Pedestrian Involvement Research Articles

Investigating the impact of temporal instability in smart roadway retrofitting on terrain-related crash injury severity

The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions.

Are Pedestrian Crashes Becoming More Severe? A Breakdown of Pedestrian Crashes in Urban Tennessee

Similar to the nationwide trend of declining pedestrian safety, pedestrian fatalities in Tennessee increased by 117 % between 2009 and 2019. During the same time, pedestrians involved in traffic crashes have only increased by 26 %, suggesting that the reason for the increased number of fatalities is that pedestrian crashes are becoming more severe. The media, and safety advocates speculate about the different scenarios that could be responsible for the decrease in pedestrian safety. Past studies have failed to integrate pedestrian involvement into their studies and confirm whether pedestrian crashes are becoming more severe. Our study collected all reported pedestrian crashes and their outcomes from crash data kept by the Tennessee police, thus providing fatality and involvement numbers. Excluding crashes on controlled access roads, we measured the pedestrian fatality rate (PFR) for essential variables for each year from 2009 to 2019. An upward PFR graph shows that pedestrian crashes are becoming more severe compared with the past. To substantiate our claims with adequate statistical significance, we employed separate Poisson regression models for the pedestrian fatality count and PFR associated with each attribute of pedestrian crashes. The results show that an increase in severity is linked with multilane urban arterials with speeds above 35 mph at midblock locations. The study results also confirm that cars are still the cause of the high fatality rate in Tennessee rather than sport utility vehicles or other large vehicles. We recommend that cities reduce design speeds to 35 mph, increase safe pedestrian crossing opportunities, and install more pedestrian-scale lighting infrastructure on urban arterials.

Factors Associated with Work Zone Crashes

Nationally, the annual number of fatal crashes in work zones over the past decade (2010–2019) increased from 521 to 762, which led to rising fatalities from 546 in 2010 to 842 in 2019. Traffic safety in work zones with the aim of reducing fatal and serious injury crashes has been under investigation by researchers and related agencies for decades. The main objective of this study was to investigate the factors associated with work zone crashes to recommend policies targeted at addressing them. Data collected by the Kansas Department of Transportation on crashes that occurred between 2016 and 2020 were used for this research. For the statistical model, binary logistic regression was used in which the dichotomous response variable was defined as either crashes in work zones or crashes outside work zones. According to the model’s results, several significant factors were associated with work zone crashes. Those that had a reasonable expectation of improvement through state policies included the following: alcohol involvement; driver distraction; large commercial vehicle involvement; pedestrian involvement; single-vehicle crashes; speed limits, and speeding. Although these may already be areas targeted in a state’s strategic highway safety plan, special consideration of the overrepresentation of these factors in work zones can provide additional policy options for consideration. Existing countermeasures can be explored, or new countermeasures can be developed based on the needs of the existing conditions.

Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns

Walking plays an important role in overcoming many challenges nowadays, and governments and local authorities are encouraging healthy and environmentally sustainable lifestyles. Nevertheless, pedestrians are the most vulnerable road users and crashes with pedestrian involvement are a serious concern. Thus, the identification of pedestrian crash patterns is crucial to identify appropriate safety countermeasures. The aims of the study are (1) to identify the road infrastructure, environmental, vehicle, and driver-related patterns that are associated with an overrepresentation of pedestrian crashes, and (2) to identify safety countermeasures to mitigate the detected pedestrian crash patterns. The analysis carried out an econometric model, namely the mixed logit model, and the association rules and the classification tree algorithm, as machine learning tools, to analyse the patterns contributing to the overrepresentation of pedestrian crashes in Italy. The dataset consists of 874,847 crashes—including 101,032 pedestrian crashes—that occurred in Italy from 2014 to 2018. The methodological approach adopted in the study was effective in uncovering relations among road infrastructure, environmental, vehicle, and driver-related patterns, and the overrepresentation of pedestrian crashes. The mixed logit provided a clue on the impact of each pattern on the pedestrian crash occurrence, whereas the association rules and the classification tree detected the associations among the patterns with insights on how the co-occurrence of more factors could be detrimental to pedestrian safety. Drivers’ behaviour and psychophysical state turned out to be crucial patterns related to pedestrian crashes’ overrepresentation. Based on the identified crash patterns, safety countermeasures have been proposed.

Factors affecting fatal PTW at-fault crash outcome metrics at intersections and non-intersections in South Korea

Fatal crashes involving Powered Two-Wheelers (PTWs) are a major health concern in South Korea due to the increase in their usage in the logistics industry. Owing to the way, they are operated on the roadways, most riders end up in fatal crashes. Interestingly, little research exists regarding the impact of risk factors on fatal crashes involving at-fault PTW riders. This study employs a copula-based regression technique to simultaneously model the relationship between crash-risk factors and crash outcome metrics of fatal PTW rider-at-fault crashes, namely the number of crash casualties (casualty size) and the number of vehicles involved in a crash (crash size) at intersections and non-intersection segments. The proposed method was superior compared to the SEM-based bivariate regression approach, and the estimation results showed that there exists a positive relationship between both outcome variables. From the analysis, it was identified that while "other violations" comprising speeding and wrongful overtaking had varying effects on crash size outcomes at the intersection and non-intersection segments, variables such as daytime, winter, head-on collisions, and pedestrian involvement had positive impact on the crash consequence metrics irrespective of the crash location. Insights drawn from the study are used in recommending appropriate countermeasures for improving PTW safety.

Factors explaining pedestrian-involved fatality crashes on National Highways in India

Pedestrians continue to face high risk of getting involved in fatal and serious injury crashes all over the world. In many high-income countries, pedestrian involvement in fatal crashes occur mostly in urban areas. However, in many low- and middle-income countries in Asia and Africa, pedestrian involvement in fatal crashes occur on intercity highways too. This research analyses fatal pedestrian crash characteristics, and identifies probable contributory factors to pedestrian involvement in fatal crashes using logistic regression for two-, four-, and six-lane National Highways. The fatal pedestrian crash density is found to be the highest at 1.37 crashes/km/year on six-lane divided NH-1. The binary logistic regression estimation results for pedestrian involvement in the fatal crash model revealed that the predictors: “number of lanes” and “time of crash” are found to be significant at 95% level. The model results for the variable “number of lanes” highlights the need to study pedestrian crossing behaviour on highways in detail. The design standards for pedestrian crossing facilities in urban areas may not be suitable for National Highways in particular multi-lane highways. In-depth research is required to understand the suitability of various traffic calming measures and other possible interventions which can ensure pedestrian safety on highways.

Factors related to highway crash severity in Brazil through a multinomial logistic regression model

Reducing the number of deaths by road crashes is an important priority for many countries around the world. Although focusing on the occurrence of crashes can provide safety policies that help reduce its numbers, studying their severity can provide different measures that may help further reduce the number of deaths by focusing on the most severe problems first. In this paper, a multinomial logistic regression model is fitted to nationwide highway crash data in Brazil from 2017 to 2019 to identify and estimate the associated factors to crash severity. Severity is classified as without injury, with injured victims or with fatal victims. Amongst other observations, results indicate that pedestrian involvement in highway crashes increase dramatically their severity. Also, conditions that favor greater speeds (clear weather, times when there are fewer vehicles on the road) are also related to an increase in crash severity, pointing to a proportional relation with traffic fluidity. Moreover, some observed limitations on the data may indicate that Brazil would benefit greatly from national crash records standards and unified databases, especially crossmatching crash reports with health data.

A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques

Every year in Italy, there are about 20,000 road accidents involving pedestrians, with a significant number of injuries and deaths. Out of these, about 30% occur at pedestrian crossings, where pedestrians should be protected the most. Here, we propose a new accident prediction model to improve pedestrian safety assessments that allows us to accurately identify the sites with the largest potential safety improvements and define the best treatments to be applied. The accident prediction model was developed using the ISTAT dataset, including information about the fatal and injurious crashes that occurred in Italy in a 5-year period. The model allowed us to estimate the risk level of a road section through a machine-learning approach. Gradient Boosting seems to be an appropriate tool to fit classification models for its flexibility that allows us to capture non-linear relationships that would be difficult to detect via a classical approach. The results show the ability of the model to perform an accurate analysis of the sites included in the dataset. The locations analyzed have been classified based on the potential risk in the following three classes: High, medium, and low. The proposed model represents a solid and reliable tool for practitioners to perform accident analysis with pedestrian involvement.

Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

This paper presents the results of an analysis focusing on large truck-involved work zone fatal crashes using seven-year crash data in the State of Florida. Decision tree/random forest models were applied to specifically detect critical crash patterns that result in a fatality outcome. Because of the imbalanced nature of crash severity data (very low frequency of fatal crashes compared with property damage only or injury), data were treated using random and systematic over-sampling techniques. Marginal effects were addressed using Shapley values to increase model explainability. From a methodological perspective, results showed that the combination of over-sampling techniques with ensemble random forests could significantly improve model performance in predicting fatal crashes (compared with conventional logistic regression models). Primary contributors included pedestrian involvement, lighting conditions, safety equipment, driver condition, driver age, and work zone locations. For pedestrian crashes, factors such as dark-not lighted conditions, distracted truck driver, and driver’s age (young drivers outside city limits, senior drivers inside city limits) were highly likely to be fatal. For non-pedestrian crashes, the combination of front airbag deployment with any restraint system other than shoulder and belt was quite likely to be fatal. Also, abnormal driver conditions increased the risk of a fatal outcome. Additionally, the presence of female drivers (as the second driver in multiple vehicle crashes) highly decreased crash severity, probably because females typically drive more carefully than males. Interestingly, truck driver actions and maneuvers as well as roadway design and other physical environment features (i.e., number of lanes, median type, roadway grade, and alignment) did not show significant contribution to the model.

A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics

Road safety is a major public health issue, with road crashes accounting for one-fourth of all documented injuries. In these crashes, pedestrians are more vulnerable to fatal and/or severe injuries than car occupants. Therefore, it is necessary to have a better understanding of the relationship between pedestrian crashes and possible influencing factors, including road environment, traffic conditions, and population characteristics. In conventional studies, separate prediction models were established for pedestrian crashes and other crash types, which could have ignored possible correlations among the different crash types. Additionally, these influencing factors can contribute to pedestrian crashes in two manners, i.e., contributing to crash occurrence and propensity of pedestrian involvement. Furthermore, extensive pedestrian count data were generally not available, affecting the estimation of pedestrian crash exposure. In this study, a joint probability model is adopted for the simultaneous modeling of crash occurrence and pedestrian involvement in crashes; effects of possible influencing factors, including land use, road networks, traffic flow, population demographics and socioeconomics, public transport facilities, and trip attraction attributes, are considered. Additionally, trip generation and pedestrian activity data, based on a comprehensive household travel survey, are used to determine pedestrian crash exposure. Markov chain Monte Carlo full Bayesian approach is then applied to estimate the parameters. Results indicate that crash occurrence is correlated to traffic flow, number of non-signalized intersections, and points of interest such as restaurants and hotels. By contrast, population age, ethnicity, education, household size, road density, and number of public transit stations could affect the propensity of pedestrian involvement in crashes. These findings indicate that better design and planning of built environments are necessary for safe and efficient access for pedestrians and for the long-term improvement of walkability in a high-density city such as Hong Kong.

Aggregation framework for TSK fuzzy and association rules: interpretability improvement on a traffic accidents case

The number and diversity of machine learning applications causes an increasing need for understanding computational models and used data. This paper deals with a framework design of easily interpretable rules of the Takagi-Sugeno-Kang (TSK) fuzzy model. The proposed framework aggregates TSK fuzzy rules and association rules by calculating overlapping value intervals of variables appearing in both antecedent and consequent parts of fuzzy and association rules. Besides a simple insight into rule interconnections of the rule-based models, the framework provides an assessment of fuzzy rule importance, and in accordance with other rules and the complete TSK fuzzy model. The proposed framework is developed and illustrated by analysing traffic accidents with pedestrian involvement. It provides a deeper understanding of the built rule-based model, as well as more readable identification of significant accident causes. The framework can be used in many domains of analysis modelling and decision making processes where computational model understanding is crucial.

Determining Causal Factors of Severe Crashes on the Fort Peck Indian Reservation, Montana

Indian reservations have been struggling with the highest rate of crashes that lead to fatal and incapacitating injuries across the United States for decades. The US government has been striving to improve roadway safety on Indian reservations to reduce such crashes. However, the rustic nature of the reservations, issues of jurisdictional coordination and collaboration, inadequate resources, and limited crash data make it challenging for the tribes to reduce the number of severe crashes. Determining factors associated with crashes is one of the most efficient and effective ways to select appropriate countermeasures for improving roadway safety and reducing crashes. Due to the unique nature of each of the reservations, factors contributing to crashes vary across the reservations as well as across the different roadways within the reservations. Only a few researches have investigated factors contributing to crashes on Indian reservations, and no studies have determined the factors separately for different roadways within the reservations. Therefore, this study was conducted to identify the contributory factors to fatal and injury crashes in the Fort Peck Indian Reservation (FPIR). The crash database covering a ten-year period from 2005 to 2014 was obtained from the Montana Department of Transportation (MDT). During this period, 940 crashes occurred on state, county, city, and tribally owned roads. Binary logistic regression models were developed to determine the factors associated with fatal and injury crashes for all roads within the FPIR and separately for the roads maintained by different agencies. The analysis identified unique contributory factors to fatal or injury crashes for different roadways, which justified separating crashes based on different road types. Impaired driving, adverse weather condition, collision with a ditch/embankment, pedestrian involvement, and overturn/rollover crashes were some of the factors that significantly contribute to increasing the risk associated with fatal and injury crashes. Impaired driving was found to be the most significant factor contributing to crash severity in all three roadways. Indian reservation roads were found to be possessing the highest risk of fatal and injury crashes due to impaired driving among the three roadway systems. The results of the study provide the Fort Peck Tribes with the opportunity to determine the countermeasures for safety improvements on their roadway systems efficiently.

Safety of Freeway Service Patrol Operations: Case Study of Florida Road Rangers

Safety service patrols are a proven strategy to mitigate the effects of traffic incidents through quick clearance, incident management, and assistance to other incident responders like police, fire, emergency medical services, and towing. As encountered by other responders, working on or near roadways presents unique hazards for safety service patrol vehicles and operators. Road Rangers are Florida’s branded safety service patrols and, as a mature program with over 100 beats, a suitable case study for safety. This research combined an analysis of Road Ranger traffic crash data for 3 years with a comprehensive safety survey of more than 200 operators to determine safety characteristics related to service patrols. Comparing 200 Road Ranger traffic crashes from 2014 through 2016 with all Florida freeway crashes for the same time period revealed that Road Ranger crashes are five times more likely to involve a parked vehicle, and involve two or more vehicles 95% of the time. Pedestrian involvement, nighttime, shoulder locations, and work zones have higher representation for Road Ranger crashes, but weather is not a factor. Alcohol is three times more likely, drug use five times more likely, and distraction slightly higher when Road Ranger vehicles are struck. A survey of 217 Road Ranger drivers revealed that they are keenly aware of important safety topics like high-visibility safety apparel, non-traffic side vehicle approaches, and the dangers of working where there is limited lateral buffer space. Drivers overwhelmingly believe that they have the training and equipment necessary to do their jobs safely.

2384 - Risk Factors for Pedestrian Injury on Tribal Lands

Background American Indians have the highest rate of pedestrian death per capita among ethnic groups in the United States. In North Dakota, the incidence rate for pedestrian involvement in crashes 3.9% on tribal lands compared to less than 0.1% among other rural populations in the state. The cultural foundation of the American Indians includes a proclivity toward walking and community-based solutions. Nationally, contributing factors such as limited sight zone, alcohol involvement, no sidewalk facility, and uncontrolled intersections are attributed in these crashes. Methods Descriptive Statistics and Case Study. Information regarding pedestrian motor vehicle crash (MVC) involvement is available nationally for fatal crashes. The fatal crash data will be coupled with all injury-level crashes in North Dakota to look at these crashes from a local perspective as well as the national view. This state database is a census of police-reported traffic crashes in the state. Both data sets include data on incident, vehicle, and occupant crash elements. While these data do not provide a means for predicting pedestrian crash involvement, it can be used to gain a better understanding of the crash events. Results Quantitative Problem Scope and Trend Discussions and Tribal Community Case Study Solutions/Interventions. This analysis will use crash records from the years 2012 to 2016. Descriptive statistical analysis of crash records is used in a diagnostic process to distinguish crash elements associated with tribal lands MVC where pedestrian involvement. The pedestrian-involved MVC crashes on other roads will be compared for statistical similarity. Significant differences in driver, vehicle, environment, and roadway elements will be examined using the chi-square test. Gaining a better understanding of these tribal pedestrian-involved crashes will help decision makers to more effectively select the most appropriate evidence based solutions. Conclusions Investigation into crashes the occurred locally on tribal lands and other rural locations provides insight to aid tribal communities as they prevent future traffic injuries.

Control methods for automated testing of preventive pedestrian protection systems

Preventive pedestrian protection systems have significant potential to reduce the number of accidents with pedestrian involvement. However, owing to their high complexity new test methods have to be developed. The new safety functions are validated by means of driving tests reproducing safety-critical traffic situations with pedestrian involvement on a testing ground. This paper presents new control methods for the automated testing of preventive pedestrian protection systems. These include new control algorithms for automated lateral and longitudinal vehicle guidance as well as two different concepts for motion control of a pedestrian dummy. The presented measurement results confirm that the developed methods enable highly accurate and reproducible driving tests.

Development of a test rig for the precise positioning of a pedestrian dummy in driving tests

Preventive pedestrian protection systems are validated by means of driving tests reproducing safety–critical traffic situations with pedestrian involvement on a testing ground. Hence, a test rig is needed to position the dummy which replicates the pedestrian during the manoeuvre. This paper presents the development of a new test rig ensuring a precise and reproducible positioning of the pedestrian dummy according to the tolerances defined in the corresponding test specifications. The test rig is designed for the use in outdoor environment and features a high flexibility achieved by a modular design, which enables also an easy assembly. Furthermore, a special dummy mounting is developed preventing damage due to collisions between the Vehicle Under Test (VUT) and the pedestrian dummy. Finally, based on measurement results of an appropriate test scenario the high positioning accuracy of the new test rig is presented.

Differential patterns, trends and hotspots of road traffic injuries on different road networks in Vellore district, southern India.

To describe spatial and temporal profiles of Road Traffic Injuries (RTIs) on different road networks in Vellore district of southern India. Using the information in the police maintained First Information Reports (FIRs), daily time series of RTI counts were created and temporal characteristics were analysed with respect to the vehicle, road types and time of the day for the period January 2005 to May 2007. Daily incidence and trend of RTIs were estimated using a Poisson regression analysis. Of the reported 3262 RTIs, 52% had occurred on the National Highway (NH). The overall RTI rate on the NH was 8.8/100000 vehicles per day with significantly higher pedestrian involvement. The mean numbers of RTIs were significantly higher on weekends. Thirteen percentage of all RTIs were associated with fatalities. Hotspots are major town junctions, and RTI rates differ over different stretches of the NH. In India, FIRs form a valuable source of RTI information. Information on different vehicle profile, RTI patterns, and their spatial and temporal trends can be used by administrators to devise effective strategies for RTI prevention by concentrating on the high-risk areas, thereby optimising the use of available personnel and resources.

ROAD TRAFFIC INJURIES IN SOUTH INDIA

Background and Aims: Road traffic injuries (RTIs) contribute 18% of total deaths in India and the number of RTI-related fatalities has been increasing by 8% annually. This study provides a detailed profile of RTIs on the National, State Highways (NH, SH) and local roads in Vellore district and identifies hotspots of RTIs using GIS methodology. Methods: 3262 RTI records were abstracted from the police filed First Information Reports (FIRs) between January, 2005 and May, 2007. Each record contained information on accident’s location, time, vehicle and injury characteristics. The NH vehicular density and RTIs were mapped. Highway junctions at risk for fatal RTIs and segments with high RTI rates were identified. Poisson regression analysis was used to compare RTIs across road categories and event time. Results: The highest proportion of RTIs was observed on NH (52%) with 3.77 injuries/day, compared to SH (30%) and local roads (18%), which had 50% pedestrian involvement, in contrast to NH and SH (34% & 40%). Over the study period, the number of fatal RTIs on NH had increased by 57%. A significant 5 and 7-fold increase respectively were observed in all RTIs and fatal RTIs on the local roads. NH RTI rate (8.8/100,000 vehicles/day) varied from 5.8/100,000 (weekends midnight-to-6am) to 13.2/100,000 (weekdays 6pm-to-midnight). RTIs involving heavy motor vehicles (HMV) were more on weekends compared to weekdays (39.4% vs. 32.6%, p<0.05).Majority of RTIs on the NH had occurred at five major junctions, two of which had the highest fatality rates (17%). One NH segment exhibited the highest rate for fatal RTIs (1.2/100,000). Conclusions: Despite the limited specificity of the police reports, this study has demonstrated the patterns and growing trends of RTIs in India. This study also demonstrates the value of GIS for policy makers in the design, maintenance of highways and in implementing preventive strategies.

Comparison of Rural andUrban Ambulance Crashes in Pennsylvania

Objective. To describe andcompare the characteristics of, andassociated injuries caused by, ambulance crashes that occur in rural versus urban areas. Methods. Crash data collected by the Pennsylvania Department of Transportation were obtained for ambulance crashes from 1997 to 2001. Crash demographics (e.g., location of crash, road conditions, andintersection type) andinjuries reported by police were analyzed to determine differences, if any, between crashes occurring in rural andurban areas. Results. 311 rural and1,434 urban ambulance crashes were identified. Day andtime of crash, light conditions, androad type were similar. Rural crashes were more likely to occur on snowy roads (13% vs. 5%) andat nighttime without street lighting (25% vs. 4%). Operator error was the most common cause of crashes (75% for rural; 93% for urban), whereas vehicle or environmental conditions more frequently affected rural drivers (25% vs. 7%). Urban crashes were more likely to involve angled collisions with other vehicles (54% vs. 19%), intersections (67% vs. 26%), andoccur at a stop sign or signal (53% vs, 14%). Rural crashes often involved striking a fixed object (33% vs. 7%). Urban crashes more often involved more than one vehicle (88% vs. 56%) andmore than four people total (35% vs. 23%). Pedestrian involvement was rare in both groups (< 5%). Injury severity was similar between both types of crashes, although rural crashes more frequently did not involve any injuries (33% vs. 20%). Alcohol and/or drug use by drivers was rare (< 1%). Conclusion. Rural ambulance crashes usually do not involve other vehicles andare more often due to environmental or vehicle factors. Urban ambulance crashes typically involve intersections, other vehicles, andtraffic signals. Although more people andvehicles are often involved in urban ambulance crashes, the severity of injuries sustained are similar.

SOME INSIGHTS ON ROADWAY INFRASTRUCTURE DESIGN FOR SAFE ELDERLY PEDESTRIAN TRAVEL

This paper presents insights into the relationship between road infrastructure and elderly pedestrian involvement in traffic accidents. We combine insights from empirical studies involving the probability of a pedestrian accident with insights from studies involving the probability of injuries to elderly pedestrians who are involved in vehicle-pedestrian accidents. The combined insights provide some direction to the methodology for identifying non-motorized improvements for supporting safe elderly travel. The results of the study indicate that after controlling for vehicle volumes, road infrastructure variables posing the greatest risk of pedestrian accidents in urban corridors include the presence of center turning lanes, traffic signal spacing exceeding 0.5 miles and roadway illumination. Center turning lanes indicate the presence of long corridors which may induce elderly pedestrians especially to attempt to cross roadways mid-block using center turning lane sections as refuges. Presence of traffic signals provides reduced pedestrian accident risk if the spacing is less than 0.5 miles. Especially for elderly pedestrians, the availability of protected crossings at signalized intersections is important considering the fact they cannot travel long block lengths in order to use signalized crossings. Presence of continuous roadway lighting decreases elderly pedestrian accident risk. The results also show the greatest impacts on injury severity probabilities are from the occurrence of elderly pedestrian accidents in non-intersection locations. Specifically, if elderly pedestrians are involved in marked crosswalk accidents, the probability of lower severity injury is higher; in contrast, if they are involved in unmarked, non-intersection locations such as mid-blocks, the probability of high severity injury is higher. We obtained these results through the use of Bayesian analysis. Bayesian analysis allows us to use subjective prior information on the distribution of parameters in combination with information from the observed data. The advantage of Bayesian analysis in the assessment of key road variables on safe elderly travel is that we can examine the robustness of results.