Roadway Characteristics Research Articles

Midblock Pedestrian Signal Safety Effectiveness

The midblock pedestrian signal (MPS) operates as a coordinated actuated vehicular traffic signal that enables pedestrians to cross at midblock. The MPS has been used in multiple locations, including for more than 40 years in Los Angeles. It differs from a typical pedestrian crossing signal by allowing the red display for vehicular traffic to flash at the same time as the pedestrian timing for the crossing, reducing delay to vehicular traffic. The research team built a database of crash and roadway characteristics data for treated and control sites located in three states (California, Utah, and Texas). Three control groups were considered: all control sites (included intersections with two, three, or four legs and traffic control signals); two-leg sites with any type of pedestrian traffic control other than the MPS; and two-leg sites with nonactive or not present pedestrian traffic control devices. It was found that the MPS is associated with a reduction in the number of crashes involving pedestrians and a reduction in the number of all fatal and injury crashes when the control group is two-leg sites with nonactive or not present pedestrian traffic control devices. The following crash modification factors for the MPS were identified: 0.554 for crashes involving pedestrians; and 0.660 for all crashes.

An exploratory analysis of two-vehicle crashes for distracted driving with a mixed approach: Machine learning algorithm with unobserved heterogeneity

Two-vehicle crashes resulting from distracted driving led to a higher number of fatalities and serious injuries over time. This study utilized machine learning and econometric models to investigate two-vehicle-involved distracted driving crashes from the Crash Report Sampling System within the United States. XGBoost and Random Forest were utilized to identify the top variables based on SHAP value, although mixed logit with unobserved heterogeneity was used to model injury severity. The model results indicate that there is a complex interaction of driver characteristics, such as demographics (male drivers), driver actions (careless driving, driving more than the speed limit of more than 15 mph, hitting a stopped vehicle), a driver without violation history, turning violation, drinking, roadway characteristics (non-interstate highways, undivided and divided roadways with positive barrier, curved roadways, dry surface), environmental conditions (rainy weather), vehicle attributes (motorcycle, displacement volume up to 2500 cc, newer vehicle within five years of crash-involvement), temporal characteristics (4–6 PM, July–September, and year 2017). These findings underscore the importance of driving behavior and roadway design. As such, prioritizing efforts to address distracted driving behavior through driver training and law enforcement, as well as considering its implications for roadway design and maintenance, becomes crucial.

Safety Effectiveness of Rectangular Rapid Flashing Beacons Pedestrian Enhancement

A rectangular rapid flashing beacon (RRFB) is a pedestrian crosswalk countermeasure system that cautions drivers by providing them with real-time warning about the presence of pedestrians in an upcoming crosswalk. This paper investigated the safety effectiveness of existing RRFBs installed in the state of Florida between 2013 and 2018. Additionally, traffic volume, roadway characteristics, and land-use mix were included as explanatory variables. The study evaluated 154 treated sites with RRFB installations and 158 control sites with similar characteristics. Safety performance functions (SPF) were developed, and crash modification factors (CMF) were calculated using the empirical Bayes (EB) methodology for various crash types. A CMF of 0.31 for total pedestrian crashes was estimated, showing that RRFBs have the potential to reduce 69% of total pedestrian-involved crashes. CMF for total vehicular crashes and rear-end crashes were also investigated to ascertain whether RRFBs have adverse effects on rear-end crashes. CMF for total vehicular crashes was found to be 1.14, showing that RRFBs do not have positive effect in reducing total vehicular crashes. Similarly, it was observed that CMF for the rear-end crashes was 1.11, indicating RRFBs might have adverse effect on rear-end crashes because vehicles needs to slow down or stop due to the presence of pedestrians. CMFunctions for significant variables for pedestrian, rear-end, and total crash models were also estimated in this study. An economic analysis indicated that the implementation of RRFBs will yield a benefit to cost ratio (calculated over 3 years) of approximately 85.5 with a 4% discount rate.

Crash severity analysis of single-vehicle rollover crashes in Namibia: A mixed logit approach

Road traffic crashes are a leading cause of serious injuries and fatalities globally and place unnecessary developmental and economic burdens on low- and middle-income countries (LMIC) as they account for the vast majority of the world's road related deaths. This is typically due to both the increased frequency of dangerous crash types and the increased severity of said crash types. Rollover crashes while quite rare are a particularly dangerous crash type among other various crash types. In the case of Namibia, rollover crashes reportedly accounted for 34% of both road related injuries and fatalities in Namibia for 2020. When compared to high-income countries the issue of rollover crash severity in Namibia and like sub-Saharan African (SSA) countries becomes apparent. Therefore, it crucial to understand the contributing factors and their associated effects on rollover crash severities in these countries. This study aims to investigate and identify the significant factors influencing crash severities and their associated impact magnitudes on single-vehicle rollover crashes in Namibia by adopting a mixed logit with heterogeneity in means and variances approach to account for unobserved heterogeneity in the data. Although it is not without its limitations the dataset used in this study includes single-vehicles rollover crash instances from 2014 to 2016 within Namibia and is able to provide unique details for the crash observations including various driver, environmental, roadway, and vehicle characteristics. Results from this study indicate several factors including weekends, open roadways, and minibuses to be significantly increasing the crash severity of single-vehicle rollover crashes. Additionally, results provide a basis for which researchers and policy makers can understand rollover crashes in Namibia and adopt an appropriate approach to address this issue, such as, Safe Systems. Such an approach would include but not be limited to the implementation of roadside features, educational campaigns, speed enforcement, and vehicle standards policy.

Application of Realistic Artificial Data for Testing Various Crash Safety Analyses: A Case Study for Rural Two-Lane Undivided Highways

Traffic safety research will continue to create new and improved methods for the analysis of safety data. Even if these models perform well, the precise underlying crash mechanism remains unknown. The missing piece is a tool that may be used to evaluate how well a method identifies the cause-and-effect relationship in the data. To meet these safety analysis needs, a high-resolution disaggregate data generating process called realistic artificial data (RAD) was developed. This tool simulates crash incidence on transportation facilities, capturing real-world causal links between individual roadway characteristics and crashes. The objective of this study was to check if the stochasticity embedded in the RAD generation process will be consistent for different random seeds and miles of data generated from the tool. To accomplish this, 10 different datasets were generated from the RAD tool and estimated using the negative binomial model; parameter estimates from the model were checked using a revised Wald statistic. The t-statistic estimates showed that the differences among the parameter values across the dataset are within a statistically acceptable level. Given the stability of the tool, the RAD framework can be useful in addressing the known limitations and knowledge gap in assessing the extent to which a statistical method succeeds in identifying the cause-and-effect relationship in the data. This in return can help guide and improve the practical application of statistical methods and eventually lead to more effective safety countermeasures that can reduce highway-related injuries and fatalities.

Toolkit for the Assessment of Safety at Bus Stops

Safety at bus stops is a major concern of transit agencies. This research aimed to develop a data-driven method for estimating bus stop safety scores that will assist transit agencies in ranking and prioritizing bus stops for improvement considerations. This study presents a bus stop scoring methodology that uses a data-driven approach based on multiple data sources. The methodology was developed using six criteria: bus stop characteristics inventory, roadway characteristics inventory (RCI), land use and demographics, pedestrian crash frequency, annual average daily traffic, and annual ridership. Crash data were collected from SignalFour Analytics, and roadway characteristics data were retrieved from the Florida Department of Transportation RCI database. The Florida Geographic Data Library was used for retrieving census and land use data, while the Florida Transit Information System was employed to extract information on transit stops. A data-driven method was used to develop a priority list for improvements among the selected bus stops. The methodology was implemented in a case study including three transit agencies: Hillsborough Area Regional Transit Authority, Jacksonville Transportation Authority, and Miami-Dade Transit. A bus stop priority list was generated for each agency, with the highest-ranking bus stop location having the greatest need for safety improvements. Based on the analysis, safety measures that could improve safety at the highest-ranking bus stop locations include: installing a shelter, installing pedestrian safety features, conducting pedestrian safety outreach programs, and/or conducting further site investigation.

Hybrid electric vehicle data-driven insights on hot-stabilized exhaust emissions and driving volatility

Despite the fuel use and emission benefits of hybrid electric vehicles (HEVs), few studies have characterized in detail emission patterns and driving volatility profiles from HEVs in different road types under real-world driving conditions. This article examined the relationship between hot-stabilized emissions, engine variables, internal combustion engine status, roadway characteristics, and vehicular jerk types. Data were collected from a Toyota HEV subcompact on a driving route over rural, urban, and highway roads in the Aveiro region (Portugal). Driving volatility was represented by six driving styles based on combinations of acceleration/deceleration and vehicular jerk. Clustering and Disjoint Principal Component Analysis (CDPCA) was applied to extract principal components and identify specific clusters among variables. Both route type and internal combustion engine (ICE) operating behavior showed to have an impact on the distribution of vehicular jerk types. The proposed CDPCA constrained to the road sector exhibited different shapes in the clusters of the jerk types between ICE operation status. This article can provide insights into emissions monitoring analysis of the new generation of HEVs about the description of volatile driving behaviors.

Factors associated with driver injury severity of motor vehicle crashes on sealed and unsealed pavements: Random parameter model with heterogeneity in means and variances

The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored. This study collected a four-year crash dataset (2015–2018) from South Australia to explore this issue. The data shows 3,812 and 1,086 crashes at sealed and unsealed pavement surfaces, respectively, during those years. This study examines the consequence of sealed and unsealed pavements on driver injury severity outcomes of motor vehicle crashes. A mixed logit model was developed by accounting for heterogeneity in means and variances of the random parameters. The variables were distributed among several categories: driver, temporal, spatial, roadway characteristics, crash type, vehicle type, and vehicle movement. Four random parameters were observed in the sealed model, whereas five parameters were in the unsealed one. Moreover, the sealed pavements model showed substantial heterogeneity in means of four of the random parameters, while the unsealed pavements model has some heterogeneity in both means and variances of some of the random parameters. Marginal effect results indicate that two indicator variables have enlarged the likelihood of driver severe injury consequences in sealed, alcohol involvement and posted speed limit > 100 km/hr. Additionally, four other significant variables sustain the probability of severe injury outcomes at unsealed pavement like male drivers, middle-aged drivers, rollover crash types, and crashes at straight roads. Based on these variables, various countermeasures were recommended to enhance the safety of both types of pavements.

Failure mechanism and control technology of deep soft-rock roadways: Numerical simulation and field study

Large deformations such as roof subsidence, floor heave, and two-sided deformations occur frequently in deep soft-rock roadways. The deformation becomes more severe under the combined effect of high in-situ and mining-induced stresses, which detrimentally affect the safe mining of coal. Based on the geological conditions and roadway failure characteristics of the Nanyaotou coal mine in Shanxi province, China, we used comprehensive numerical simulations and field observations to study roadway deformation and failure. The deformation mechanism of deep soft-rock roadways under dynamic pressures is described, and the corresponding control measures are proposed. The deformation and fracture development characteristics of roadways surrounding rocks were explored with a primary support scheme, and its effects were evaluated. The radius of the plastic zone and the displacement deformation of the roadway were studied by using theoretical analysis, and a combined-support design of “anchor bolt + anchor cable + shotcrete + deep and shallow borehole grouting + inverted arch” was proposed to limit deformations and relieve the stress in the surrounding rocks. Numerical simulations and field monitoring showed that the combined support scheme can effectively mitigate the large deformations of ventilation roadways and provide guidance for the stable control of deep soft-rock roadways.

Investigating snowplow-related injury severity along mountainous roadway in Wyoming

Snow removal and deicing using snowplow trucks assist transportation agencies to enhance roadway safety and mobility. However, due to slower travel speeds during these operations, motorists often end up in crashes for poor visibility and disturbance of the snow. Despite the risk associated with snowplows, no previous study was found that exclusively investigate the factors associated with injury severity in snowplow-involved crashes. Therefore, this paper presents an extensive exploratory analysis and fills this knowledge gap by identifying the significant contributing factors affecting the occupant injury severity from the aspects of crashes with snowplow involvement. The study utilized eleven years (2010-2020) of historical snowplow-related crash data from Wyoming. Both the binary logit model and mixed binary logit model were developed to investigate the impacts of the various occupant, vehicle, crash, roadway, and environmental characteristics on the corresponding occupant injury severity. As one of the important findings from this research concludes that other vehicle drivers are more responsible than snowplow drivers contributing to more severe injuries in crashes involving snowplows. Recommendations suggested based on the modeling results are expected to help transportation agencies and policymakers take necessary actions in reducing snowplow-involved crashes by targeting appropriate strategies and proper resource allocation.

Identifying the Local Impacts of Speed-Related Factors on Tunnel Entrance Crash Severity

According to crash statistics, freeway tunnel entrance zones have higher injury severity rates than other freeway sections. The concentration of traffic injuries is a combination of high speed and low driver alertness. The government of South Korea has deployed the variable speed limit (VSL) as a safety countermeasure to mitigate the consequences of tunnel entrance crashes. This measure has not been quantitatively reviewed, therefore, the effectiveness of implementing VSL at tunnel entrance zones is unknown. The aim of this was to predict the injury severity of crashes occurring at the tunnel entrance zone given the nature of the crash, the size of the tunnel, the roadway geometric characteristics, and environmental factors. It was anticipated that a statistical model calibrated with historical crash data and tunnel features would be able to justify the current locations of VSL implementation and prioritize new VSL implementations. A geographically weighted logistic regression was employed and benchmarked by the conventional logistic regression model, considering the varying geographic locations of freeway tunnel entrances. The geographically weighted logistic regression model presented consistent findings and a slightly better statistical goodness-of-fit than the conventional logistic model. Moreover, the geographically weighted regression captured the local impact of low-level traffic speed on crash injury severities. The findings in this study could help researchers and practitioners evaluate site-specific traffic safety improvement strategies.

Analysis of Injury Severity of Work Zone Truck-Involved Crashes in South Carolina for Interstates and Non-Interstates

This study investigates factors contributing to the injury severity of truck-involved work zones crashes in South Carolina (SC). The outcome of interest is injury or property damage only crashes, and the explanatory factors examined include the occupant, vehicle, collision, roadway, temporal, and environmental characteristics. Two mixed (random parameter) logit models are developed, one for non-interstates with speed limits less than 60 miles per hour (mph) and one for interstates with speed limits greater than or equal to 60 mph, using South Carolina statewide truck-involved work zone crash data from 2014 to 2020. Results of log-likelihood ratio tests indicate that separate speed models are warranted. The factors that were found to contribute to injury at the 90% confidence level in both models (interstate and non-interstate) are (1) dark lighting conditions, (2) female (at-fault) drivers, and (3) driving too fast for roadway conditions. Significant factors that apply only to non-interstates are SC or US primary roadways, activity area of the work zone, at-fault drivers under 35, sideswipe collision, presence of workers in the work zone, and collision with fixed objects. Significant factors that apply only to interstates are three or more vehicles, rear-end collision, location before the first work zone sign, and weekdays.

Leveraging probe data to model speeding on urban limited access highway segments: Examining the impact of operational performance, roadway characteristics, and COVID-19 pandemic

Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic.

Time and Distance Gaps of Primary-Secondary Crashes Prediction and Analysis Using Random Forests and SHAP Model

Secondary crashes (SCs) are typically defined as the crash that occurs within the spatiotemporal boundaries of the impact area of the primary crashes (PCs), which will intensify traffic congestion and induce a series of road safety issues. Predicting and analyzing the time and distance gaps between the SCs and PCs will help to prevent the occurrence of SCs. In this paper, a combined data-driven method of static and dynamic approaches is applied to identify SCs. Then, the random forests (RF) method is implemented to predict the two gaps using temporal, primary crash, roadway, and real-time traffic characteristics data collected from 2016 to 2019 at California interstate freeways. Subsequently, the SHapley Additive explanation (SHAP) approach is employed to interpret the RF outputs. The results show that the traffic volume, speed, lighting, and population are considered the most significant factors in both gaps. Furthermore, the main and interaction effects of factors are also quantified. High volume possibly promotes the time and distance gaps, while low volume inhibits them. And volume affects the distance gap inconsiderably when it falls between 300 and 400 veh/5 min. Traffic conditions with high speed and low volume are strongly associated with short-time and short-distance gaps. Darker surroundings probably accelerate the occurrence of SCs. Moreover, crashes involving the violation categories of improper turns or unsafe lane changes likely result in long time and distance gaps. These results have important implications for proposing traffic management and improving road safety.

Temporal instability and age differences of determinants affecting injury severities in nighttime crashes

Driving at nighttime may make drivers more likely to be involved in fatal crashes. To investigate the temporal instability and age differences of contributors determining different injury severity levels in nighttime crashes, this paper estimates three groups of random parameters logit models with heterogeneity in the means and variances (young/middle-age/old groups). Nighttime single-vehicle crashes in this study are gathered over four years in California, from January 1, 2014, to December 31, 2017, provided by Highway Safety Information System, including single-vehicle crashes occurring under dark, dawn, and dusk lighting conditions. Simultaneously, to investigate the temporal instability and transferability of nighttime crash severity relating to drivers of different ages, three disaggregate groups are defined: young drivers (15–29 years old), middle-age drivers (30–49 years old), old drivers (over 49 years old). Three injury-severity categories are determined as outcome variables: severe injury, minor injury, and no injury, while multiple factors are investigated as explanatory variables, including driver characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics, and temporal characteristics. Two series of likelihood ratio tests are undertaken to unveil the contributors determining nighttime crash injury severities varying among drivers of different ages over time. Besides, the current study also compares the differences between out-of-sample and within-sample predictions. The results indicate the unstable direction of predictions across different age groups over time and underscore the necessity to adequately accommodate the temporal instability and age differences in accident prediction. More studies can be conducted to accommodate the self-selectivity issue and the out-of-sample prediction differences between using the parametric models and non-parametric models.

Modeling injury severities of single and multi-vehicle freeway crashes considering spatiotemporal instability and unobserved heterogeneity

ABSTRACT Single and multi-vehicle (SMV) crashes remain a significant issue, causing serious safety and economic concerns, and therefore deserve more attention. Using crash data in the Beijing-Shanghai and Changchun-Shenzhen freeways over the five years (2015–2019), this paper explored the transferability and heterogeneity for crash type (single-vehicle versus multi-vehicle crashes) and spatiotemporal stability of determinants affecting the injury severity. The random parameters logit approach with heterogeneity in means and variances was used to model three possible crash injury severity outcomes (measured by the most severely injured individual in the crash) of no injury, minor injury, and severe injury and identify the determinants in terms of driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. Remarkable differences were observed in the SMV crashes, and the contributing factors also reported considerable temporal and (or) spatial instabilities. The insights of this study should be valuable to help freeway designers and decision-makers understand the contributing mechanism of the factors and develop the proper management strategies and enforcement countermeasures.

Evaluation of Real-Time Perception of Deformation State of Host Rocks in Coal Mine Roadways in Dusty Environment

Intelligent mining needs to achieve real-time acquisition of surrounding rock deformation data of roadways and analysis and provide technical support for intelligent mining construction. To solve problems such as significant error, information lag, and low acquisition frequency of surrounding rock monitoring technology at the current stage, a perception method, RSBV of roadway deformation situation, based on binocular vision is proposed, which realizes the dynamic, accurate real-time acquisition of host rocks’ relative deformation in a dusky environment. The low illumination image enhancement method is used to preprocess original images, which reduces the impact of low illumination and high dust; the K-medoids algorithm segments the target image, and the SIFT algorithm extracts feature points from the ROI (region of interest). The influence of eliminating background images on the feature point extraction is revealed, and the efficiency of feature extraction is improved; the method of SIFT feature-matching with epipolar constraints is studied, which improves the accuracy and speed of feature points. The roadway deformation characteristics are analyzed, and the reflective target is used as the monitoring point. A roadway deformation acquisition and analysis platform based on binocular vision is built in a dim environment. Zhang’s method is selected to calibrate the camera parameters, and stereo rectification is carried out for the target motion image. The adaptability of the RSBV method to different surrounding rock deformation scales is studied and compared with the measurement results of the SGBM algorithm. The results show that the error of the RSBV method is controlled within 1.6%, which is 2.88% lower than the average error of the SGBM algorithm. The average time for processing a group of binocular images is 1.87 s, which is only 20% of the SGBM algorithm. The research result provides a reliable theoretical basis for the real-time and accurate evaluation of the surrounding rock deformation mechanism.

How effective is reducing traffic speed for safer work zones? Methodology and a case study in Pennsylvania

Transportation agencies post and enforce reduced speed limits in work zones to ensure work zone safety, since traffic speed is found to be associated with work zone crash risks. However, prior findings on the relationship between speed and crash rate in work zones are inconsistent. This may be attributed to the methods of statistical associations between traffic speed and crash risks that do not necessarily discover true causal relations. In fact, work zone presence could lead to the reduction of actual traffic speed that influences crash risks, where it may also directly impose effects on crash risks as a result of work zone configurations. The actual traffic speed (not posted speed limit) is also known as a “mediator” where work zones can indirectly impact the crash risks. It is challenging to rigorously separate the causal effect of traffic speed on work zone crash risk from that directly caused by work zones. The underlying causal relation could help to determine what reduced post speed limit (with enforcement) is necessary to ensure work zone safety under the most desired “actual traffic speed”. This study proposes to use the sequential g-estimation and the regression discontinuity design to estimate the controlled direct effect of traffic speed on work zone crashes. Two research gaps are identified and filled: inaccurate inferences of the effect of reduced speed limit in work zones as a result of ignoring (1) potential post-treatment bias since traffic speed is a mediator; and (2) potential confounding bias caused by unobservable roadway characteristics. The proposed methodology was applied to 4008 work zones in Pennsylvania from 2015 to 2017, and the results were validated through a series of robustness tests. The results indicate that the direct causal effect of the presence of work zones on crash risk is significantly positive when the traffic speed is relatively low (i.e., lower than 55 mph in this case study), while traffic speed has a positive causal effect on crash occurrences when the actual traffic speed is high (i.e., greater or equal to 55 mph). It suggests that strictly enforcing reduced posted speed limits in work zones is particularly effective when the actual traffic speed is greater than 55 mph. This is particularly true on roadways with high traffic volume (i.e., AADT > 20,000 vehicles per day), long, and daytime work zones (i.e., > 3000 m). On the other hand, the effect of enforcing reduced speed on work zone safety is unclear when the actual speed is already low. In this case, improving work zone configurations and driving behaviors may be more effective in reducing crash risks.

Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data

Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.

Impact of highway design on traffic safety: How geometric elements affect accident risk

The study conducted in India using Fuzzy Inference System (FIS) is a novel approach to predicting traffic accident rates on rural highways. By using various highway geometric elements as input data, the study was able to identify risk variables related to roadway features and predict accident rates using FIS. The findings of the study suggest that FIS is a valuable tool for predicting accident rates and can help identify the factors that contribute to accidents. The study also found statistically significant positive connections between geometric elements and accident rates, which highlights the importance of highway design and safety measures. The use of Fuzzy Inference Systems in accident prediction is a promising approach as it allows for a more comprehensive understanding of the complex relationships between various factors contributing to accidents. The model was tested using simulation and data analysis and was found to fit well with real-world data, indicating its potential for practical applications in road safety management. Overall, this study provides important insights into the use of FIS in predicting accident rates on rural highways and can help guide future research in this area. It also highlights the importance of considering various highway geometric elements in designing safer highways and implementing appropriate safety measures to reduce accident rates. However, the road accidents can have significant impacts on ecological cycles, including habitat fragmentation, wildlife mortality, pollution, and climate change.