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.

Effectiveness of road safety interventions: An evidence and gap map.

Background: Road Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low- and middle-income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high-income countries have successfully reduced RTI by using a public health approach and implementing evidence-based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high-income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge. Objectives: The objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels. Search Methods: The EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019. Selection Criteria: The interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post-crash pre-hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non-fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes. Data Collection and Analysis: The EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case-control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2. Main Results: The EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post-crash pre-hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non-fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety-six percent of the studies were reported from high-income countries (HIC), 4.5% from upper-middle-income countries, and only 1.4% from lower-middle and low-income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality. Authors' Conclusions: The EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence-synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

A matched case-control approach to identify the risk factors of fatal pedestrian crashes on a six-lane rural highway in India.

Globally, the increase in pedestrian fatalities due to road traffic crashes (RTCs) on transport networks has been a major concern. In low- and middle-income countries (LMICs), pedestrians face a high risk due to RTCs on the rural highway network. The safety evaluation methods, such as observational before-after, empirical Bayes, full Bayes, and cross-sectional methods have been used to identify risk factors of RTCs. However, these methods are data-intensive and have associated limitations. Thus, this study employed a matched case-control method to identify the risk factors of fatal pedestrian crashes. This study utilized crash, traffic volume, speed, geometric, and roadside environment data of a 175 km six-lane rural highway in India. The identified major risk factors, such as clear zone width, the presence of habitation, service roads, and horizontal curve sections, increase the likelihood of a fatal pedestrian crash. This study provides specific insights for modifying the speed limit of highway sections passing through habitation. On such highway sections, designers should shift focus to pedestrian safety. It also suggests that the service road design needs to be reconsidered from a pedestrian safety viewpoint. The proposed method can be used in any other setting having similar traffic and socio-economic conditions.

Developing a national database of police-reported fatal road traffic crashes for road safety research and management in India.

Strengthening crash surveillance is an urgent priority for road safety in low- and middle-income countries. We reviewed the online availability and completeness of First Information Reports (FIRs; police reports) of road traffic crashes in India. We developed a relational database to record information extracted from FIRs, and implemented it for one state (Chhattisgarh, 2017-2019). We found that FIRs can be downloaded in bulk from government websites of 15 states and union territories. Another 14 provide access online but restrict bulk downloading, and 7 do not provide online access. For Chhattisgarh, 87% of registered FIRs could be downloaded. Most FIRs reported the date, time, collision-type, and vehicle-types, but important crash characteristics (e.g. infrastructure attributes) were missing. India needs to invest in building the crash surveillance capacity for research and safety management. However, in the interim, maintaining a national database of a sample of FIRs can provide useful policy guidance.

Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi.

Pedestrian safety is a serious concern in the developing nations of the world. It is evident from the past studies that built-environment characteristics near bus-stops, play a crucial role on the frequency and overall share of pedestrian deaths and injuries in road traffic crashes. The present study aims to identify critical built-environment features around vulnerable bus-stops in an Indian city and evaluate the odds of risk that prevails on the safety of pedestrians near bus stops. Hotspot analysis was conducted to finalise 177 bus stop sites within high-crash clusters in the study area. Built-environment attributes considered were based on sidewalk, crosswalk and bus stop conditions near such vulnerable locations. This study includes a video graphic and manual field survey conducted during the day and night-time. Logistic regression was applied to estimate the impact of built environment features on pedestrian crashes. Width and disability friendliness of sidewalks, presence of bus bays and on-street parking have significant impacts on pedestrian fatalities at locations with a higher share of pedestrian fatalities during the day. On the other hand, presence of zebra crossings at junctions, proper bus stop lighting and high sidewalks reduce the odds of pedestrian crashes at night near bus stops.

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

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

A systematic review and meta-analysis of the impact of curbs on crash outcomes.

INTRODUCTION: Road traffic crashes involving vertical curbs are commonly reported to occur on highways and expressways in India. We found a gap in terms of systematically assessing the evidence of the impact of curbs on road safety outcomes in the real world. METHOD: We conducted a systematic review and meta-analysis of the impact of curbs on the risk of road traffic injuries. We used keywords in a database of records prepared by an earlier evidence gap map (EGM). The EGM used a comprehensive search strategy including 6 academic database, 17 organizational websites, hand searching, contacting experts and back referencing. RESULTS: We found 4 studies that evaluated impact of a curbed median or a curbed shoulder. We found that the presence of a curb on a median increases the risk for all crashes, all single-vehicle crashes, all median-related crashes and median-related injury crashes. The data also indicate that the severity of accidents reduces for curbs on median while it increases for curbs on shoulder, though the latter effect is not statistically significant. All the epidemiological studies were conducted on rural highways and did not report effects for different traffic speeds or vehicle types. However, our review of crash tests and simulation studies indicates that the impact of a curb design may be highly sensitive to speed and vehicle types. CONCLUSIONS: The safety impacts of a curb depend on the context of the road. In an urban road, a curb should ensure safety of pedestrians from an errant vehicle. On high-speed rural roads, curbs should be avoided and treatments should facilitate safe departure of the vehicle from the roadway.

Identification and analysis of offenders causing hit and run accidents using classification algorithms.

Hit-and-run crashes are significant concern for many countries. Due to lack of information of offending vehicles it is difficult to understand dynamics of these crashes to have a prevention plan. The paper aims to identify the impacting vehicle in hit-and-run crashes. We studied fatal road crashes of New Delhi for eleven years (2006-2016) and found that approximately 40% fatal crashes are hit-and-run with unknown impacting vehicles. We proposed a framework using eleven different machine learning-based classification algorithms - Logistic-Regression, KNN, SVM-Linear and RBF-Kernel, Naïve-Bayes, Random-Forest, DecisionTree, AdaBoost, Multilayer-Perceptron, CART and Linear-Discriminant-Analysis. We found SVM-linear-kernel gave best results. Results reveal that cars, buses, and heavy vehicles are involved vehicles in hit-and-run crashes. Buses were primary cause leading to 39% of hit-and-run during 2006-2009 thereafter cars increased drastically. Our framework is robust and scalable to any city. The outcomes provide inputs to traffic engineers for better policy prescription and road user safety.

A performance analysis of prediction techniques for impacting vehicles in hit-and-run road accidents.

Road accidents are globally accepted challenges. They are one of the significant causes of deaths and injuries besides other direct and indirect losses. Countries and international organizations have designed technologies, systems, and policies to prevent accidents. However, hit-and-run accidents remain one of the most dangerous types of road accidents as the information about the vehicle responsible for the accident remain unknown. Therefore, any mechanism which can provide information about the impacting vehicle in hit-and-run accidents will be useful in planning and executing preventive measures to address this road menace. Since there exist several models to predict the impacting unknown vehicle, it becomes important to find which is the most accurate amongst those available. This research applies a process-based approach that identifies the most accurate model out of six supervised learning classification models viz. Logistic Reasoning, Linear Discriminant Analysis, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbor and Support Vector Machine. These models are implemented using five-fold and ten-fold cross validation, on road accident data collected from five mid-sized Indian cities: Agra, Amritsar, Bhopal, Ludhiana, and Vizag (Vishakhapatnam).This study investigates the possible input factors that may have effect on the performance of applied models. Based on the results of the experiment conducted in this study, Support Vector Machine has been found to have the maximum potentiality to predict unknown impacting vehicle type in hit-and-run accidents for all the cities except Amritsar. The result indicates that, Classification and Regression Trees have maximum accuracy, for Amritsar. Naïve Bayes performed very poorly for the five cities. These recommendations will help in predicting unknown impacting vehicles in hit-and-run accidents. The outcome is useful for transportation authorities and policymakers to implement effective road safety measures for the safety of road users.