Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes

Objectives: Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle–pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle–pedestrian crashes to identify the hotspots and hot times.

Methods: Data for all vehicle–pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle–pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle–pedestrian crashes for different age groups and genders.

Results: Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle–pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle–pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle–pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars.

Conclusions: This research reveals that temporal and spatial distributions of vehicle–pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to increase the effectiveness of the countermeasures in preventing and reducing vehicle–pedestrian crashes.     

Considering built environment and spatial correlation in modeling pedestrian injury severity

Objective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.

Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.

Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.

Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.     

Investigating factors influencing pedestrian injury severity at intersections

Objective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables.

Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age.

Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers.

Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed.     

Safe speed limits for a safe system: The relationship between speed limit and fatal crash rate for different crash types

Objective: The objective of this article is to provide empirical evidence for safe speed limits that will meet the objectives of the Safe System by examining the relationship between speed limit and injury severity for different crash types, using police-reported crash data.

Method: Police-reported crashes from 2 Australian jurisdictions were used to calculate a fatal crash rate by speed limit and crash type. Example safe speed limits were defined using threshold risk levels.

Results: A positive exponential relationship between speed limit and fatality rate was found. For an example fatality rate threshold of 1 in 100 crashes it was found that safe speed limits are 40 km/h for pedestrian crashes; 50 km/h for head-on crashes; 60 km/h for hit fixed object crashes; 80 km/h for right angle, right turn, and left road/rollover crashes; and 110 km/h or more for rear-end crashes.

Conclusions: The positive exponential relationship between speed limit and fatal crash rate is consistent with prior research into speed and crash risk. The results indicate that speed zones of 100 km/h or more only meet the objectives of the Safe System, with regard to fatal crashes, where all crash types except rear-end crashes are exceedingly rare, such as on a high standard restricted access highway with a safe roadside design.     

Impact of perceptual countermeasures on driving behavior at curves using driving simulator

Objective: The probability of crash occurrence on horizontal curves is 1.5 to 4 times higher than that on tangent sections. A majority of these crashes are associated with human errors. Therefore, human behavior in curves needs to be corrected.

Methodology: In this study, 2 different road marking treatments, optical circles and herringbone patterns, were used to influence driver behavior while entering a curve on a 2-lane rural road section. A driving simulator was used to perform the experiment. The simulated road sections are replicas of 2 real road sections in Flanders.

Results: Both treatments were found to reduce speed before entering the curve. However, speed reduction was more gradual when optical circles were used. A herringbone pattern had more influence on lateral position than optical circles by forcing drivers to maintain a safe distance from opposing traffic in the adjacent lane.

Conclusion: The study concluded that among other low-cost speed reduction methods, optical circles are effective tools to reduce speed and increase drivers’ attention. Moreover, a herringbone pattern can be used to reduce crashes on curves, mainly for head-on crashes where the main problem is inappropriate lateral position.     

Motorcycle crashes potentially preventable by three crash avoidance technologies on passenger vehicles

Objective: The objective of this study was to identify and quantify the motorcycle crash population that would be potential beneficiaries of 3 crash avoidance technologies recently available on passenger vehicles.

Methods: Two-vehicle crashes between a motorcycle and a passenger vehicle that occurred in the United States during 2011–2015 were classified by type, with consideration of the functionality of 3 classes of passenger vehicle crash avoidance technologies: frontal crash prevention, lane maintenance, and blind spot detection. Results were expressed as the percentage of crashes potentially preventable by each type of technology, based on all known types of 2-vehicle crashes and based on all crashes involving motorcycles.

Results: Frontal crash prevention had the largest potential to prevent 2-vehicle motorcycle crashes with passenger vehicles. The 3 technologies in sum had the potential to prevent 10% of fatal 2-vehicle crashes and 23% of police-reported crashes. However, because 2-vehicle crashes with a passenger vehicle represent fewer than half of all motorcycle crashes, these technologies represent a potential to avoid 4% of all fatal motorcycle crashes and 10% of all police-reported motorcycle crashes.

Discussion: Refining the ability of passenger vehicle crash avoidance systems to detect motorcycles represents an opportunity to improve motorcycle safety. Expanding the capabilities of these technologies represents an even greater opportunity. However, even fully realizing these opportunities can affect only a minority of motorcycle crashes and does not change the need for other motorcycle safety countermeasures such as helmets, universal helmet laws, and antilock braking systems.     

A clustering approach to integrate traffic safety in road maintenance prioritization

Objectives: We combine data on roads and crash characteristics to identify patterns in road traffic crashes with regard to road characteristics. We illustrate how combined analysis of data regarding road maintenance, maintenance costs, road characteristics, crash characteristics, and geographical location can enrich road maintenance prioritization from a traffic safety perspective.

Methods: The study is based on traffic crash data merged with road maintenance data and annual average daily traffic (AADT) collected in Denmark. We analyzed 3,964 crashes that occurred from 2010 to 2015. A latent class clustering (LCC) technique was used to identify crash clusters with different road and crash characteristics. The distribution of crash severity and estimated road maintenance costs for each cluster was found and cluster differences were compared using the chi-square test. Finally, a map matching procedure was used to identify the geographical distribution of the crashes in each cluster.

Results: Results showed that based on road maintenance levels there was no difference in the distribution of crash severity. The LCC technique revealed 11 crash clusters. Five clusters were characterized by crashes on roads with a poor maintenance level (levels 4 and 3). Only a few of these crashes included a vulnerable road user (VRU) but many occurred on roads without barriers. Four clusters included a large share of crashes on acceptably maintained roads (level 2). For these clusters only small variations in road characteristics were found, whereas the differences in crash characteristics were more dominant. The last 2 clusters included crashes that mainly occurred on new roads with no need for maintenance (level 1). Injury severity, estimated maintenance costs, and geographical location were found to be differently distributed for most of the clusters.

Conclusions: We find that focusing solely on road maintenance and crash severity does not provide clear guidance of how to prioritize between road maintenance efforts from a traffic safety perspective. However, when combined with geographical location and crash characteristics, a more nuanced picture appears that allows consideration of different target groups and perspectives.     

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

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

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

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

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

Wrong-way driving crashes: A multiple correspondence approach to identify contributing factors

Objective: Wrong-way driving (WWD) crashes result in 1.34 fatalities per fatal crash, whereas for other non-WWD fatal crashes this number drops to 1.10. As such, further in-depth investigation of WWD crashes is necessary. The objective of this study is 2-fold: to identify the characteristics that best describe WWD crashes and to verify the factors associated with WWD occurrence.

Methods: We collected and analyzed 15 years of crash data from the states of Illinois and Alabama. The final data set includes 398 WWD crashes. The rarity of WWD events and the consequently small sample size of the crash database significantly influence the application of conventional log-linear models in analyzing the data, because they use maximum-likelihood estimation. To overcome this issue, in this study, we employ multiple correspondence analysis (MCA) to define the structure of the crash data set and identify the significant contributing factors to WWD crashes on freeways.

Results: The results of the present study specify various factors that characterize and influence the probability of WWD crashes and can thus lead to the development of several safety countermeasures and recommendations. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions were among the most significant contributors to WWD crashes.

Conclusions: Despite many other methods that identify only the contributing factors, this method can identify possible associations between various contributing factors. This is an inherent advantage of the MCA method, which can provide a major opportunity for state departments of transportation (DOTs) to select safety countermeasures that are associated with multiple safety benefits.     

Local vs. national: Epidemiology of pedestrian injury in a mid-Atlantic city

Objective: Understanding pedestrian injury trends at the local level is essential for program planning and allocation of funds for urban planning and improvement. Because we hypothesize that local injury trends differ from national trends in significant and meaningful ways, we investigated citywide pedestrian injury trends to assess injury risk among nationally identified risk groups, as well as identify risk groups and locations specific to Baltimore City.

Methods: Pedestrian injury data, obtained from the Baltimore City Fire Department, were gathered through emergency medical services (EMS) records collected from January 1 to December 31, 2014. Locations of pedestrian injuries were geocoded and mapped. Pearson's chi-square test of independence was used to investigate differences in injury severity level across risk groups. Pedestrian injury rates by age group, gender, and race were compared to national rates.

Results: A total of 699 pedestrians were involved in motor vehicle crashes in 2014—an average of 2 EMS transports each day. The distribution of injuries throughout the city did not coincide with population or income distributions, indicating that there was not a consistent correlation between areas of concentrated population or concentrated poverty and areas of concentrated pedestrian injury. Twenty percent (n = 138) of all injuries occurred among children age ≤14, and 22% (n = 73) of severe injuries occurred among young children. The rate of injury in this age group was 5 times the national rate (Incident Rate Ratio [IRR] = 4.81, 95% confidence interval [CI], [4.05, 5.71]). Injury rates for adults ≥65 were less than the national average.

Conclusions: As the urban landscape and associated pedestrian behavior transform, continued investigation of local pedestrian injury trends and evolving public health prevention strategies is necessary to ensure pedestrian safety.     

Identifying the crash characteristics on freeway segments based on different ramp influence areas

Objective: This study aimed to explore the relationship between crash types and different freeway segments and identify the factors contributing to crashes on different freeway segments. Unlike most of the previous studies on freeway segments, this study separately investigates basic freeway segments, single ramp influence segments, and multiple ramp influence segments.

Methods: Nonlinear canonical correlation analysis (NLCCA) and proportionality test were used to identify the relationship between crash types and different freeway segments. The data sets for the different freeway segments accumulated for this study consist of 9,867 crash samples with complete information on all 22 chosen variables. A multinomial logit model (MNL) was used to estimate the influence of crash factors on different freeway segments.

Results: The results show that weaving and diverge overlap influence segments (WD) are more likely to have injury or fatal crashes; diverge and diverge overlap influence segments (DD) are more likely to have property damage–only (PDO) crashes; merge and merge overlap influence segments (MM) are more likely to have sideswipe crashes; and WD have non-sideswipe crashes; WD and weaving overlap influence segments (MW) are more likely to have rear end crashes; and MM segments are less likely to have hit object crashes. The contributing factors are identified by MNL and the results show that different traffic variables, environmental variables, vehicle variables, driver variables, and geometric variables significantly affected the likelihood of crashes on different freeway segments.

Conclusions: Investigation of crash types and factors contributing to crashes on different freeway segments is based on multiple ramp influence segments, which can promote a better understanding of the safety performance of various freeway segments.     

Trauma demography and clinical epidemiology of motorcycle crash–related head injury in a neurosurgery practice in an African developing country

Objective: Though motor vehicle crashes (MVCs) were the main cause of head trauma from road traffic injuries (RTIs), motorcycle crashes (MCCs) are now a major cause of RTI-related head injury (HI) in many developing countries.

Methods: Using a prospective database of HIs from a neurosurgical practice in a sub-Saharan African developing country, a cross-sectional survey was conducted for the trauma demography and clinical epidemiology of this MCC-related HI.

Results: Motorcycle crashes accounted for 57% (473/833) of all RTI-related HIs in this registry. The victims, with a mean age of 33.1 years (SD = 18.3), consisted mainly of males (83.1%), those of low socioeconomic status (>90%), and those aged between 20 and 40 years old (56%). MCCs involved only riders in 114 cases (114/473, 32.1%), of which 69% were motorcycle–motorcycle crashes. The HI was moderate–severe in 50.8%; clinical symptomatology of significant HI included loss of consciousness (92%), anisocoria (35%), Abbreviated Injury Scale head (AIS–head) score > 3 (28%), and CT-Rotterdam score > 3 (30%). Extracranial systemic injury involved the limbs most frequently, with an Injury Severity Score (ISS) >25 in 49%. The fatality rate was 24%.

MCC-related HI among pedestrian victims involved more vulnerable age groups (the young and elderly) but have lower mean ISS compared to motorcycle passengers (mean ISS = 23.5 [11.6] vs. 27.4 [13.0]; 95% confidence interval [CI], 1.27–6.49; P = .004). In addition, compared to a contemporary cohort of MVC-related HIs in our registry, MCC victims were older (mean age 34.8 years [18.0] vs. 30.8 [18.4]; P = .002); had higher proportions of certain extracranial trauma like long bone fractures (71 vs. 29%; P = .02); and suffered fewer surgical brain lesions (25.5 vs. 17.2%; P = .004).

Conclusions: Motorcycle crashes are now a significant threat to the heads, limbs, and lives of vulnerable road users in developing countries.     

Pedestrian crashes at priority-controlled junctions,roundabouts, and signalized junctions: The UK case study

Objectives: Over half of pedestrians killed and seriously injured in Great Britain in 2015 were involved in crashes at junctions. This study investigates the nature of these crashes.

Methods: A study was conducted into pedestrian casualty crashes at priority controlled junctions, roundabouts and signalised junctions in England between 2005 and 2015 using information from the UK STATS19 accident database, the UK National Travel Survey and the UK National Census. Consideration was given to coding frequencies of contributory factors, exposure (in terms of miles walked or driven) as well as age, gender and the resident deprivation index of the road users involved.

Results and Conclusions: In terms of indicative blame, the coding frequencies of subjectively determined pedestrian actions and behaviour factors which might have contributed to pedestrian casualty crashes were found to be between 1.6 and 2.8 times the frequencies of driver actions and behavioural factors. Substantial social gradients were found in pedestrian casualty rates per miles walked and in the driver involvement rates per mile driven with those from the most deprived quintile having higher rates. In addition, it was found that female pedestrians, aged 60 years and over, had higher pedestrian casualty rates, per billion miles walked, for all three junction types, when compared with males and females under the age of 60 years, apart from male pedestrians aged 16 years and younger at priority controlled junctions.     

Reliability and validity of the Multidimensional Driving Style Inventory in Chinese drivers

Objective: The Multidimensional Driving Style Inventory (MDSI) has been widely used in assessing the associations between driving styles and traffic violations and accidents in different cultural contexts. Due to the lack of a valid instrument to assess driving style, studies concerning driving style and its influence factors are limited in China. Thus, this study aimed to adapt and validate a Chinese version of the MDSI.

Methods: Seven hundred and sixty drivers aged from 19 to 60 years old were asked to complete the MDSI and a personality scale (trait anger, sensation seeking, altruism, and normlessness). Exploratory factory analysis (EFA) and confirmatory factor analysis (CFA) were used to obtain the factorial structure of the MDSI. The external validity of the MDSI was then evaluated by examining the associations between driving styles and personality traits, demographic variables, and traffic violations and crashes.

Results: EFA revealed a 6-factor structure of the MDSI (i.e., risky, anxious, angry, distress reduction, careful, and dissociative driving styles). CFA confirmed that the model fit of the MDSI was acceptable. The MDSI factors were moderately or weakly correlated with trait anger, sensation seeking, altruism, and normlessness. Significant gender and age differences in driving styles were found. Moreover, drivers who had traffic violations or crashes in the past year scored higher on risky and angry driving styles and lower on careful driving style than those who had not have traffic violations or crashes.

Conclusions: The Chinese version of the MDSI proved to be a reliable, valid, and highly useful instrument. It could be used to assess Chinese drivers who are at risk due to their maladaptive driving styles.     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

Fatally injured drivers in Norway 2005–2015—Trends in substance use and crash characteristics

Objective: Norway introduced a “Vision Zero” strategy in 2001, using multiple approaches, aiming toward a future in which no one will be killed or seriously injured in road traffic crashes (RTCs). Official statistics show that the number of fatally injured road users has declined substantially from 341 deaths in 2000 to 117 in 2015. In-depth crash investigations of all fatal RTCs started in Norway in 2005. The aim of this study was to investigate whether fatal crash characteristics, vehicle safety features, and prevalence of drugs and/or alcohol among fatally injured drivers and riders has changed during 2005–2015, accompanying the reduction in road fatalities.

Methods: Data on all car/van drivers and motorcycle/moped riders fatally injured in RTCs during 2005–2015 were extracted from Norwegian road traffic crash registries and combined with forensic toxicology data.

Results: The proportion of cars and motorcycles with antilock braking systems and cars with electronic stability control, increased significantly during the study period. The prevalence of nonuse of seat belts/helmets and speeding declined among both fatally injured drivers and riders. In addition, the prevalence of alcohol declined, though no significant change in the total prevalence of other substances was noted.

Conclusion: The observed changes toward more safety installations in cars and motorcycles and lower prevalence of driver-related risk factors like alcohol use, speeding, and nonuse of seat belts/helmets among fatally injured drivers/riders may have contributed to the decrease in road traffic deaths.     

A comparison of overtime patrol stops made inside and out of cluster identified hotspots

Objective: The annual rate of impaired driving crashes in the United States has remained nearly constant over the last decade. While engineers, educators, enforcement, and emergency response personnel have worked diligently in their combined efforts to reduce the loss of life, there is still significant progress to be made. One area of recent interest is the use of data driven enforcement. The basis for data driven enforcement is the use of statistical clustering to identify geographic areas that represent the location of problem identification for various criminal or traffic offenses. In the case of impaired driving fatalities, the clustering represents locations with high rates of impaired driving crashes. Law enforcement officers and supervisors may allocate resources towards more specifically and efficiently addressing problem areas.

Methods: While data driven enforcement has been proven to be an effective tool in addressing crime and traffic safety problems, it has been a slow process for agencies to adopt data driven techniques. This study aims to explore the difference in traffic stops made inside and outside of hotspot identified areas. The study uses data from the Stark County Operating a Vehicle Impaired Task Force between 2013 and 2014.

Results: The analysis determined that stop occurring in hotspot defined areas are more likely to result in impaired driving arrests and seatbelt citations. Additionally it is found that the average cost of impaired driving arrests is significantly cheaper for stops occurring inside of hotspot areas.

Conclusion: Clustering as a means of directing law enforcement efforts are a way to increase the productivity and benefits of law enforcement agencies with limited finances or personnel. From this study it is seen that traffic stops made within defined cluster or hot spot areas are more effective in resulting in OVI arrests.     

Severity of motorcycle crashes in Dar es Salaam,Tanzania

Objective: Motorcycles are a common mode of transportation in low- and middle-income countries. Tanzania, in particular, has experienced an increased use of motorcycles in the last decade. In Dar es Salaam, motorcycles provide door-to-door travel and often operate where more conventional services are uneconomical or physically impossible to maneuver. Although motorcycles play a crucial role in improving mobility in the city, they have several safety issues. This study focuses on identifying factors influencing the severity of motorcycle crashes.

Method: A multinomial logit analysis was conducted to identify factors influencing the severity of motorcycle crashes in Dar es Salaam, Tanzania. The severity categories were fatal, severe injury, minor injury, and property damage only (PDO). The analysis was based on a total of 784 motorcycle crashes that occurred from 2013 to 2016.

Results: The following factors were found to increase the probability of a fatality: Speeding, driving under the influence, head-on impact, presence of horizontal curves, reckless riding, off-peak hours, violations, and riding without a helmet. The results indicate that crashes occurring on weekdays, during peak hours, at intersections, involving a rear-end impact, in daylight, on street roads, and under clear weather conditions decrease the probability of a fatality. However, minor injury and PDO crashes were found to be associated with crashes occurring during peak hours, at intersections, and on street roads, as well as failure to yield right-of-way.

Conclusions: Several countermeasures are recommended based on the study findings. The recommended countermeasures focus on the holistic safety improvement strategies constituting the three Es of highway safety, namely, engineering, education, and enforcement.