Mobile phone use by young drivers: effects on traffic speed and headways

Abstract

This paper investigates the effects of mobile phone use while driving on traffic speed and headways, with particular focus on young drivers. For this purpose, a field survey was carried out in real road traffic conditions, in which drivers' speeds and headways were measured while using or not using a mobile phone. The survey took place within a University Campus area, allowing to distinguish between settings approximating to either free flow or interrupted flow conditions. Linear and loglinear regression methods were used to investigate the effects of mobile phone use and several other young driver characteristics, such as gender, driving experience and annual distance travelled, on vehicle speeds and headways. Separate models were developed for average free flow, interrupted flow, as well as for total average speed. Results show that mobile phone use leads to a statistically significant reduction in traffic speeds of young drivers in all types of traffic conditions. Furthermore, male and female drivers reduce their speed similarly when using a mobile phone while driving. However, male drivers using their mobile phone drive at lower speeds than female drivers not using their mobile phones. Sensitivity analysis revealed that, among all explanatory variables, the effect of mobile phone use on speed was most important. Accordingly, vehicle headways appear to increase for drivers using their mobile phone. However, this effect could not be statistically validated, due to the strong correlation between speed and headway.     

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

Eco-driving behaviors of electric vehicle users: A survey study

Previous research has shown that electric vehicle (EV) users could behave differently compared to internal combustion engine vehicle (ICEV) drivers due to their consciousness or practices of eco-driving, but very limited research has fully investigated this assumption. This research explores this topic through investigating EV drivers’ eco-driving behaviors and motivations. We first conducted a questionnaire survey on EV drivers’ driving behavior and some hypothetical decisions of their driving. It indicates various characteristics between EV and ICEV commuters, including self-reported daily driving habits, preferences of route choices, tradeoff between travel time and energy saving, and adoption of in-vehicle display (IVD) technologies. Then, through statistical analysis with Fisher’s exact test and Mann-Whitney U test, this research reveals that, compared to ICEV drivers, EV drivers possess significantly calmer driving maneuvers and more fuel-efficient driving habits such as trip chaining. The survey data also show that EV drivers are much more willing to save energy in compensation of travel time. Furthermore, the survey data indicate that EV drivers are more willing to adopt eco-friendly IVD technologies. All these findings are expected to improve the understanding of some unique behavior found in EV drivers.     

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.     

Do cooperative systems make drivers’ car-following behavior safer?

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.     

A Bayesian Network model for contextual versus non-contextual driving behavior assessment

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.     

The effects of in-car speed limiters: field studies

Field trials in three European countries, the Netherlands, Spain and Sweden were carried out in order to investigate the effects of an in-car speed limiter. The trials were carried out on urban and rural roads including motorways. A so-called unobtrusive instrumented car was used, where all the measuring equipment was hidden. All the speed limit categories in the respective countries, ranging from 30 to 120 km/h were included. The results showed that the effects of the limiter were greatest in free driving conditions outside platoons. However, the limiter also had effects in congested traffic. Momentary high speeds were suppressed effectively, which resulted in less variation of speeds. Approach speeds at roundabouts, intersections and curves became smoother, car-following behaviour became safer in the speed range of 30–50 km/h. On the other hand, in the speed range of 70–90 km/h a slightly higher number of short time-gaps suggested less safe car-following behaviour. Other negative behavioural effects were slightly increased travel time and the increased frustration and stress for the drivers caused by the limiter. The majority of the subjects accepted the speed limiter as a driver-operated system. Half of the drivers would accept the limiter voluntarily in their cars.     

Modeling traffic's flow-density relation: Accommodation of multiple flow regimes and traveler types

This research investigates freeway-flow impacts of different traveler types by specifying and applying a latent-segmentation model of congested and uncongested driving behaviors. Drivers in uncongested conditions are assumed to drive at self-chosen speeds, while drivers in congested conditions are assumed to take speed as given and choose a spacing (between their vehicle and the previous vehicle). Several classes of driver-vehicle combinations are distinguished in a data set based on double-loop-detector pulses and a household travel survey. These classifications are made on the basis of vehicle type and gender, leading to class estimates of speeds and spacings. The segmentation model is specified as a logit function of density, weather, and vehicle type, leading to estimates of congested-condition probabilities. Unobserved heterogeneity is incorporated in all models via common error assumptions.Results indicate that segmentation models are promising tools for traffic data analysis and that information on travelers, their vehicles, and weather conditions explains significant variation in flow data. By clarifying a greater understanding of traffic conditions and traveler behavior explains much scatter in the fundamental relation between flow, speed, and density, can assist regions in their traffic-management efforts and engineers in their design of roadway facilities. Ultimately, such improvements to travel networks should enhance quality of life.     

Driving with intelligent speed adaptation: Final results of the Belgian ISA-trial

In October 2002 the first ISA-trial in Belgium was started in Ghent. Thirty-four cars and three buses were equipped with the “active accelerator pedal”. In this system a resistance in the accelerator is activated when the driver attempts to exceed the speed limit. If necessary, the driver can overrule the system. The main research goals of the trial in Ghent were to evaluate the effects of ISA on speed-change, traffic safety, drivers’ attitude, behaviour and drivers’ acceptance. To study these effects of the ISA-system both surveys and logged speed data were analyzed. In the surveys drivers noticed that the pedal assisted them well in upholding the speed limits and that the system increased driving comfort. Most important drawbacks were technical issues. Data analysis shows a reduction in the amount of speeding due to the ISA-system. There is however still a large remaining percentage of distance speeding, especially in low speed zones. Differences between drivers are large. For some drivers speeding even increases despite activation of the system. For less frequent speeders average driving speed almost always increases and for more frequent speeders average speed tends to decrease. With the system, less frequent speeders tend to accelerate faster towards the speed limit and drive exactly at the speed limit instead of safely below, which causes average speeds to go up.     

Tracking the position of neighboring vehicles using wireless communications

We investigate four communication schemes for Cooperative Active Safety System (CASS) and compare their performance with application level reliability metrics. The four schemes are periodic communication, periodic communication with model, variable communication, and variable communication with repetition. CASS uses information communicated from neighboring vehicles via wireless network in order to actively evaluate driving situations and provide warnings or other forms of assistance to drivers. In CASS, we assume that vehicles are equipped with a GPS receiver, a Dedicated Short Range Communications (DSRC) transceiver, and in-vehicle sensors. The messages exchanged between vehicles convey position, speed, heading, and other vehicle kinematics. This information is broadcast to all neighbors within a specified communication range. Existing literature surmises that in order for CASS to be effective, it may need a vehicle to broadcast messages periodically as often as every 100 ms. In this paper, we introduce the concept of running a kinematic model in-between message transmissions as a means of reducing the communication rate. We use traffic and network simulators to compare the performance of the four schemes. Our performance measure metrics include communication losses as well as average position errors.     

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.     

Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed

Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.     

Speed management via in-car devices: effects,implications, perspectives

The objectives of this paper are to identify in-vehicle systems for speed management that have been or are being developed, and to suggest recommendations for the implementation of systems that will effectively influence driving speeds and thereby significantly increase safety. The best safety effect is expected from an "intelligent" gas pedal, more specifically the automatic speed limiter. However, in terms of user acceptance, this system is least liked, although, acceptance seems to improve after it has been tried out. Nonetheless, the final goal for implementation should be a mandatory speed limiter system, starting with voluntary usage supported with educational measures. A period of car producers' standardisation of ISA-systems should be followed by legislation prescribing that all new vehicles are to be fitted with the system. Finally, some questions regarding further research are outlined.     

Mental models of driving and speed: biases,choices and reality

This paper provides a review of research performed by Svenson with colleagues and others work on mental models and their practical implications. Mental models describe how people perceive and think about the world including covariances and relationships between different variables, such as driving speed and time. Research on mental models has detected the time-saving bias [Svenson, O. (1970). A functional measurement approach to intuitive estimation as exemplified by estimated time savings. Journal of Experimental Psychology, 86, 204–210]. It means that drivers relatively overestimate the time that can be saved by increasing speed from an already high speed, for example, 90–130?km/h, and underestimate the time that can be saved by increasing speed from a low speed, for example, 30–45?km/h. In congruence with this finding, mean speed judgments and perceptions of mean speeds are also biased and higher speeds given too much weight and low speeds too little weight in comparison with objective reality. Replacing or adding a new speedometer in the car showing min per km eliminated or weakened the time-saving bias. Information about braking distances at different speeds did not improve overoptimistic judgments of braking capacity, but information about collision speed with an object suddenly appearing on the road did improve judgments of braking capacity. This is relevant to drivers, politicians and traffic regulators.     

Interface design considerations for an in-vehicle eco-driving assistance system

This high-fidelity driving simulator study used a paired comparison design to investigate the effectiveness of 12 potential eco-driving interfaces. Previous work has demonstrated fuel economy improvements through the provision of in-vehicle eco-driving guidance using a visual or haptic interface. This study uses an eco-driving assistance system that advises the driver of the most fuel efficient accelerator pedal angle, in real time. Assistance was provided to drivers through a visual dashboard display, a multimodal visual dashboard and auditory tone combination, or a haptic accelerator pedal. The style of advice delivery was varied within each modality. The effectiveness of the eco-driving guidance was assessed via subjective feedback, and objectively through the pedal angle error between system-requested and participant-selected accelerator pedal angle. Comparisons amongst the six haptic systems suggest that drivers are guided best by a force feedback system, where a driver experiences a step change in force applied against their foot when they accelerate inefficiently. Subjective impressions also identified this system as more effective than a stiffness feedback system involving a more gradual change in pedal feedback. For interfaces with a visual component, drivers produced smaller pedal errors with an in-vehicle visual display containing second order information on the required rate of change of pedal angle, in addition to current fuel economy information. This was supported by subjective feedback. The presence of complementary audio alerts improved eco-driving performance and reduced visual distraction from the roadway. The results of this study can inform the further development of an in-vehicle assistance system that supports ‘green’ driving.     

Exploring relationships between driving events identified by in-vehicle data recorders,infrastructure characteristics and road crashes

There is an increasing interest in technology-based solutions that can assist drivers in reducing their risk of involvement in road crashes. Previous studies showed that driving events produced by in-vehicle data recorders (IVDR) are applicable for identification of unsafe driving patterns, while combined examinations of driving events and road infrastructure characteristics are rare. This study explored the relationship between the IVDR-driving events, road characteristics and crashes, to examine a potential of the events for predicting crashes and identification of high-risk locations on the road network. The study database included 3500 segments of the interurban roads in Israel, for which the automatically produced IVDR events were matched with road infrastructure characteristics and crashes. Negative-binomial regression models were adjusted for the relationships between road characteristics and driving events, and subsequently, between events and crashes, given the exposure. Significant impacts were found, yet various event types showed different relations to the infrastructure characteristics and different effects on crashes, on various road types. Better road conditions were associated with a decrease in “braking” events and an increase in the “speed alert” events, where road layout constraints and junction proximity were associated with an opposite effect on events. “Braking” and total events showed better potential for predicting crashes on single-carriageway roads, with a positive link to crashes, where for other road types the “speed alert” events were stronger related to crashes, but with a negative link. The heterogeneity of findings indicates a need in further research of the above relationship, with a particular focus on definitions of driving events produced by the IVDR or other technologies.     

Interface design of eco-driving support systems – Truck drivers’ preferences and behavioural compliance

The aim of the study was to investigate the perceived usefulness of various types of in-vehicle feedback and advice on fuel efficient driving. Twenty-four professional truck drivers participated in a driving simulator study. Two eco-driving support systems were included in the experiment: one that provided continuous information and one that provided intermittent information. After the simulator session, the participants were interviewed about their experiences of the various constituents of the systems. In general, the participants had a positive attitude towards eco-driving support systems and behavioural data indicated that they tended to comply with the advice given. However, different drivers had very different preferences with respect to what type of information they found useful. The majority of the participants preferred simple and clear information. The eco-driving constituents that were rated as most useful were advice on gas pedal pressure, speed guidance, feedback on manoeuvres, fuel consumption information and simple statistics. It is concluded that customisable user interfaces are recommended for eco-driving support systems for trucks.     

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.     

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.