Non-motorized Road Users Research Articles

Evaluating the Current State of Pedestrian Facilities in Peri-Urban and Urban Areas: A Case Study of Pietermaritzburg City

Background Pedestrian infrastructure is pivotal for advancing the movement of nonmotorized road users and plays a transformative role in transitioning toward sustainable transportation, especially in rural territories influenced by urban dynamics and interconnected by daily commuter flows. Both walking and cycling stand out as paramount sustainable transport means, offering substantial health and environmental benefits, including reduced noise and air pollution. Multiple factors, such as convenience, safety, accessibility, service cost, flexibility, and the quality of pedestrian pathways, shape individuals' propensity to walk. Notably, in peri-urban contexts, the cost of service becomes a significant determinant, as a notable proportion of the population earns minimal wages or faces unemployment. Objective This study sought to examine the current state of pedestrian walkways within Pietermaritzburg and surrounding areas, through which the accessibility, availability, and quality of these facilities were assessed. The study has suggested possible solutions to address the challenges faced by pedestrians. Methods By conducting semi-structured interviews and leveraging ArcGIS mapping utilities, this research has delineated the existing scenario and offered an initial redesign tailored to pedestrian requirements. Results The data have starkly revealed pervasive inadequacies in the existing pedestrian infrastructure. The functionality and safety of current amenities have been manifestly undermined by these shortcomings. The continuing use of these dilapidated facilities may pose alarming threats not only to pedestrians, but also to other road users.

Safety Evaluation of the First Bus Rapid Transit System in Tanzania

The first bus rapid transit (BRT) system in Tanzania and the third in Africa became operational in 2016. As it was introduced to relieve congestion and promote efficient means of public transportation, most previous researchers have focused on its effectiveness on travel time reliability. While travel time is important, the safety aspect of the BRT system is equally important. This study conducted a comprehensive safety evaluation of the BRT system in Tanzania using five years (2016–2020) of crash data. The hot spot analysis was conducted to identify locations experiencing more crashes. The text mining approach was used to understand the key themes from crash narratives. The established themes were further analyzed using a Bayesian network (BN) to understand their association with the severity of crashes that involved BRT buses. Analysis of the results showed that major intersections and locations with commercial activities are the hot spots for crashes involving BRT buses. The text network revealed that most crashes involving a BRT bus and pedestrians were associated with drivers failing to obey traffic control devices. Crashes involving a collision between BRT buses and non-motorized road users were more likely to be severe. The BN findings showed that distraction, collision with non-motorized road users, and disregarding traffic control devices are associated with a higher likelihood of severe crashes. The findings are anticipated to assist agencies in improving the safety of existing and future BRT systems.

Safe streets for some: A review of local active transportation responses across the U.S. during the COVID-19 pandemic

Introduction & research objectivesThe COVID-19 pandemic significantly disrupted daily travel. This paper contrasts 51 US cities’ responses, namely street reallocation criteria and messaging related to physical activity (PA) and active transportation (AT) during the early months of the pandemic. This study can be utilized by cities for aiding in the creation of locally responsive policies that acknowledge and remedy a lack of safe active transportation. MethodsA content analysis review was conducted of city orders and documents related to PA or AT for the largest city by population in all 50 US states and the District of Columbia. Authoritative documents issued from each city's public health declaration (ca. March 2020) to September 2020 were reviewed. The study obtained documents from two crowdsourced datasets and municipal websites. Descriptive statistics were used to compare policies and strategies, with a focus on reallocation of street space. ResultsA total of 631 documents were coded. Considerable variation existed in city responses to COVID-19 that impacted PA and AT. Most cities’ stay-at-home orders explicitly permitted outdoor PA (63%) and many encouraged PA (47%). As the pandemic continued, 23 cities (45%) had pilot programs that reallocated street space for non-motorized road users to recreate and travel. Most cities explicitly mentioned a rationale for the programs (e.g., to provide space for exercise (96%) and to alleviate crowding or provide safe AT routes (57%)). Cities used public feedback to guide placement decisions (35%) and several welcomed public input to adjust initial actions. Geographic equity was a criterion in 35% of programs and 57% considered inadequately sized infrastructure in decision-making. ConclusionsIf cities want to emphasize AT and the health of their citizens, safe access to dedicated infrastructure needs to be prioritized. More than half of study cities did not instate new programs within the first 6 months of the pandemic. Cities should study peer responses and innovations to inform and create locally responsive policies that can acknowledge and remedy a lack of safe AT.

Contributing factors to right-turn crash severity at signalized intersections: An application of econometric modeling

Motorists are required to interact with both roadway infrastructure and various users. The complexity of the driving task in certain scenarios can influence the frequency and severity of crashes. Turning vehicles at intersections, for example, pose a collision risk for both motorized and non-motorized road users. The primary goal of this paper is to investigate the underlying factors which contribute to right-turn crashes at signalized intersections. Five years of crash data across Oregon were collected. A random parameters binary logit model was developed to predict the likelihood of whether a crash resulted in an injury or fatality. It was found that 14 variables were statistically significant in contributing to crash severity. The results obtained show that dry conditions and a posted speed limit of 30 mi/hr or 35 mi/hr contributed to a higher percentage of severe crashes, while fixed-object crashes and snowy weather had a higher likelihood of resulting in no injury crashes. Time-of-day (9:00 p.m. to 6:00 a.m.), lighting conditions (dusk), gender (male driver), crash type (vehicle–pedestrian and rear-end), and driver-level crash cause (driver sped too fast for conditions, driver did not yield right-of-way, and driver disregarded the traffic control device) all led to an increase in probability of a fatal or injury crash. The vehicle–pedestrian conflict variable had the highest impact on increasing the probability of such a crash while turning right at a signalized intersection. This observation is important because right turns are often permitted during the pedestrian walk and clearance indications, and often drivers do not give right-of-way to pedestrians.

External communication of automated shuttles: Results, experiences, and lessons learned from three European long-term research projects.

Automated shuttles are already seeing deployment in many places across the world and have the potential to transform public mobility to be safer and more accessible. During the current transition phase from fully manual vehicles toward higher degrees of automation and resulting mixed traffic, there is a heightened need for additional communication or external indicators to comprehend automated vehicle actions for other road users. In this work, we present and discuss the results from seven studies (three preparatory and four main studies) conducted in three European countries aimed at investigating and providing a variety of such external communication solutions to facilitate the exchange of information between automated shuttles and other motorized and non-motorized road users.

Epidemiology of adult trauma injuries in Malawi: results from a multisite trauma registry

BackgroundLarge-scale multisite trauma registries with broad geographic coverage in low-income countries are rare. This lack of systematic trauma data impedes effective policy responses.MethodsAll patients presenting with trauma at 10 hospitals in Malawi from September 2018 to March 2020 were enrolled in a prospective registry. Using data from 49,241 cases, we analyze prevalence, causes, and distribution of trauma in adult patients, and timeliness of transport to health facilities and treatment.ResultsFalls were the most common mechanism of injury overall, but road traffic crashes (RTCs) were the most common mechanism of serious injury, accounting for (48%) of trauma admissions. This pattern was consistent across all central and district hospitals, with only one hospital recording < 40% of admissions due to RTCs. 49% of RTC-linked trauma patients were not in motorized vehicles at the time of the crash. 84% of passengers in cars/trucks/buses and 48% of drivers of cars/trucks/buses from RTCs did not wear seatbelts, and 52% of motorcycle riders (driver and passenger) did not wear helmets. For all serious trauma cases (defined as requiring hospital admission), median time to hospital arrival was 5 h 20 min (IQR 1 h 20 min, 24 h). For serious trauma cases that presented on the same day that trauma occurred, median time to hospital arrival was 2 h (IQR 1 h, 11 h). Significant predictors of hospital admission include being involved in an RTC, age > 55, Glasgow Coma Score < 12, and presentation at hospital on a weekend.ConclusionsRTCs make up almost half of hospitalized trauma cases in this setting, are equally common in referral and district hospitals, and are an important predictor of injury severity. Pedestrians and cyclists are just as affected as those in vehicles. Many of those injured in vehicles do not take adequate safety precautions. Most trauma patients, including those with serious injuries, do not receive prompt medical attention. Greater attention to safety for both motorized and especially non-motorized road users, and more timely, higher quality emergency medical services, are important policy priorities for Malawi and other developing countries with high burdens of RTC trauma.

Automatic Identification of Vehicle Partial Occlusion in Data Collected by Roadside LiDAR Sensors

Light detection and ranging (LiDAR) sensors are receiving an increasing amount of attention in traffic detection because of their powerful capacity for providing accurate trajectory data of vehicles and non-motorized road users. When installed at the roadside, LiDAR faces the same occlusion problem as other over-roadway sensors (such as video cameras)—the integrity and reliability of object detection can be reduced when occlusion occurs. Existing occlusion reasoning methods are either developed for video sensors or require sensor fusion. Fast and accurate occlusion determination is an indispensable step in the process of vehicle detection, classification, and tracking. This paper presents a novel algorithm to automatically identify the occurrence of vehicle partial occlusion and the corresponding occluding/occluded relationship from roadside LiDAR data. According to the inherent characteristics of LiDAR sensors, two specifically designed heatmaps (ClusterID heatmap and Distance heatmap) were generated and used as the basis for fast occlusion search. Using the field data collected from two testbeds, it was shown that the proposed method could achieve about 95.60% accuracy for partial occlusion identification and a processing speed of 2 ms per frame (32-laser LiDAR sensor with 0.1 s per frame).

Simulation of dispersion effects by considering interactions of pedestrians and bicyclists using an agent space model

As the number of bicyclists in urban areas continues to increase, the need to realistically model the movement and interactions of bicyclists in mixed urban traffic is rapidly gaining importance. Therefore, this paper presents an agent space model (ASM) to elucidate the movements of bicyclists and pedestrians on shared roads. The ASM model, via simulation, particularly illustrates the dispersion phenomenon observed for non-motorized road users. The mutual interactions and diverse bicyclist and pedestrian properties were also incorporated into this model. The mutual interactions were realised through agent spaces of different sizes in conflict and overtaking behaviours for the following combinations: bicyclist-to-pedestrian, bicyclist-to-bicyclist, pedestrian-to-bicyclist, and pedestrian-to-pedestrian, which were obtained through experiments. The hypothesis test indicated that different agent spaces exist for different types of interactions. The experimental data were used to obtain several variables that describe the elements of road user agent spaces, including longitudinal and lateral distances and the dynamic relationship between the longitudinal distance and speed. The simulation results indicated that with an increase in the number of pedestrians, the maximum capacity decreased and the dispersion degree increased. The following psychological and physiological factors affect the degree of dispersion of bicyclists: travelling speed, reaction time, intensity, probability of selecting the head-on direction, and probability of selecting the right-hand direction. In addition, lane formation was observed in all simulations. The results also demonstrated that dedicated bicycle lanes will significantly reduce the dispersion degree. Moreover, the safety and efficiency effects of different forms of bicycle lanes were analysed from the perspective of the degree of dispersion. The simulation results can provide specific guidelines for understanding the causes of phenomena such as dispersion and lane formation, as well as for studying the traffic dynamics, effects of dedicated bicycle lanes, and macroscopic characteristics according to different bicyclist-pedestrian ratios.

Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data

As the Autonomous Vehicle (AV) industry is rapidly advancing, the classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes significant training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227 × 227 images to be used for AlexNet and SqueezeNet; and constructing 224 × 224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. Moreover, we trained resnet101 and shufflenet for a very short time using one epoch of data and then used them as weak learners, which yielded 98.49% classification accuracy. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

Pedestrian temporal gap acceptance behavior at unsignalized intersections in Kanpur, India

Vehicle heterogeneity, lack of lane discipline, lack of infrastructure facilities, and weak enforcement of traffic rules are all characteristics of Indian traffic. These characteristics make Indian roads difficult for non-motorized road users, especially pedestrians. Most intersections in urban areas in India are unsignalized, which results in complex interactions between pedestrians and motorists. This study aims to understand pedestrian gap acceptance behavior at unsignalized intersections in India under heterogeneous traffic conditions. Pedestrian data were collected from six unsignalized intersections in the city of Kanpur. The critical gap was estimated using Wu’s (2006) macroscopic model, and then the effects of several variables on the critical gap were studied. A discrete choice model (binary logit model) was developed to understand the choice of accepted and rejected vehicular gaps depending on traffic, road, and peer pedestrian characteristics. The mean critical headway was 3.76 s using Wu’s model. The behavioral analysis revealed that pedestrians accept rolling gaps to cross unsignalized intersections, resulting in a reduction of the size of the critical gap. The adoption of rolling gap behavior can be attributed to the unyielding behavior of motorists, which forces pedestrians to accept short vehicular gaps in the traffic stream.

Methodology and evidence from a case study in Rome to increase pedestrian safety along home-to-school routes

Abstract Home-to-school routes are very sensitive areas: they represent, for children, a learning tool for their everyday activities, but if poorly designed, maintained and equipped they can expose them to traffic risks. Sidewalks' inappropriate level of service and poor maintenance, especially, are main factors contributing to walking unsuitability, thus to poor comfort and safety levels for young pedestrians, and more in general for all the vulnerable non-motorized road users. This paper deals with a methodology specifically developed to highlight the quality of the urban environment where the home-to-school routes are located, according to four main criteria: wellbeing, usage, appearance, and safety and security. Each criterion is associated with a checklist including the most relevant features to assess, with a focus on maintenance as a key parameter to create safe and comfortable routes to school. An application, a case study in Rome, where the lack of regular maintenance results into a network of unsafe sidewalks, is also presented, analyzing three different areas where a number of schools are located. Detours when approaching school premises were surveyed, due to the levels of distresses and linked to the poor comfort and safety levels. As the mutual influence of built environment over road safety and maintenance requirements for home-to-school paths is not largely investigated thus far, the paper's goal is to provide advanced knowledge for studies and applications further afield.

Total Capacity of Roundabouts Analyzed by a Conflict Technique

The conventional method of analysis for capacity of roundabouts is based on gap-acceptance methods or on empirical regression or a combination of both. The influence of pedestrians is modeled by reduction factors with a questionable empirical background. Thus, the current methods are based on an incoherent mix of approaches. Moreover, they do not account for the interaction between the different elements. The paper presents a new model which treats the whole intersection as one entity. Here all the conflict points where different streams (vehicles and non-motorized road users) intersect within the roundabout are identified. Each conflict point is treated as one queuing system with a simplified queuing mechanism. In addition, the interactions between the consecutive queuing systems are taken into account according to the theory of chains of queues where the distance (= storage area) between the conflict points becomes important. The paper explains the development of the model, presents the mathematical derivations of ready-to-use capacity equations plus parameter calibration by existing data, and demonstrates its real-world application. The advantage of the technique is that all conflicts—both between vehicle streams and with pedestrians at entries and exits—are treated by the same congruent methods. In addition, the interactions between consecutive arms of the roundabout are modeled. The method can also model limited priority (e.g., for pedestrians at crosswalks) for all conflict points. Finally, the conflicts that are decisive for the performance of the whole intersection are identified. The method can be easily implemented into computer software.

Automated class identification of modes of travel in shared spaces: a case study from India

This paper presents a classification approach for road-user modes of travel. The classification does not assume well organized, and lane disciplined traffic. Instead, it relies on specific characteristics intrinsic for each road-user to predict the corresponding class. The classification relies on extracting the geometric and movement characteristics of road-users. As such, it is possible to classify road-users in shared space facilities and sites with high level of non-compliance. The classification is a multi-step procedure. First, movement features are used to discriminate between motorized and non-motorized road-users. Then, complementary features based on road-user geometry are added to differentiate between vehicles, rickshaws, powered two-wheelers, and buses. Experiments are performed on a video data set from a shared facility in New Delhi, India. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 90 percent. By considering the movement attributes, the approach is tolerant to considerable variations in road-user physical details which often arises from choices of camera positions and partial occlusions. The research is part of the long-term goal to develop an automated video-based road safety and data collection system for developing countries.

Law Accommodating Nonmotorized Road Users and Pedestrian Fatalities in Florida, 1975 to 2013.

To examine the effect of Florida's adoption of Statute 335.065-a law requiring the routine accommodation of nonmotorized road users (i.e., a "Complete Streets" policy)-on pedestrian fatalities and to identify factors influencing its implementation. We used a multimethod design (interrupted time-series quasi-experiment and interviews) to calculate Florida's pedestrian fatality rates from 1975 to 2013-39 quarters before and 117 quarters after adoption of the law. Using statistical models, we compared Florida with regional and national comparison groups. Semistructured interviews were conducted with 10 current and former Florida transportation professionals in 2015. Florida's pedestrian fatality rates decreased significantly-by at least 0.500% more each quarter-after Statute 335.065 was adopted, resulting in more than 3500 lives saved across 29 years. Interviewees described supports and challenges associated with implementing the law. Florida Statute 335.065 is associated with a 3-decade decrease in pedestrian fatalities. The study also reveals factors that influenced the implementation and effectiveness of the law. Public Health Implications. Transportation policies-particularly Complete Streets policies-can have significant, quantifiable impacts on population health. Multimethod designs are valuable approaches to policy evaluations.

Driverless vehicles' potential influence on bicyclist facility preferences

ABSTRACTResearch in the field of autonomous vehicle technology focuses on its enhanced safety and convenience for vehicle occupants. The present paper seeks to establish a line of inquiry that addresses the implications of autonomous vehicle technology for nonmotorized road users, in the present case, bicyclists. Studies show that motorized traffic volume and speed affect nonmotorized agents' behavior and facility preference, but the degree to which this will apply to a driverless environment requires further study. We developed a stated-preference survey that had respondents select their preferred facility in a variety of hypothetical scenarios with and without the presence of driverless vehicles and on street types of varying motorized traffic volumes and speeds. A Random Parameters Logit Model is estimated to analyze the links between facility preferences, sociodemographics, street types, and the existence of driverless vehicles. The model results suggest that increases in motorized traffic volumes and speeds correlate with a greater preference for separated facilities. The presence of driverless vehicles amplifies this preference. Controlling for other factors, under driverless vehicle conditions, the odds of selecting protected facilities, such as buffered bicycle lanes and cycle tracks, were more than double the odds found under current conditions. We conclude with recommendations for infrastructure and policy and suggestions for future research in this nascent field of study.

Evaluating the influential priority of the factors on insurance loss of public transit.

Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest.

Determinants of pedestrian and bicyclist crash severity by party at fault in San Francisco, CA

Pedestrian and bicyclist safety is of growing concern, especially given the increasing numbers of urban residents choosing to walk and bike. Sharing the roads with automobiles, these road users are particularly vulnerable. An intuitive conceptual model is proposed of the determinants of injury severity in crashes between vehicles and nonmotorized road users. Using 10 years of crash data from San Francisco, CA, we estimate logistic regression models to illuminate key determinants of crash severity for both pedestrian and bicyclist collisions. The analyses are separated by party at fault to test the novel hypothesis that environmental factors affecting driver speed and reaction time may be especially important when the driver is not at fault. Pedestrian results are broadly consistent with prior research, and offer considerable support for this hypothesis. The strongest predictors of injury severity include pedestrian advanced age, driver sobriety, vehicle type, and a set of variables that help determine driver speed and reaction time. Bicyclist results were weaker overall, and the distinction by party at fault was less important.

Epidemiology and mapping of serious and fatal road traffic injuries in Guyana: results from a cross-sectional study.

To describe the epidemiology of Guyana's road traffic injuries and perform the first geocoding of road traffic injuries in this setting. This was a registry-based retrospective cross-sectional study investigating collisions resulting in serious and fatal injuries. Police reports from two police divisions were used to identify victim, second party (ie, non-victim) and collision characteristics of all serious and fatal collisions between January 2012 and June 2015. Collisions with available location data were geocoded using Geographic Information Systems. Distributions of characteristics were compared for urban and rural areas. Multivariable logistic regression was used to assess variables associated with fatal collisions. The study included 751 collisions, resulting in 1002 seriously or fatally injured victims. Fatally injured victims tended to be older, male and either pedestrians or cyclists. Fatal collisions tended to take place in rural areas, occur on weekends and involve speeding. Fifty-three per cent of fatalities occurred due to non-motorised road users being struck by motorised road users, and the most common fatal collision type was between pedestrians and motor vehicles (35%). The distribution of collisions was similar for urban (43.8%) and rural (56.2%) areas. Fatal collisions were more likely to occur in rural settings. Road traffic injuries pose a considerable public health burden in Guyana. These results suggest a pattern of high mortality in rural collisions and a disproportionate burden of injuries on vulnerable road users. The spatial distribution of collisions should be considered in order to target interventions and improve road traffic safety.

Social thinking and risk between road users: The case of the pedestrian in two cultural contexts

Interactions between motorized and non-motorized road users constitute situations of risk for the latter. Improving the safety of pedestrians, who are the most vulnerable users, is a challenge for public health in society. The aims of this study concern the social representation of the pedestrian and the impact of the cultural variable in the construction of this knowledge; a field in which there exists little research. In this perspective, the discourse of two groups of students, one from a French city (N=85) and one from Singapore (N=124) are compared. The use of free associations and specific analyses makes it possible to isolate the words or expressions most frequently associated with pedestrians and those that discriminate the two populations. This study reveals that the pedestrian is associated with risk in both cultural environments. Nevertheless the French express more fear and apprehension and the Singaporeans’ representation is both more descriptive and personified. These results are discussed in relation to the cultural contexts.

Alcohol intoxication in non‐motorised road trauma

To determine the proportion of non-motorised road users involved in road traffic crashes that presents to hospital intoxicated. We undertook a retrospective cohort study using data collected from the Alfred Trauma Registry. All patients presenting to an adult major trauma centre in Victoria, Australia from July 2009 to June 2014 who were involved in a road traffic crash as a non-motorised road user - pedestrians, pedal-cyclists, non-motorised scooter users, horse riders - were included. Patients who had a blood alcohol measurement were included, and intoxication was defined as a blood alcohol concentration ≥0.05 g/100 mL. There were 1323 patients included for analysis with data on presenting blood alcohol concentration. Alcohol was detected in 248 (18.7%; 95% CI: 16.7-20.9) patients, whereas 211 (15.9%; 95% CI: 14.1-18.0) were intoxicated. Among all included pedestrians, 161 (24.7%) were intoxicated; among all included pedal-cyclists, 47 (7.3%) were intoxicated. Intoxicated patients were significantly younger, and a higher proportion were males and more likely to present after hours and on public holidays (P < 0.01). Survival to hospital discharge and inpatient rehabilitation requirements were similar among intoxicated and non-intoxicated patients. Intoxication was common among non-motorised road users, and the proportion of intoxicated patients in this subgroup appears unchanged over time despite public awareness programmes. The true burden of intoxication in non-motorised road users remains unknown because of a lack of routine testing. Legislation directed at testing for intoxication of non-motorised users and introduction of penalties should be considered to improve safety of all road users.