Driving Big Data: A First Look at Driving Behavior via a Large-Scale Private Car Dataset

Li, Tong; Alhilal, Ahmad; Zhang, Anlan; Hoque, Mohammad A.; Chatzopoulos, Dimitris; Xiao, Zhu; Li, Yong; Hui, Pan

doi:10.1109/icdew.2019.00-34

Cited by 20 publications

(4 citation statements)

References 20 publications

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“…As one of the most critical urban transport, taxis are widespread in urban areas and run for almost 24 hours every day. Lots of works are based on taxi trajectories [46], [111], [61], [71], [112]. In practice, trajectory datasets can be used to detect traffic anomalies, unexpected crowds, and even individual anomalies.…”

Section: Trajectorymentioning

confidence: 99%

Urban Anomaly Analytics: Description, Detection, and Prediction

Zhang

et al. 2022

IEEE Trans. Big Data

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Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening are of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.

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Section: Trajectorymentioning

confidence: 99%

Urban Anomaly Analytics: Description, Detection, and Prediction

Zhang

et al. 2022

IEEE Trans. Big Data

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“…DATASET DESCRIPTION In order to evaluate our system, we use real-world driving data. We use a dataset collected from 68,000 privately owned vehicles for one month (1-31 July 2016) in China [22]. The dataset contains trips and trajectories of the cars (see Table I).…”

Section: E Potential Accidentsmentioning

confidence: 99%

CAD3: Edge-facilitated Real-time Collaborative Abnormal Driving Distributed Detection

Alhilal

Braud

Sut

et al. 2021

2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)

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Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways. Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles. However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system. In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection. CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time. Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale. We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China. At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.

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“…In [24], the representation of EVs' energy scheduling in an energy system is depicted as a joint modeling. A model in [25] represents the economic concerns of EVs and cars, and it is then tackled using an evolutionary algorithm.…”

Section: Introductionmentioning

confidence: 99%

Economic energy scheduling of electrical microgrid considering optimal participation of the electric vehicles

Du,

Yang,

Guo

2024

STET

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This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, taking these uncertainties into consideration to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimize cost of microgrid by 4.31%, 3.82% and 1.87% than genetic algorithm (GA), Particle swarm optimization (PSO) algorithm and Teaching learning-based optimization (TLBO) algorithm, respectively.

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Driving Big Data: A First Look at Driving Behavior via a Large-Scale Private Car Dataset

Cited by 20 publications

References 20 publications

Urban Anomaly Analytics: Description, Detection, and Prediction

Urban Anomaly Analytics: Description, Detection, and Prediction

CAD3: Edge-facilitated Real-time Collaborative Abnormal Driving Distributed Detection

Economic energy scheduling of electrical microgrid considering optimal participation of the electric vehicles

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