Urban Traffic Prediction through the Second Use of Inexpensive Big Data from Buildings

Zheng, Zimu; Wang, Dan; Pei, Jian; Yuan, Yi; Fan, Cheng; Xiao, Fu

doi:10.1145/2983323.2983357

Cited by 24 publications

(23 citation statements)

References 25 publications

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“…Reusing building data: Building sensing data is originally dedicated to management and control purposes [21]; therefore, reusing building sensing data for traffic prediction will not cost any extra cost. Beyond building management, reusing building sensing data has attracted considerable research interests from smart city applications, including urban transportation [8], crowd flow patterns [6], and data integration [22]. According to [23], a single set of traffic monitoring systems with camera detectors could easily cost $2500 USD, and over 100 million dollars of such devices can only cover a quarter of roads in a typical metropolitan city [8].…”

Section: Related Workmentioning

confidence: 99%

“…Beyond building management, reusing building sensing data has attracted considerable research interests from smart city applications, including urban transportation [8], crowd flow patterns [6], and data integration [22]. According to [23], a single set of traffic monitoring systems with camera detectors could easily cost $2500 USD, and over 100 million dollars of such devices can only cover a quarter of roads in a typical metropolitan city [8]. In comparison with conventional methods [11], [15]- [17] that fully rely on sensing data from traffic monitoring systems, it is considerably low-cost while highly reliable to make a second use of building sensing data to predict nearby traffic.…”

Section: Related Workmentioning

confidence: 99%

“…Hu et al [24] proposed a communication sharing architecture for smart buildings to organize in-building IoT devices with heterogeneous data communication. Moreover, Zheng et al [8] proposed to use indoor CO 2 data to estimate building occupancy and further developed an occupancytraffic model for traffic speed prediction. Inspired by the above existing works, it is of great significance to unify traffic monitoring systems with external sensing infrastructures to enhance traffic prediction accuracy and reduce marginal cost.…”

Section: Related Workmentioning

confidence: 99%

“…Reusing existing building sensing data is with great significance for ubiquitous sensing of smart cities, as building data is cost-efficiency and sustainable [5]. Assuming building sensing data is available for the long-term, it has recently enabled a series of new applications in urban sensing, such as developing urban 3D mobility models [6] and identifying abnormal appliances [7], [8]. In this paper, as a preliminary exploration (illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…28 Nonparametric models are the method for solving optimization problems without objective function or constraint function. 28 Nonparametric models are the method for solving optimization problems without objective function or constraint function.…”

Section: Nonparameter Modelsmentioning

confidence: 99%

Big data‐driven machine learning‐enabled traffic flow prediction

Kong

Jiang

et al. 2018

Trans Emerging Tel Tech

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Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. The key challenge of traffic flow prediction network is how to construct an adaptive model relying on historical data. Existing big data‐driven traffic flow prediction networking approaches mainly use shallow learning, and there are unsatisfying for many realistic applications, which inspire us to rethink the traffic flow big data prediction problem with deep learning. In this paper, we propose a novel prediction approach based on machine learning. In addition to the minimum prediction error as the goal, we present the long short‐term memory model, which is a typical machine learning algorithm with deep learning network. This method is applied into the real‐world traffic big data from performance measurement system. Experimental results show that the proposed machine learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network.

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