Posted Speed Limit Research Articles

Do enhanced school zone policies improve pedestrians' safety? A deep learning-based case study of Osan City, South Korea

This study investigates the effectiveness of strengthened penalty policies in South Korean school zones by analyzing the changes in road users' behaviors, focusing on pedestrian-vehicle interactions. This study employed three surrogate safety measurements: vehicle speed, Pedestrian Safety Margins (PSM), and Predicted Collision Risk (PCR) level. The comprehensive analysis covers a spectrum of behaviors, from simple to complex, assessing the policy's impact in urban environments. The findings reveal several important insights. First, the policy enforcement resulted in a positive impact on vehicle speeds, with average speeds aligning with posted speed limits. Second, an increase was observed in yielding behaviors, particularly in school zones. However, much of this behavior appeared to be cosmetic, emphasizing the need for more safety-oriented yielding practices. Finally, the policy enforcement had a mixed impact on PCR levels, with a reduction in danger levels in school zones, yet an unexpected increase in danger levels in non-school zones. This study provides valuable insights into the effectiveness of strengthened penalty policies in school zones, particularly the development of a safe and sustainable urban environment.

Evaluation of Selected Factors Affecting the Speed of Drivers at Signal-Controlled Intersections in Poland

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals.

Impact of urban road characteristics on vehicle speed: Insights from Brescia, Italy

In managing road infrastructures, a key benchmark is the 85th percentile of vehicle speeds (V85). While V85 can be derived from spot speed samples, these are often lacking on each urban road. Thus, prediction models become valuable tools for examining the relationship between V85 and road characteristics. Although various models exist for rural roads, the impact of roadside characteristics and markings on V85 in urban road networks has been partially investigated, and the effect of traffic calming measures remains fragmented.This study aims to address these gaps by applying a methodology that sheds light on the effects of some variables that influence V85 on urban roads. Specifically, the methodology selects and segments roads along the urban road network of the municipality of Brescia (Italy) and collects data on both road characteristics and 48,000+ spot speed information. Following data cleansing, it processes these data according to three different multiple regression models to analyse the influence of various predictors on V85. Once the best model is estimated, its performance is evaluated, and the final list of significant predictors is obtained.The results revealed that V85 increases with longer homogeneous segments, greater distance to successive intersections, bituminous conglomerate roads with more lanes, and the presence of trees, visible road markings, and posted speed limits. Conversely, V85 decreases in the presence of on-street parking and other obstacles (e.g., walls and road posts), when the density of road intersections and pedestrian crossings increases, when the left crossbar width increases and when the land use crossed is commercial or office, residential or industrial. Nevertheless, no significant effect was found for several traffic calming measures included in the model.These findings can assist road authorities in verifying road operating conditions and planning infrastructure interventions to reduce speeds, thereby creating a safer urban environment for all users.

The role of posted speed limit on pedestrian and bicycle injury severities: An investigation into systematic and unobserved heterogeneities

The posted speed limit, as a proxy of actual speed, is one of the most fundamental predictors of active travelers’ (pedestrian and bicyclist) injury outcomes when involved in crashes with motor vehicles. Although earlier studies predominantly considered posted speed limit as an exogenous variable and provided highly insightful findings, majorities of them assume the effects of active traveler behavior to remain the same across different posted speed limits, which in turn neglect the heterogeneity in active traveler behaviors on high-speed roads vs. low-speed roads. This study proposes to develop a latent segmentation-based active traveler injury severity model to relax the homogeneity assumption of the posted speed limit by active traveler behavior. Specifically, this study proposes to estimate a latent segmentation-based correlated random parameters generalized ordered logit model to examine active travel injury severity mechanisms. The proposed model accommodates systematic heterogeneity in the effects of posted speed limit, crash year and active traveler group by using a piecewise linear function in injury severity component of the latent segment model. The model is demonstrated by using active traveler crash data from Queensland, Australia, for the years 2015 through 2019. To demonstrate the implications of the estimated models, a number of hypothetical scenario analyses are performed with a specific focus on active traveler behavior and reduction in posted speed limits. The outcomes from the hypothetical scenario analysis highlighted that a 76 % (73 %) reduction in active traveler fatalities can be achieved by converting 50–60 km/hr roadways to 10–40 km/hr roadways in the urban areas (rural areas) of Queensland. The outcomes of the study will allow us to identify effective speed management strategies while targeting those with high-risk behavior.

Correlation between carbon emissions, fuel consumption of vehicles and speed limit on expressway

This paper aimed to investigate the correlation between carbon emissions, fuel consumption, and speed limit. A theoretical model was derived based on the energy conservation law, which expresses the relationship between vehicle's fuel consumption and speed. Subsequently, a total of 40 sets of fuel consumption data were collected through field tests to verify the accuracy of the theoretical model at different speeds and different road longitudinal slope combinations. The fuel consumption was then converted to carbon emissions according to the carbon emission factors specified by Intergovernmental Panel on Climate Change (IPCC). In the field experiment, two types of cars and trucks, which are most common on the expressways in China, were selected. Finally, the travel speed under different posted speed limits was obtained through the previously established model, and the carbon emission changes of different vehicle types at different limited speeds are calculated. The results show that the speed limit has a significant impact on fuel consumption and carbon emissions. When the speed limit increased from 80 to 120 km/h, average vehicle speeds increased about 21%–27%, and fuel consumption and carbon emissions increased from approximately 33%–38%. Another interesting result was that the vehicle's fuel consumption and carbon emissions are only affected by speed. The results of the study explore the effect of speed limits on carbon emissions and provide evidence for road managers to set reasonable speed limits.

Evaluation of Run-off-Road Crashes Involving Battery-Electric Vehicles

Electric vehicles (EVs) are increasing in popularity and are among the fastest-growing segments in new vehicle sales. Battery-electric vehicles (BEVs) are the highest-selling class of EV. BEVs use a unique frame construction (i.e., a “skateboard” chassis) with a heavy battery pack located below the occupant’s floorpan, and are heavier and have lower centers-of-gravity than internal combustion engine vehicles (ICEVs)—factors that could affect their crashworthiness with roadside features. A crash study was performed to compare the crash conditions for BEVs to ICEVs and hybrid-electric vehicles (HEVs) covering the numbers of vehicles, posted speed limits, crash locations, time of day, weather conditions, crash severities, and roadside fixed objects struck. Eight state departments of transportation provided crash data spanning 5 years that included vehicle identification numbers or make, model, and year combinations, which were used to isolate BEVs from ICEVs. Crash results were reviewed and EV crash conditions were found to be similar to those of ICEV and HEV crash conditions. Moreover, the frequency of run-off-road crashes was similar for BEVs and ICEVs. Because no substantial differences were observed between the two datasets, it is recommended that impact conditions described in the Manual for Assessing Safety Hardware be utilized during preliminary evaluation of roadside hardware using electric vehicles.

Uncovering pedestrian midblock crash severity patterns using association rules mining

The current study investigated the contributing factors and temporal variation in pedestrian crashes at midblock, with a particular focus on the severity levels: fatal/severe, moderate injury, and minor/no injury. It used association rules mining to uncover patterns between crash-contributing factors. By generating, evaluating, and visualising association rules for each severity level within each cluster, significant findings were discovered. Significant associations are observed between fatal crashes on weekdays and factors such as alcohol or drug impairment and nighttime. Similarly, factors including one-way roadway type, summer, and 25 MPH posted speed limit have a strong association with moderate injury crashes during weekdays. On weekends, nighttime crashes with non-motorised vehicles have the strongest association with fatal/severe injury crashes. Moreover, the generated rules for nighttime pedestrian fatal/severe injury crashes highlighted physical or drug-impaired pedestrians as the predominant attribute. The findings can enhance pedestrian safety at midblocks through targeted interventions.

Bus rapid transit as arterial corridor traffic calming: The relationship between transit infrastructure and motor vehicle operating speeds

Objectives This article presents an analysis of the traffic-calming effects of bus rapid transit (BRT) by studying changes to motor vehicle speeds before and after implementation of Albuquerque Rapid Transit (ART) infrastructure in Albuquerque, New Mexico. Methods While ART construction was completed in spring 2018, the BRT buses did not operate until December 2019; providing a unique opportunity to explore the influence of BRT infrastructure sans BRT buses (i.e., to tease apart the effects of BRT infrastructure and operations). We used validated data from StreetLight InSight to compare before/after changes to average motor vehicle speeds and 85th percentile motor vehicle speeds at 46 ART sites and 36 control sites. Results Findings suggest that infrastructure associated with BRT systems can improve traffic safety by reducing vehicle speeds. Speed decreases at the ART sites were especially strong in terms of 85th percentile decreases, suggesting that the BRT infrastructure is especially effective at limiting excessive speeding. Motor vehicle 85th-percentile speeds along the ART corridor were reduced by 11.5% (compared to a 5.8% decrease at control sites). The 85th-percentile speeds at the ART sites decreased from 32.3 mph to 28.6 mph, which is an especially important range for vulnerable road-user safety outcomes. While ART intersections saw the largest decreases in absolute speeds (a reduction of 4.1 mph in 85th-percentile speeds), ART mid-block sites had larger decreases relative to the control mid-block sites (decreases in 85th-percentile speeds were 73.7% greater at ART mid-block sites than at control mid-block sites). BRT-related lane reductions were linked with particularly strong speed reductions; there were 85th-percentile speed reductions of 4.1 mph (12.6%) when general vehicle lanes were removed versus 2.2 mph (7.8%) when lanes were not removed. Conclusions Speed reductions were experienced across the ART corridor even though 87.0% of BRT locations did not have a change in posted speed limit, suggesting that physical changes to the roadway associated with BRT were impactful in terms of speed reductions and in turn could possibly promote traffic injury prevention by decreasing the number and severity of crashes.

Evaluating the effectiveness of brief anti-speeding messages disseminated within warning notices to driving offenders

Road safety messaging represents a long-standing strategy in efforts to reduce speeding, a behaviour which accounts for an estimated 30 % of all road crashes on Australian roads. This study aimed to further examine the effectiveness of such a strategy via the use of a novel message medium that included anti-speeding messaging disseminated to speeding offenders via warning notices issued by a Start Government Transport agency. Informed by the Step approach to Message Design and Testing (SatMDT), survey content was devised to compare the effectiveness of the messages across various outcome measures including intentions to stay within the posted speed limit. A total of 219 speeding offenders completed the online survey. Overall, the results found some positive responses regarding Intervention condition participants’ responses to the anti-speeding messaging featured within the warning notices. Among some of the key findings were that the Intervention condition participants reported relatively low levels of message rejection. Additionally, females responded more favourably to both messages than males. Collectively, the findings suggest that there is no harm in including such messages within warning notices providing such messages are concept-tested prior to use. Overall, the findings suggest this approach may represent a low-cost option for targeted message delivery.

Perceived effectiveness of traditional and technology-based speeding-related countermeasures

Introduction: Speeding behaviour contributes significantly to road crashes and subsequent injuries and fatalities. The purpose of this study was to examine which traditional countermeasures (i.e., police enforcement and on-road signs) and technology-based countermeasures (i.e., advanced driver assistance systems [ADAS] and in-vehicle speed audio alerts) drivers perceived as effective in assisting them to comply with posted speed limits. Methods: Participants (N = 680; Mage = 49.34 years) who held a current driver’s licence completed a 20-minute online survey. Participants in the experimental condition were randomly assigned to read one of four scenarios which differed based on location (urban or regional) and posted speed limit (60 km/hr or 100 km/hr) or to the control condition (no scenario), before answering questions about the perceived effectiveness of the traditional and technology-based interventions. For the experimental conditions, participants were instructed to respond to these questions based on how they would drive in the situation outlined in the scenario. Results: Low-level speeding behaviour was common, with 40.7 % reporting regularly driving 5 km over the posted speed limit in a 60 km/hr speed zone and 50.4 % reporting regularly driving 5 km over the posted speed limit in a 100 km/hr speed zone. A mixed ANOVA revealed that participants perceived police enforcement activities to be the most effective at assisting them to comply with posted speed limits when compared to other traditional and technology-based approaches. Further, ADAS was rated by participants as significantly more effective at assisting them to comply with the posted speed limit in the 100 km/hr urban condition compared to the 60 km/hr urban condition. Conclusions: Low-level speeding behaviour remains common practice and there is a need for continued roadside police presence to discourage this behaviour. Drivers with ADAS-equipped vehicles could also be encouraged to use systems, such as adaptative cruise control, to assist with speed management on high-speed roads.

Speeding behavior and associated factors in Buenos Aires, Argentina

Objective Excessive speed is a major risk factor for serious injuries and death. However, speeding remains a pervasive problem around the world. This study aimed to investigate the factors associated with speeding behavior in the city of Buenos Aires, Argentina. Methods A sample of vehicles (n = 34,967) from ten locations in the city was observed in two waves during 2021. Measurements were made at different times and days of the week. Observation sites were free of intersections, traffic lights, speed bumps and cameras, allowing drivers to speed freely. Data on speed, drivers and vehicle types were collected. Factors associated with speeding were identified through logistic regression analyses. Results 15.3% of vehicles were observed to be speeding. Roads with posted speed limits of 40 km/h showed higher speeding compared to 60 km/h roads. 77% of vehicles traveled above 30 km/h on local roads, and 30% above 50 km/h on avenues. Motorcycles, both commercial and private, showed a higher percentage of speeding compared to all other vehicles. Speeding was lower among women, among adults over 60 years of age, and among those using cell phones. Conclusion It is crucial to strengthen strategies for increased compliance with speed limits. Actions targeting motorcyclists must be a priority.

Safety-in-Numbers for route choice of bicycle trips: A choice experiment approach for commuters

Safety-in-Numbers (SiN) implies that the risk of collision per road user is less when there are more road users. Although the available literature has confirmed the existence of SiN as an objective measure of safety, the effect on perceived safety, especially in the context of bicycle riders, has received much less attention. This study investigates the SiN effect on the perceived safety of bicycle riders that influences route choice behavior. A stated preference survey was performed in the South Delhi district of Delhi. The effect of attributes like posted speed limit, the volume of motorized traffic, bicycle infrastructure, and bicycle traffic/ crowding on route choice behavior was investigated. A binary logit model was developed to quantify the effect of these attributes on route choice. The results indicate that, in general, riders prefer routes with more bicycle traffic, hence validating SiN. But the effect does not always hold. For some riders, in the presence of dedicated bicycle infrastructure, when the perceived safety is higher, the presence of more bicycle traffic acts as crowding and demotivates riders to choose that route. The study also reveals that riders prefer routes with a low volume of motorized traffic and dedicated bicycle infrastructure. The outcomes suggest that a policy that encourages infrastructural development to provide lateral separation will encourage more people, hence increasing bicycle mode share as well as the perceived safety of riders.

Toll road crash severity using mixed logit model incorporating heterogeneous mean structures

The current study examined 1,465 crash observations (2017–2021) from Louisiana, identifying significant variables grouped into three major categories: drivers’, crash, and road characteristics. Considering crash injury severity as a dependent variable, we employed classic Multinomial Logit (MNL) model, and several other models to address unobserved heterogeneity in crash data including Random Parameter Logit (RPL), Random Parameter Logit with Heterogeneity in Means (RPLHM), and Random Parameter Logit with Heterogeneity in Means and Variance (RPLHMV). Our findings highlight the impact of factors such as driver gender, age, traffic violations, driver distractions, crash types, surface conditions, and roadway attributes on crash injury severity. These insights emphasise the complexity of toll road safety and inform targeted interventions to mitigate crash injury severity. Notably, male drivers and those under 25 years old increased property damage likelihood, while factors like driver distractions and lower posted speed limits reduced the likelihood of severe injuries or fatalities.

Analysis of crash severity in rear-end and angle collisions on an urban roundabout in heterogeneous non-lane-based traffic conditions

ABSTRACT This study examines crash risk factors and develops a severity model for rear-end and angle collisions in roundabouts and its vicinity in non-lane-based heterogeneous traffic. The rear-end severity is primarily influenced by posted speed limits, violation type, collision partner, single-vehicle collision, and vehicle age. Whereas type of violation, collision partner, and presence of road surface condition are the critical influencing factors for determining the severity outcomes of angle collision. The marginal effects of angle collision results indicated that the probability of fatal injury increased by 28% due to heavy-vehicle-vulnerable road user collisions. In the rear-end case, risk factors such as collision partners (heavy vehicles-vulnerable road users) increased by 18% and single-vehicle collisions increased by 25%, highly influencing the severity of fatal injury at roundabouts. Notably, rear-end collisions exhibit higher severity compared to angle collisions. This study provides valuable insights for developing strategies to mitigate roundabout collision severity.

Visual hazardous models: A hybrid approach to investigate road hazardous events

Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009–2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 820 times more fatal/grievous injuries than simple injuries on expressways per 1000 crosswalk crashes (posted speed limit: 100 km per hour). Working pedestrian age group (25–64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.

The influence of roadway characteristics and built environment on the extent of over-speeding: An exploration using mobile automated traffic camera data

Over-speeding is a pivotal factor in fatal traffic crashes globally, necessitating robust speed management strategies to augment road safety. In 2021, the National Highway Traffic Safety Administration reported over 12,000 speed-related fatalities in the United States alone. Previous studies aggregated over-speeding tendency; however, the extent of over-speeding has a significant implication on the crash outcome. This study delves into the prevalence and magnitude of over-speeding in various scenarios, utilizing data from traffic cameras in Edmonton, Canada, and employing a negative binomial statistical model for analysis. The model elucidates the significance and likelihood of over-speeding tendencies by incorporating temporal and built environment variables—year, month, number of lanes, dwelling unit types, school-related, and open green space. Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of various factors. Notably, the estimated impact of the posted speed limit is up to over two times for the disaggregated models compared to the aggregated model. Further, the summer months exhibit a roughly 25% uptick in speed limit violations for aggregated models compared to about 40% for disaggregated approaches. Conversely, a discernible decline in over-speeding tendencies is observed with camera enforcement, showcasing a 25% reduction over four years. Built environment variables presented mixed results, with one-unit dwellings associated with a 12% increase in over-speeding, while proximity to schools indicated a 10% decrease. These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and countermeasures to curtail speed limit violations and bolster overall road safety conditions.

Exploring Uncertainties for Crashed Vehicle Travelling Velocity Tolerance in One-Dimensional Crash Configuration

Evidence suggests that measurement uncertainty is among the most important factors for reliability assessment. Uncertainty plays a vital role in maximising velocity calculation accuracy in crash reconstruction works. It serves as a reliable calculation tolerance, whereby its contribution becomes more significant when the calculated initial velocity is nearly approaching the posted speed limit. The results suggested that damage width and midpoint offset have a low sensitivity of Delta-V from 0.5 km/h to 0.75 km/h for up to 80 cm and 60 cm measurement errors, respectively. For the crush profile variable, a lesser measurement error of 8 cm results in 0.9 km/h deviation in Delta-V. Meanwhile, vehicle mass, drag factor and post-impact displacement have a higher sensitivity of Delta-V, as compared to the two previous variables. The calculation results may deviate from the actual figure for 1 km/h with a missing 75 kg adult occupant. Deviation of almost 2 km/h initial velocity was observed for as low as 0.5 drag factor determination fault. Moreover, with 1 m displacement measurement inaccuracy is also giving rise to the resulting initial speed of 2 km/h. Overall, the principle direction of force recorded the greatest velocity sensitivity among the investigated variables. Within 20o and 45o of inaccurate principle direction of force, the Delta-V deviation increases exponentially, up to 4.25 km/h. The presented findings are beneficial in terms of crash investigator judgment for the thoroughness measurement while conducting the assessment of the crashed vehicle and the crash scene. This investigation will also contribute to enhancing our understanding of tolerance determination for more accurate velocity estimation.

Data-driven exploration of traffic speed patterns to identify potential road links for variable speed limit sign implementation

ABSTRACT The focus of this study is to identify potential road links suitable for implementing a variable speed limit (VSL) sign by analyzing real-world traffic speed data collected over one year in Charlotte, North Carolina, United States. Exploratory and bivariate analyses were conducted to examine variations in traffic speed patterns during weekdays and weekends across eight specific timespans. The results revealed that road links with lower posted speed limits consistently experienced traffic speeds exceeding the posted speed limits. The mean traffic speeds are generally close to the posted speed limits for road links with higher posted speed limits while the 85th percentile speeds exceeded the posted speed limits, indicating a potential need for VSL sign implementation. The road links with 40 mph or 50 mph posted speed limits have a unique pattern compared to road links of other posted speed limit clusters. The mean traffic speed on these road links decreased as the standard deviation increased. The findings contribute to an improved understanding of traffic speed patterns and provide valuable insights for implementing a VSL sign.

Predicting pedestrian-involved crash severity using inception-v3 deep learning model

This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016–2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into three categories: fatal, injury, and no injury. The Boruta algorithm was applied to determine the importance of variables and investigate contributing factors to pedestrian crash severity, revealing several associated aspects, including pedestrian gender, pedestrian and driver impairment, posted speed limits, alcohol involvement, pedestrian age, visibility obstruction, roadway lighting conditions, and both pedestrian and driver conditions, including distraction and inattentiveness. To address data imbalance, the study employed Random Under Sampling (RUS) and the Synthetic Minority Oversampling Technique (SMOTE). The DeepInsight technique transformed numeric data into images. Subsequently, five crash severity prediction models were developed with Inception-v3, considering various scenarios, including original, under-sampled, over-sampled, a combination of under and over-sampled data, and the top twenty-five important variables. Results indicated that the model applying both over and under sampling outperforms models based on other data balancing techniques in terms of several performance metrics, including accuracy, sensitivity, precision, specificity, false negative ratio (FNR), false positive ratio (FPR), and F1-score. This model achieved prediction accuracies of 93.5%, 77.5%, and 85.9% for fatal, injury, and no injury categories, respectively. Additionally, comparative analysis based on several performance metrics and McNemar’s tests demonstrated that the predictive performance of the Inception-v3 deep learning model is statistically superior compared to traditional machine learning and statistical models. The insights from this research can be effectively harnessed by safety professionals, emergency service providers, traffic management centers, and vehicle manufacturers to enhance their safety measures and applications.

Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques

Objective Motorcycle crashes often result in severe injuries on roads that affect people’s lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. Methods This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. Results The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. Conclusion The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.