Safety Countermeasures Research Articles

Investigation of pedestrian and bicyclist safety in public transportation systems

IntroductionUnderstanding the crash risk factors associated with pedestrians and bicyclists is vital to promoting pedestrian- and bicycle-friendly communities and ensuring the sustainability of the transportation system. Transit stations perform as important mode transfer locations for bicyclists and pedestrians. However, past studies on contributing factors to pedestrian and bicycle crash in transit station service areas are limited. This study develops a model to guide the prioritization of areas for bicyclist and pedestrian safety improvement. MethodThis study analyzes pedestrian and bicyclist safety in subway station service areas using publicly available crash data from New York City (NYC) area. For the model development, various subway station and built-environment characteristics related variables were aggregated to the subway station service area level. Spatial regression and random parameter models were developed to account for spatial heterogeneity and spatial dependency in the crash data. Performance measures of the developed models showed that the Semi-Parametric Geographically Weighted Poisson Regression (S-GWPR) model better explained the variation in the crash frequency. ResultsThe results showed that subway stations in NYC with high ridership were positively associated with pedestrian and bicycle crashes. The number of bus stops in a subway station service area was also positively associated with pedestrian and bicycle crashes. This study also found that additional bike lanes can reduce bicycle-involved crashes. ConclusionAreas surrounding transit stations, where pedestrian and bicyclist activities are usually high, require special attention to deploy appropriate safety countermeasures. The findings of this study suggest that cities can install more bike lanes to promote the safety of bicyclists. Using the results of this study, traffic safety planners can prioritize areas for the implementation of safety countermeasures in NYC.

Injury severity analysis of time-of-day fluctuations and temporal volatility in reverse sideswipe collisions: A random parameter model with heterogeneous means and heteroscedastic variances

Problem: Sideswipe collisions in the opposite direction often result in more severe injuries than the typical same-direction crashes, especially when light trucks are involved. This study investigates the time-of-day fluctuations and temporal volatility of potential factors that affect the injury severity of reverse sideswipe collisions. Methods: A series of random parameters logit models with heterogeneous means and heteroscedastic variances are developed and utilized to explore unobserved heterogeneity inherent in variables and preclude biased parameter estimation. The segmentation of estimated results is also examined through temporal instability tests. Results: Based on crash data in North Carolina, a number of contributing factors are identified that have profound associations with obvious and moderate injuries. Meanwhile, significant temporal volatility is observed in the marginal effects of several factors such as driver restraint, alcohol or drugs impact, Sport Utility Vehicle (SUV) at fault, and adverse road surface across three different periods. Fluctuations in the time of day indicate that restraint with belts is more effective in mitigating the obvious injury in the nighttime, and high-class roadway sustains a higher probability of resulting in more serious injury compared to the daytime. Practical Applications: The findings of this study could help further guide the implementation of safety countermeasures related to atypical sideswipe collisions.

An advanced approach to the system safety in sociotechnical systems

The safety performance of sociotechnical systems (STSs) and their main components (e.g., human, organization, and technology) vary due to numerous performance shaping factors (PSFs). However, previous research mainly focused on studying limited PSFs related to human functions, while organization and technology functions have often been ignored. This paper aims to propose a systematic approach to identify PSFs, quantify their importance level and influence on the performance of complex sociotechnical systems’ functions. To this end, we first developed a holistic PSFs Taxonomy based on sociotechnical systems design and then employed novel Interval-Valued Spherical Fuzzy Sets (IVSFS) and Best Worst Method to quantify the importance of performance. We tested the proposed model’s capability on maintenance operations in the chemical process plants and compared the model with the previous research considering fourteen criteria. The findings revealed the approach's effectiveness in dealing with epistemic uncertainty, vagueness, and fuzziness in the knowledge acquisition process. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors to maintenance operations. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance.

Real-time driving risk assessment using deep learning with XGBoost

Traffic crashes typically occur in a few seconds and real-time prediction can significantly benefit traffic safety management and the development of safety countermeasures. This paper presents a novel deep learning model for crash identification based on high-frequency, high-resolution continuous driving data. The method consists of feature engineering based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) and classification based on Extreme Gradient Boosting (XGBoost). The CNN-GRU architecture captures the time series characteristics of driving kinematics data. Compared to normal driving segments, safety-critical events (SCEs)—i.e., crashes and near-crashes (CNC)—are rare. The weighted categorical cross-entropy loss and oversampling methods are utilized to address this imbalance issue. An XGBoost classifier is utilized instead of the multi-layer perceptron (MLP) to achieve a high precision and recall rate. The proposed approach is applied to the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) data with 1,820 crashes, 6,848 near-crashes, and 59,997 normal driving segments. The results show that in a 3-class classification system (crash, near-crash, normal driving segments), the accuracy for the overall model is 97.5%, and the precision and recall for crashes are 84.7%, and 71.3% respectively, which is substantially better than benchmarks models. Furthermore, the recall of the most severe crashes is 98.0%. The proposed crash identification approach provides an accurate, highly efficient, and scalable way to identify crashes based on high frequency, high-resolution continuous driving data and has broad application prospects in traffic safety applications.

A user-friendly lithium battery simulator based on open-source CFD

The growing use of lithium-ion batteries (LIBs) for automotive and stationary storage applications has put increasingly stringent requirements on battery thermal management and battery safety. An open-source platform that can bridge battery electrochemical models and computational fluid dynamics (CFD) can be of great benefit for designing advanced battery thermal management systems and safety countermeasures by allowing the simulation and prediction of battery responses to various thermofluidic environments and thermal boundaries. Here we develop a user-friendly battery simulator based on the open-source CFD code OpenFOAM. The simulator contains the in-house solvers for the two mostly used physics-based battery models, the single particle model, and the pseudo-two-dimensional model. GUIs are also developed based on Qt for simulation automation and ease of use. To demonstrate the functionality of the developed simulator, the electrochemical performance and internal states of half LIB cells and full LIB cells with different chemistries at different operating conditions are simulated. The obtained results agree well with other existing battery simulators. Due to its native integration with OpenFOAM, the new battery simulator is readily extendable to incorporate various CFD models and other physics to meet the simulation needs of thermal management and safety design for LIBs.

Pattern recognition from injury severity types of frontage roadway crashes

Frontage roads are the supporting roadways that are along freeways and fully controlled principal arterial roadway networks in the U.S. These roads are designed in a way to provide access between the freeways, principal arterials, and surrounding business entities. For Texas, these roadways are the leading design resolution for providing access along rural freeways and principal arterial roadways. These roadways are generally two-ways for rural and less developed urban areas and are mostly one-way for urban and city-centered roadways. Although frontage roadways possess major safety concerns, the safety performance of these roadways has not been well studied. This study collected six years of frontage road crash data from Texas to determine the patterns of associated factors by applying a dimension reduction method known as cluster correspondence analysis (CCA). The results revealed four clusters for each of the two datasets based on crash injury types. For fatal and injury crashes, the major clusters are distraction-related crashes at signalized intersections, segment-related crashes at dark unlighted conditions, yield signed intersection locations and segments with no TCDs, and intersection crashes on undivided roadways. For the no injury crash dataset, the key clusters are segment crashes in dark conditions and rain, crashes at signalized intersections with both drivers going straight, segment crashes with both drivers going straight with marked lanes or no TCDs, and intersection-related collisions on undivided roadways. Based on the evaluation results, suitable safety countermeasures and policy initiatives to reduce frontage road crash frequencies can be singled out.

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.

Analysis of contributory factors of fatal pedestrian crashes by mixed logit model and association rules

Pedestrians are the most vulnerable road users and pedestrian crashes are a major concern both for their number and their severity. In Italy, pedestrians account for 34% of the road fatalities in urban area. To improve pedestrian safety, this study is aimed at analysing the roadway, environmental, vehicle, driver and pedestrian-related factors that are associated with fatal pedestrian crashes in Italy and providing insights for the development of effective countermeasures. This study used an econometric model, the mixed logit model, and a machine learning algorithm, the association rules, to analyse 101,032 pedestrian crashes that occurred in Italy. Study results identified several factors associated with fatal pedestrian crashes. The mixed logit identified 46 significant indicator variables (1 with random parameter), and the association rules provided 119 valid rules. F-measure and G-mean showed higher prediction performance of the mixed logit over the association rules. Study results recommend using both models as complementary approaches since their combination is effective in providing meaningful insights about pedestrian crash contributory factors and their interdependencies. To address the contributory factors identified by the study, behavioural/engineering pedestrian safety countermeasures are recommended. The findings provided new insights for transportation agencies to develop effective countermeasures for pedestrian safety improvement.

Developing Commercial Motor Vehicle Crash-Specific Safety Performance Functions at Interchange Ramp Terminals in Kentucky

Commercial motor vehicle (CMV) drivers often experience difficulties in turning and crossing at ramp terminals. CMV-involved crashes could cause queue spillback at ramp terminals and possibly nearby freeway mainline and crossroads. The safety and mobility of ramp terminals for accommodating CMVs is thus of significant importance. However, interchange ramp terminals have rarely been investigated in previous safety studies, especially with regard to CMV-involved crashes. This study aimed at examining CMV-related crashes that occurred at ramp terminals. Heterogeneous negative binomial (HTNB) and traditional negative binomial (NB) models were fitted and compared while developing a safety performance function (SPF) for predicting CMV crashes and identifying high-risk ramp terminals. Information on crash history and site-specific characteristics was collected at 285 ramp terminals in the state of Kentucky between 2015 and 2019. The results showed that the HTNB model outperformed the NB model in relation to various goodness-of-fit measures (e.g., likelihood ratio test “LRT”, Akaike information criterion “AIC” and McFadden Pseudo R-squared statistic). The predicted crash frequencies while applying the HTNB model were then used to identify and rank high-risk ramp terminals. Ramp terminals with signalized traffic control type, with greater average daily traffic on the exit ramp, with two or more traffic lanes on the exit ramp, and adjacent to commercial or industrial areas were found to experience more CMV crashes. Several safety countermeasures were proposed to alleviate CMV-involved crashes at ramp terminals. One example is ensuring the presence of physical medians on crossroads (or major roads) ahead of exits ramps.

Development of estimation method for material property under high strain rate condition utilizing experiment and analysis

Safety countermeasures have been implemented in nuclear power plants in Japan under the new nuclear regulation. The importance of structural integrity of components which are attacked by tornado missile has been growing as one of the countermeasures. In the case of impact analysis for tornado missile, strain rate dependency needs to be considered in the stress strain relationship, which is different from static analysis. However, it is difficult to obtain the data of stress strain relationship which strain rate is over 103 s−1 due to the restriction of test equipment. Therefore, a new method to estimate strain rate dependency of stress strain relationship on the high strain rate condition was developed. An inverse analysis using the deformation data obtained by the Taylor impact test and FEM analysis was applied in the method. Stress strain relationship estimated by the method was verified by comparing it to Split Hopkinson Bar (SHB) test result. It was confirmed that the method could estimate strain rate dependency of stress strain relationship accurately.

Investigating the role of beliefs in influencing the hand-held and hands-free mobile phone use among pedestrians in India

Mobile phone distraction is a significant contributor to pedestrian injuries. However, mobile phone engagement among pedestrians has been scarcely explored in a developing country like India. The present study utilized the beliefs-based theory of planned behaviour to examine the association between pedestrian beliefs towards distracted walking (behavioural, normative, and control) and their mobile phone use frequencies. Based on a survey of 560 pedestrians (64.6% males), it was found that the major use of mobile phones was for listening to music (30.7%), followed by receiving a call (25%), making a call (18.9%), texting (9.8%), navigation (8.5%) and internet browsing (7.1%). A series of multivariate ANOVAs and logistic regression models were developed to investigate the relationships between the beliefs and frequencies of mobile phone use in hands-free and hand-held conditions. Significant multivariate differences were found for behavioural and normative beliefs in hands-free conditions and all three types of beliefs in hand-held conditions. The frequency of mobile phone use was significantly predicted by normative beliefs (p < 0.001) in the hands-free condition, and by behavioural (p = 0.041) and normative beliefs (p = 0.004) in the hand-held condition. The findings may assist the road safety countermeasures in addressing the issue of pedestrian distraction.

Nursing Safety Management and Countermeasures of Patients with Chronic Renal Failure and Uremia

Nursing Safety Management and Countermeasures of Patients with Chronic Renal Failure and Uremia

A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records

In recent years, individual drivers‘ crash risk assessments have received much attention for identifying high-risk drivers. To this end, we propose a probabilistic assessment method of crash risks with a reproducible long-term dataset (i.e., traffic violations, license, and crash records). In developing this method, we used 7.75 million violations and crashes of 5.5 million individual drivers in Seoul, South Korea, from June 2013 to June 2017 (four years). The stochastic process of the Bayesian network (BN), whose structure is optimized by tabu-search, successfully evaluates individual drivers' crash and violation probability. In addition, the cluster analysis classifies drivers into five distinctive groups according to their estimated violation and crash probabilities. As a result, this study found that the estimated average crash rate within a cluster converges with the actual crash rate by the proposed framework without privacy issues. We also confirm that violation records and expected crash probability are strongly correlated, and there is a direct relationship between a driver’s previous violations and crash record and the future at-fault crash. The proposed assessment method is valuable in developing proactive driver education programs and safety countermeasures, including adjusting the penalty system and developing user-based insurance by recognizing dangerous drivers and identifying their properties.

Early Response to COVID-19

Early Response to COVID-19

Severity Analysis of Large-Truck Wrong-Way Driving Crashes in the State of Florida

Wrong-way driving (WWD) crashes lead to severe injuries and fatalities, especially when a large truck is involved. This study investigates the factors associated with crash-injury severity in large-truck WWD crashes in Florida. Various driver, roadway, weather, and traffic characteristics were explored as explanatory variables through a random parameter ordered logit model. The study also accounted for heterogeneity by identifying random parameters in the model and introducing interaction effects as potential sources of such heterogeneity. The findings indicate that not using a seatbelt, driving under the influence of drugs, and a driving speed of 50–74 mph were more likely to result in fatal crashes. On the contrary, female drivers, private roadways, and sideswipe collisions showed negative impacts on crash-injury severity. The model identified two random parameters, including a speed of 25–49 mph and early-morning crashes. The interaction effects showed that when driving at a speed of 25–49 mph, young drivers (under 20 years old) and middle-aged drivers (36–50 years old) were the sources of heterogeneity, decreasing crash-injury severity. Understanding the contributing factors of large-truck WWD crashes can help policymakers develop safety countermeasures to reduce the associated injury severity and improve truck safety.

Dynamic All-Red Extension: An Innovative Safety Countermeasure to Treat Red Light Running Crashes

The purpose of this project was to evaluate the safety effectiveness of Dynamic All-Red Extension (DARE) systems. North Carolina Department of Transportation has used DARE to combat red light running (RLR) crashes on major road approaches of selected rural, isolated traffic signals since 2011. Sixteen intersections were included in the before–after analysis. DARE systems in this study were implemented to target a specific crash type: frontal impact crashes (angle, left turn, right turn, head on) caused by vehicles traveling through the intersection and running the red light. Although there was some variability in the magnitude and significance of the results among treatment groups, there was a general reduction in the frequency of target RLR crashes. The crash modification factor (CMF) using empirical Bayes (EB) methodology was 0.93 (7% reduction in target RLR crashes) for all 16 treatment sites; for seven multilane at two-lane sites where DARE was the sole treatment, CMF was 0.65 (35% reduction in target RLR crashes), which was statistically significant at the 95% confidence level. The benefit–cost ratio of the treatment was 143:1, realized from low installation cost and the high cost of RLR crashes. The treatment appeared promising as a systemic safety treatment applied to isolated intersections with higher speed limits, especially for intersections with multilane major roads. Safety professionals should consider adding this safety countermeasure to the limited list of tools available to treat RLR crashes. DARE was inexpensive, demonstrated safety benefits, and caused minimal delays in rural applications without creating driver habituation.

A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime

This study explored the effects of contributing factors on crash frequency, by injury severity of all, daytime, and nighttime crashes that occurred on freeways. With three injury severity outcomes classified as light injury, minor injury, and severe injury, the effects of the explanatory variables affecting the crash frequency were examined in terms of the crash, traffic, speed, geometric, and sight characteristics. Regarding the model estimations, the lowest AIC and BIC values (2263.87 and 2379.22, respectively) showed the superiority of the random-parameter multivariate negative binomial (RPMNB) model in terms of the goodness-of-fit measure. Additionally, the RPMNB model indicated the highest R2 (0.25) and predictive accuracy, along with a significantly positive α parameter. Moreover, transferability tests were conducted to confirm the rationality of separating the daytime and nighttime crashes. Based on the RPMNB models, several explanatory variables were observed to exhibit relatively stable effects whereas other variables presented obvious variations. This study can be of certain value in guiding highway design and policies and developing effective safety countermeasures.

An empirical analysis of driver injury severities in work-zone and non-work-zone crashes involving single-vehicle large trucks

Objectives Florida ranks among the states with the highest rates of work-zone crashes involving large trucks. With significant emphasis in Florida’s strategic highway safety plan, understanding work-zone crashes involving large trucks and resulting injury severities is critically important. This study investigated the contributing factors influencing the driver injury severity of single-large-truck crashes in work zones, benchmarked against non-work zones in Florida. Methods Using work-zone and non-work-zone crash data from 2011 to 2019 (inclusive), driver-injury severities in single-large trucks crashes were studied using random parameters logit models that allow for possible heterogeneity in the means and variances of parameter estimates. The available data included a wide variety of factors known to influence driver injury severity, including spatial and temporal; vehicle and traffic; roadway, harmful events, and driver characteristics. Results The model estimates produced fundamental shift in unobserved heterogeneity for work-zone and non-work-zone crashes involving single large trucks. More importantly, the likelihood of large truck drivers’ injury severity is about fourteen-times higher on rural and six-times higher on urban interstate highways and 1.3 times lower with 10 miles per hour below the posted speed limit for large trucks inside work zones relative to non-work zones. The model results also indicate that the likelihood of severe driver injury is higher for heavy truck (more than 26000 pounds), a lane-shift work-zone configuration, and careless driving in work-zone crashes involving single large trucks. Conclusions The model findings add valuable insights to have profound effects in the safety performance of large trucks and in-vehicle safety technologies, such as, Advanced Driver Assistance Systems, for careful driving along the work-zone segments with lower speed, leading to Automated Driving Systems. These measures include various policy-related safety countermeasures including revisiting traffic control plan for lane-shift on highways specifically for large trucks and developing training modules for Florida registered truck drivers.

In-vehicle warnings for work zone and related rear-end collisions: A driving simulator experiment

Work zone area on roads is a critical component of road networks which concerns the safety of workers and passing by drivers. However, the passive speed reduction and lane changes caused by lane closure have led to frequent rear-end collisions in work zone areas. To help drivers better anticipate work zone situation and reduce collision risks, this paper proposes two types of in-vehicle warnings for work zone areas: Leading Vehicle Brake Warning (LVBW), and Lane-Closed Warning & Leading Vehicle Brake Warning (LCW & LVBW). The LVBW delivers a danger warning message to drivers upon the brake of the leading vehicle, while the LCW & LVBW provides an additional work-zone position message to remind drivers to decelerate in advance. A driving simulator experiment was conducted with 44 participants (24 males and 20 females) to test drivers’ performance in work zone area under different conditions, comprising two warning types (LVBW vs. LCW & LVBW), four warning times (3 s, 5 s, 7 s and 9 s) and two visibility conditions (clear and foggy weather). The results showed significant safety benefits of the lane-closed warning message under the LCW & LVBW condition. In contrast, the warning of leading vehicle’s brake in both LVBW and LCW & LVBW conditions had limited efficacy, which indicates that earlier warning about lane-closure is important to assist drivers in anticipating the complex situations in work zones. Drivers’ speed control and collision avoidance performances were impaired in fog, but the impairment was compensated by the warning messages. Compared with male drivers, female drivers tend to be more cautious when approaching the work zone areas. Overall, this study plays a pioneering role in developing effective safety countermeasures for work zone areas and providing strong support for implementing in-vehicle warning technologies.

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.