Aspects Of Driver Behavior Research Articles

Simulation-based driver scoring and profiling system

This paper describes a rule-based Driver Scoring System model, derived from behavioral data collected using a driving simulator. It introduces a novel approach to establish driver profiles through feature engineering of acquired dataset, with features evaluating various aspects of driver behavior. The research aims to provide employers and drivers with profile-specific feedback and recommendations to design training protocols. Principal Component Analysis is applied on preprocessed dataset from 412 drivers for dimensionality reduction and feature selection. The K-means clustering algorithm is used for data analysis, resulting in three distinct clusters. The Kruskal-Wallis test, supplemented by post hoc Dunn testing is employed to determine statistical significance between clusters. Clusters are portrayed using descriptive statistics, specifically the mean scores and overall driver performance averages. Our method delineates three driver profiles, with two driving styles reflecting desirable driving skills and good overall performance, while the third represents unacceptable driving skills and bad overall performance.

How do contract types and incentives influence driver behavior?\u2212An analysis of the Kigali bus network

The rapidly growing city of Kigali has a bus network that is undergoing increased development as underlined in its Transport Master Plan. Two schemes of bus driver remuneration coexist in the city: One constitutes a hybrid salary and commission system, while the other pays a fixed monthly salary. This paper examines the effect of these differing compensation schemes on driver behavior in Kigali using survey data from 2019. The analysis applies linear models incorporating various aspects of driver behavior in a principal-agent framework. The results indicate that the performance-based compensation scheme is associated with higher per-trip passenger fluctuation and faster driving (possibly due to drivers aiming to accrue a higher income) compared to the fixed-wage system. Policy implications comprise the inclusion of further criteria in incentive contracts to internalize potential negative externalities on society, e.g., to hinder the endangerment of passenger safety by appropriately incentivizing drivers. In conclusion, bus drivers who are compensated by performance are more likely to alter their behavior, responding to the incentive scheme through several channels.

Comprehensive Analysis of Dynamic Message Sign Impact on Driver Behavior: A Random Forest Approach

This study investigates the potential effect(s) of different dynamic message signs (DMSs) on driver behavior using a full-scale high-fidelity driving simulator. Different DMSs are categorized by their content, structure, and type of messages. A random forest algorithm is used for three separate behavioral analyses—a route diversion analysis, a route choice analysis, and a compliance analysis—to identify the potential and relative influences of different DMSs on these aspects of driver behavior. A total of 390 simulation runs are conducted using a sample of 65 participants from diverse socioeconomic backgrounds. Results obtained suggest that DMSs displaying lane closure and delay information with advisory messages are most influential with regards to diversion, while color-coded DMSs and DMSs with avoid route advice are the top contributors potentially impacting route choice decisions and DMS compliance. In this first-of-a-kind study, based on the responses to the pre- and post-simulation surveys as well as results obtained from the analysis of driving-simulation-session data, the authors found that color-coded DMSs are more effective than alphanumeric DMSs, especially in scenarios that demand high compliance from drivers. The increased effectiveness may be attributed to reduced comprehension time and ease with which such DMSs are understood by a greater percentage of road users.

Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control.

Successful driving involves steering corrections that respond to immediate positional errors while also anticipating upcoming changes to the road layout ahead. In popular steering models these tasks are often treated as separate functions using two points: the near region for correcting current errors, and the far region for anticipating future steering requirements. Whereas two-point control models can capture many aspects of driver behavior, the nature of perceptual inputs to these two "points" remains unclear. Inspired by experiments that solely focused on road-edge information (Land & Horwood, 1995), two-point models have tended to ignore the role of optic flow during steering control. There is recent evidence demonstrating that optic flow should be considered within two-point control steering models (Mole, Kountouriotis, Billington, & Wilkie, 2016). To examine the impact of optic flow and road edges on two-point steering control we used a driving simulator to selectively and systematically manipulate these components. We removed flow and/or road-edge information from near or far regions of the scene, and examined how behaviors changed when steering along roads where the utility of far-road information varied. While steering behaviors were strongly influenced by the road-edges, there were also clear contributions of optic flow to steering responses. The patterns of steering were not consistent with optic flow simply feeding into two-point control; rather, the global optic flow field appeared to support effective steering responses across the time-course of each trajectory.

Towards Robust Vehicular Context Sensing

In-vehicle context sensing can detect many aspects of driver behavior and the environment, such as drivers changing lanes, potholes, road grade, and stop signs, and these features can be used to improve driver safety and comfort, and engine efficiency. In general, detecting these features can use either onboard sensors on the vehicle ( car sensors ) or sensors built into mobile devices ( phone sensors ) carried by one or more occupants, or both. Furthermore, traces of sensor readings from different cars, when crowd-sourced , can provide increased spatial coverage as well as disambiguation. In this paper, we explore, by designing novel detection algorithms for the four different features discussed above, three related questions: How is the accuracy of detection related to the choice of phone versus car sensors? To what extent, and in what ways, does crowd-sourcing contribute to detection accuracy? How is accuracy affected by phone position? We have collected hundreds of miles of vehicle traces with annotated groundtruth, and demonstrated through evaluation that our detection algorithms can achieve high accuracy for each task (e.g., $>$ 90% for lane change determinations) and that crowd-sensing plays an indispensable role in improving the detection performance (e.g., improving recall by 35% for lane change determinations on curves). Our results can give car manufacturers insight into how to augment their internal sensing capabilities with phone sensors, or give mobile app developers insight into what car sensors to use in order to complement mobile device sensing capabilities.

Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing

This article reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intradriver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this article is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling.

Exploring associations between self-regulatory mechanisms and neuropsychological functioning and driver behaviour after brain injury

ABSTRACTThe objective of this prospective one-year follow-up study was to explore the associations between self-regulatory mechanisms and neuropsychological tests as well as baseline and follow-up ratings of driver behaviour. The participants were a cohort of subjects with stroke and traumatic brain injury (TBI) who were found fit to drive after a multi-disciplinary driver assessment (baseline). Baseline measures included neuropsychological tests and ratings of self-regulatory mechanisms, i.e., executive functions (Behavior Rating Inventory of Executive Function–Adult Version; BRIEF-A) and impulsive personality traits (UPPS Impulsive Behavior Scale). The participants rated pre-injury driving behaviour on the Driver Behaviour Qestionnaire (DBQ) retrospectively at baseline and after one year of post-injury driving (follow-up). Better performance on neuropsychological tests was significantly associated with more post-injury DBQ Violations. The BRIEF-A main indexes were significantly associated with baseline and follow-up ratings of DBQ Mistakes and follow-up DBQ Inattention. UPPS (lack of) Perseverance was significantly associated with baseline DBQ Inattention, whereas UPPS Urgency was significantly associated with baseline DBQ Inexperience and post-injury DBQ Mistakes. There were no significant changes in DBQ ratings from baseline (pre-injury) to follow-up (post-injury). It was concluded that neuropsychological functioning and self-regulatory mechanisms are related to driver behaviour. Some aspects of driver behaviour do not necessarily change after brain injury, reflecting the influence of premorbid driving behaviour or impaired awareness of deficits on post-injury driving behaviour. Further evidence is required to predict the role of self-regulatory mechanisms on driver behaviour and crashes or near misses.

Driving Simulation of the Safety and Operation Performance at a Freeway Toll Plaza

Toll plaza designs have implemented electronic toll collection and other technologies to improve toll systems; however, an increase in crashes has appeared with these improvements. To study safely the pertinent aspects of driver behavior in toll plazas with electronic toll collection, a cockpit driving simulator housed at the University of Puerto Rico at Mayagüez was used. Specifically, in this study a comparison was made of two configurations of the signs that indicated the corresponding speed limit and toll station for each lane in the area before the toll plaza. One configuration corresponded to the current condition of the signage in Puerto Rico, with signs located at the roadside; the second configuration presented a proposed overhead signage treatment. A representative group of 20 subjects was selected to test the effectiveness of the two signage configurations on the approach zone leading to the toll plaza, calculating the standard deviation of roadway position, speed, and acceleration noise in five zones. The behavior of drivers using the proposed signage configuration appeared to be safer than the behavior of drivers following the current signage configuration. Specifically, at each of five zones in which behavior was sampled on the approach to the toll plaza, drivers using the proposed configuration changed lanes more smoothly and reduced their vehicles’ velocity more when approaching the toll plaza. Nevertheless, there was no significant difference between configurations in acceleration noise. The results of this study provide strong evidence that driving simulators can be used effectively to identify efficient and inexpensive alternative signage configurations at toll plazas.

Modeling the Strategic Behavior of Drivers for Multi-Lane Highway Driving

Current state-of-the-art highway traffic flow simulators rely extensively on models using formulas similar to those describing physical phenomena, such as forces, viscosity, or potential fields. These models have been carefully calibrated to represent the overall flow of traffic and they can also be extended to account for the cognitive limitations of the driver, such as reaction times. However, there are some aspects of driver behavior, such as strategic planning, that are difficult to formulate mathematically. In this article, we describe the YAES-DSIM highway simulator, which integrates virtual physics models with an agent-based model. The virtual physics component models the physical vehicle and the subconscious aspects of the driver behavior, while the agent component is responsible for the strategic and tactical decisions, which are difficult to model using virtual physics. We focus on the lane change decisions of the drivers, with special attention to the optimal lane positioning for a safe exit. We have used the model to simulate the flow of traffic on Highway 408 in Orlando, Florida, and to study the impact of various tactical and strategic decisions on the efficiency and safety of the traffic.

Individual Differences in Driver Over-Confidence

This study aimed to extend understanding of individual differences in over-confidence in driver safety. First, we discriminated general driving confidence from confidence in coping with impairments such as fatigue and distraction. Second, we discriminated three aspects of overconfidence specified in terms of a recent Bayesian belief updating model of over-confidence (Moore and Healy, 2008): overestimation, overplacement and overprecision. Calibration tasks were used for this purpose. Results showed that the magnitude of overconfidence differed across the various metrics, and different metrics were only modestly intercorrelated. Confidence in handling impairment appears to be distinct from general confidence. Both forms of confidence were negatively related to dislike of driving but related differently to other aspects of driver behavior and stress. Risk factors related to self-estimation (violations, thrill-seeking) may be distinct from factors related to overplacement (aggression). Discrimination of multiple metrics for driving overconfidence may support better matching of safety interventions to the individual driver.

An investigation of the relationship between the driving behavior questionnaire and objective measures of highway driving behavior

The Driver Behavior Questionnaire (DBQ) is a well-documented instrument for obtaining self-report information on aberrant driving behaviors. The DBQ contains three subscales to capture different aspects of driver behavior: errors, lapses and violations. Previous research has demonstrated a relationship between DBQ scores and retrospective and prospective crash involvement. There is little or no published information, however, on the relationship between DBQ scores and actual driving behavior that may bear some relationship to crash risk. The present study focuses on the relationship between DBQ subscales and observed highway driving behaviors. A sample of 108 drivers in self-reported good health and with a safe recent driving history was balanced by gender across three age groups (20–29, 40–49, 60–69). Prior to driving, participants completed a 24-item U.S. version of the DBQ. The relationships between subscales of the DBQ and driving behavior were examined. The results indicated that drivers with high violations scores drove faster, had poorer lateral control, changed lanes more frequently, spent more time in the left lane, and had more sudden unidirectional accelerations. High lapses scores were related to high steering wheel reversal rates and less consistent throttle control. No main effects were observed between errors scores and any of the driving behavior measures. The magnitude of the relationships observed here are likely conservative because drivers involved in accidents in the past year were excluded.

Self Explaining Roads and situation awareness

This paper places theories of SA into contact with the issue of Self Explaining Roads. Twelve drivers took part in an on-road study and performed a verbal commentary as they drove around a defined test route. The verbal transcripts were partitioned into six road types, and driver SA was modeled using semantic networks. The content and structure of these networks was analysed and cognitively salient endemic road features were extracted. These were then compared with aspects of driver behaviour. The findings highlight the systemic nature of the driver–vehicle–road interaction, and show that SA is highly contingent on road type. The findings also reveal that motorways/freeways are the most cognitively compatible road type and that incompatibilities grow rapidly as road types become increasingly minor and less overtly ‘designed’. The paper is exploratory in nature but succeeds in innovating a theoretically robust means of examining road environments under naturalistic conditions. It also succeeds in providing numerous insights and hypotheses for a developing program of work.

Results of Car Following Analyses Using Global Positioning System

One of the key elements in the evolution of traffic flow theory has been the development of the car following theory. Applications of this research can be seen in traffic simulation models, such as CORSIM, that use car following models to simulate traffic flow and predict operational performance characteristics on arterial roadways. Researchers at Louisiana State University have recently developed a technique using the Global Positioning System to record and analyze car following behavior. In this study, these procedures were used to collect vehicle motion data and compare aspects of driver behavior recorded in the field to those predicted by the General Motors car following models. These comparisons were also used to develop numeric values of driver sensitivity and time lag from field data.

Driving and energy conservation

Probably one of the major causes of energy resource depletion and a significant source of air pollution is personal automobile usage. There is a resurgence in interest of psychological aspects of driver behaviour, particularly energy efficient automobile usage. In this paper we suggest that psychologists should actively contribute and collaborate with multidisciplinary teams who would have an input on policy formulation which would encourage energy conserving behaviours, particularly in the personal use of cars. It is also argued that psychologists should embrace technological solutions to some of these environmental problems and support measures that would encourage consumers to adopt these environment friendly solutions.

Direct observation of driving, self reports of driver behaviour, and accident involvement

Forty-eight drivers answered a set of written questions about their driving style and drove a pre-defined, mixed urban and motorway route under observation. For 20 drivers there was a second observer in the car to check on inter-observer reliability. Relationships were examined between self-reports of driver behaviour and observers' reports, and between both of these and the number of accidents in which the drivers had been involved in the past three years. The results indicated that there was good inter-observer agreement on a number of important variables including speed, calmness, and attentiveness. Inter-observer reliability for other variables was less good; among these were preferred distance to car in front, use of indicators, and aggressiveness. Observers also showed good agreement on overall ratings of driver skill and safety. Observed speed on the motorway correlated well with drivers' self reports of normal driving speed. Observer ratings of calmness correlated significantly with self-reports of calmness. Also observer ratings of attentiveness and carefulness correlated significantly with self-reports of deviant driving behaviour. Observed speed on the motorway showed a clear positive correlation with self-reported accident involvement. The results indicate that self-reports of certain aspects of driver behaviour can be used as surrogates for observational measures, thus providing a convenient extension to the researcher's methodological armoury. One such aspect is speed which appears to play an important role in accident involvement.

Driver behaviour and business outcomes: An alternative approach to the psychology of the owner–manager

Previous psychological approaches to the study of owner–managers are reviewed and an alternative model based on the transactional analysis concept of Drivers is put forward. This model is tested out via a series of interviews and observations of 20 owner–managers of small and medium–sized businesses based in the North East of England. The relationship between driver behaviour and business performance is explored and the negative consequences of driver behaviour for the business are identified. Driver behaviour is also viewed as potentially beneficial and the inherent positive aspects of driver behaviour are proposed as the opposite end of a continuum of behaviours which are at the heart of business success. On a methodological level the need for an approach grounded in the owner–managers behaviour is confirmed.

Some Aspects Of Driver Behaviour In SignalisedIntersections

Some Aspects Of Driver Behaviour In SignalisedIntersections