Understanding the impacts of mobile phone distraction on driving performance: A systematic review

The use of mobile phones while driving—one of the most common driver distractions—has been a significant research interest during the most recent decade. While there has been a considerable amount research and excellent reviews on how mobile phone distractions influence various aspects of driving performance, the mechanisms by which the interactions with mobile phone affect driver performance is relatively unexamined. As such, the aim of this study is to examine the mechanisms involved with mobile phone distractions such as conversing, texting, and reading and the driving task, and subsequent outcomes. A novel human-machine framework is proposed to isolate the components and various interactions associated with mobile phone distracted driving. The proposed framework specifies the impacts of mobile phone distraction as an inter-related system of outcomes such as speed selection, lane deviations and crashes; human-car controls such as steering control and brake pedal use and human-environment interactions such as visual scanning and navigation. Eleven literature-review/meta-analyses papers and 62 recent research articles from 2005 to 2015 are critically reviewed and synthesised following a systematic classification scheme derived from the human-machine system framework. The analysis shows that while many studies have attempted to measure system outcomes or driving performance, research on how drivers interactively manage in-vehicle secondary tasks and adapt their driving behaviour while distracted is scant. A systematic approach may bolster efforts to examine comprehensively the performance of distracted drivers and their impact over the transportation system by considering all system components and interactions of drivers with mobile phones and vehicles. The proposed human-machine framework not only contributes to the literature on mobile phone distraction and safety, but also assists in identifying the research needs and promising strategies for mitigating mobile phone-related safety issues. Technology based countermeasures that can provide real-time feedback or alerts to drivers based on eye/head movements in conjunction with vehicle dynamics should be an important research direction.     

Categorization of the lane change decision process on freeways

Traffic operations for new road layouts are often simulated using microscopic traffic simulation packages. These traffic simulation packages usually simulate traffic on freeways by a combination of a car-following model and a lane change model. The car-following models have gained attention of researchers and are well calibrated versus data. The proposed lane change models are often representations of assumed reasonable behavior, not necessarily corresponding to reality. The current simulation packages apply solely one specific type of model for car-following or lane changing for all vehicles during the simulation. This paper investigates the decision process of lane changing maneuvers for a variety of drivers based on a two-stage test-drive. Participants are asked to take a drive on a freeway in the Netherlands in a camera-equipped vehicle. Afterwards, the drivers are asked to comment on their choices related to lane and speed choice, while watching the video. This paper reveals that different drivers have completely different strategies to choose lanes, and the choices to change lane are related to their speed choice. Four distinct strategies are empirically found. These strategies differ not only in parameter values, as is currently being modeled in most simulation packages, but also in their reasoning. Most remarkably, all drivers perceive their strategy as an obvious behavior and expect all other drivers to drive in a similar way. In addition to the interviews of the participants in the test-drive, 11 people who did not take part in the experiment were interviewed and questioned on lane change decisions. Moreover, the findings of this study have been presented to various groups of audience with different backgrounds (about 150 people). Their comments and feedback on the derived driving strategies have added some value to this study. The findings in this paper form a starting point for developing a novel lane change model which considers four different driving strategies among the drivers on freeway. This is a significant contribution in the area of driving behavior modeling, since the existing microscopic simulators consider only one type of lane change models for all drivers during the simulation. This could lead to significant changes in the way lane changes on freeways are modeled.     

Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework

The objective of this paper is to quantify and characterize driver behavior under different roadway geometries and weather conditions. In order to explore how a driver perceives the rapidly changing driving surrounding (i.e. different weather conditions and road geometry configurations) and executes acceleration maneuvers accordingly, this paper extends a Prospect Theory based acceleration modeling framework. A driving simulator is utilized to conduct 76 driving experiments. Foggy weather, icy and wet roadway surfaces, horizontal and vertical curves, and different lane and shoulder widths are simulated while having participants driving behind a yellow cab at speeds/headways of their choice. After studying the driving trends observed in the different driving experiments, the extended Prospect Theory based acceleration model is calibrated using the produced trajectory data. The extended Prospect Theory based model parameters are able to reflect a change in risk-perception and acceleration maneuvering when receiving different parameterized exogenous information. The results indicate that drivers invest more attention and effort to deal with the roadway challenges compared to the effort to deal with the weather conditions. Moreover, the calibrated model is used to simulate a highway segment and observe the produced fundamental diagram. The preliminary results suggest that the model is capable of capturing driver behavior under different roadway and weather conditions leading to changes in capacity and traffic disruptions.     

Effects of low speed limits on freeway traffic flow

Recent years have seen a renewed interest in Variable Speed Limit (VSL) strategies. New opportunities for VSL as a freeway metering mechanism or a homogenization scheme to reduce speed differences and lane changing maneuvers are being explored. This paper examines both the macroscopic and microscopic effects of different speed limits on a traffic stream, especially when adopting low speed limits. To that end, data from a VSL experiment carried out on a freeway in Spain are used. Data include vehicle counts, speeds and occupancy per lane, as well as lane changing rates for three days, each with a different fixed speed limit (80 km/h, 60 km/h, and 40 km/h). Results reveal some of the mechanisms through which VSL affects traffic performance, specifically the flow and speed distribution across lanes, as well as the ensuing lane changing maneuvers. It is confirmed that the lower the speed limit, the higher the occupancy to achieve a given flow. This result has been observed even for relatively high flows and low speed limits. For instance, a stable flow of 1942 veh/h/lane has been measured with the 40 km/h speed limit in force. The corresponding occupancy was 33%, doubling the typical occupancy for this flow in the absence of speed limits. This means that VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here, where speed limits have a reduced ability to limit flows. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Additionally, results show that lower speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rate. This means that VSL strategies aiming to homogenize traffic and reduce lane changing activity might not be successful when adopting such low speed limits. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane is avoided. These findings are useful for the development of better traffic models that are able to emulate these effects. Moreover, they are crucial for the implementation and assessment of VSL strategies and other traffic control algorithms.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

Assessing the long term benefit of banning the use of hand-held wireless devices while driving

An increasing number of legislative efforts have been undertaken to prohibit the use of hand-held wireless devices while driving. As of July 2012, ten states and the District of Columbia enforce laws banning the use of hand-held cell phones while driving. Thirty-nine states and the District of Columbia have banned text messaging while driving. Recent studies of driver behavior suggest that hand-held wireless device usage negatively impacts driver performance. However few studies at the aggregate level address the plausible link between the use of hand-held wireless devices while driving, increased risk of automobile accidents, and government legislative efforts to reduce such risk. This paper analyzes data at the aggregate level and builds a regression model to estimate the long term accident rate reduction due to a hand-held ban. This model differs from previous studies, which consider short term accident rate reduction, by considering time trends in the accident rate due to the ban. Additionally, counties considered in this analysis are placed into groups based on driver density, defined by the number of licensed drivers per centerline mile of roadway, and a separate analysis is performed within these groups. This approach allows one to better quantify the effect of hand-held bans in counties of different driver densities. Results from this paper suggest that bans on hand-held wireless device use while driving reduce the rate of personal injury accidents in counties with high levels of driver density, but may increase accident rates in counties with low driver density levels. These results can inform transportation policymakers interested in reducing automobile-accident-risk attributable to the use of hand-held wireless devices while driving.     

Modeling of individual vehicle safety and fuel consumption under comprehensive external conditions

This research intends to explore external factors affecting driving safety and fuel consumption, and build a risk and fuel consumption prediction model for individual drivers based on natural driving data. Based on 120 taxi drivers’ natural driving data during 4 months, driving behavior data under various conditions of the roadway, traffic, weather, and time of day are extracted. The driver's fuel consumption is directly collected by the on-board diagnostics (OBD) unit, and safety index is calculated based on Data Threshold Violations (DTV) and Phase Plane Analysis with Limits (PPAL) considering speed, longitudinal and lateral acceleration. By using a linear mixed model explaining the fixed effect of the external conditions and the random effect of the driver, the influences of various external factors on fuel consumption and safety are analyzed and discussed. The prediction model lays a foundation for drivers' fuel consumption and risk prediction in different external conditions, which could help improve individual driving behavior for the benefit of both fuel consumption and safety.     

A rule-based neural network approach to model driver naturalistic behavior in traffic

This paper proposes a rule-based neural network model to simulate driver behavior in terms of longitudinal and lateral actions in two driving situations, namely car-following situation and safety critical events. A fuzzy rule based neural network is constructed to obtain driver individual driving rules from their vehicle trajectory data. A machine learning method reinforcement learning is used to train the neural network such that the neural network can mimic driving behavior of individual drivers. Vehicle actions by neural network are compared to actions from naturalistic data. Furthermore, this paper applies the proposed method to analyze the heterogeneities of driving behavior from different drivers’ data.Driving data in the two driving situations are extracted from Naturalistic Truck Driving Study and Naturalistic Car Driving Study databases provided by the Virginia Tech Transportation Institute according to pre-defined criteria. Driving actions were recorded in instrumented vehicles that have been equipped with specialized sensing, processing, and recording equipment.     

Integrated driving behavior modeling

This paper develops, implements and tests a framework for driving behavior modeling that integrates the various decisions, such as acceleration, lane changing and gap acceptance. Furthermore, the proposed framework is based on the concepts of short-term goal and short-term plan. Drivers are assumed to conceive and perform short-term plans in order to accomplish short-term goals. This behavioral framework supports a more realistic representation of the driving task, since it captures drivers’ planning capabilities and allows decisions to be based on anticipated future conditions.An integrated driving behavior model, which utilizes these concepts, is developed. The model captures both lane changing and acceleration behaviors. The driver’s short-term goal is defined by the target lane. Drivers who wish to change lanes but cannot change lanes immediately, select a short-term plan to perform the desired lane change. Short-term plans are defined by the various gaps in traffic in the target lane. Drivers adapt their acceleration behavior to facilitate the lane change using the target gap. Hence, inter-dependencies between lane changing and acceleration behaviors are captured.     

Naturalistic driving data collection to investigate into the effects of road geometrics on track behaviour

Road designers assume that drivers will follow the road alignment with trajectories centred in the lane, and move at the design speed parallel to the road centreline (i.e., the horizontal alignment). Therefore, they assume that if the horizontal alignment indicates the “designed trajectory”, the driving path indicates the “operating trajectory”. However, at present, they do not have the necessary tools to measure the relationship between the designed alignment and possible vehicle trajectories.The paper has two objectives: (a) to develop an understanding of the root causes of differences between road alignment and vehicle trajectories; and (b) to define and calibrate a model that estimates the local curvature of trajectories on the basis of the designed horizontal alignment.The two objectives were pursued by carrying out a naturalistic survey using vehicles equipped with high precision GPS in real-time kinematics (RTK) mode driven by test drivers on road sections of known geometric characteristics. The results provide an insight into the effects of road geometrics on driver behaviour, thus anticipating possible driving errors or unexpected/undesired behaviours, information which can then be used to correct possible inconsistencies when making decisions at the design stage.     

Impact of traveler advisory systems on driving speed: some new evidence

This paper explores the effects of driving behavior using in-vehicle and out-of-vehicle traffic advisory information relating to adverse weather and incident conditions. A full-size, fixed-based driving simulator is used to collect data on drivers’ speed behavior under four different advisory-information conditions: in-vehicle messages, out-of-vehicle messages, both types of messages, and no messages. The findings of this study suggest an interesting phenomenon in that, while messages are significant in reducing speeds in the area of adverse conditions, drivers tend to compensate for this speed reduction by increasing speeds downstream when such adverse conditions do not exist. As a result, the net safety effects of such message systems are ambiguous.     

Personalised assistance for fuel-efficient driving

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.     

Impact of stop-and-go waves and lane changes on discharge rate in recovery flow

In an effort to uncover traffic conditions that trigger discharge rate reductions near active bottlenecks, this paper analyzed individual vehicle trajectories at a microscopic level and documented the findings. Based on an investigation of traffic flow involving diverse traffic situations, a driver’s tendency to take a significant headway after passing stop-and-go waves was identified as one of the influencing factors for discharge rate reduction. Conversely, the pattern of lane changers caused a transient increase in the discharge rate until the situation was relaxed after completing the lane-changing event. Although we observed a high flow from the incoming lane changers, the events ultimately caused adverse impacts on the traffic such that the disturbances generated stop-and-go waves. Based on this observation, we regard upstream lane changes and stop-and-go waves as the responsible factors for the decreased capacity at downstream of active bottlenecks. This empirical investigation also supports the resignation effect, the regressive effect, and the asymmetric behavioral models in differentiating acceleration and deceleration behaviors.     

The “field of safe travel” revisited: interpreting driving behaviour performance through a holistic approach

The present paper proposes a conceptual framework for the driver’s visual–spatial perceptual processes. Based on a theoretical analysis of driving proposed by Gibson and Crooks [(1938). A theoretical field-analysis of automobile-driving. The American Journal of Psychology, 51, 453–471. doi:10.2307/1416145], the developed field of safe travel (FoST) framework suggests that at any moment the driver constructs a “field” by integrating two perceptual entities: (i) the possible available spatial fields for locomotion and (ii) the driver’s mental image of ego-vehicle outer-line and motion dynamics. This framework is used to reinterpret in a unified way a number of disparate research findings reported in the literature concerning specific driving sub-tasks (e.g. lane keeping and car following). It is argued that the FoST framework may be used to predict drivers’ behaviour in various traffic/situation environments based on their prioritisation between the above two perceptual entities. Implications of the proposed framework at a theoretical and practical level, in view of the future of driving with multiple levels of automation, are also discussed.     

Effect of variable message signs on driver speed behavior on a section of expressway under adverse fog conditions—A driving simulator approach

Variable message signs (VMS) are used to provide dynamic information and one current application is to show different speed limits under different conditions. As speed is an important contributor to road accidents and also affects driver speed behavior, the present study focuses on how effective traffic advisory information is when helping drivers to divert from potentially dangerous conditions. Graphical representation of an Expressway section made it easy to isolate the effects of speed etc. by drivers with information provided through VMS under adverse fog conditions. Understanding and reacting to the VMS system by drivers is essential for its success. If drivers do not react by changing speed behavior then the VMS system will fail and further implementation may cease. In this paper an Analysis of Variance model, which is appropriate to the proposed experimental conditions, is used to study how subjects (drivers) will perceive provided information and also to find the effect of VMS on driver speed behavior on the simulated Expressway section.     

Modelling dual carriageway lane changing using neural networks

Neural networks offer a potential alternative method of modelling driver behaviour within road traffic systems. This paper explores the application of neural networks to modelling the lane-changing decisions of drivers on dual carriageways. Two approaches are considered. The first, preliminary approach uses a prediction type of neural network with a single hidden layer and the back propagation learning algorithm to model the behaviour of an individual driver. A series of consecutive time-scan traffic patterns, which describe the driver's environment and changes over time as the selected vehicle travels along a link, are input to the neural network, which then predicts the new lane and position of the vehicle. Training data are collected from a human subject using an interactive driving simulation. The trained neural network successfully exhibited the rudiments of driving behaviour in terms of lane and speed changes. A major disadvantage of this approach was the difficulty in recording real-life data, which are required to train the neural network, for individual drivers. The second approach concentrates specifically on lane changing and makes use of a learning vector quantization classification type of neural network. Input to the neural network still consists primarily of time-scan traffic patterns, but the format is changed to facilitate the possibility of data acquisition using image processing. The neural network output classifies the input data by determining the new lane for the vehicle concerned. Performance in both testing and training was very good for data generated by the rule-based driver-decision model of a microscopic simulation. Performance in testing was less satisfactory for data taken directly from a road and highlighted the need for extensive data sets for successful training.     

Vicious or virtuous circles? Exploring the vulnerability of drivers to break low urban speed limits

Levels of support for 20 mph limits in Great Britain are consistently high. However, these positive attitudes are not translating into similarly positive behaviour changes in terms of complying with these new speed limits. Recent research from the authors studied the complex relationship between support and compliance, with qualitative findings suggesting that copycat driving could create a ‘vicious circle effect’ that leads to increased levels of non-compliance. However it is also possible that an alternative ‘virtuous circle’ effect may emerge from the high levels of societal support for 20 mph limits pressurising drivers to comply with speed limits. In this work the authors investigated these issues and we report on data and analysis of a large scale survey of drivers and residents undertaken in Great Britain. We explain the origins of vicious and virtuous circles in driver behaviour and study the data from the survey, offering an analysis of attitudes and claimed behaviours that has implications for policy-makers and professionals working with low urban speed limits. We discuss the issues for speed limit enforcement, making reference to the public relations ‘battle’ for public opinion. It is concluded that normative compliance, triggered by community and other campaigning, may be the most realistic mechanism for countering the difficulties of government funding in promoting compliance.     

Personalised feedback and eco-driving: An explorative study

Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO₂ emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour.     

Speed-spacing dependency on relative speed from the adjacent lane: New insights for car following models

This paper examines the traffic dynamics underlying a recently observed phenomenon, the so called “sympathy of speeds” whereby a high occupancy vehicle (HOV) lane seemingly exhibits lower vehicular capacity and lower flow at speeds throughout the congested regime compared to the adjacent general purpose (GP) lanes. Unlike previous studies this paper examines a time-of-day HOV lane. During the non-HOV periods the study lane reverts to a GP lane, thereby providing a control condition for the specific lane and location. This work uses the single vehicle passage (svp) method to group vehicle passages before measuring the traffic state and extends the svp to bin vehicles in the study lane based on the relative speed to the adjacent lane. The extended svp method allows the work to also study the impacts during the non-HOV periods when the study lane serves GP vehicles. This work finds that: (1) during the non-HOV periods the study lane exhibited behavior indistinguishable from the adjacent GP lane. (2) The sympathy of speeds persists throughout the day, even when the study lane serves GP vehicles. (3) The relative speed to the adjacent lane provided a better predictor of behavior than whether or not the HOV restriction is active. In short, the car following behavior that gives rise to the sympathy of speeds is unrelated to the HOV restriction per se, persisting under GP operations as well.This dependency on the relative speed in the adjacent lane is an important finding given the fact that most existing car following models assume that the longitudinal acceleration of a following vehicle is strictly a function of the relationship to the leading vehicle in the same lane. Because drivers in general adopt a larger spacing when faced with a high differential in speed between lanes means that car following behavior also depends on the relative speed to the adjacent lane. This fact has likely gone unnoticed to date because generally the conditions that give rise to a differential in speeds between lanes are usually short lived, and thus, do not become apparent in conventional macroscopic data except under exceptional circumstances that include confounding factors like HOV operations.