Think Like A Graph: Real-Time Traffic Estimation at City-Scale

Liu, Zhidan; Zhou, Pengfei; Li, Zhenjiang; Li, Mo

doi:10.1109/tmc.2018.2873642

Cited by 39 publications

(14 citation statements)

References 33 publications

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“…As presented in Figure 4, the proposed method is an unsupervised learning method and only needs input variables, which are spatiotemporal population data. Liu et al [55] developed a graph processing framework based traffic estimation (GPTE), which can capture traffic correlation from taxi data and enable advanced traffic estimation at city-scale based on graph-parallel processing method. From these previous traditional machine learning-based methods, we can conclude that these models mainly focus on shortterm traffic flow prediction and receive high prediction accuracy.…”

Section: Traditional Machine Learning Methodsmentioning

confidence: 99%

Urban flow prediction from spatiotemporal data using machine learning: A survey

et al. 2020

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Section: Traditional Machine Learning Methodsmentioning

confidence: 99%

Urban flow prediction from spatiotemporal data using machine learning: A survey

et al. 2020

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“…The main advantage of road-based sensors is that they can provide reliable data by capturing all vehicles passing by the corresponding roads [25]. Recently, opportunistic sensing and crowdsensing techniques have been utilized to collect GPS data [11], [26], [27], and cellular record data [28]- [30] from floating vehicles and mobile passengers. These trajectories provide detailed mobility traces for network-wide traffic sensing and prediction.…”

Section: Related Workmentioning

confidence: 99%

BuildSenSys: Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning

Fan

Xiang

Chen

et al. 2021

IEEE Trans. on Mobile Comput.

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With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperforms all baseline methods with up to 65.3% accuracy improvement (e.g., 2.2% MAPE) in predicting nearby traffic volume. We believe that this work can open a new gate of reusing building sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation.

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“…Soon after, some dynamic and extended searchable encryption schemes have emerged [18][19][20][21][22], but the above searchable encryption schemes cannot be used to implement route finding with support for semantic search on encrypted graph. Recently, some researchers have studied and implemented some query schemes on the encrypted graph [23][24][25][26][27]. Chase et al studied the query problem on the encrypted graph and proposed the structured encryption method [23].…”

Section: Introductionmentioning

confidence: 99%

Privacy‐Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

Chen

et al. 2021

Wireless Communications and Mobile Computing

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The arrival of cloud computing age makes data outsourcing an important and convenient application. More and more individuals and organizations outsource large amounts of graph data to the cloud computing platform (CCP) for the sake of saving cost. As the server on CCP is not completely honest and trustworthy, the outsourcing graph data are usually encrypted before they are sent to CCP. The optimal route finding on graph data is a popular operation which is frequently used in many fields. The optimal route finding with support for semantic search has stronger query capabilities, and a consumer can use similar words of graph vertices as query terms to implement optimal route finding. Due to encrypting the outsourcing graph data before they are sent to CCP, it is not easy for data customers to manipulate and further use the encrypted graph data. In this paper, we present a solution to execute privacy-guarding optimal route finding with support for semantic search on the encrypted graph in the cloud computing scenario (PORF). We designed a scheme by building secure query index to implement optimal route finding with support for semantic search based on searchable encryption idea and stemmer mechanism. We give formal security analysis for our scheme. We also analyze the efficiency of our scheme through the experimental evaluation.

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Think Like A Graph: Real-Time Traffic Estimation at City-Scale

Cited by 39 publications

References 33 publications

Urban flow prediction from spatiotemporal data using machine learning: A survey

Urban flow prediction from spatiotemporal data using machine learning: A survey

BuildSenSys: Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning

Privacy‐Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

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