scholarly journals Investigating the Effect of Social and Cultural Factors on Drivers in Malaysia: A Naturalistic Driving Study

2021 ◽  
Vol 18 (22) ◽  
pp. 11740
Author(s):  
Ward Ahmed Al-Hussein ◽  
Miss Laiha Mat Kiah ◽  
Lip Yee Por ◽  
Bilal Bahaa Zaidan
Keyword(s):  
Traffic Safety ◽  
Data Acquisition System ◽  
Cultural Factors ◽  
Road Accidents ◽  
Speed Limits ◽  
Crash Data ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Instrumented Vehicle ◽  
Social And Cultural Factors

Road accidents are increasing every year in Malaysia, and it is always challenging to collect reliable pre-crash data in the transportation community. Existing studies relied on simulators, police crash reports, questionnaires, and surveys to study Malaysia’s drivers’ behavior. Researchers previously criticized such methods for being biased and unreliable. To fill in the literature gap, this study presents the first naturalistic driving study in Malaysia. Thirty drivers were recruited to drive an instrumented vehicle for 750 km while collecting continuous driving data. The data acquisition system consists of various sensors such as OBDII, lidar, ultrasonic sensors, IMU, and GPS. Irrelevant data were filtered, and experts helped identify safety criteria regarding multiple driving metrics such as maximum acceptable speed limits, safe accelerations, safe decelerations, acceptable distances to vehicles ahead, and safe steering behavior. These thresholds were used to investigate the influence of social and cultural factors on driving in Malaysia. The findings show statistically significant differences between drivers based on gender, age, and cultural background. There are also significant differences in the results for those who drove on weekends rather than weekdays. The study presents several recommendations to various public and governmental sectors to help prevent future accidents and improve traffic safety.

Naturalistic Data Collection of Driver Performance in Familiar and Unfamiliar Vehicles

2005 ◽  
Vol 49 (22) ◽  
pp. 1994-1998
Author(s):  
Suzanne E. Lee ◽  
Thomas A. Dingus ◽  
Sheila G. Klauer ◽  
Vicki L. Neale ◽  
Jeremy Sudweeks
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Data Set ◽  
Driver Performance ◽  
Final Data ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Instrumented Vehicles ◽  
Instrumented Vehicle ◽  
Privately Owned

The 100-Car Naturalistic Driving Study was the first large-scale instrumented vehicle study with no special driver instructions, unobtrusive data collection instrumentation, and no in-vehicle experimenter. The final data set includes approximately 2,000,000 vehicle miles, almost 43,000 hours of data, 241 primary and secondary drivers, 12 to 13 months of data collection for each vehicle, and data from a highly capable instrumentation system. In addition, 78 of 102 vehicles were privately owned and 22 were leased. After 12 months, leased vehicles were provided to 22 private vehicle drivers who then drove the leased vehicles for an additional four weeks. Driving performance for the same drivers in familiar and unfamiliar instrumented vehicles was then compared. Results provided evidence of increased relative risk for the same driver for weeks 1 through 4 of driving an unfamiliar leased vehicle as compared to the same period of driving their privately owned vehicle.

Using Naturalistic Driving Study Data to Evaluate the Effects of Intersection Balance on Driver Behavior at Partial Cloverleaf Interchange Terminals

2018 ◽  
Vol 2672 (38) ◽  
pp. 255-265
Author(s):  
Jin Wang ◽  
Huaguo Zhou
Keyword(s):  
Traffic Safety ◽  
Driver Behavior ◽  
Study Data ◽  
Statistical Characteristics ◽  
Proof Of Concept ◽  
Left Turn ◽  
Sight Distance ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Entrance Ramps

Past studies showed that poor intersection balances at partial cloverleaf (parclo) interchange terminals significantly impact traffic safety and sight distance of drivers making left turns to entrance ramps. Some state traffic agencies have recommended a “balance” guideline that the length between the left-turn stop line on crossroads to the middle of the intersection should not be greater than 60% of the entire length of the intersection. However, a scarcity of research exists on how the balance of an intersection affects driver behavior, which has been identified as a critical contributing factor to intersection-related crashes. This study utilizes the Naturalistic Driving Study (NDS) data to evaluate the effects of intersection balance on driver behavior at parclo interchange terminals for proof-of-concept. A small but representative data sample was collected from the second Strategic Highway Research Program’s (SHRP 2) NDS dataset. It demonstrates statistical characteristics and overall trends of driver speed, acceleration/deceleration rates, and risk perception with the changing of intersection balances. Conclusions provide guidance on optimal intersection balance design that may help drivers make smoother and safer transitions from crossroads to entrance ramps at parclo interchange terminals.

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.

Driver Inattention: A Contributing Factor to Crashes and Near-Crashes

2005 ◽  
Vol 49 (22) ◽  
pp. 1922-1926 ◽  
Author(s):  
Sheila G. Klauer ◽  
Vicki L. Neale ◽  
Thomas A. Dingus ◽  
David Ramsey ◽  
Jeremy Sudweeks
Keyword(s):  
Performance Metrics ◽  
State Legislatures ◽  
Washington Dc ◽  
Cell Phone Use ◽  
Naturalistic Driving Study ◽  
Driver Inattention ◽  
Collection Period ◽  
Naturalistic Driving ◽  
Instrumented Vehicle ◽  
Post Hoc

Driver distraction, or inattention, has been receiving wide media attention recently as many state legislatures are considering various levels of restricting cell phone use. Research has been conducted using a variety of experimental methods to determine the level of risk associated with driving inattention. While most of this research suggests that inattention impairs driving, there have been no studies to directly link driving inattention to crashes. Data from the 100-Car Naturalistic Driving Study, an instrumented vehicle study for which data was collected on 100 drivers in the Washington, DC metropolitan area for 12 months, were used in the following analyses. Crashes and near-crashes were identified in the data using post-hoc triggers based upon driving performance metrics, (i.e. hard braking). Results suggest that inattention contributed to 78% of all crashes collected over the 12 month data collection period.

Initial Analyses from the SHRP 2 Naturalistic Driving Study: Addressing Driver Performance and Behavior in Traffic Safety

2013 ◽  
Author(s):  
Shauna Hallmark ◽  
Dan McGehee ◽  
Karin M. Bauer ◽  
Jessica M. Hutton ◽  
Gary A. Davis ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Driver Performance ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
And Behavior

The Path of Standard Albanian Language Formation

2018 ◽  
Vol 5 (2) ◽  
pp. 106-115
Author(s):  
Sindorela Doli Kryeziu
Keyword(s):  
Single Unit ◽  
Cultural Factors ◽  
Language History ◽  
Whole Process ◽  
History Of ◽  
Social And Cultural Factors ◽  
Cultural Body ◽  
Over Time ◽  
Language Literature

Abstract In our paper we will talk about the whole process of standardization of the Albanian language, where it has gone through a long historical route, for almost a century.When talking about standard Albanian language history and according to Albanian language literature, it is often thought that the Albanian language was standardized in the Albanian Language Orthography Congress, held in Tirana in 1972, or after the publication of the Orthographic Rules (which was a project at that time) of 1967 and the decisions of the Linguistic Conference, a conference of great importance that took place in Pristina, in 1968. All of these have influenced chronologically during a very difficult historical journey, until the standardization of the Albanian language.Considering a slightly wider and more complex view than what is often presented in Albanian language literature, we will try to describe the path (history) of the standard Albanian formation under the influence of many historical, political, social and cultural factors that are known in the history of the Albanian people. These factors have contributed to the formation of a common state, which would have, over time, a common standard language.It is fair to think that "all activity in the development of writing and the Albanian language, in the field of standardization and linguistic planning, should be seen as a single unit of Albanian culture, of course with frequent manifestations of specific polycentric organization, either because of divisions within the cultural body itself, or because of the external imposition"(Rexhep Ismajli," In Language and for Language ", Dukagjini, Peja, 1998, pp. 15-18.)

Entering the Clubhouse

2010 ◽  
Vol 22 (2) ◽  
pp. 21-35 ◽  
Author(s):  
Yasmin B. Kafai ◽  
Deborah A Fields ◽  
William Q. Burke
Keyword(s):  
Online Communities ◽  
File Sharing ◽  
Cultural Factors ◽  
Ethnographic Study ◽  
After School ◽  
End User ◽  
Programming Software ◽  
Social And Cultural Factors ◽  
Learning Programming ◽  
School Club

Previous efforts in end-user development have focused on facilitating the mechanics of learning programming, leaving aside social and cultural factors equally important in getting youth engaged in programming. As part of a 4-month long ethnographic study, we followed two 12-year-old participants as they learned the programming software Scratch and its associated file-sharing site, scratch.mit.edu, in an after-school club and class. In our discussion, we focus on the role that agency, membership, and status played in their joining and participating in local and online communities of programmers.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

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