Mining Road Network Correlation for Traffic Estimation via Compressive Sensing

Liu, Zhidan; Li, Zhenjiang; Li, Mo; Xing, Wei; Lu, Dongming

doi:10.1109/tits.2016.2514519

Cited by 69 publications

(33 citation statements)

References 22 publications

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“…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%

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%

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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“…After intensive experimentation, we set the maximum length of a road segment to 200 m. If the length of a road link exceeds 200 m, we divide it to several segments. We set the time interval to 15 min, which is a typical value in transport studies and applications [56,57]. Regarding the set of GPS records, P, we filter the related records of a synopsis by the location of road segment p and the time t. Noted that taxis travel slowly or stop while looking for passengers, which will underestimate road traffic.…”

Section: Data Processingmentioning

confidence: 99%

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

Wang

Lü

2020

Sensors

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Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.

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“…To solve such a sensing vacancy issue, one effective solution is to learn data's inner correlation from the historical data, so that the unknown sensor values can be derived by the readings from the space with the sensor coverage. In this case, the created knowledge is the inner correlation explored from the raw sensing data [9]. However, the missing values may exist in the historical data as well, which could degrade the accuracy to derive the correlation.…”

Section: B Knowledge Creationmentioning

confidence: 99%

Vision and Challenges for Knowledge Centric Networking

Wang

et al. 2019

IEEE Wireless Commun.

Self Cite

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In the creation of a smart future information society, Internet of Things (IoT) and Content Centric Networking (CCN) break two key barriers for both the front-end sensing and back-end networking. However, we still observe the missing piece of the research that dominates the current networking traffic control and system management, e.g., lacking of the knowledge penetrated into both sensing and networking to glue them holistically. In this paper, we envision to leverage emerging machine learning or deep learning techniques to create aspects of knowledge for facilitating the designs. In particular, we can extract knowledge from collected data to facilitate reduced data volume, enhanced system intelligence and interactivity, improved service quality, communication with better controllability and lower cost. We name such a knowledge-oriented traffic control and networking management paradigm as the Knowledge Centric Networking (KCN). This paper presents KCN rationale, KCN benefits, related works and research opportunities.

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Mining Road Network Correlation for Traffic Estimation via Compressive Sensing

Cited by 69 publications

References 22 publications

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

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

Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories

Vision and Challenges for Knowledge Centric Networking

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