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To describe the sociodemographic data of injured pedestrians, temporal patterns of injury, injury patterns, and the independent predictors of hospital admission. A two year cross-sectional study was conducted at the Saint Ann's Bay Regional Hospital in pedestrians with injuries post collision with a motor vehicle. A census was performed in all patients who received either emergency room treatment, hospital admission, or surgical intervention. A 30-item interviewer questionnaire was administered to collect the data. A logical regression model was used to determine independent predictors for hospital admission. Ninety pedestrians were included. Age range: 6-86 years old (Mean=39.9). Males were 63.3%, 75.6% were employed, 31% had a chronic illness and 27% reported marijuana use. Most injuries occurred in April, lowest injury rates occurred in August and September. Twenty two percent of collisions occurred on Saturdays. Most injuries occurred at 5pm and 3pm. Many (54.4%) had a fracture, 73.5% were closed. Approximately 32% had contusions and 6.7% had lacerations. Independent predictors of admission were history of marijuana use and having a fracture. Those with history of marijuana use were 4.21 times more likely to be admitted. Those with fractures were 7.10 times more likely to be admitted. Injury patterns spanned a wide age range. They often involved a high energy mechanism of injury as evidenced by the frequency of fractures, hospital admission and surgery intervention rates. The data also suggests a need to implement marijuana testing programmes in our road users.

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Pedestrian detection based on deep learning methods have reached great success in the past few years with several possible real-world applications including autonomous driving, robotic navigation, and video surveillance. In this work, a new neural network two-stage pedestrian detector with a new custom classification head, adding the triplet loss function to the standard bounding box regression and classification losses, is presented. This aims to improve the domain generalization capabilities of existing pedestrian detectors, by explicitly maximizing inter-class distance and minimizing intra-class distance. Triplet loss is applied to the features generated by the region proposal network, aimed at clustering together pedestrian samples in the features space. We used Faster R-CNN and Cascade R-CNN with the HRNet backbone pre-trained on ImageNet, changing the standard classification head for Faster R-CNN, and changing one of the three heads for Cascade R-CNN. The best results were obtained using a progressive training pipeline, starting from a dataset that is further away from the target domain, and progressively fine-tuning on datasets closer to the target domain. We obtained state-of-the-art results, MR-2 of 9.9, 11.0, and 36.2 for the reasonable, small, and heavy subsets on the CityPersons benchmark with outstanding performance on the heavy subset, the most difficult one.

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In recent years, the application of artificial intelligence (AI) in the automotive industry has led to the development of intelligent systems focused on road safety, aiming to improve protection for drivers and pedestrians worldwide to reduce the number of accidents yearly. One of the most critical functions of these systems is pedestrian detection, as it is crucial for the safety of everyone involved in road traffic. However, pedestrian detection goes beyond the front of the vehicle; it is also essential to consider the vehicle's rear since pedestrian collisions occur when the car is in reverse drive. To contribute to the solution of this problem, this research proposes a model based on convolutional neural networks (CNN) using a proposed one-dimensional architecture and the Inception V3 architecture to fuse the information from the backup camera and the distance measured by the ultrasonic sensors, to detect pedestrians when the vehicle is reversing. In addition, specific data collection was performed to build a database for the research. The proposed model showed outstanding results with 99.85% accuracy and 99.86% correct classification performance, demonstrating that it is possible to achieve the goal of pedestrian detection using CNN by fusing two types of data.

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Introducción. Los accidentes de tránsito (AT) son un problema de salud pública a nivel mundial, se estiman 1,3 millones de muertes anuales, y en los sobrevivientes deja secuelas traumáticas que pueden disminuir la calidad de vida. De las víctimas, los peatones lesionados representan el 22%. El objetivo del presente estudio fue determinar los factores asociados al requerimiento de rehabilitación física de los peatones lesionados por algún AT en el territorio peruano. Métodos. Se realizó un estudio observacional de corte transversal con los datos secundarios brindados por el Centro Nacional de Epidemiología, Prevención y Control de Enfermedades (CDC Perú) por medio de fichas epidemiológicas. Resultados. Se analizaron 14 186 registros que involucran a peatones lesionados por atropellamiento en carretera por algún vehículo motorizado entre el 2016 y el 2021. El 87,89% de las víctimas no requirieron rehabilitación posterior al suceso. Existe una mayor probabilidad de rehabilitación si el lesionado tiene una mayor edad y si la hora del accidente fue entre las 00:00 y 05:59 horas del día. Existe una menor probabilidad de rehabilitación si el accidente lo ocasiona un motocarro. Conclusiones. Existe una gran cantidad de accidentados jóvenes y adultos, sin embargo, la edad mayor se comporta como un factor asociado a la necesidad de rehabilitación, así mismo, el tipo de carretera y el tipo de vehículo causante influyen en el desenlace de la víctima.

Introduction. Traffic accidents (TA) are a public health problem worldwide, with an estimated 1.3 million deaths per year from collisions and leaves traumatic sequels in the survivors. Of the total number of victims affected in an AT, injured pedestrians represent 22%. The objective of this study was to determine the factors associated with the requirement of physical rehabilitation of pedestrians injured by any TA in the Peruvian territory. Methods. An observational cross-sectional study was carried out with the data provided by the National Center for Epidemiology, Prevention, and Control of Diseases (CDC Peru) through epidemiological records. A regression analysis was performed, and prevalence ratios (PR) were reported, having the rehabilitation requirement as a dependent variable. Results. A total of 14,186 records of pedestrians injured by being hit by a motor vehicle on the road between 2016 and 2021 were analyzed. 87.89% of the victims did not require post-event rehabilitation. There is a greater probability of rehabilitation if the injured person is older and if the time of the accident occurred between 00:00 and 05:59 hours of the day. There is a lower probability of rehabilitation if the accident is caused by a minor motor vehicle. Conclusions. Many young people and adults are injured; however, older age is a factor associated with the need for rehabilitation, likewise, the type of road and the type of vehicle responsible influence the outcome of the victim.

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Background: Traffic accidents are an important issue for public health and a threat for sustainable development, with pedestrians and cyclists having been recognized as the most vulnerable actors on the streets. The objective of this study was to analyze the profiles of pedestrians and cyclists who died as a result of traffic accidents in Colombia during the 1998-2019 period. Methods: An observational and descriptive study, with the deaths due to traffic accidents in Colombia between 1998 and 2019 as data source. Secondary data were taken from the Vital statistics of Colombia (EEVV), published by Departamento Administrativo Nacional de Estadística (DANE). A trend analysis of the number of deaths during the period under study was performed, and such number was examined against sex to identify potential differences. Multiple correspondence analysis was employed to elaborate the profile of pedestrians and cyclists who die due to traffic accidents. Three profiles were prepared for each road actor: a global profile, one for 1998, and another for 2019. Results: The mortality profiles are different for pedestrians and cyclists, and, in turn, there are also demographic, geographic, and socioeconomic conditions in each type of road actor, which determine higher mortality risks. High population density, younger age group in the cyclists and adults among the pedestrians, low schooling levels and absence of health insurance are suggested as key factors in these profiles. Related to sex, for men is not possible to establish a profile. Women's cases are commonly related to health insurance, age, and population density. Conclusions: Several contextual and demographic characteristics in pedestrians and cyclists allow delimiting mortality profiles. The profiles that were identified suggest the need to articulate road safety policies with other social and development policies in order to coordinate and integrate intersectoral actions that reduce mortality in these road actors.

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BACKGROUND: Using smartphones during a task that requires upright posture is suggested to be detrimental for the overall motor performance. The aim of this study was to determine the role of age and specific aspects of cognitive function on walking and standing tasks in the presence of smartphone use. METHODS: 51 older (36 women) and 50 young (35 women), mean age: 66.5 ± 6.3 and 22.3 ± 1.7 years, respectively, were enrolled in this study. The impact of using a smartphone was assessed during a dynamic (timed up and go, TUG) and a static balance test (performed on a force platform). Multivariate analyses of variance were applied to verify main effects of age, task, estimates of cognitive function and interactions. RESULTS: Compared to young, older individuals exhibited a poorer performance on the dynamic and on the static test (age effect: p = 0.001 for both variables). Dual-tasking with a smartphone had a negative impact on both groups (task effect: p = 0.001 for both variables). The negative impact, however, was greater in the older group (age × task effect: p = 0.001 for both variables). Executive function and verbal fluency partially explained results of the dynamic and static tests, respectively. CONCLUSIONS: The negative impact of using a smartphone while performing tasks similar to daily activities is higher in older compared to young people. Subclinical deficits in distinct aspects of cognitive function partially explain the decreased performance when dual-tasking.

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Trauma disproportionately affects vulnerable road users, especially the elderly. We analyzed the spatial distribution of elderly pedestrians struck by vehicles in the urban area of Maringa city, from 2014 to 2018. Hotspots were obtained by kernel density estimation and wavelet analysis. The relationship between spatial relative risks (RR) of elderly run-overs and the built environment was assessed through Qualitative Comparative Analysis (QCA). Incidents were more frequent in the central and southeast regions of the city, where the RR was up to 2.58 times higher. The QCA test found a significant association between elderly pedestrian victims and the presence of traffic lights, medical centers/hospitals, roundabouts and schools. There is an association between higher risk of elderly pedestrians collisions and specific elements of built environments in Maringa, providing fundamental data to help guide public policies to improve urban mobility aimed at protecting vulnerable road users and planning an age-friendly city.

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OBJECTIVE: In this study, we aim to identify social typologies of pedestrian crashes considering demographics, health impacts, involved vehicle, temporality of the collision, and place of impact in Hermosillo, Mexico. METHODS: A socio-spatial analysis was performed by using local urban planning information and vehicles-pedestrian crashes records collected by the police department (N = 950) between 2014 and 2017. Multiple Correspondence Analysis and Hierarchical Cluster Analysis were used to determine typologies. Geographical distribution of typologies was obtained with spatial analysis techniques. RESULTS: The results suggest there are four typologies, which portray the physical vulnerability of pedestrians, which reflect the vulnerability to collisions associated to the variables age, gender, and street speed limits. Findings show that children are more likely to be injured during weekends in residential zones (Typology 1), while older females are more likely to be injured during the first three days of the week (Monday - Wednesday) in the downtown area (Typology 2). Injured males during the afternoon in arterial streets represented the most frequent cluster (Typology 3). Also, males were likely to be severely injured by heavy trucks during nighttime in peri-urban areas (Typology 4). These findings indicate that vulnerability and risk exposure vary according to the type of pedestrian involved in the crash, which are linked to the types of places they visit. CONCLUSIONS: The design of the built environment plays a major role in the number of pedestrian injuries particularly when it favors motor vehicles over pedestrians or non-motorized vehicles. Because traffic crashes are considered preventable events, cities must embrace a diversity of mobility modes and incorporate the appropriate infrastructures that safeguard the lives of all their travelers, especially pedestrians.

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Introduction: Wearable assistive devices for the visually impaired whose technology is based on video camera devices represent a challenge in rapid evolution, where one of the main problems is to find computer vision algorithms that can be implemented in low-cost embedded devices. Objectives and Methods: This work presents a Tiny You Only Look Once architecture for pedestrian detection, which can be implemented in low-cost wearable devices as an alternative for the development of assistive technologies for the visually impaired. Results: The recall results of the proposed refined model represent an improvement of 71% working with four anchor boxes and 66% with six anchor boxes compared to the original model. The accuracy achieved on the same data set shows an increase of 14% and 25%, respectively. The F1 calculation shows a refinement of 57% and 55%. The average accuracy of the models achieved an improvement of 87% and 99%. The number of correctly detected objects was 3098 and 2892 for four and six anchor boxes, respectively, whose performance is better by 77% and 65% compared to the original, which correctly detected 1743 objects. Discussion: Finally, the model was optimized for the Jetson Nano embedded system, a case study for low-power embedded devices, and in a desktop computer. In both cases, the graphics processing unit (GPU) and central processing unit were tested, and a documented comparison of solutions aimed at serving visually impaired people was performed. Conclusion: We performed the desktop tests with a RTX 2070S graphics card, and the image processing took about 2.8 ms. The Jetson Nano board could process an image in about 110 ms, offering the opportunity to generate alert notification procedures in support of visually impaired mobility.

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The use of IoT systems that support the construction of smart cities is a global trend that directly affects the quality of life of citizens. for vehicular and pedestrian traffic, the detection of living beings and especially of humans, is a way of quantifying different variables pertinent to the improvement of roads, traffic flows, frequency of visits, among others. the implementation of low-cost systems that do not involve high-processing systems makes the solutions more scalable at a global level. The data acquired by this type of device offers advantages to the different entities in statistics and public consultations, thus contributing to their growth. In this article, an assistance system for the task of pedestrian flow detection is designed and constructed. It integrates strategically located arrays of sensors to detect the direction and general location, which include microwave sensors to detect motion, and infrared presence sensors. The results demonstrate that the system manages to establish the direction of flow of the individual and laterally of the displacement and differentiation between humans and objects for assistance to other systems of counting or analysis of pedestrian flow.

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BACKGROUND & AIMS: While physical inactivity can contribute to chronic diseases, regular activity like walking can help prevent them. In 2010, one in three adults in the U.S. Virgin Islands (USVI) was physically inactive, higher than most U.S. states and territories. There are few walkable destinations and sidewalks along streets in the USVI. Since community- and street-scale design features can influence walking, we convened a 3-day walkability institute in the USVI to (1) learn about physical activity and best practices for design and (2) develop public health infrastructure that supports implementation. Island teams were formed to develop and implement a territory-wide action plan, focused on passing a Complete Streets policy, and demonstration projects on the islands of St. Croix, St. John, and St. Thomas to advance and pass this policy. An example of the demonstration projects and their significance is the completed one in St. Croix, which is the focus of this article. METHODS: Island teams applied critical components of functioning program infrastructure as described in the Component Model of Infrastructure (CMI) such as engaged data, multilevel leadership, responsive plans and planning, and networked partnerships. We evaluated whether a crosswalk installation in St. Croix could alter driver and pedestrian behavior and create a safer environment for pedestrians. Observers recorded pedestrian crossing time, driver speed, and other behaviors before and after crosswalk installation. RESULTS: Pedestrians took significantly fewer average seconds to cross the street in the postdemonstration period (9.83) compared with predemonstration (13.4) (p = .03). Average car speed declined between the predemonstration (24.3) and long-term demonstration periods (p < .01) and from the postdemonstration (24.7) to the long-term demonstration period (18.2) (p < .01). A greater percentage of pedestrians used the crosswalk to cross the street between the postdemonstration (12.5%) and long-term demonstration periods (53.7%) (p < .01). IMPLICATIONS: The demonstration project in St. Croix shows that improvements to built environment infrastructure can increase safety for pedestrians, thus improving walkability in the USVI. We discuss the importance of CMI elements observed in the success of the St. Croix demonstration and its effectiveness in promoting a Complete Streets policy and the lack of these elements on St. John hindering progress there. Public health practitioners can apply the CMI to future physical activity promotion projects in the USVI and other settings as having functioning program infrastructure helps overcome challenges including natural disasters and a global pandemic and can achieve progress toward sustained policy and systems change.

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The demand for more sustainable structures has been shown as a growing tendency, and engineers can use optimization techniques to aid in the design and sizing stage, achieving solutions that minimize its cost and environmental and social impacts. In pedestrian bridges, which are subjected to human-induced vibrations, it is also important to ensure the users' comfort, besides the security verifications. In this context, the objective of this paper is to perform a multi-objective optimization of a steel-concrete composite pedestrian bridge, minimizing cost, carbon dioxide emissions, and vertical acceleration caused by human walking. For this, the Multi-Objective Harmony Search (MOHS) was applied to obtain non-dominated solutions and compose a Pareto Front. Two scenarios were considered with different unit emissions obtained from a life cycle assessment in the literature. Results show that by increasing 15% the structure cost, the vertical acceleration is reduced from 2.5 to 1.0 m/s2. For both scenarios, the optimal ratio for the web height and total span (Le) lies between Le/20 and Le/16. The web height, the concrete strength, and the slab thickness were the design variables with more influence on the value of the vertical acceleration. The Pareto-optimal solutions were considerably sensitive to the parameters varied in each scenario, changing concrete consumption and dimensions of the welded steel I-beam, evidencing the importance of carrying out a sensitivity analysis in optimization problems.

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Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

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Using smartphones during a task that requires an upright posture can be detrimental for the overall motor performance. The aim of this study was to determine the risks of accidents caused by the use of smartphones by pedestrians while walking in a controlled (laboratory) and a non-controlled (public street) environment. Two hundred and one participants, 100 men and 101 women, all young adults, were submitted to walking activities while texting messages and talking on the phone. The risk of accident was measured by the time and the number of steps necessary to walk a 20 ft distance. Assessments were performed with no external distractors (laboratory) and on a public street with vehicles, pedestrians, lights, and noises. Multivariate analysis of variance tests provided the main effect of task (using × not using smartphone), environment (laboratory × street), sex (men × women), and interactions. Significance was set at 5%. The results showed that using a smartphone while walking demanded a greater number of steps and time to perform the task (main effect of task: 0.84; p = 0.001). The risk of accident was higher on the streets where, due to traffic hazards, pedestrians performed the task faster and with a lower number of steps (the main effect of environment: 0.82; p = 0.001). There was no difference of risks between men and women (main effect of sex: 0.01; p = 0.225), whether in the laboratory or on the street (main effect of sex × environment: 0.01; p = 0.905). The task × environment interaction showed that using a smartphone on the street potentiates risks of accidents of pedestrians (main effect of task × environment: 0.41; p = 0.001). In conclusion, using a smartphone while walking can be risky for pedestrians, especially in a traffic environment. People should avoid using their smartphone while crossing streets.

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Pedestrian detection (PD) systems capable of locating pedestrians over large distances and locating them faster are needed in Pedestrian Collision Prediction (PCP) systems to increase the decision-making distance. This paper proposes a performance-optimized FPGA implementation of a HOG-SVM-based PD system with support for image pyramids and detection windows of different sizes to locate near and far pedestrians. This work proposes a hardware architecture that can process one pixel per clock cycle by exploring data and temporal parallelism using techniques such as pipeline and spatial division of data between parallel processing units. The proposed architecture for the PD module was validated in FPGA and integrated with the stereo semi-global matching (SGM) module, also prototyped in FPGA. Processing two windows of different dimensions permitted a reduction in miss rate of at least 6% compared to a uniquely sized window detector. The performances achieved by the PD system and the PCP system in HD resolution were 100 and 66.2 frames per second (FPS), respectively. The performance improvement achieved by the PCP system with the addition of our PD module permitted an increase in decision-making distance of 3.3 m compared to a PCP system that processes at 30 FPS.

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Between 2009 and 2019, pedestrian fatalities in the U.S. increased 51.0% while all other traffic fatalities increased 0.4%. To mitigate pedestrian safety issues, practitioners increasingly use police-reported data to identify and treat locations that experience either serious or fatal injuries. We investigated how many and which types of pedestrian injuries were misclassified by police-reported data in New Mexico between 2014 and 2018 by matching pedestrian-vehicle crash victims reported in New Mexico Department of Transportation (NMDOT) crash data to patients treated at University of New Mexico Health-Science Center, an American College of Surgeons-certified level 1 trauma center (n = 3097 pedestrians in NMDOT data; n = 512 matched pedestrians). Findings suggest that injuries involving older pedestrians, males, alcohol, more serious injuries, and those that occur at night are more likely to match to the hospital data. Of the non-fatally injured pedestrians who police estimated as seriously-injured (n = 207), 21.7% were no more than minorly-injured (n = 45) (KABCO A and ISS < 9). Of pedestrians who police estimated as minorly-injured (n = 239), 55.6% were seriously-injured (n = 133) (KABCO B,C,O and ISS ≥ 9). Of pedestrians with true serious injuries (n = 295) (ISS ≥ 9), 45.1% were under-estimated by police (n = 133) (KABCO B,C,O and ISS ≥ 9) whereas 29.8% of pedestrians with true minor injuries (n = 151) (ISS < 9) were over-estimated by police (n = 45) (KABCO A and ISS < 9). Minorly-injured pedestrians who were over-estimated by police (KABCO A and ISS < 9) were more likely to have lower extremity injuries (62.2% vs 42.5%, p-value = 0.013) compared to minorly-injured pedestrians whose injury severities were estimated correctly (KABCO B,C,O and ISS < 9). Seriously-injured pedestrians who were under-estimated (KABCO B,C,O and ISS ≥ 9) were less likely to have injuries to the head (39.8% vs. 55.6%, p-value = 0.003), spine (30.1% vs. 50.0%, p-value < 0.001), thorax (53.4% vs. 66.7%, p-value = 0.0139), or abdomen (18.8% vs. 32.1%, p-value = 0.005) compared to seriously-injured pedestrians whose injury severities were estimated correctly (KABCO A and ISS ≥ 9). This research illustrates the importance of linking police and health outcome databases to provide a more complete understanding of traffic safety.

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BACKGROUND: Research on the effects of restricting bar opening hours and alcohol sales in middle-income countries is very limited. We assessed compliance with and possible effects of a law enacted in Zacatecas, Mexico on December 30, 2017 and implemented in 2018 and 2019 that established a 2 AM bar closing time and 10 PM cut-off for alcohol sales by off-premises stores. METHODS: Monthly observations of bars and off-premises stores and alcohol mystery shopping visits from 2018 to early 2020 were conducted to assess compliance with the law. Breath tests were conducted in 2018 and 2019 with samples of pedestrians in the nighttime entertainment districts of Zacatecas and a comparison city (Aguascalientes). Surveys of bar owners/managers and staff, emergency medical personnel (EMP), and police officers were conducted in Zacatecas in 2018 and 2019 to assess awareness and support of the law and possible effects of the law on alcohol-related problems such as violence and injuries. RESULTS: Monthly observations indicated that a substantial percentage of bars and off-premises package stores did not comply with the law. Pedestrian breath tests in 2018 and 2019 indicated significant reductions in blood alcohol concentration and heavy drinking among pedestrians in Zacatecas from 11 PM to 2 AM compared to Aguascalientes, but not after 2 AM. Surveys of bar owners/managers indicated that most were aware and supportive of the law. EMP surveys indicated reductions in incidents of physical fighting and drunk or injured customers during the annual September fair in Zacatecas. CONCLUSIONS: This study suggests that restricting bar opening hours and alcohol sales may not result in full compliance by bars and off-premises stores, but may help to reduce excessive alcohol use and related harms in a middle-income country. A more rigorous evaluation with pre-intervention data is needed, however, to fully address this latter question.

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In the face of cooperative intelligent transportation systems (C-ITS) advancements, the inclusion of vulnerable road users (VRU), i.e., pedestrians, cyclists, and motorcyclists, has just recently become a part of the discussion. Including VRU in C-ITS presents new challenges, most notably the trade-off between the increase in VRU safety and the aggravation in channel congestion resulting from VRU-generated messages. However, previous studies mainly focus on network-related metrics without giving much consideration to VRU safety-related metrics. In this context, we evaluated such a trade-off with a study of motion-based message generation rules for VRU transmissions. The rules were analyzed using theoretical and simulation-based evaluations. In addition to studying the message generation rules using channel load metrics, such as channel busy ratio (CBR) and packet delivery ratio (PDR), we introduced a new metric: the VRU Awareness Probability (VAP). VAP uses the exchange of messages from active VRU to measure the probability of VRU detection by nearby vehicles. Results show that fixed message-filtering mechanisms reduce the overall channel load, but they could negatively impact VRU detection. We established the importance of quantifying the VRU awareness and its inclusion in C-ITS analysis because of its direct impact on VRU safety. We also discussed approaches that include VRU context and dynamism to improve the definition of message generation rules.

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This study revises the non-local macroscopic pedestrian flow model proposed in [R. M. Colombo, M. Garavello, and M. Lécureux-Mercier. A class of nonlocal models for pedestrian traffic. Math. Models Methods Appl. Sci., 22(4):1150023, 2012] to account for anisotropic interactions and the presence of walls or other obstacles in the walking domain. We prove the well-posedness of this extended model and we apply high-resolution numerical schemes to illustrate the model characteristics. In particular, numerical simulations highlight the role of different model parameters in the observed pattern formation.

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OBJECTIVE: This study aimed to explore how pedestrians´ safety perception concerning the built environmental characteristics can assist in designing a safer built environment in an urban area in Mexico. METHODS: The study involved two stages of data collection. In the first stage, a physical audit on selected urban roads was performed to assess the characteristics that may increase the perceived risk of a collision. An observational framework to evaluate the crossing areas, sidewalks and organizational factors was developed and used for data collection. In the second stage, an on-street questionnaire was applied to collect the perception of a group of 299 pedestrians about safety risks, road characteristics and their ideas for designing a safer built environment. RESULTS: The physical road audit identified several features in the crossing areas and sidewalks, such as parked cars, movable and fixed obstacles, and lack of traffic signage, which may increase the risk of a pedestrian being involved in a collision. More than half of the road users who were interviewed either agree (27%) or strongly agree (29%) with the statement that crossing the roads in the area was safe. However, pedestrians also identified the following elements as detrimental for the safe use of roads: lack of traffic lights, too much traffic, lack of signs, and parked cars that obstruct visibility. Participants also raised issues beyond the physical infrastructure; for instance, a lack of respect shown by drivers to pedestrians. For designing a safer built environment, participants suggested several ideas highlighting pedestrianization of the road and widening the sidewalks, along with restricting parking of cars on the road. CONCLUSIONS: This combination of findings provide valuable support for the premise that pedestrians may have a good sense of recognizing safety problems and the ability to see the solutions. Although the research was undertaken in the context of a municipality in Guadalajara, the role of pedestrian safety perception may be applicable in other urban settings in low and middle-income countries (LMICs), where local authorities are in charge of designing the road environment. This study highlights the relevance of including pedestrians' participation for a safer and human-centred design of our cities.

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