Multivariate analysis of car-following behavior data using a coupled hidden Markov model

Zou, Yajie; Zhu, Ting; Xie, Yuanchang; Zhang, Yunlong; Zhang, Yue

doi:10.1016/j.trc.2022.103914

Cited by 13 publications

(9 citation statements)

References 39 publications

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“…Chen et al [ 12 ] quantified various factors (such as environmental, economic, social impacts, recycling modes) using an integrated framework supported by the analytical network process (ANP) and fuzzy comprehensive evaluation model. Zou et al [ 13 ] introduced the CHMM method to analyze the interaction between driving behavior variables, which can better understand and explain driving behavior and original driving patterns, confirming the importance of considering the dependence between different variables. In order to overcome the uncertainty of the model, Wu et al [ 14 ] applied the Bayesian model averaging (BMA) method to consider the advantages of different distributions in headway modeling.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City

Zhang,

Sui,

Zhang

2024

PLoS ONE

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Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents’ urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model’s dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City

Zhang,

Sui,

Zhang

2024

PLoS ONE

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“…To develop ADASs and AVs that can operate correctly in real traffic situations, a robust tool to describe rare events, such as near-collision and collision events, is needed. Yajie Zou et al developed a coupled hidden Markov model (CHMM) that can explain the intra-heterogeneity of individual drivers [18], and Jon Ander Ruiz Colmenares et al conducted research to derive driving behavior that causes motion sickness using machine learning techniques [19]. Yuchuan Du et al developed a deep reinforcement learning technique that enables autonomous vehicles to perform comfortable and energy-efficient speed control on rough pavement [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of Following Vehicles’ Driving Patterns Using Spectral Analysis Techniques

Chae

Kim

2023

Sustainability

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Despite the potential benefits of autonomous vehicles (AVs) of reducing human driver errors and enhancing traffic safety, a comprehensive evaluation of recent AV collision data reveals a concerning trend of rear-end collisions caused by following vehicles. This study aimed to address this issue by developing a methodology that identifies the relationship between driving patterns and the risk of collision between leading and following vehicles using spectral analysis. Specifically, we propose a process for computing three indices: reaction time, stimulus compliance index, and collision-risk aversion index. These indices consistently produced reliable results under various traffic conditions. Our findings align with existing research on the driving patterns of following vehicles. Given the consistency and robustness of these indices, they can be effectively utilized in advanced driver assistance systems or incorporated into AVs to assess the likelihood of collision risk posed by following vehicles and develop safer driving strategies accordingly.

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“…KL divergence is used to compare the diference between driving patterns by comparing feature distributions, which better takes into account the randomness of driving behavior data. Other commonly used methods for this purpose include the Jensen-Shannon (JS) divergence [23] and the Cauchy-Schwarz divergence [24]. Tese driving behavior evaluation methods consider the infuence of the randomness of driving data and are capable of measuring the diference of information contained in two temporal driving data.…”

Section: Introductionmentioning

confidence: 99%

Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data

Tang,

Zou,

Zhang

et al. 2024

Journal of Advanced Transportation

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Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety.

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Multivariate analysis of car-following behavior data using a coupled hidden Markov model

Cited by 13 publications

References 39 publications

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City

Investigation of Following Vehicles’ Driving Patterns Using Spectral Analysis Techniques

Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data

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