道路机动车活动水平调查及其污染物排放测算应用——上海案例研究

采用上海市车辆数据和机动车年检站实地调查方式获取车辆技术及分布,用GPS设备采集机动车行驶工况,通过交通流量录像的现场计数的方法获得道路车流分布。调查结果显示,在用汽车排放水平85％以上都达到或超过国Ⅱ标准;在低速、怠速工况区间内,社会车辆的比功率比例最低;道路轻型客车（含出租车）日平均车流量占总车流量的80％以上。机动车活动水平的详细调查将有助于上海实际道路机动车排放清单的精确计算,并获取主要的排放源及其影响因素。     

Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

Introduction: COVID-19 has disrupted daily life and societal flow globally since December 2019; it introduced measures such as lockdown and suspension of all non-essential movements. As a result, driving activity was also significantly affected. Still, to-date, a quantitative assessment of the effect of COVID-19 on driving behavior during the lockdown is yet to be provided. This gap forms the motivation for this paper, which aims at comparing observed values concerning three indicators (average speed, speeding, and harsh braking), with forecasts based on their corresponding observations before the lockdown in Greece. Method: Time series of the three indicators were extracted using a specially developed smartphone application and transmitted to a back-end platform between 01/01/2020 and 09/05/2020, a time period containing normal operations, COVID-19 spreading, and the full lockdown period in Greece. Based on the collected data, XGBoost was employed to identify the most influential COVID-19 indicators, and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models were developed for obtaining forecasts on driving behavior. Results: Results revealed the intensity of the impact of COVID-19 on driving, especially on average speed, speeding, and harsh braking per 100 km. More specifically, speeds were found to increase by 2.27 km/h on average compared to the forecasted evolution, while harsh braking/100 km increased to almost 1.51 on average. On the bright side, road crashes in Greece were reduced by 49% during the months of COVID-19 compared to the non-COVID-19 period.     

Modeling drivers’ reaction when being tailgated: A Random Forests Method

Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles’ reaction using existing naturalistic driving data. Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle’s reaction based on corresponding factors including driver, vehicle, and environmental variables. Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers’ reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.     

Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator

Introduction: Driver’s evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver’s evasive action. Thus, this study determines the driver’s evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver’s evasive action time (i.e., duration of evasive action) on rear-end collision risk. Method: Fifty drivers’ car-following behavior on a freeway was observed using a driving simulator. An SSM called “Deceleration Rate to Avoid Crash (DRAC)” and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios – Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. Results: DRAC decreased with the evasive action time and DRAC was closely related to drivers’ gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. Conclusions: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver’s evasive action and the speed variations among different drivers.     

Socio-demographic and experience factors affecting drivers’ runoff risk along horizontal curves of two-lane rural highway

Introduction: An improper driving strategy is one of the causative factors for a high probability of runoff and overturning crashes along the horizontal curves of two-lane highways. The socio-demographic and driving experience factors of a driver do influence driving strategy. Hence, this paper explored the effect of these factors on the driver’s runoff risk along the horizontal curves. Method: The driving performance data of 48 drivers along 52 horizontal curves was recorded in a fixed-base driving simulator. The driving performance index was estimated from the weighted lateral acceleration profile of each driver along a horizontal curve. It was clustered and compared with the actual runoff events observed during the experiment. It yielded high, moderate, and low-risk clusters. Using cross-tabulation, each risk cluster was compared with the socio-demographic and experience factors. Further, generalized mixed logistic regression models were developed to predict the high-risk and high to moderate risk events. Results: The age and experience of drivers are the influencing factors for runoff crash. The high-risk event percentage for mid-age drivers decreases with an increase in driving experience. For younger drivers, it increases initially but decreases afterwards. The generalized mixed logistic regression models identified young drivers with mid and high experience and mid-age drivers with low-experience as the high-risk groups. Conclusions: The proposed index parameter is effective in identifying the risk associated with horizontal curves. Driver training program focusing on the horizontal curve negotiation skills and graduated driver licensing could help the high-risk groups. Practical applications: The proposed index parameter can evaluate driving behavior at the horizontal curves. Driving behavior of high-risk groups could be considered in highway geometric design. Motor-vehicle agencies, advanced driver assistance systems manufacturers, and insurance agencies can use proposed index parameter to identify the high-risk drivers for their perusal.     

Drugged driving among US adults: Results from the 2016–2018 national survey on drug use and health

Introduction: Drugged driving, the act of driving while under the influence of one or more illicit substances, remains a critical problem needing to be addressed. The present study sought to analyze risk factors associated with past-year driving under the influence of alcohol and marijuana among a national sample of U.S. adults. Method: Pooled data from the 2016–2018 National Survey on Drug Use and Health were analyzed among 124,466 adults ages 18 years or older. Results: Weighted logistic regression analyses revealed that 8.52% of adults drove under the influence of alcohol in the past year and 4.49% of adults drove under the influence of marijuana in the past year. Of concern, compared to heterosexuals, greater than 1 in 10 sexual minorities drove under the influence of alcohol in the past year. Moreover, a sizeable percentage of adults used other drugs while under the influence of alcohol including inhalants (70.3%), cocaine (68.9%), and hallucinogens (63.7%). Moreover, a great percentage of adults (53.4%) reported using methamphetamine while also under the influence of marijuana. Practical Applications: We believe our findings can inform harm reduction efforts and prevention messaging surrounding the dangers of drugged driving.     

Driving anger expression in Germany—Validation of the Driving Anger Expression Inventory for German drivers

Objective: The main objective of this article is to examine whether the Driving Anger Expression Inventory (DAX) applies to German drivers because this scale has previously been given to drivers in many different countries.

Methods: We applied German versions of the DAX, the Driving Anger Scale (DAS), and the State-Trait Anger Expression Inventory (STAXI) to a sample of 501 German drivers. We computed confirmatory factor analysis and principal axis factoring (PAF) analysis to examine the structure of driving anger expression in Germany. Finally, we related the drivers’ anger exp ression scores to their driving anger experiences and their general anger propensities to assess the validity of the DAX for German drivers.

Results: Results indicated that the DAX’s original factor structure does not apply to German drivers because the confirmatory factor analysis did not show a good model fit. An item analysis revealed that many DAX items had no meaningful variability. They were excluded from further analysis. The subsequent PAF analysis indicated that German drivers do not use personal physical aggression to express their driving anger. Instead, they reported unique preventive anger expression management behavior. In addition, their driving anger expressions were significantly related to their driving anger experiences and their general anger propensities indicated the validity of the refined DAX for German drivers.

Conclusions: We conclude that German drivers do not use strong behaviors to express their driving anger. Many statements of Deffenbacher et al.’s (Behav Res Ther. 40:717–737, 2002) original American questionnaire were not applicable for our sample of German drivers. These findings are in line with several other studies showing discrepancies in driving anger expression in various countries. Future investigations should examine the reasons for discrepancies in driving anger expression.     

In-vehicle workload assessment: effects of traffic situations and cellular telephone use

PROBLEM: Assessment of drivers' on-road workload is an important traffic safety consideration. This study was conducted to examine the effects of cellular phone communication on driving performance, with particular emphasis on variations in task demand in different traffic situations. METHOD: Twelve participants were asked to drive on urban roads and motorways with or without concomitant mathematical-addition tests relayed via cellular phone. Measurements included task and driving performance, physiological responses, and compensatory behavior. RESULTS: Analysis of task performance revealed that mean response time was markedly increased (11.9%) for driving on urban roads compared to motorways. The mean driving speed only decreased 5.8% in the presence of phone tasks in comparison to normal driving without distractions. In addition, overall physiological workload increased through compensatory behavior in response to the phone tasks. CONCLUSIONS: Driving with phone use in different traffic environments induced measurable variations in driver workload. IMPACT ON INDUSTRY: When faced with heavy traffic, a greater safety margin is typically adopted, with more lowered driving speed and restricted phone use, and it can be assumed that there is a general trade-off between tasks to preserve driving safety.     

Evaluating impaired drivers confidence and intention to "(Please) drink responsibly"

Introduction

Currently, alcohol industry-sponsored advertisements subsume traditional designated driver and don't drink and drive messages within responsible drinking campaigns. Yet, to date, there remains a dearth of literature specifically examining the attitudinal beliefs impaired drivers attach to the responsible drinking message.

Objective

This investigation sought to examine the responsible drinking attitudes and beliefs of impaired drivers, specifically examining their confidence and intention to drink responsibly the next time they consumed alcohol.

Methods

A random sample of 729 students attending a large, public Texas university completed a web-based administration of the Characteristic of Responsible Drinking Survey (CHORDS).

Results

Participants in this sample who had driven while impaired by alcohol exhibited significantly less confidence in refraining from drinking and driving and reported significantly lower intentions to designate a driver, take a taxi, or use a safe-ride program the next time they consumed alcohol. Additionally, they also reported less confidence, and lower intentions, to ensure their blood alcohol concentrations remained below the legal limit (0.08%) the next time they consumed alcohol.

Conclusions

Drivers who had driven while impaired significantly differed in their confidence and intention to drink responsibly the next time they consumed alcohol. Logistic regression results indicate that by increasing one's confidence in responsible drinking, and increasing their intention to drink responsibly, the likelihood of impaired driving can be decreased.

Impact on industry

Results from this investigation demonstrate one’s responsible drinking attitudinal beliefs accounts for a significant amount of the variance associated with one’s alcohol-related behaviors. Thus, further research should examine and establish how individuals conceptualize and practice responsible drinking.