Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia

Cyclist safety around trams: A market survey

Tram systems present safety risks to cyclists, however only limited research has explored this topic, of which most has focused on crash and hospital data, and severe crash events. This paper presents the first known cyclist market survey focused specifically on the experience of cyclists related to safety around trams, including unreported incidents and those that did not result in hospital attendance. Findings suggest that track-skid incidents are more common than track-wedge incidents, in contrast to previous research that emphasizes track-wedging as a larger issue than skidding. This is may be explained by the differing outcomes, with track-wedging more likely to result in injury. This research is thus significant in identifying track skidding as a major risk concern, causing a majority of crashes, while also confirming that track wedging is the major severity concern. In the last five years, 21% of respondents were involved in at least one tram-track-related crash. This was less than the share of respondents involved in falls (50%), crashes relating to road defects (36%) or collisions with motor vehicles (29%). However, half of survey respondents (52%) reported cycling on roads with tram tracks for 0-20% of their cycling, which might suggest that tram track-related crash rates are high given that most inner-city cycling occurs on roads without tracks. Track-skidding was found to be associated with wet conditions. Those involved in at least one track-skid in the last five years where more likely to have been cycling more than 3 years, but involvement in track-wedging was more likely amongst those cycling > 10 years and aged < 45 years. Implications for research and practice are suggested.

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.

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest

Accurate crash frequency prediction is critical for proactive safety management. The emerging connected vehicles technology provides us with a wealth of vehicular motion data, which enables a better connection between crash frequency and driving behaviors. However, appropriately dealing with the spatial dependence of crash frequency and multitudinous driving features has been a difficult but critical challenge in the prediction process. To this end, this study aims to investigate a new Artificial Intelligence technique called Geographical Random Forest (GRF) that can address spatial heterogeneity and retain all potential predictors. By harnessing more than 2.2 billion high-resolution connected vehicle Basic Safety Message (BSM) observations from the Safety Pilot Model Deployment in Ann Arbor, MI, 30 indicators of driving volatility are extracted, including speed, longitudinal and lateral acceleration, and yaw rate. The developed GRF was implemented to predict rear-end crash frequency at intersections. The results show that: 1) rear-end crashes are more likely to happen at intersections connecting minor roads compared to major roads; 2) a higher number of hard acceleration and deceleration events beyond two standard deviations in the longitudinal direction is a leading indicator of rear-end crashes; 3) the optimal GRF significantly outperforms Global Random Forest, with a 9% lower test error and a substantially better fit; and 4) geographical visualization of variable importance highlights the presence of spatial non-stationarity. The proposed framework can proactively identify at-risk intersections and alert drivers when leading indicators of driving volatility tend to worsen.

Ride-hailing services: Competition or complement to public transport to reduce accident rates. The case of Madrid

Introduction

The transport and mobility sector is experiencing profound transformations. These changes are mainly due to: environmental awareness, the increase in the population of large urban areas and the size of cities, the aging of the population and the emergence of relevant technological innovations that have changed consumption habits, such as electronic commerce or the sharing economy. The introduction of new services such as Uber or Cabify is transforming urban and metropolitan mobility, which has to adapt to this new scenario and the very concept of mobility.

Objective

Thus, the purpose of this study was to evaluate whether ride-hailing platforms substitute or complement public transport to reduce accident rates, considering the two basic transport zones of Madrid: “The Central Almond” and the periphery.

Methods

The data were collected from the 21 districts of Madrid for the period 2013–2019, and they were analyzed by a Random Effects Negative Binominal model.

Results

The results obtained in this study suggest that since the arrival of Uber and Cabify to the municipality of Madrid the number of fatalities and serious injuries in traffic accidents has been reduced. Traffic accidents on weekends and holidays, with at least one serious injury or death, have also been reduced. However, the number of minor injuries has increased in the central districts of Madrid.

Conclusion

Overall, what was found in this study supports the hypothesis that these services replace the urban buses. However, these services improve the supply to users with greater difficulties to access taxis or public transport, constituting an alternative mode of transport for high-risk drivers. Therefore, such findings may be quite useful for policy makers to better define regulatory policies for these services.

Does the Implementation of Ride-Hailing Services Affect Urban Road Safety? The Experience of Madrid

In recent years, changes have occurred in consumption, ownership, and social relations, giving rise to new economic models in which technology enables new ways of connecting, creating, and sharing value. The nature of transport has transformed with the emergence of mobile applications, such as Uber and Cabify, which offer an alternative to the services traditionally provided by the taxi and chauffeur-driven hire vehicle (CDV) sectors. These services have developed within a context of market regulation of the taxi and CDV which are subject to considerable unjustified restrictions for entering and operating in the market, including the numerus clausus of licenses, the limited geographical scope of the license and, in the case of taxis, the regulation of prices as inflexible public rates. Bearing in mind the latest legislative changes affecting mostly the provision of the services of these platforms, this study analyzes whether the number of traffic accident victims has fallen since the introduction of these services in the city of Madrid using a Random Effects Negative Binominal model. The results show that the deployment of these platforms is associated with a reduction of 25% in the number of serious injuries and deaths.

Analysis of the Impact of Ride-Hailing Services on Motor Vehicles Crashes in Madrid

In most cities, discretionary passenger transport by car is predominantly supplied by taxi services. These services face competition from new digital platforms (UBER, Cabify, etc.) that connect users with the services offered by authorized drivers with a license for rented vehicles with drivers (VTC). However, very little is known about the impacts that these services produce in cities where they operate. So far, most studies on this issue have focused on cities of the United States of America, and they broadly found a positive impact in terms of road safety. Road safety has become one of the priority focuses for ensuring social welfare, to the point of being integrated into the Sustainable Development Goals as a primary value to achieve sustainable, safe and responsible mobility. Within this context, the objective of this paper is to analyze the impact of ride-hailing platforms on the frequency of traffic accidents with at least one fatally or seriously injured person in the municipality of Madrid from 2014 to 2018. To do this, a regression analysis has been carried out using a random effects negative binomial regression (RENB). The results of the model show that Uber and Cabify services are associated with a decrease in fatal and serious accidents in Madrid.

A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response

With the development and maturation of vehicle-based data acquisition technology, in-vehicle data is increasingly being used to explore road safety. This paper reports on research that analyzed the real-time tire force data (kinetic response) obtained from vehicle kinetic experiments, and constructed a new approach for identifying the high-risk of crashes on freeway segments with horizontal curvature. First, the road was divided into 1km units. Then, taking into account the characteristics of freeway alignment, each segment with horizontal curve was selected as the object of subsequent analysis. Automotive instrumentation was used to obtain a measure of tire force in the course of normal driving. The entire data set was preprocessed according to rate of change and the density of the data was reduced. By defining the outliers of the kinetic data and conducting factor analysis, two representative crash risk indicators of longitudinal and lateral stability were obtained. Negative binomial regression model (NBR model) and random effects negative binomial regression model (RENBR model) were constructed and jointly applied based on the new indicators to predict the risk value of horizontal curve segments. The method showed good prediction performance (71.8 %) for high-risk road segments with design flaws, but the predicted effect for low-risk road segments was not ideal. This study not only illustrated the effectiveness of in-vehicle data in assessing road crash risk by coupling multiple kinetic parameters, but also provided support for freeway safety research using surrogate measures of risk when there is a lack of crash statistics.

Assessing the impact of traffic signal performance on crash frequency for signalized intersections along urban arterials: A random parameter modeling approach

The recent development of Automated Traffic Signal Performance Measures (ATSPMs), has provided new opportunities and insights into traffic signal operations. As agencies begin to make decisions regarding investment in infrastructure and operation systems, it is imperative to understand the impacts these systems may have on safety. Past research has thoroughly investigated the impact of geometry and signal timing parameters on the safety of intersections, but little is understood on the relationship between improved signal performance and safety. This study uses vehicle trajectory data to create performance metrics for 121 signalized intersections on ten corridors near Columbus, Ohio. These metrics are used to understand the relationship between signal performance and safety. Two performance metrics, percent arrivals on green (POG) and level of travel time reliability (LOTTR), were used along with other volume and geometric data to model the total crash frequency on signalized mainline approaches. The crash data were modeled using a random parameters negative binomial approach. In consideration of potential unobserved heterogeneity between intersections, a correlated random parameters specification was tested alongside the traditional uncorrelated random parameters and fixed parameters model. Based on goodness of fit measures, the correlated random parameter model was chosen to interpret results because this model explains the complex cross-correlation among the estimates of random parameters. The elasticity values revealed a one percent increase in percent arrivals on green is associated with a reduction in total crashes by 1.12 %. The results of this study show the investment in signal operations and optimization result in an improvement in safety at signalized intersections. Further research should be explored to expand this study to additional intersections over a larger time period.

An Examination of the Safety Impacts of Bus Priority Routes in Major Israeli Cities

Bus priority routes (BPRs) promote public transport use in urban areas; however, their safety impacts are not sufficiently understood. Along with proven positive mobility effects, such systems may lead to crash increases. This study examines the safety impacts of BPRs, which have been introduced on busy urban roads in three major Israeli cities—Tel Aviv, Jerusalem and Haifa. Crash changes associated with BPR implementation are estimated using after–before or cross-section evaluations, with comparison-groups. The findings show that BPR implementation is generally associated with increasing trends in various crash types and, particularly, in pedestrian crashes at junctions. Yet, the results differ depending on BPR configurations. Center lane BPRs are found to be safer than curbside BPRs. The best safety level is observed when a center lane BPR is adjacent to a single lane for all-purpose traffic. Local public transport planners should be aware of possible negative implications of BPRs for urban traffic safety. Negative safety impacts can be moderated by a wider use of safety-related measures, as demonstrated in BPRs’ operation in Haifa. Further research is needed to delve into the reasons for the negative safety impacts of BPRs under Israeli conditions relative to the positive impacts reported in other countries.

Analysis of commercial truck drivers' potentially dangerous driving behaviors based on 11-month digital tachograph data and multilevel modeling approach

This study analyzed the potentially dangerous driving behaviors of commercial truck drivers from both macro and micro perspectives. The analysis was based on digital tachograph data collected over an 11-month period and comprising 4373 trips made by 70 truck drivers. First, different types of truck drivers were identified using principal component analysis (PCA) and a density-based spatial clustering of applications with noise (DBSCAN) at the macro level. Then, a multilevel model was built to extract the variation properties of speeding behavior at the micro level. Results showed that 40% of the truck drivers tended to drive in a substantially dangerous way and the explained variance proportion of potentially extremely dangerous truck drivers (79.76%) was distinctly higher than that of other types of truck drivers (14.70%˜34.17%). This paper presents a systematic approach to extracting and examining information from a big data source of digital tachograph data. The derived findings make valuable contributions to the development of safety education programs, regulations, and proactive road safety countermeasures and management.

A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data

The primary objective of this study is to investigate how the deep learning approach contributes to citywide short-term crash risk prediction by leveraging multi-source datasets. This study uses data collected from Manhattan in New York City to illustrate the procedure. The following multiple datasets are collected: crash data, large-scale taxi GPS data, road network attributes, land use features, population data and weather data. A spatiotemporal convolutional long short-term memory network (STCL-Net) is proposed for predicting the citywide short-term crash risk. A total of nine prediction tasks are conducted and compared, including weekly, daily and hourly models with 8 × 3, 15 × 5 and 30 × 10 grids, respectively. The results suggest that the prediction performance of the proposed model decreases as the spatiotemporal resolution of prediction task increases. Moreover, four commonly-used econometric models, and four state-of-the-art machine-learning models are selected as benchmark methods to compare with the proposed STCL-Net for all the crash risk prediction tasks. The comparative analyses suggest that in general the proposed STCL-Net outperforms the benchmark methods for different crash risk prediction tasks in terms of higher prediction accuracy rate and lower false alarm rate. The results verify that the proposed spatiotemporal deep learning approach performs better at capturing the spatiotemporal characteristics for the citywide short-term crash risk prediction. In addition, the comparative analyses also reveal that econometric models perform better than machine-learning models in weekly crash risk prediction tasks, while they exhibit worse results than machine-learning models in daily crash risk prediction tasks. The results can potentially guide transportation safety engineers to select appropriate methods for different crash risk prediction tasks.

Investigating factors of crash frequency with random effects and random parameters models: New insights from Chinese freeway study

In response to the rapid economic growth in China, its freeway system has become the longest in the world and likely will continue to expand. Unfortunately, the safety issues on freeways in China have grown as well and are of great concern to Chinese transportation authorities and drivers. While many proven safety countermeasures developed and implemented by other countries are available for reference, they may be not fully transferrable to China due to the differences in driving cultures and conditions. As a result, an investigation of China's unique safety factors and effective relevant countermeasures are urgently needed. The study presented in this paper thoroughly investigated the factors contributing to freeway crashes in China based on detailed crash data, traffic characteristics, freeway geometry, pavement conditions, and weather conditions. To properly account for the over-dispersion of data and unobserved heterogeneity, a random effects negative binomial (RENB) model and a random parameters negative binomial (RPNB) model were applied, along with a negative binomial (NB) model. The analysis revealed a large number of crash frequency factors, including several interesting and important factors rarely studied in the past, such as the safety effects of climbing lanes. Moreover, the RENB and RPNB models were found to considerably outperform the NB model; however, although the RPNB exhibited better goodness-of-fit than the RENB model, the difference was rather small. The findings of this study shed more light on the factors influencing freeway crashes in China. The results will be useful to highway designers and engineers for creating, building, and operating safe freeways as well as to safety management departments for developing effective safety countermeasures. The study presented in this paper also provides additional guidance for choosing relevant methods to analyze safety and to identify safety factors.

Exploring road design factors influencing tram road safety - Melbourne tram driver focus groups

Melbourne, Australia has the largest tram/streetcar network in the world including the largest mixed traffic tram operating environment. Therefore, Melbourne tram drivers are responsible for controlling one of the heaviest vehicles on road ranging from shared tram lanes to exclusive tram lanes. In addition to different tram lane configurations, tram drivers need to follow different traffic signal phases at intersections including tram priority signals as well as need to serve passengers at various types of closely spaced tram stops. Despite all these challenges, no research has explored tram driver perceptions of the risk factors on different tram route road design configurations. Therefore, the aim of this study is to investigate how tram drivers' safety perceptions alter along various tram route sections, signal settings and stop configurations. A tram driver focus group approach was adopted for this research involving thirty tram drivers (4 female and 26 male drivers). The tram drivers' age ranged from 29 to 63 years, with an average age of 47.6 years (standard deviation of 10.1 years), and their experience of tram driving ranged from 1.17 to 31 years, with an average experience of 12.5 years (standard deviation of 10.2 years). The participating tram drivers perceived that the raised tram tracks and tramways with raised yellow curbing beside tracks are safer lane priority features on the Melbourne tram network compared to full-time, part-time and mixed traffic tram lanes. They regarded 'hook turns' as a safe form of tram signal priority treatment at intersections and platform tram stops as the safest tram stop design for all passengers among all other tram stop designs in Melbourne. Findings of this research could enhance the understanding of crash risk factors for different tram route features and thus can offer effective planning strategies for transit agencies to improve tram road safety.