Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects

Objective: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized typical maximum likelihood estimation binary logit models, and none have employed real-time traffic data. To fill this gap, this study focused on investigating factors contributing to HR crashes, as well as the severity levels of HR.

Methods: This study analyzed 4-year crash and real-time loop detector data by employing hierarchical Bayesian models with random effects within a sequential logit structure. In addition to evaluation of the impact of random effects on model fitness and complexity, the prediction capability of the models was examined. Stepwise incremental sensitivity and specificity were calculated and receiver operating characteristic (ROC) curves were utilized to graphically illustrate the predictive performance of the model.

Results: Among the real-time flow variables, the average occupancy and speed from the upstream detector were observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference between upstream and downstream speeds were correlated with the occurrence of severe HR crashes. In addition to real-time factors, other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street lights, driving under the influence of alcohol, width of inner shoulder, and nighttime.

Conclusions: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed toward mitigating risky driving under the aforementioned traffic conditions. Moreover, enforcement agencies may employ alcohol checkpoints to counter driving under the influence (DUI) at night. With regard to engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and street lights should be installed and maintained in working condition to make roads less prone to such crashes.     

Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

Introduction: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, “adverse weather,” which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. Methods: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. Result: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade.     

Examination of the severity of two-lane highway traffic barrier crashes using the mixed logit model

IntroductionVehicles in transport sometimes leave the travel lane and encroach onto natural or artificial objects on the roadsides. These types of crashes are called run-off the road crashes, which account for a large proportion of fatalities and severe crashes to vehicle occupants. In the United States, there are about one million such crashes, with roadside features leading to one third of all road fatalities. Traffic barriers could be installed to keep vehicles on the roadways and to prevent vehicles from colliding with obstacles such as trees, boulder, and walls. The installation of traffic barriers would be warranted if the severity of colliding with the barrier would be less severe than colliding with other fix objects on the sides of the roadway. However, injuries and fatalities do occur when vehicle collide with traffic barriers. A comprehensive analysis of traffic barrier features is lacking due to the absence of traffic barrier features data. Previous research has focused on simulation studies or only a general evaluation of traffic barriers, without accounting for different traffic barrier features.MethodThis study is conducted using an extensive traffic barrier features database for the purpose of investigating the impact of different environmental and traffic barrier geometry on this type of crash severity. This study only included data related to two-lane undivided roadway systems, which did not involve median barrier crashes. Crash severity is modeled using a mixed binary logistic regression model in which some parameters are fixed and some are random.ResultsThe results indicated that the effects of traffic barrier height, traffic barrier offset, and shoulder width should not be separated, but rather considered as interactions that impact crash severity. Rollover, side slope height, alcohol involvement, road surface conditions, and posted speed limit are some factors that also impact the severity of these crashes. The effects of gender, truck traffic count, and time of a day were found to be best modeled with random parameters in this study. The effects of these risk factors are discussed in this paper.Practical applicationsResults from this study could provide new guidelines for the design of traffic barriers based upon the identified roadway and traffic barrier characteristics.     

大数据在环境执法与监测中的应用创新

当前背景下,大数据的有效应用以及普及,其很大程度上推动了各个领域创新发展的进程,尤其是在一些企业中,其通过对大数据的应用,实现了对数据的分析以及统计,有效优化了生产工艺,也满足了现阶段节能降耗的要求。因此,在进行环境执法与监测工作阶段,为了可以更好地提升监测水平,也应该积极的对大数据技术进行应用,合理创新。     

安全大数据共享影响因素分析及其模型构建

为促进安全大数据共享能力建设,打破传统安全信息不对称困境,最终塑造完善的安全大数据共享观。首先,对安全大数据及其共享的内涵进行分析,运用文献综述法,从安全大数据共享的困境出发,以安全大数据共享观念文化、数据有效性、技术环境和制度政策4个视角归纳出12个影响因素;然后,对安全大数据共享机理进行详细分析;最后,以12个影响要素为出发点、安全大数据共享互动流程为路径、共享平台建设为着重点,建立安全大数据共享模型。结果表明:模型可为安全大数据共享流程化提供参考。     

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.     

Motorcycle active safety systems: Assessment of the function and applicability using a population-based crash data set

Objective: Motorcycles and mopeds, often referred to as powered 2-wheelers (PTWs), play an important role in personal mobility worldwide. Despite their advantages, including low cost, space occupancy, and fuel efficiency, the risk of sustaining serious or fatal injuries is higher than that for occupants of passenger cars. The development of safety systems specific for PTWs represents a potential way to reduce casualties among riders. With the proliferation of new active and passive safety technologies, the question as to which might offer the most value is important. In this context, a prioritization process was applied to a set of PTW active safety systems to evaluate their applicability to crash scenarios alone and in combination. The systems included in the study were antilock braking (ABS), autonomous emergency braking (AEB), collision warning, curve warning, and curve assist.

Methods: With the functional performance of the 5 safety systems established, the relevance of each system to specific crash configurations and vehicle movements defined by a standardized accident classification system used in Victoria, Australia, was rated by 2 independent reviewers, with a third reviewer acting as a moderator where disagreements occurred. Ratings ranged from 1 (definitely not applicable) to 4 (definitely applicable). Using population-based crash data, the number and percentage of crashes that each safety system could potentially influence, or be relevant for, was defined. Applying accepted injury costs permitted the derivation of the societal economic cost of PTW crashes and the potential reductions associated with each safety system given a theoretical crash avoidance effectiveness of 100%.

Results: In the 12-year period 2000–2011, 23,955 PTW riders and 1292 pillion passengers were reported to have been involved in a road crash, with over 500 killed and more than 10,000 seriously injured; only 3.5% of riders/pillion passengers were uninjured. The total economic cost associated with these injured riders and pillion passengers was estimated to be AU$11.1 billion (US$7.70 billion; €6.67 billion). The 5 safety systems, as single solutions or in combination, were relevant to 57% of all crashes and to 74% of riders killed. Antilock braking was found to be relevant to the highest number of crashes, with incremental increases in coverage when combined with other safety systems.

Conclusions: The findings demonstrate that ABS, alone and in combination with other safety systems, has the potential to mitigate or possibly prevent a high percentage of PTW crashes in the considered setting. Other safety systems can influence different crash scenarios and are also recommended. Given the high cost of motorcycle crashes and the increasing number of PTW safety technologies, the proposed approach can be used to inform the process of selection of the most suitable interventions to improve PTW safety.     

Development of a method to rate the primary safety of vehicles using linked New Zealand crash and vehicle licensing data

Objective: Vehicle safety rating systems aim firstly to inform consumers about safe vehicle choices and, secondly, to encourage vehicle manufacturers to aspire to safer levels of vehicle performance. Primary rating systems (that measure the ability of a vehicle to assist the driver in avoiding crashes) have not been developed for a variety of reasons, mainly associated with the difficult task of disassociating driver behavior and vehicle exposure characteristics from the estimation of crash involvement risk specific to a given vehicle. The aim of the current study was to explore different approaches to primary safety estimation, identifying which approaches (if any) may be most valid and most practical, given typical data that may be available for producing ratings.

Methods: Data analyzed consisted of crash data and motor vehicle registration data for the period 2003 to 2012: 21,643,864 observations (representing vehicle-years) and 135,578 crashed vehicles. Various logistic models were tested as a means to estimate primary safety: Conditional models (conditioning on the vehicle owner over all vehicles owned); full models not conditioned on the owner, with all available owner and vehicle data; reduced models with few variables; induced exposure models; and models that synthesised elements from the latter two models.

Results: It was found that excluding young drivers (aged 25 and under) from all primary safety estimates attenuated some high risks estimated for make/model combinations favored by young people. The conditional model had clear biases that made it unsuitable. Estimates from a reduced model based just on crash rates per year (but including an owner location variable) produced estimates that were generally similar to the full model, although there was more spread in the estimates. The best replication of the full model estimates was generated by a synthesis of the reduced model and an induced exposure model.

Conclusions: This study compared approaches to estimating primary safety that could mimic an analysis based on a very rich data set, using variables that are commonly available when registered fleet data are linked to crash data. This exploratory study has highlighted promising avenues for developing primary safety rating systems for vehicle makes and models.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.     

Measurement of driving patterns of older adults using data logging devices with and without global positioning system capability

BACKGROUND: Methods to study driving patterns and exposure of older drivers have typically relied on surveys or driving diaries. Electronic data logging devices may offer a reliable, alternative method of measuring driving exposure, and global positioning system (GPS) technology may be able to provide further information about driving patterns. OBJECTIVES: The aim of this study was to compare a driving diary with two electronic data logging devices, one of which had GPS capability, in order to identify which method best assesses the driving exposure and habits of older drivers as well as the method most acceptable to study participants. METHOD: In this prospective cohort study we recruited 20 participants aged 70 years or more (mean 78; range 70-85) (15 men and 5 women). The participants' driving patterns were recorded for one week with an electronic data logging device with GPS (FleetPulse), followed by recording for a further week with an electronic data logging device without GPS (CarChip). During both time periods the subjects also completed a standard driving diary. RESULTS: More comprehensive information, including braking and acceleration patterns, duration of driving time, time of day, and maximum speeds, was collected with the electronic devices than with the driving diary. There was excellent correlation between the driving diary data and those obtained with the CarChip (r = 0.9; p < 0.01). The correlation between the driving diary data and the FleetPulse data was moderate (r = 0.56; p = 0.02). The subjects clearly preferred the electronic monitoring devices over the driving diary. GPS data were able to demonstrate driving routes. CONCLUSIONS: Electronic data logging devices are a valid method for recording the driving patterns of older adults. These devices also reduce burden and improve the completeness of data.     

Analysis of bus collision and non-collision incidents using transit ITS and other archived operations data

Introduction

This paper analyzes factors contributing to bus operations safety incidents at TriMet, the transit provider for the Portland Oregon metropolitan region.

Method

The analysis focuses on 4,631 collision and non-collision incidents that occurred between 2006 and 2009. Empirical analysis of these incidents draws on a wide array of operator-level data recovered by transit ITS technologies in combination with information from TriMet's human resources, scheduling, and customer relations databases. Incident frequencies are estimated in relation to operators' demographic characteristics, employment status, assigned work characteristics, service delivery and performance indicators, temporal factors, and customer information.

Results

Apart from identifying factors that are empirically related to the frequency of safety incidents, the findings offer insights into operations policies and practices that hold promise for improving safety.

Impact on Industry

Potential for safety improvement based on analysis of archived operations and human resource data.