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.

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.

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.

Driver speed selection and crash risk: Insights from the naturalistic driving study

2017 ◽  
Vol 63 ◽  
pp. 187-194 ◽  
Author(s):  
Raha Hamzeie ◽  
Peter T. Savolainen ◽  
Timothy J. Gates
Keyword(s):  
Crash Risk ◽  
Naturalistic Driving Study ◽  
Speed Selection ◽  
Naturalistic Driving

scholarly journals Parameter uncertainty of a dynamic multispecies size spectrum model

2016 ◽  
Vol 73 (4) ◽  
pp. 589-597 ◽  
Author(s):  
Michael A. Spence ◽  
Paul G. Blackwell ◽  
Julia L. Blanchard
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Size Structure ◽  
Management Strategies ◽  
Optimization Methods ◽  
Series Data ◽  
Size Spectrum ◽  
Aquatic Communities ◽  
The North ◽  
Spectrum Model

Dynamic size spectrum models have been recognized as an effective way of describing how size-based interactions can give rise to the size structure of aquatic communities. They are intermediate-complexity ecological models that are solutions to partial differential equations driven by the size-dependent processes of predation, growth, mortality, and reproduction in a community of interacting species and sizes. To be useful for quantitative fisheries management these models need to be developed further in a formal statistical framework. Previous work has used time-averaged data to “calibrate” the model using optimization methods with the disadvantage of losing detailed time-series information. Using a published multispecies size spectrum model parameterized for the North Sea comprising 12 interacting fish species and a background resource, we fit the model to time-series data using a Bayesian framework for the first time. We capture the 1967–2010 period using annual estimates of fishing mortality rates as input to the model and time series of fisheries landings data to fit the model to output. We estimate 38 key parameters representing the carrying capacity of each species and background resource, as well as initial inputs of the dynamical system and errors on the model output. We then forecast the model forward to evaluate how uncertainty propagates through to population- and community-level indicators under alternative management strategies.

Crash and Near-Crash Risk Assessment of Distracted Driving and Engagement in Secondary Tasks: A Naturalistic Driving Study

2018 ◽  
Vol 2672 (38) ◽  
pp. 245-254 ◽  
Author(s):  
Peter R. Bakhit ◽  
BeiBei Guo ◽  
Sherif Ishak
Keyword(s):  
Traffic Safety ◽  
Real World ◽  
Secondary Task ◽  
Safety Concern ◽  
Distracted Driving ◽  
Crash Risk ◽  
Secondary Tasks ◽  
Depth Analysis ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.

scholarly journals Incidence forecasting of new invasive pest of coconut rugose spiraling whitefly (Aleurodicus rugioperculatus) in India using ARIMAX analysis

2021 ◽  
Vol 23 (2) ◽  
pp. 194-199
Author(s):  
K.ELANGO ◽  
S. JEYARAJAN NELSON ◽  
P.DINESHKUMAR
Keyword(s):  
Tamil Nadu ◽  
Prediction Models ◽  
Time Series Data ◽  
Management Strategies ◽  
Climatic Conditions ◽  
Invasive Pest ◽  
Series Data ◽  
Maximum Temperature ◽  
Weather Factors ◽  
Extent Of Damage

The rugose spiraling whitefly (RSW), Aleurodicus rugioperculatus Martin is a new invasive pest occurring in several crops including coconut since 2016 in India from Tamil Nadu, Karnataka, Kerala and Andhra Pradesh. The population dynamics of new invasive whitefly species, A. rugioperculatus study indicated that RSW was found throughout the year on coconut and the observation recorded on weekly interval basis shows that A. rugioperculatus population escalated from the first week of July 2018 (130.8 nymph/ leaf/ frond) reaching the maximum during the first week of October (161.0 nymph/ leaf/ frond) which subsequently dwindled to a minimum during April. Due to variation in the agro-climatic conditions of different regions, arthropods show varying trends in their incidence also in nature and extent of damage to the crop. Influence of weather parameters on rugose spiralling whitefly incidence is lacking, which is essential for developing management strategies. The forecasting model to predict rugose spiralling whitefly incidence in coconut was developed by ARIMAX model of weekly cases and weather factors. In exploring different prediction models by fitting covariates to the time series data, ARIMA (0,2,1) with Maximum temperature was found best model for predicting the rugose  spiralling whitefly incidence and all covariates were found non-significant predictors except maximum temperature.

scholarly journals On the safety effects of spatially aggregated traffic flow parameters at macro-levels

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881940
Author(s):  
Chen Wang ◽  
Lin Liu ◽  
Zhenbo Lu
Keyword(s):  
Traffic Flow ◽  
Management Strategies ◽  
Central Area ◽  
Model Performance ◽  
Graph Cut ◽  
Crash Risk ◽  
Traffic Density ◽  
Minimization Method ◽  
Flow Parameters ◽  
Regional Management

Traffic flow parameters have been found to significantly affect crash risk at micro-levels. If such effects do exist at macro-levels, at least two benefits could be expected: (1) the performance and estimates of planning-based crash models could be improved and (2) useful safety knowledge could be provided for regional traffic management. In this article, a flow-based spatial unit was developed by a graph-cut minimization method, based on which regional management strategies are often applied. The graph-cut method partitioned the central area of Kunshan, China, into multiple sub-regions (i.e. graph-cut unit), considering traffic density homogeneity. Bayesian Poisson lognormal models with conditional autoregressive priors were utilized to examine the safety effects of traffic flow parameters, based on the traditional planning-based units and the flow-based graph-cut units. According to the results, no significant traffic flow effect was found for the traffic analysis zone–based model. Traffic flow parameters resulted in a decreased model performance and potential endogeneity issues for the census tract–based model. However, traffic flow effects were found significant for the graph-cut-based model, with an improved model performance. In general, the safety effects of macro-level traffic flow need to be considered for flow-based units developed for regional management.

scholarly journals Analysis of a long-term Forest Accountancy Network to Support Management and Controlling

2018 ◽  
Vol 3 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Markus Dög ◽  
Johannes Wildberg ◽  
Bernhard Möhring
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Time Series Data ◽  
Management Strategies ◽  
Series Data ◽  
Forest Economics ◽  
Private Forest ◽  
Forest Landowners ◽  
Forest Enterprises

Abstract Multifunctional forestry in Germany is characterized by long production periods and complex biological-technical processes. Private forest enterprises are complex systems which are closely interwoven with the economic environment. To ensure their economic success, forest landowners need to take the economic development into consideration and adapt their management strategies. Management accounting is an important source for information needed to fulfil main tasks of accounting that help to manage forest enterprises: ‘description’, ‘explanation’ and ‘decision making’. To get general data, long time series data, taken from Forest Accountancy Networks (FAN), can be analysed. For more than 45 years, data from the FAN Westfalen-Lippe in Germany has been collected and analysed by the department of Forest Economics and Forest Management at the University of Göttingen. The long-term development and adaptation strategies of defined groups of private forest enterprises can be illustrated using this data. These valuable time series can support decision-making processes for private forest landowners and provide tools for forest policy. The data shows that private forest enterprises, with spruce as the dominating tree species, have performed above average in terms of operating revenues and profit margins, but are also more susceptible to calamities resulting in higher involuntary timber harvests.

scholarly journals Sustainable Knowledge Management and Its Impact on the Performances of Biotechnology Organizations

2019 ◽  
Vol 11 (2) ◽  
pp. 359 ◽  
Author(s):  
Alexandra Zbuchea ◽  
Florina Pînzaru ◽  
Mihail Busu ◽  
Sergiu-Octavian Stan ◽  
Alina Bârgăoanu
Keyword(s):  
Knowledge Management ◽  
Time Series Data ◽  
Descriptive Analysis ◽  
Management Strategies ◽  
Series Data ◽  
Growth Strategies ◽  
Knowledge Based ◽  
Capital Productivity ◽  
Statistical Hypotheses ◽  
Biotechnology Sector

Starting from the findings of specialized studies on knowledge management in the field of biotechnology, this paper aims to present the factors that underline sustainable performances of Romanian biotechnology organizations. Particularly, descriptive analysis of these factors has outlined a picture of the current situation of biotechnology in Romania. The design of an exploratory knowledge management model for organizations in the biotechnology sector was achieved and validated through a panel data model. Starting from a model of growth based on productivity, capital inflow, and human capital, three statistical hypotheses were validated by a time series data panel regression model using EViews 9.0 software. The data were collected for the enterprises active in the field of biotechnology for a period of nine years. The paper highlights the fact that the economic performance of biotechnology organizations is determined by the flow of capital, productivity, and the workforce. Knowledge-based growth strategies are essential in the econometric model presented. Nevertheless, in terms of knowledge management strategies, the sector has not reached its maturity, and full sustainability is not a norm.

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