using geographic and human
mobility data. Second, we combine each unplanned area and its surrounding
context representation as a tuple, and categorize all the tuples into positive
(well-planned areas) and negative samples (poorly-planned areas). Third, we
develop an adversarial land-use configuration approach, where the surrounding
context representation is fed into a generator to generate a land-use
configuration, and a discriminator learns to distinguish among positive and
negative samples.Comment: Proceedings of the 28th International Conference on Advances in
Geographic Information Systems (2020
Understanding taxi travel demand patterns through Floating Car Data
This paper analyses the current structure of taxi service use in Rome, processing taxi Floating Car Data (FCD). The methodology used to pass from the original data to data useful for the demand analyses is described. Further, the patterns of within-day and day-to-day service demand are reported, considering the origin, the destination and other characteristics of the trips (e.g. travel time). The analyses reported in the paper can help the definition of space-temporal characteristics of future Shared Autonomous Electrical Vehicles (SAEVs) demand in mobility scenarios
Detection of Anomalous Traffic Patterns and Insight Analysis from Bus Trajectory Data
© 2019, Springer Nature Switzerland AG. Detection of anomalous patterns from traffic data is closely related to analysis of traffic accidents, fault detection, flow management, and new infrastructure planning. Existing methods on traffic anomaly detection are modelled on taxi trajectory data and have shortcoming that the data may lose much information about actual road traffic situation, as taxi drivers can select optimal route for themselves to avoid traffic anomalies. We employ bus trajectory data as it reflects real traffic conditions on the road to detect city-wide anomalous traffic patterns and to provide broader range of insights into these anomalies. Taking these considerations, we first propose a feature visualization method by mapping extracted 3-dimensional hidden features to red-green-blue (RGB) color space with a deep sparse autoencoder (DSAE). A color trajectory (CT) is produced by encoding a trajectory with RGB colors. Then, a novel algorithm is devised to detect spatio-temporal outliers with spatial and temporal properties extracted from the CT. We also integrate the CT with the geographic information system (GIS) map to obtain insights for understanding the traffic anomaly locations, and more importantly the road influence affected by the corresponding anomalies. Our proposed method was tested on three real-world bus trajectory data sets to demonstrate the excellent performance of high detection rates and low false alarm rates
Research on Visualization of Multi-Dimensional Real-Time Traffic Data Stream Based on Cloud Computing
AbstractBased on efficient continuous parallel query series algorithm supporting multi-objective optimization, by using visual graphics technology for traffic data streams for efficient real-time graphical visualization, it improve human-computer interaction, to realize real-time and visual data analysis and to improve efficiency and accuracy of the analysis. This paper employs data mining processing and statistical analysis on real-time traffic data stream, based on the parameters standards of various data mining algorithms, and by using computer graphics and image processing technology, converts graphics or images and make them displayed on the screen according to the system requirements, in order to track, forecast and maintain the operating condition of all traffic service systems effectively
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