Systematic literature review of 10 years of cyclist safety research

Safety-in-Numbers for route choice of bicycle trips: A choice experiment approach for commuters

Safety-in-Numbers (SiN) implies that the risk of collision per road user is less when there are more road users. Although the available literature has confirmed the existence of SiN as an objective measure of safety, the effect on perceived safety, especially in the context of bicycle riders, has received much less attention. This study investigates the SiN effect on the perceived safety of bicycle riders that influences route choice behavior. A stated preference survey was performed in the South Delhi district of Delhi. The effect of attributes like posted speed limit, the volume of motorized traffic, bicycle infrastructure, and bicycle traffic/ crowding on route choice behavior was investigated. A binary logit model was developed to quantify the effect of these attributes on route choice. The results indicate that, in general, riders prefer routes with more bicycle traffic, hence validating SiN. But the effect does not always hold. For some riders, in the presence of dedicated bicycle infrastructure, when the perceived safety is higher, the presence of more bicycle traffic acts as crowding and demotivates riders to choose that route. The study also reveals that riders prefer routes with a low volume of motorized traffic and dedicated bicycle infrastructure. The outcomes suggest that a policy that encourages infrastructural development to provide lateral separation will encourage more people, hence increasing bicycle mode share as well as the perceived safety of riders.

Unraveling the relation between cycling accidents and built environment typologies: Capturing spatial heterogeneity through a latent class discrete outcome model

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death. Transport authorities must understand why accidents occur, to reduce the risk of those who cycle. This study applies a new modeling framework to analyze cycling accident severities. We employ a latent class discrete outcome model, where classes are derived from a Gaussian-Bernoulli mixture, applied to data from Berlin, and augmented with volunteered geographic information. We jointly estimate model components, combining machine learning and econometric approaches, allowing for more intricate and flexible representations while maintaining interpretability. Results show the potential of our approach. Risk factors are indexed depending on where accidents occurred and their contribution. We can discover complex relations between specific built environments and accident characteristics and uncover differences in the impact of certain accident factors on one environment typology but not others. Using multiple data sources also proves helpful as an additional layer of knowledge, providing unique value to understand and model cycling accidents. Another critical aspect of our approach is the potential for simulation, where locations can be examined through simulated accident features to understand the inherent risk of various locations. These findings highlight the ability to capture heterogeneity in accidents and their relation to the built environment. Capturing such relations allows for more direct countermeasures to risky situations or policies to be designed, simulated, and targeted.

Systematic review, evaluation and comparison of different approaches for the implementation of road network safety analysis

Introduction

Road safety is still a major issue all around the world. The capability to analyze the road network and identify high risk sections is crucial in road safety management. Therefore, it is essential for road administrations, practitioners, and researcher to have a clear and practical framework of the available road network safety analysis procedures. The aim of this study is to provide such a framework by carrying out an exhaustive analysis of the main procedures available all around the world.

Method

The proposed literature review has started considering a web search on Web of Science (WoS). Then, a systematic review of each publication has been carried out using the Bibliometrix software, to identify the main characteristics of the publications within the specific topic. Then, the most relevant and widespread safety analysis procedures have been considered and the following aspects have been analyzed: the type of approach (crash analysis, crash prediction models procedures, based on road safety inspections, etc.), which and how many data are required (crashes, traffic, visual inspections, geometrical data, etc.), which is the effectiveness of the procedure, and which are the segmentation criteria used (fixed length, variable length based on geometry, traffic, etc.).

Results

Ten different procedures for road network safety analysis have been considered for detailed analysis. The research findings highlight that each procedure has its own pros and cons.

Conclusions

The choice of the best procedure to use is highly related to the characteristics of the road network that need to be analyzed, to the availability of data, and to the main elements the Road Authorities (RA) wants to give priority to.

Practical applications

This collection and review of different procedures will be of great interest for RAs, practitioners, and researchers in the process of selecting the most useful procedure to use to carry out a road network safety analysis.

A Journey of Challenges and Victories: A Bibliometric Worldview of Nanomedicine since the 21st Century

Nanotechnology profoundly affects the advancement of medicine. Limitations in diagnosing and treating cancer and chronic diseases promote the growth of nanomedicine. However, there are very few analytical and descriptive studies regarding the trajectory of nanomedicine, key research powers, present research landscape, focal investigative points, and future outlooks. Herein, articles and reviews published in the Science Citation Index Expanded of Web of Science Core Collection from first January 2000 to 18th July 2023 are analyzed. Herein, a bibliometric visualization of publication trends, countries/regions, institutions, journals, research categories, themes, references, and keywords is produced and elaborated. Nanomedicine-related academic output is increasing since the COVID-19 pandemic, solidifying the uneven global distribution of research performance. While China leads in terms of publication quantity and has numerous highly productive institutions, the USA has advantages in academic impact, commercialization, and industrial value. Nanomedicine integrates with other disciplines, establishing interdisciplinary platforms, in which drug delivery and nanoparticles remain focal points. Current research focuses on integrating nanomedicine and cell ferroptosis induction in cancer immunotherapy. The keyword "burst testing" identifies promising research directions, including immunogenic cell death, chemodynamic therapy, tumor microenvironment, immunotherapy, and extracellular vesicles. The prospects, major challenges, and barriers to addressing these directions are discussed.

Riding practices of e-bike riders after the implementation of electric bike management regulations: An observational study in Hangzhou, China

Objective

This study aimed to understand the riding behaviors of electric bike (e-bike) users in Hangzhou after the "Regulations of Zhejiang Province on the Administration of Electric Bicycles".

Methods

The study consisted of two parts, including a questionnaire survey of local e-bike users in Shangcheng District and Jiande County in Hangzhou City, and a cross-sectional observational study of 16 intersections.

Results

A total of 789 e-bike riders participated in the questionnaire survey, and the riding behavior of 99,407 e-bike users was observed. The main purpose of using e-bike was work and daily life, 46.0% of them used e-bikes more than 5 days a week, and 58.5% used e-bikes for less than 30 min each time. A vast majority (81.7%) of e-bike riders believe that the implementation of Zhejiang Regulations has significantly improved the safety level of e-bike riding in the region. The field survey found that the correct rates of helmet wearing by e-bike riders and passengers were 78.83% and 42.27%. The main violations were invalid/non-helmet wearing (21.17%), followed by carrying passengers and running red lights (7.94% and 4.26%). The rates of invalid/non-helmet wearing and running red lights were significantly higher during non-morning rush hour, weekends, and roads without separate non-motorized vehicle lanes than in other conditions (all P < 0.05). Additionally, sunny days and crossroads were risk factors for passenger-carrying and invalid/non-helmet wearing compared to rainy/cloudy days and T-intersections.

Conclusions

The phenomenon that e-bike users' correct practice lags far behind the awareness of various violations has shown some improvement. To further enhance safety measures for e-bike riders, it is necessary to promote education, improve infrastructure, and strengthen law enforcement, in support of the "Zhejiang Regulations" and behavioral interventions.

Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Cycling is a sustainable and healthy mode of transportation with direct links to reducing traffic congestion, lowering greenhouse gas emissions, and improving air quality. However, from a safety perspective, bicyclists represent a risky road user group with a higher likelihood of sustaining severe injuries when involved in vehicle crashes. With various determinants known to affect bicyclist injury severity and vary across locations, this study investigates the factors affecting bicyclist injury severity and temporal instability, considering the location of crashes. More specifically, the objective of this study is to understand differences in injury severities of intersection and non-intersection-related single-bicycle-vehicle crashes using four year crash data from the state of Florida. Random parameters logit models with heterogeneity in the means and variances are developed to model bicyclist injury severity outcomes (no injury, minor injury, and severe injury) for intersection and non-intersection crashes. Several variables affecting injury severities are considered in model estimation, including weather, roadway, vehicle, driver, and bicyclist characteristics. The temporal stability of the model parameters is assessed for different locations and years using a series of likelihood ratio tests. Results indicate that the determinants of bicyclist injury severities change over time and location, resulting in different injury severities of bicyclists, with non-intersection crashes consistently resulting in more severe bicyclist injuries. Using a simulation-based out-of-sample approach, predictions are made to understand the benefits of replicating driving behaviour and facilities similar to intersections for non-intersection locations, which could benefit in reducing bicyclist injury severity probabilities.

Examining safe spaces for pedestrians and e-bicyclists at urban crosswalks: An analysis based on drone-captured video

Despite numerous theoretical and empirical studies exploring the spatial needs of road users, a significant gap remains in validating these findings with extensive real-world data sets. This study presents the idea of "safe spaces," outlining the buffer zones that both walkers and e-bicyclists maintain when crossing streets, while also taking safety and psychological well-being into consideration. We used drones to gather the study's trajectory data at three significant crossings in Xi'an, China. Multi-coordinate system transformation enabled us to determine the relative positions of individuals and moving objects within their visual domain. Relative position frequency heat maps were generated to explore safe distance demands in different directions. The safety space was then fitted using the least squares method. Our research demonstrates that whereas e-bicyclists maintain semi-elliptical safe spaces at street crossings, walkers maintain semi-circular safe spaces, and the sizes of these zones increase in direct proportion to their relative speeds. However, the safe space size oscillates within a defined range above a particular speed threshold. Notably, e-bicyclists require larger safety distances forward and are more sensitive to speed variations. Our knowledge of the dynamics of safe spaces for walkers and e-bicyclists at street crossings is improved by this work, with emphasis on the role of speed and pedestrian numbers in shaping these spaces. The incorporation of real-world data from drone footage significantly strengthens the validity and reliability of our findings, bridging a crucial empirical gap in the existing literature. Additionally, this paper introduces a crowding coefficient based on safe space and proposes a new method using safe space to aid traffic conflict metrics PET, providing valuable insights for identifying crowd congestion levels and capturing traffic conflict events. The practical implications of our findings extend to urban planning, traffic management, and safety of vulnerable road users. Ultimately, this research contributes to the development of safer and more efficient urban environments for all road users.

Deep transfer learning-based anomaly detection for cycling safety

INTRODUCTION

Despite the general improvements in road safety, with the growing number of bicycle users, cycling safety is still a challenge as demonstrated by the fact that it is the only road transport mode with an increase in the number of fatalities in EU cities.

PROBLEM

Moreover, to analyze the problem to improve the road transport system, the traditional network screening based on crash statistics is a reactive approach and less effective due to the lack of suitable bicycle data availability, as well. In such a framework, new opportunities for data collection in smart cities and communities are emerging as proactive approaches to identify critical locations where safety treatments can be effectively applied to prevent bicycle crashes.

METHOD

This research applied a deep transfer learning model to detect anomalies in cycling behavior that can be associated with traffic conflicts or near-miss crashes.

RESULTS

The paper presents how to build a users' tailored riding model named DTL AD to detect and localize riding anomalies by using a set of data in the National Marine Electronics Association (NMEA) string of Global Navigation Satellite System (GNSS) recorded with instrumented bicycles by different cyclists.

CONCLUSION

More specifically, DTL AD exploits a convolutional autoencoder (CAE) with transfer learning to reduce data labelling and training effort.

PRACTICAL APPLICATION

A case study demonstrates the identification of anomalies in cycling behavior visually represented on Geographic Information Systems (GIS) maps, showing how data clustering is well located in high-risk areas.

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

BSafe-360: An All-in-One Naturalistic Cycling Data Collection Tool

The popularity of bicycles as a mode of transportation has been steadily increasing. However, concerns about cyclist safety persist due to a need for comprehensive data. This data scarcity hinders accurate assessment of bicycle safety and identification of factors that contribute to the occurrence and severity of bicycle collisions in urban environments. This paper presents the development of the BSafe-360, a novel multi-sensor device designed as a data acquisition system (DAS) for collecting naturalistic cycling data, which provides a high granularity of cyclist behavior and interactions with other road users. For the hardware component, the BSafe-360 utilizes a Raspberry Pi microcomputer, a Global Positioning System (GPS) antenna and receiver, two ultrasonic sensors, an inertial measurement unit (IMU), and a real-time clock (RTC), which are all housed within a customized bicycle phone case. To handle the software aspect, BSafe-360 has two Python scripts that manage data processing and storage in both local and online databases. To demonstrate the capabilities of the device, we conducted a proof of concept experiment, collecting data for seven hours. In addition to utilizing the BSafe-360, we included data from CCTV and weather information in the data analysis step for verifying the occurrence of critical events, ensuring comprehensive coverage of all relevant information. The combination of sensors within a single device enables the collection of crucial data for bicycle safety studies, including bicycle trajectory, lateral passing distance (LPD), and cyclist behavior. Our findings show that the BSafe-360 is a promising tool for collecting naturalistic cycling data, facilitating a deeper understanding of bicycle safety and improving it. By effectively improving bicycle safety, numerous benefits can be realized, including the potential to reduce bicycle injuries and fatalities to zero in the near future.

Analysis of Risky Riding Behavior Characteristics of the Related Road Traffic Injuries of Electric Bicycle Riders

Electric bicycle (EB) riders, being vulnerable road users (VRUs), are increasingly becoming victims of road traffic injuries (RTIs). This study aimed to determine the current status and epidemiological characteristics of RTIs among EB riders through a questionnaire survey and roadside observations in Shantou to provide a scientific basis for the prevention and control of electric bicycle road traffic injuries (ERTIs). A total of 2412 EB riders were surveyed, and 34,554 cyclists were observed in the study. To analyze the relationship between riding habits and injuries among EB riders, chi-square tests and multi-factor logistic regression models were employed. The findings reveal that the prevalence of ERTIs in Shantou was 4.81%, and the most affected group was children under 16 years old, accounting for 9.84%. Risky behavior was widespread among EB riders, such as the infrequent wearing of safety helmets, carrying people on EBs, riding on sidewalks, and listening to music with headphones while bicycling. Notably, over 90% of those who wore headphones while bicycling engaged in this risky behavior. The logistic regression analysis showed that honking the horn (odds ratio (OR): 2.009, 95% CI: 1.245-3.240), riding in reverse (OR: 4.210, 95% CI: 2.631-6.737), and continuing to ride after a fault was detected (OR: 2.010, 95% CI: 1.188-3.402) all significantly increased the risk of ERTIs (all p < 0.05). Risky riding behavior was significantly less observed at traffic intersections with traffic officers than at those without (all p < 0.001).

Texting While Driving: A Literature Review on Driving Simulator Studies

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.