Offer Grembek

Pedestrian Crash Risk on Boundary Roadways: University Campus Case Study

Prominent pedestrian trip attractors, such as college campuses and major urban parks, are often surrounded by roadways with high volumes of motor vehicle traffic. Although many pedestrians cross busy boundary roadways, relatively little is known about the pedestrian crash risk along these types of facilities. This study quantifies pedestrian crash risk at roadway intersections on the boundary of the University of California, Berkeley, campus during typical spring and fall semester weekdays. Manual pedestrian counts were extrapolated with data from three automated counter locations to represent pedestrian exposure. Pedestrian crash risk was highest at intersections along the boundary roadways with the lowest pedestrian volumes. In addition, pedestrian risk in the evening (6:00 p.m. to midnight) was estimated to be more than three times higher than in the daytime (10:00 a.m. to 4:00 p.m.). The crash risk estimation approach presented can be used to study pedestrian safety on the boundary of campuses and other major attractors so that agencies can identify and prioritize engineering, education, and enforcement treatments to reduce pedestrian injuries.

Crashes on and near college campuses: : a comparative analysis of pedestrian and bicyclist safety

Problem, research strategy, and findings: College campuses are multimodal settings with very high levels of walking and biking in conjunction with high levels of vehicular traffic, which increases risks for bicyclists and pedestrians. In this study, we examine crash data (both police reported and self-reported) and urban form data from three U.S. campuses to understand the spatial and temporal distribution of crashes on the campuses and their immediate periphery. To account for underreporting of pedestrian and bicycle crashes, we developed and circulated an online survey, which helped identify collision hotspots across the three campuses. We then studied these locations to determine their characteristics, generate a typology of campus danger zones, and recommend design and policy changes that could improve pedestrian and cycling safety. We find a significant underreporting of crashes, and unequal spatial and temporal distributions of campus crashes. We identify three particular types of danger zones for pedestrians and cyclists: campus activity hubs, campus access hubs, and through traffic hubs. Injuries tended to be more serious for those crashes taking place on campus peripheries. Takeaway for practice: The intermingling of motorized and non-motorized modes creates significant opportunities for crashes. Planners should be aware of the existing underreporting and give special attention to the three types of danger zones. In addition to the recommendations of the literature for the creation of campus master plans for walking and biking, campuses should conduct safety audits and surveys to identify hotspots and consider specific design improvements for each of the three danger zones to lessen modal conflict.

Investigating the underreporting of pedestrian and bicycle crashes in and around university campuses − a crowdsourcing approach

In this paper, the non-motorized traffic safety concerns in and around three university campuses are evaluated by comparing police-reported crash data with traffic safety information sourced from the campus communities themselves. The crowdsourced traffic safety data comprise of both self-reported crashes as well as perceived hazardous locations. The results of the crash data analysis reveal that police-reported crashes underrepresent non-motorized safety concerns in and around the campus regions. The spatial distribution of police-reported crashes shows that police-reported crashes are predominantly unavailable inside the main campus areas, and the off-campus crashes over-represent automobile involvement. In comparison, the self-reported crash results report a wide variety of off-campus collisions not involving automobiles, while also highlighting the issue of high crash concentrations along campus boundaries. An assessment of the perceived hazardous locations (PHLs) reveals that high concentrations of such observations at/near a given location have statistically significant association with both survey-reported crashes as well as future police-reported crashes. Moreover, the results indicate the presence of a saturation point in the relationship between crashes and PHLs wherein beyond a certain limit, an increasing number of traffic safety concerns may not necessarily correlate with a proportional increase in the number of crashes. These findings suggests that augmenting our existing knowledge of traffic safety through crowdsourcing techniques can potentially help in better estimating both existing as well as emerging traffic safety concerns.

Dynamic programming-based hot spot identification approach for pedestrian crashes

Network screening techniques are widely used by state agencies to identify locations with high collision concentration, also referred to as hot spots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile collisions. In comparison, pedestrian hot spot detection has typically focused on analyzing pedestrian crashes in specific locations, such as at/near intersections, mid-blocks, and/or other crossings, as opposed to long stretches of roadway. In this context, the efficiency of the some of the widely used network screening methods has not been tested. Hence, in order to address this issue, a dynamic programming-based hot spot identification approach is proposed which provides efficient hot spot definitions for pedestrian crashes. The proposed approach is compared with the sliding window method and an intersection buffer-based approach. The results reveal that the dynamic programming method generates more hot spots with a higher number of crashes, while providing small hot spot segment lengths. In comparison, the sliding window method is shown to suffer from shortcomings due to a first-come-first-serve approach vis-à-vis hot spot identification and a fixed hot spot window length assumption.

Comparison of Pedestrian Count Expansion Methods: Land Use Groups versus Empirical Clusters

Expansion factors based on the trends in long-term count data are useful tools for estimating daily, weekly, or annual volumes from short-term counts, but it is unclear how to differentiate locations by activity pattern. This paper compares two approaches to developing factor groups for hour-to-week pedestrian count expansion factors. The land use (LU) classification approach assumes that surrounding LUs affect the pedestrian activity at a location, and it is easy to apply to short-term count locations based on identifiable attributes of the site. The empirical clustering (EC) approach uses statistical methods to match locations based on the actual counts, which may produce more accurate volume estimates, but presents a challenge for determining which factor group to apply to a location. We found that both the LU and EC approaches provided better weekly pedestrian volume estimates than the single factor approach of taking the average of all locations. Further, the differences between LU and EC estimation errors were modest, so it may be beneficial to use the intuitive and practical LU approach. LU groupings can also be modified with insights from the EC results, thus improving estimates while maintaining the ease of application. Ideal times for short-term counts are during peak activity periods, as they generally produce estimates with fewer errors than off-peak periods. Weekly volume estimated from longer-duration counts (e.g., 12 h) is generally more accurate than estimates from shorter-duration counts (e.g., 2 h). Practitioners can follow this guidance to improve the quality of weekly pedestrian volume estimates.

A State-of-the-Art Review on Empirical Data Collection for External Governed Pedestrians Complex Movement

Complex movement patterns of pedestrian traffic, ranging from unidirectional to multidirectional flows, are frequently observed in major public infrastructure such as transport hubs. These multidirectional movements can result in increased number of conflicts, thereby influencing the mobility and safety of pedestrian facilities. Therefore, empirical data collection on pedestrians’ complex movement has been on the rise in the past two decades. Although there are several reviews of mathematical simulation models for pedestrian traffic in the existing literature, a detailed review examining the challenges and opportunities on empirical studies on the pedestrians complex movements is limited in the literature. The overall aim of this study is to present a systematic review on the empirical data collection for uni- and multidirectional crowd complex movements. We first categorized the complex movements of pedestrian crowd into two general categories, namely, external governed movements and internal driven movements based on the interactions with the infrastructure and among pedestrians, respectively. Further, considering the hierarchy of movement complexity, we decomposed the externally governed movements of pedestrian traffic into several unique movement patterns including straight line, turning, egress and ingress, opposing, weaving, merging, diverging, and random flows. Analysis of the literature showed that empirical data were highly rich in straight line and egress flow while medium rich in turning, merging, weaving, and opposing flows, but poor in ingress, diverging, and random flows. We put emphasis on the need for the future global collaborative efforts on data sharing for the complex crowd movements.

Developing a safety heatmap of uncontrolled intersections using both conflict probability and severity

This paper presents a method to assess the safety of uncontrolled intersections considering two major properties of traffic conflicts-conflict probability and severity. This method assesses both the safety level of the entire intersection in addition to the distribution of safety within it. Intersections are modeled by a two-dimensional Cartesian coordinate system and the internal space of intersections is divided into cells. First, the vehicle movement characteristics of an uncontrolled intersection are modeled. Second, the conflict probability of each cell within the intersection is estimated considering the approaching probability and lateral migration probability of vehicles. The quantification of conflict severity is based on kinetic energy loss of potential crashes. Cluster analysis is used to combine conflict probability and severity to model the safety assessment of each cell. Third, the application of the method is discussed, and an overall safety index of intersections is proposed which considers weighted safety level and relative value of areas of different safety levels. Finally, a case study, which includes three different designs, is presented along with safety heatmaps to demonstrate the results. The results not only demonstrate the validity of the model, but also indicate that the proposed method can be applied to: i) safety evaluation of build-up intersections; ii) dangerous position management within an intersection; iii) safety assessment of designed intersections, and iv) safety level comparison among different intersections or various designs for a single intersection. Using this method, engineers and planners can better evaluate and improve the safety of existing or future uncontrolled intersections.

After-effects and disabilities in traffic crash victims in Northern Brazil

ABSTRACT Objectives: The objective of this study was to identify the characteristics related to crash and victim, as well as the after-effects/disabilities and consequences arising from traffic crashes occurring in the city of Rio Branco–Acre. Methods: This is an analytical descriptive cross-sectional study conducted in the City of Rio Branco–Acre. The study population consisted of 405 residents of the city who were victims of traffic crashes, of all age groups and genders, who were hospitalized for the first time as a result of the crash in public hospitals and the health system network, as recorded in the Hospital Information System, and who were discharged between January 1 and December 31, 2010. The data sources included hospital record consultations and active searches for the victims. Hierarchical logistic regression was performed to evaluate the factors associated with the after-effects. Results: The majority of the study population was motorcycle victims (68.6%), male, and young (20–39 years). Concerning the after-effects, the following were significantly associated: factors related to the presence of a postcrash activity limitation (odds ratio [OR] = 2.39; 95% confidence interval [CI], 2.39–6.76), length of hospital stay in days (OR = 1 03; 95% CI, 1.01–1.06), and surgical treatment (OR = 1.82; 95% CI, 1.03–3.21). Those who suffered damage to soft tissue and nerves or facial injury showed an odds ratio of 2 to 4 times of having an after-effect/disability, independent of the victims personal attributes. Conclusion: The mechanism, such as the origin of the pattern of injuries, explains the exposure factors shown by each attribute of the victim and their characteristics. Many of the injuries were precursors to after-effects/disabilities, which, due to their nature and extent, result in the modification of the apparently healthy living standards of young victims who are routinely injured in traffic crashes. Therefore, public policies for prevention should be formulated, reformulated, and implemented, taking into account each attribute of the victims and their social conditions, because these are closely related to their habits and customs. This is a starting point for promoting changes to the current reality that traffic crashes present in the morbidity and mortality of the population.

Database for Active Transportation Infrastructure and Volume

Information about pedestrian infrastructure and volume is indispensable to monitoring, evaluating, and improving the environment for comfortable and safe walking. However, determining and organizing the various types of data in a way that is easy to update and analyze can present challenges. This study designed and developed a relational database for pedestrian infrastructure and volume and included two core components (node table and approach table) and several subcomponents (tables for crosswalks, sidewalks, buffers, signs, transits, bikeways, bicycle parking, and volumes). Important measurements were proposed on the basis of the literature and practice review and grouped into different component categories on the basis of their attributes and relationships. Links were defined according to their relative locations to connect all the components. An infrastructure data collection pilot was conducted across 100 mi (161 km) of California highways using computer imagery, and across 7 mi (11.27 km) of those highways by field inventory, to prove the feasibility of the database. Time costs associated with collecting infrastructure data for the entire state highway system were estimated to be 4,006 h and 8,935 h for using computer and field collection methods, respectively. This study demonstrated that the database was easy to maintain, flexible to update, and feasible for data collection both by computer imagery and in the field. Although most data in the database were related to pedestrians, basic bicyclist-related information was also included to demonstrate the transferability of the database to store bicyclist infrastructure and volume in the future.

Relative Vulnerability Matrix for Evaluating Multimodal Traffic Safety

The multimodal transportation network includes a mix of inherently different modes. In addition to differences in price, range, and comfort of travel, these modes differ in mass and velocity, which correspond to different orders of magnitude in the kinetic energy carried. This discrepancy in kinetic energy affects both the level of protection of each mode, and the level of damage it can inflict on users of other modes. Unfortunately, accounting for both sides of a crash is often overlooked. While the quantities and variables of collected data continue to increase, the analyses conducted and the tools developed remain focused on the victims of crashes. The existing approach limits the ability to explore the underlying mechanism of traffic crashes since there are two sides to every crash. This manuscript proposes a framework for studying traffic safety that takes into account the interaction between all modes in a network. At the core of the framework is a square matrix, I. The rows and columns represent different modes such that element Iij is the number of injuries that were suffered by mode i, which were inflicted by mode j. The distinction between suffered and inflicted injuries is not related to the fault of the involved parties. The distinction lies in which of the two parties experienced the injury. For example, if two vehicles are involved in a crash that resulted in a single injury, the vehicle that experienced the injury is identified as the one that suffered the injury while the other vehicle is the one that inflicted the injury. If an injury is experienced in both vehicles then both vehicles suffered one injury and inflicted one injury. A relative vulnerability index can be calculated for specific mode-pairs, for individual modes, and for an entire geographical region. An empirical application using data from California reveals, amongst other things, that the relative vulnerability of pedestrian and bicyclist are orders of magnitude higher than motorized modes. Applying this methodology to different locations around the globe would provide insights the relative vulnerability of different modes under different mode-splits, different road designs, and different road user cultures.