Given the underlying road network of an urban area, the problem of urban dynamics prediction aims to capture the patterns of urban dynamics and to forecast short-term urban traffic status continuously from the histori...
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ISBN:
(纸本)9781665423984
Given the underlying road network of an urban area, the problem of urban dynamics prediction aims to capture the patterns of urban dynamics and to forecast short-term urban traffic status continuously from the historical observations. This problem is of fundamental importance to urban traffic management, planning, and various business services. However, predicting urban dynamics is challenging due to the highly dynamic (i.e., varying across geographical locations and evolving over time) and uncertain (i.e., affected by unexpected factors) nature of urban traffic systems. Recent works adopt meta-learning approaches to capture irregular and rare patterns but make unrealistic assumptions such as single-domain uncertainties and explicit temporal task segmentation. In this paper, we solve the urban dynamics prediction problem from the Bayesian meta-learning perspective and propose a novel domain adaptable continuous meta-learning approach (DAC-ML) that does not require task segmentation. Trained on a sequence of spatial-temporal urban dynamics data, DAC-ML aims to detect and infer unobserved latent variations (from task and domain levels) and generalize well in a sequential prediction setting, where the underlying data generating process varies over time. Experimental results on three real-world datasets demonstrate that DAC-ML can outperform baselines in urban dynamics prediction, especially when obvious urban dynamics and temporal uncertainties are present.
With the advances of sensory, satellite and mobile communication technologies in recent decades, locational data become widely available. A lot of work has been developed to find useful information from these data, an...
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ISBN:
(纸本)9781450327459
With the advances of sensory, satellite and mobile communication technologies in recent decades, locational data become widely available. A lot of work has been developed to find useful information from these data, and various approaches have been proposed. In this work, we aim to use one specific type of locational data network connection logs of mobile devices, which is widely available and easily accessible to telecom companies, to identify and extract active areas of users. This is a challenging topic due to the existence of inaccurate location and fluctuating log time intervals of this kind of data. In order to observe user behavior from this kind of data set, we propose a new algorithm, namely Behavior Observation Tool (BOT), which uses Convex Hull Algorithm with sliding time windows to model the user's movement, and thus knowledge about the user's lifestyle and habits can extracted from the mobile device network logs.
Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount o...
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Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects' behavior and analyze their habits. To promote the application of spatiotemporaldatamining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.
After the rapid expansion in the early stage, many enterprises have closed down or withdrawn from the Freefloating bike sharing (FFBS) market, the remaining few giants are also generally at a loss at present. One main...
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After the rapid expansion in the early stage, many enterprises have closed down or withdrawn from the Freefloating bike sharing (FFBS) market, the remaining few giants are also generally at a loss at present. One main reason causing these is the serious FFBS imbalance between supply and demand. As there is no fixed docking station, the individual-based identification method such as trip-chain, which is used for identifying travel demand and behaviour in traditional station-based bike sharing (SBBS) cannot be used in FFBS. Therefore, the lack of methods to obtain in-depth demand makes it unable to achieve reasonable rebalancing. This study constructs an individual-based spatio-temporal travel demand mining methodology, which is the first disaggregate travel demand mining model that suitable for FFBS. The proposed methodology consists of three steps. A spatio-temporal trajectory clustering algorithm is first developed to obtain an individual's frequent trajectory clusters, and then a sequential pattern mining algorithm is applied to users who have multiple spatio-temporal trajectory clusters to extract travel patterns and trajectory sequential relations in patterns. A point clustering method is used finally to identify spatial relationships among different trajectory clusters. Besides, a zone aggregating method is proposed that aggregated granularity could be flexible adjusted for zone demand imbalance analysis. Based on these, how to utilize identified frequent pattern trajectories to improve rebalancing is studied. The proposed methodology is applied to Beijing Mobike dataset, six frequent travel patterns are mined out and analyzed in detail. On this basis, imbalance and rebalancing analysis are carried out with the case study at last. Consequently, this research contributes a powerful tool to achieve accurate FFBS demand analysis and rebalancing.
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Orac...
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Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD *** problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future *** propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.
With the advances of sensory, satellite and mobile communication technologies in recent decades, locational data become widely available. A lot of work has been developed to find useful information from these data, an...
详细信息
ISBN:
(纸本)9781450327459
With the advances of sensory, satellite and mobile communication technologies in recent decades, locational data become widely available. A lot of work has been developed to find useful information from these data, and various approaches have been proposed. In this work, we aim to use one specific type of locational data -- network connection logs of mobile devices, which is widely available and easily accessible to telecom companies, to identify and extract active areas of users. This is a challenging topic due to the existence of inaccurate location and fluctuating log time intervals of this kind of data. In order to observe user behavior from this kind of data set, we propose a new algorithm, namely Behavior Observation Tool (BOT), which uses Convex Hull Algorithm with sliding time windows to model the user's movement, and thus knowledge about the user's lifestyle and habits can extracted from the mobile device network logs.
Large volumes of volunteered GPS traces in the last decade have provided location-based services with an opportunity to become more intelligent and personalized. Individual and group mobility patterns, detected from G...
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Large volumes of volunteered GPS traces in the last decade have provided location-based services with an opportunity to become more intelligent and personalized. Individual and group mobility patterns, detected from GPS traces, can be used for this purpose. In this paper, we show the potential of GPS traces, if managed properly in the database, for detecting points of interest for individual users and even recognizing individual users from their walking patterns. However, when it comes to GPS traces, databases can be very complicated and cumbersome to populate. databases provided by OSM and GeoLife do not effectively pave the path for datamining and machine learning techniques which require a much more detailed and organized database. A GPS trace database must provide statistics and detailed information about GPS traces not only for visualization purposes at the front-end, but also for cross checking purposes to eliminate erroneous records and to be applied in mobility pattern detection applications. This study provides the design of an interactive database management system for GPS traces whose applications in detecting points of interest and user identification are tested with GPS traces from the GeoLife project. The results show that while the accuracy of detected points of interest depends mostly on the size of data, the accuracy of user identification relies more upon the appropriate choice of input features to machine learning techniques.
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