On the identification of the effect of prohibiting hand-held cell phone use while driving: Comment

In a paper recently published in this journal (Nikolaev, A.G., Robbins, M.J., Jacobson, S.H., 2010. Evaluating the impact of legislation prohibiting hand-held cell phone use while driving. Transportation Research Part A 44, 182–193.), Nikolaev et al. (2010) provide evidences on the effect of hand-held cell phone bans on driving safety. More specifically, they analyze the impact of a state-wide ban on hand-held cell phone use while driving on the number of fatal automobile and personal injury accidents per 100,000 licensed drivers per year and conclude that the ban had a significant negative impact on both the mean fatal accident rate and the mean personal injury accident rate. In this paper I argue that they lack of a good identification strategy that enables them to correctly identify the causal effect of the ban. I also provide evidence that the effect they find is a combination of the ban effect and of unobservable variables not accounted for in their analysis. Finally, I provide a way where one can control for unobservables when estimating the causal effect of the ban and find that indeed that ban appears to have a negative effect on fatal automobile accidents.     

Did California’s hand-held cell phone ban reduce accidents?

On July 1st, 2008, California enacted a ban on hand-held cell phone use while driving. Using California Highway Patrol panel accident data for California freeways from January 1st, 2008 to December 31st, 2008, we examine whether this policy reduced the number of accidents on California highways. To control for unobserved time-varying effects that could be correlated with the ban, we use high-frequency data and a regression discontinuity design. We find no evidence that the ban on hand-held cell phone use led to a reduction in traffic accidents.     

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.     

Mobile phone use while driving: public opinions on restrictions

This paper reviews two road-user surveys on the use of mobile phones on the road in Finland where the mobile phone ownership rate is highest in the world (70% in August 2000). From 1998 to 1999 the proportion of drivers that chose to use a mobile phone while driving rose from 56% to 68%, while the proportion of phone using drivers who experienced dangerous situations due to phone use rose from 44% to 50%. The proportion of drivers who used their phones in some way to benefit safety on the road remained at about 55%. The youngest, novice drivers had the highest level of phone usage of all age categories. Over 48% of the interviewees believed that the government should ban the use of hand-held mobile phones while driving, and another 27% believed that all types of mobile phone use should be banned while driving. Those drivers who used their phones the most each day were more likely to want some form of restrictions, than those who had lower usage. This is a strong message to the elected lawmakers and raises the problem of exactly how regulatory bodies would go about controlling the future growth of new driver support and non-driving related communication devices in road vehicles. It was concluded that legislating for hands-free use only would be a reasonable course of action. Mandating that the current generation of equipment should be optimized for hands-free use should result in future generations of in-vehicle equipment also being optimized for hands-free use as a minimum criterion.     

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.     

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.     

Validation of in-car observations,a method for driver assessment

An in-car observation method with human observers in the car was studied to establish whether observers could be trained to observe safety variables and register driver’s behaviour in a correct and coherent way, and whether the drivers drove in their normal driving style, despite the presence of the observers. The study further discussed the observed variables from a safety perspective. First three observers were trained in the observation method and on-road observations were carried out. Their observations were then compared with a key representing a correct observation. After practising the observation method the observers showed a high correlation with the key. To establish whether the test drivers drove in a normal way during the in-car observations, comparisons of 238 spot-speed measurements were carried out. Driver’s speeds when driving their own private cars were compared with their speeds during the in-car observations. The analysis showed that the drivers drove in the same way when being observed as they did normally. Most of the variables studied in the in-car observations had a well documented relevance to traffic safety. Overall, in-car observation was shown to be a reliable and valid method to observe driver behaviour, and observed changes provide relevant data on traffic safety.     

Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment

The growing need of the driving public for accurate traffic information has spurred the deployment of large scale dedicated monitoring infrastructure systems, which mainly consist in the use of inductive loop detectors and video cameras. On-board electronic devices have been proposed as an alternative traffic sensing infrastructure, as they usually provide a cost-effective way to collect traffic data, leveraging existing communication infrastructure such as the cellular phone network. A traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network. This article presents a field experiment nicknamed Mobile Century, which was conceived as a proof of concept of such a system. Mobile Century included 100 vehicles carrying a GPS-enabled Nokia N95 phone driving loops on a 10-mile stretch of I-880 near Union City, California, for 8 h. Data were collected using virtual trip lines, which are geographical markers stored in the handset that probabilistically trigger position and speed updates when the handset crosses them. The proposed prototype system provided sufficient data for traffic monitoring purposes while managing the privacy of participants. The data obtained in the experiment were processed in real-time and successfully broadcast on the internet, demonstrating the feasibility of the proposed system for real-time traffic monitoring. Results suggest that a 2–3% penetration of cell phones in the driver population is enough to provide accurate measurements of the velocity of the traffic flow. Data presented in this article can be downloaded from http://traffic.berkeley.edu.     

A hybrid Bayesian Network approach to detect driver cognitive distraction

Driver cognitive distraction (e.g., hand-free cell phone conversation) can lead to unapparent, but detrimental, impairment to driving safety. Detecting cognitive distraction represents an important function for driver distraction mitigation systems. We developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering—to detect cognitive distraction using eye movement and driving performance measures. In this study, the algorithm was trained and tested with the data collected in a simulator-based study, where drivers drove either with or without an auditory secondary task. We calculated 19 distraction indicators and defined cognitive distraction using the experimental condition (i.e., “distraction” as in the drives with the secondary task, and “no distraction” as in the drives without the secondary task). We compared the layered algorithm with previously developed DBN and Support Vector Machine (SVM) algorithms. The results showed that the layered algorithm achieved comparable prediction performance as the two alternatives. Nonetheless, the layered algorithm shortened training and prediction time compared to the original DBN because supervised clustering improved computational efficiency by reducing the number of inputs for DBNs. Moreover, the supervised clustering of the layered algorithm revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that the layered algorithm can capitalize on the best attributes of component data mining methods and can identify human cognitive state efficiently. The study also shows the value in considering the supervised clustering method as an approach to feature reduction in data mining applications.     

Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior

In-vehicle technologies and co-operative services have potential to ease congestion problems and improve traffic safety. This paper investigates the impact of infrastructure-to-vehicle co-operative systems, case of CO-OPerative SystEms for Intelligent Road Safety (COOPERS), on driver behavior. Thirty-five test drivers drove an instrumented vehicle, twice, with and without the system. Data related to driving behavior, physiological measurements, and user acceptance was collected. A macro-level approach was used to evaluate the potential impact of such systems on driver behavior and traffic safety. The results in terms of speeds, following gaps, and physiological measurements indicate a positive impact. Furthermore, drivers’ opinions show that the system is in general acceptable and useful.     

A pattern recognition algorithm for assessing trajectory completeness

Vehicular trajectories are widely used for car-following (CF) model calibration and validation, as they embody characteristics of individual driving behaviour (each trajectory reflects an individual driver). Previous studies have highlighted that the trajectories should contain all the major vehicular interactions (driving regimes) between the leader and the follower for reliable CF model calibration and validation. Based on Dynamic Time Warping and Bottom-Up algorithms, this paper develops a pattern recognition algorithm for vehicle trajectories (PRAVT) to objectively, accurately, and automatically differentiate different driving regimes in a trajectory and then select the most complete trajectories (i.e. trajectories containing a maximum number of regimes). PRAVT is rigorously tested using synthetic data and then applied to the NGSIM data. We have observed that the NGSIM data are dominated by the trajectories which contain only three regimes, namely acceleration, deceleration, and following, 77% of the trajectories lack the standstill regime, and no trajectory in the NGSIM data is complete. These findings’ impact on how to properly utilize NGSIM data can be profound. Given the extensive use of the NGSIM data in the traffic flow community, this paper also provides insights about the types of regimes contained in each trajectory of the NGSIM data.     

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.     

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.     

Sight distance analysis of highways using GIS tools

Analyzing the distance visible to a driver on the highway is important for traffic safety, especially in maneuvers such as emergency stops, when passing another vehicle or when vehicles cross at intersections. This analysis is necessary not only in the design phase of highways, but also when they are in service. For its use in this last phase, a procedure supported by a Geographic Information System (GIS) has been implemented that determines the highway distances visible to the driver. The use of a GIS allows the sight distance analysis to be integrated with other analyses related to traffic safety, such as crash and design consistency analyses. In this way, more complete analyses could be made and costs shared. Additionally, with the procedure proposed it is possible to use data regarding the trajectory of a vehicle obtained on a highway with a Global Positioning System (GPS) device. This application is very useful when highway design data are not available. The procedure developed and its application in a case study are presented in this article.     

Modelling driver distraction effects due to mobile phone use on reaction time

Phone use during driving causes decrease in situation awareness and delays response to the events happening in driving environment which may lead to accidents. Reaction time is one of the most suitable parameters to measure the effect of distraction on event detection performance. Therefore, this paper reports the results of a simulator study which analysed and modelled the effects of mobile phone distraction upon reaction time of the Indian drivers belonging to three different age groups. Two different types of hazardous events: (1) pedestrian crossing event and (2) road crossing event by parked vehicles were included for measuring drivers’ reaction times. Four types of mobile phone distraction tasks: simple conversation, complex conversation, simple texting and complex texting were included in the experiment. Two Weibull AFT (Accelerated Failure Time) models were developed for the reaction times against both the events separately, by taking all the phone use conditions and various other factors (such as age, gender, and phone use habits during driving) as explanatory variables. The developed models showed that in case of pedestrian crossing event, the phone use tasks: simple conversation, complex conversation, simple texting and complex texting caused 40%, 95%, 137% and 204% increment in the reaction times and in case of road crossing event by parked vehicles, the tasks caused 48%, 65%, 121% and 171% increment in reaction times respectively. Thus all the phone use conditions proved to be the most significant factors in degrading the driving performance.     

北京驾驶员培训行业需求预测和发展研究

机动车驾驶员的素质能够对城市交通运行产生重大影响,良好的技术水平和高尚道德素质的驾驶员,对于保证城市交通安全运行和人民生命财产的安全至关重要。通过开展驾驶员培训市场需求总量的预测研究,能够有效引导培训市场的合理竞争,提升驾驶员培训行业的高质量发展。本文系统分析了驾驶员培训市场需求预测的方法,以北京驾培市场为实例,基于城市新总规,综合考虑经济社会发展、城市人口变化、城市机动车调控和驾驶证饱和率等多种因素,预测未来培训市场需求情况。根据预测结果和市场培训能力对比,提出了针对行业总量发展的对策建议,也能为国内城市进行驾培需求预测研究和行业发展提供参考。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

The design of an in-vehicle assistance system to support eco-driving

This driving simulator study was the second of two studies investigating the most effective and acceptable in-vehicle system for the provision of guidance on fuel efficient accelerator usage. Three eco-driving interfaces were selected for test (a second-order display visual display with auditory alerts and two haptic accelerator pedal systems) following a pilot study of 12 different interfaces. These systems were tested in a range of eco-driving scenarios involving acceleration, deceleration and speed maintenance, and assessed through their effects on fuel economy, vehicle control, distraction, and driver subjective feedback. The results suggest that a haptic accelerator pedal system is most effective for preventing over-acceleration, whilst minimal differences were observed between systems in terms of the effect of the assistance provided to prevent under-acceleration. The visual–auditory interface lowered the time spent looking towards the road, indicating a potential negative impact on driver safety from using this modality to provide continuous green driving support. Subjective results were consistent with the objective findings, with haptic pedal systems creating lower perceived workload than a visual–auditory interface. Driver acceptability ratings suggested a slight favouring of a haptic-force system for its usefulness, whereas the more subtle haptic-stiffness system was judged more acceptable to use. These findings offer suggestions for the design of a user-friendly, eco-driving device that can help drivers improve their fuel economy, specifically through the provision of real-time guidance on the manipulation of the accelerator pedal position.     

Balancing energy consumption and risk of delay in high speed trains: A three-objective real-time eco-driving algorithm with fuzzy parameters

Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient.In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays.This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination.The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected.The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service.