Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach

Guo, Yanyong; Li, Zhibin; Wu, Yao; Xu, Chengcheng

doi:10.1016/j.trf.2018.09.008

Cited by 74 publications

(67 citation statements)

References 26 publications

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“…Based on the ARIMA(p, d, q) model which includes autoregressive (AR) algorithm and moving average (MA) algorithm, the SARIMA (p, d, q)(P, D, Q) model can be defined in (1). In this study, the SARIMA model is used to remove the autocorrelation structure from the time series so as to generate the residual series for the statistical tests in the heteroscedasticity test.…”

Section: Sarima Modelmentioning

confidence: 99%

“…where n is total number of parameter; q is the periodic value; k is parameter number index; i is the cross-sectional position; The sequence y i (1) can be calculated based on the first-order accumulated generating operation (1-AGO) as shown in the (3).…”

Section: Sdgm Modelmentioning

confidence: 99%

“…There has been an increasing growth in traffic demand over the past two decades around the world. Transportation engineers are being challenged by the ever-increasing traffic demand and the corresponding traffic congestion and safety issues [1][2][3][4]. Many solutions have been investigated to mitigate the traffic congestion, in which the proactive traffic control system is great importance and efficient [5].…”

Section: Introductionmentioning

confidence: 99%

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Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Song

et al. 2019

PLoS ONE

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Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SAR-IMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.

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Section: Sarima Modelmentioning

confidence: 99%

Section: Sdgm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Song

et al. 2019

PLoS ONE

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“…Crash detection can be conducted by analyzing tra c ow data from roadway detectors, such as loops and microwaves. However, such method is o en inaccurate due to systematic errors caused by both algorithms and data quality [1][2][3][4][5]. us, in practice, crashes were o en detected by human observers through CCTV in Tra c Management Centers (TMC).…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition

Wang

Dai

Zhou

et al. 2020

Journal of Advanced Transportation

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In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.

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“…Next, the GPC for each facility which was calculated by Equation (13), as shown in Table 3. To develop the candidate cloud, the child cloud features and the forward normal cloud generator were used by Equation (9), and the three digital features of the candidate cloud for each facility was obtained by Equation (10). Then, the forward normal cloud generator algorithm was applied to calculate the degree of the membership for the four levels of GPC.…”

mentioning

confidence: 99%

Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory

Zhou

Mao

et al. 2019

Applied Sciences

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This study aims at evaluating the congestion level of pedestrians in metro stations. Twelve hours (4 h × three facilities) of video data were collected in the channel, stairway, and platform in a metro station in the city of Ningbo, China. The indicator of GPC (grade of pedestrian crowd) was proposed to quantify the congestion level of pedestrians. Four levels of congestion (level I, level II, level III, and level IV) were determined based on the GPC. A normal-cloud (NC) model was proposed and calibrated for the evaluation of three facilities including channel, stairway, and platform. The evaluation results showed that the GPC of L1-L2 and L2-L1 in channel are level II and level I, respectively. The GPC of upward and downward of stairway are level III and level I. The GPC of platform is level IV. Crowd management countermeasures were proposed for the management of pedestrians in metro station.

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Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach

Cited by 74 publications

References 26 publications

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition

Congestion Evaluation of Pedestrians in Metro Stations Based on Normal-Cloud Theory

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