Tracking Hit-and-Run Vehicle with Sparse Video Surveillance Cameras and Mobile Taxicabs

Wang, Yang; Chen, Wuji; Zheng, Wei; Huang, He; Zhang, Wen; Liu, Hengchang

doi:10.1109/icdm.2017.59

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Time-varying overall affinity matrix with dynamic traffic features involved. It has been demonstrated strong time-varying correlations between traffic conditions of different urban subregions (Wang et al 2017;. Also, there exist strong spatiotemporal correlations between traffic accidents and urban traffic conditions (Chen et al 2018b).…”

Section: Multi-task Dtgn For Accident Risk Predictionmentioning

confidence: 99%

RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework

Zhou

Wang

Xie

et al. 2020

AAAI

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Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.

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Section: Multi-task Dtgn For Accident Risk Predictionmentioning

confidence: 99%

RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework

Zhou

Wang

Xie

et al. 2020

AAAI

Self Cite

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Epidemic contact tracing with smartphone sensors

Nguyen

Luo

Watkins

2020

Journal of Location Based Services

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Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of false positives, questioning its trustworthiness and efficiency amongst the wider population for mass adoption. To this end, this paper proposes a novel, yet practical smartphone-based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the air pressure and the magnetic field for ambient environment matching. We present a model combining six smartphone sensors, prioritising some of them when certain conditions are met. We empirically verified our approach in various realistic environments to demonstrate an achievement of up to 95% fewer false positives, and 62% more accurate than Bluetooth-only system. To the best of our knowledge, this paper was one of the first work to propose a combination of smartphone sensors for contact tracing.

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