Analysis of Headway and Speed Based on Driver Characteristics and Work Zone Configurations Using Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110152
Author(s):  
Dan Xu ◽  
Chennan Xue ◽  
Huaguo Zhou
Keyword(s):  
Risk Perceptions ◽  
Time Series Data ◽  
Change Point Detection ◽  
Work Zone ◽  
Series Data ◽  
Car Following ◽  
Time Headway ◽  
Speed Distribution ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

The objective of this paper is to analyze headway and speed distribution based on driver characteristics and work zone (WZ) configurations by utilizing Naturalistic Driving Study (NDS) data. The NDS database provides a unique opportunity to study car-following behaviors for different driver types in various WZ configurations, which cannot be achieved from traditional field data collection. The complete NDS WZ trip data of 200 traversals and 103 individuals, including time-series data, forward-view videos, radar data, and driver characteristics, was collected at four WZ configurations, which encompasses nearly 1,100 vehicle miles traveled, 19 vehicle hours driven, and over 675,000 data points at 0.1 s intervals. First, the time headway selections were analyzed with driver characteristics such as the driver’s gender, age group, and risk perceptions to develop the headway selection table. Further, the speed profiles for different WZ configurations were established to explore the speed distribution and speed change. The best-fitted curves of time headway and speed distributions were estimated by the generalized additive model (GAM). The change point detection method was used to identify where significant changes in mean and variance of speeds occur. The results concluded that NDS data can be used to improve car-following models at WZs that have been implemented in current WZ planning and simulation tools by considering different headway distributions based on driver characteristics and their speed profiles while traversing the entire WZ.

Spectral Power Analysis of Drivers’ Gas Pedal Control during Steady-state Car-following on Freeways

2016 ◽  
Vol 60 (1) ◽  
pp. 729-733
Author(s):  
Fred Feng ◽  
Shan Bao ◽  
James Sayer ◽  
David LeBlanc
Keyword(s):  
Steady State ◽  
Time Series Data ◽  
Spectral Power ◽  
Cutoff Frequency ◽  
Frequency Component ◽  
Sensor Data ◽  
Series Data ◽  
Car Following ◽  
Naturalistic Driving ◽  
Lead Vehicle

This paper investigated the frequency characteristics of drivers’ gas pedal control in steady-state car-following on freeways by using vehicle sensor data from an existing naturalistic driving study. The main objectives were to examine the frequency range and distributions of a driver operating the gas pedal when following a lead vehicle, and whether the higher and lower frequency components of the gas pedal signal would vary when following a lead vehicle with varying distances. A total of 1,461 driving segments each with 90-seconds of steady-state freeway car-following were extracted from the naturalistic driving data. Fourier analysis was performed to convert the time series data of drivers’ gas pedal control to the frequency domain. The results show that during steady-state freeway car-following, the power of the gas pedal control peaks at around 0.033 Hz or 15 s per pedal movement (derived using the median of the peak frequency), and the upper limit of the frequency is around 0.94 Hz or 0.5 s per pedal movement (derived using the 95th percentile of the cutoff frequency). Further analysis showed that following a lead vehicle with smaller gap was associated with a larger proportion of the higher frequency component ( p < .001), and following a lead vehicle with larger gap was associated with a larger proportion of the lower frequency component ( p < .001). This suggests that the larger gap may allow the driver to relax control of the gas pedal with smoother operation. Potential applications of this paper include developing more realistic driver models that could be used in designing advanced driver assistance systems.

scholarly journals Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Lanfang Zhang ◽  
Cheng Chen ◽  
Jiayan Zhang ◽  
Shouen Fang ◽  
Jinming You ◽  
...  
Keyword(s):  
Time Series Data ◽  
Management Strategies ◽  
Crash Risk ◽  
Series Data ◽  
Traffic Density ◽  
Natural Setting ◽  
Lane Changing ◽  
Traffic Conditions ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

The objective of this study is to investigate lane-changing characteristics in freeway off-ramp areas using Shanghai Naturalistic Driving Study (SH-NDS) data, considering a four-lane freeway stretch in various traffic conditions. In SH-NDS, the behavior of drivers is observed unobtrusively in a natural setting for a long period of time. We identified 433 lane-changing events with valid time series data from the whole dataset. Based on the logit model developed to analyze the choice of target lanes, a likelihood analysis of lane-changing behavior was graphed with respect to three traffic conditions: free flow, medium flow, and heavy flow. The results suggested that lane-changing behavior of exiting vehicles is the consequence of the balance between route plan (mandatory incentive) and expectation to improve driving condition (discretionary incentive). In higher traffic density, the latter seems to play a significant role. Furthermore, we found that lane-change from the slow lane to the fast lane would lead to higher speed variance value, which indicates a higher crash risk. The findings contribute to a better understanding on drivers’ natural driving behavior in freeway off-ramp areas and can provide important insight into road network design and safety management strategies.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

2021 ◽  
Author(s):  
Kamil Faber ◽  
Roberto Corizzo ◽  
Bartlomiej Sniezynski ◽  
Michael Baron ◽  
Nathalie Japkowicz
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Change Point Detection ◽  
Series Data ◽  
High Dimensional ◽  
Point Detection

The Impact of The Covid-19 Pandemic on Investor Risk Tendency: The Case of Turkey

2021 ◽  
Vol 15 (9) ◽  
pp. 3046-3049
Author(s):  
Abdulkadir Kaya
Keyword(s):  
Risk Perceptions ◽  
Time Series Data ◽  
Statistical Significance ◽  
Series Data ◽  
Causality Test ◽  
Significance Level ◽  
Risk Appetite ◽  
Central Registry ◽  
The Face ◽  
The Impact

Introduction and Aim: It is an important issue that what kind of changes occur in the risks that people face in the face of emerging problems and the role of people in possible pandemics in the last twenty years and in the future. The solution of the problems that arise in the control and management of these risks attracts the attention of many researchers. In this study, the causality effect of the COVID-19 pandemic on risk appetites representing the attitudes and behaviors of securities investors. Materials and Methods: In the study; To represent the pandemic, weekly time series data of the number of COVID-19 cases (COVID) and the Risk Appetite index (RISK) announced by the Central Registry Agency for the period 30.03.2019-30.08.2021 were used. In order to determine the causality relationship, the Hatemi-J Causality test was performed. Results: It was determined that the negative shocks of the COVID variable were a cause of the positive shocks of the RISK variable at a statistical significance level of 1%. Conclusion and Suggestions: The effect of the pandemic process on the investment decisions of the investors is reduced, with the expectation that the economy and financial markets will improve, positively affecting the behavior and risk perceptions of the investors, and this expectation causes the investment behavior and risk appetite to increase. can be expressed. Keywords: COVID-19, Risk appetite, Pandemic, Hatemi-J

Nonstationary Kalman Filter for Estimation of Accurate and Consistent Car-Following Data

2005 ◽  
Vol 1934 (1) ◽  
pp. 2-12 ◽  
Author(s):  
Vincenzo Punzo ◽  
Domenico Josto Formisano ◽  
Vincenzo Torrieri
Keyword(s):  
Kalman Filter ◽  
Time Series Data ◽  
Data Consistency ◽  
Series Data ◽  
Trajectory Data ◽  
Car Following ◽  
Estimation Problems ◽  
Position Data ◽  
Data Collection Technique ◽  
Reference Measurements

Difficulty in obtaining accurate car-following data has traditionally been regarded as a considerable drawback in understanding real phenomena and has affected the development and validation of traffic microsimulation models. Recent advancements in digital technology have opened up new horizons in the conduct of research in this field. Despite the high degrees of precision of these techniques, estimation of time series data of speeds and accelerations from positions with the required accuracy is still a demanding task. The core of the problem is filtering the noisy trajectory data for each vehicle without altering platoon data consistency; i.e., the speeds and accelerations of following vehicles must be estimated so that the resulting intervehicle spacings are equal to the real one. Otherwise, negative spacings can also easily occur. The task was achieved in this study by considering vehicles of a platoon as a sole dynamic system and reducing several estimation problems to a single consistent one. This process was accomplished by means of a nonstationary Kalman filter that used measurements and time-varying error information from differential Global Positioning System devices. The Kalman filter was fruitfully applied here to estimation of the speed of the whole platoon by including intervehicle spacings as additional measurements (assumed to be reference measurements). The closed solution of an optimization problem that ensures strict observation of the true intervehicle spacings concludes the estimation process. The stationary counterpart of the devised filter is suitable for application to position data, regardless of the data collection technique used, e.g., video cameras.

scholarly journals Multiple change point detection and validation in autoregressive time series data

2020 ◽  
Vol 61 (4) ◽  
pp. 1507-1528 ◽  
Author(s):  
Lijing Ma ◽  
Andrew J. Grant ◽  
Georgy Sofronov
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Change Point Detection ◽  
Series Data ◽  
Autoregressive Time Series ◽  
Point Detection

scholarly journals Risk Factors in Work Zone Safety Events: A Naturalistic Driving Study Analysis

2019 ◽  
Vol 2673 (1) ◽  
pp. 379-387 ◽  
Author(s):  
Nipjyoti Bharadwaj ◽  
Praveen Edara ◽  
Carlos Sun
Keyword(s):  
Risk Factors ◽  
Driver Behavior ◽  
Work Zone ◽  
Crash Risk ◽  
Work Zones ◽  
Binary Logistic Regression Model ◽  
Work Zone Safety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
The Impact

Identification of crash risk factors and enhancing safety at work zones is a major priority for transportation agencies. There is a critical need for collecting comprehensive data related to work zone safety. The naturalistic driving study (NDS) data offers a rare opportunity for a first-hand view of crashes and near-crashes (CNC) that occur in and around work zones. NDS includes information related to driver behavior and various non-driving related tasks performed while driving. Thus, the impact of driver behavior on crash risk along with infrastructure and traffic variables can be assessed. This study: (1) investigated risk factors associated with safety critical events occurring in a work zone; (2) developed a binary logistic regression model to estimate crash risk in work zones; and (3) quantified risk for different factors using matched case-control design and odds ratios (OR). The predictive ability of the model was evaluated by developing receiver operating characteristic curves for training and validation datasets. The results indicate that performing a non-driving related secondary task for more than 6 seconds increases the CNC risk by 5.46 times. Driver inattention was found to be the most critical behavioral factor contributing to CNC risk with an odds ratio of 29.06. In addition, traffic conditions corresponding to Level of Service (LOS) D exhibited the highest level of CNC risk in work zones. This study represents one of the first efforts to closely examine work zone events in the Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) NDS data to better understand factors contributing to increased crash risk in work zones.

Sign in / Sign up

Export Citation Format

Share Document