Macro-level safety analysis of pedestrian crashes in Shanghai, China

Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US

Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.

A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity

Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities.

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.

Geographically weighted random forests for macro-level crash frequency prediction

Machine learning models such as random forests (RF) have been widely applied in the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a single decision tree such as overfitting and instability. However, the traditional RF is a global concept, and thus may fail to capture spatial variability. In macro-level analysis of road safety, the relationship between crash frequency and risk factors can vary spatially. To address this issue, we employ a modified RF algorithm, named geographically weighted random forest (GWRF). Based on the data from London at the level of Middle-super-output-area (MSOA), the predictive performances of RF and GWRF are compared using mean absolute error (MAE) and root mean square error (RMSE). Moreover, considering MSOAs are geographically connected with each other, several factors related to the discrepancies between adjacent zones are also included in the models. Our results indicate that GWRF outperforms the traditional RF and GWR when an appropriate bandwidth is selected. We further explore the effects of multicollinearity on model performance. The results show that prediction accuracy of GWRF models are not susceptible to the multicollinearity. However, the importance values of those variables with multicollinearity may reduce. Finally, and of equal importance, it is found that the importance of each explanatory variable varies across zones. The density of minor road makes the highest contribution to crash frequency in downtown area, while the crash frequency in peripheral area is more sensitive to the discrepancy of road environment between MSOAs. With such information, road safety interventions can be designed and implemented according to the locally important factors, avoiding thus general guidelines addressed for the entire city.

A systematic approach to macro-level safety assessment and contributing factors analysis considering traffic crashes and violations

During rapid urbanization and increase in motorization, it becomes particularly important to understand the relationships between traffic safety and risk factors in order to provide targeted improvements and policy recommendations. Violations and police enforcement are key variables, but the endogenous relationship between crashes and violations has made these variables unreliable and has limited their use. To manage this problem, this study developed a systematic approach for the joint modeling of crashes and violations to identify crash and violation hotspots and examine the mechanisms underlying macro-level contributing factors. Socio-economic, road network, public facility, traffic enforcement, and land use intensity data from 115 towns in Suzhou, China, were collected as independent variables. A bivariate negative binomial spatial conditional autoregressive model (BNB-CAR) and the potential for safety improvement (PSI) method were adopted to identify crash-prone and violation-prone areas, and an interpretable machine learning framework was applied to explore the factors' effects by area. Results showed that the proposed framework was able to accurately identify problem areas and quantify the impact of key factors, which, in Suzhou, were the number of traffic police and their daily patrol time. Considering such enforcement-related information provided important insights into reducing crash and violation frequency; for example, keeping the number of traffic police and daily patrol time under certain thresholds (number of police lower than 11 and patrol time lower than 2.3 h in this sample) was as effective as increasing these numbers for reducing the probability of high-crash and high-violation areas. The proposed approach can help traffic administrators identify the key contributing factors, especially enforcement factors, in crash-prone and violation-prone areas and provide guidelines for improvement.

Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis

Understanding the relationship between social disparities and traffic crash frequency is essential for long-term transportation planning and policymaking. Few studies have systemically examined the influence of socioeconomic and infrastructure-related disparities in macro-level traffic crash frequency. This study provides a framework to spatially examine the relationships between crash rates and demographic and socioeconomic characteristics, as well as roadway infrastructure and traffic characteristics at the Census Block Groups (CBGs) level. Spatial autocorrelation analysis was first performed on the residual of the Ordinary Least Squares (OLS) model to identify whether non-stationarity exists. Then, the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model were applied to assess the impacts of these factors on crash rates spatially and statistically. Our findings indicate that MGWR outperforms both OLS and GWR in uncovering the spatial relationships between contributing factors and both fatal and injury (FI) crashes as well as property damage only (PDO) crashes. A thorough examination of local coefficient maps highlighted six pivotal variables that significantly influenced a majority of CBGs. Improving infrastructure, including pedestrian pathways and public transit facilities, in low-income areas can offer significant benefits. These findings and recommendations can inform the development of effective strategies for reducing crashes and guide the appropriate selection of modeling techniques for macro-level crash analysis.

An integrated approach of machine learning and Bayesian spatial Poisson model for large-scale real-time traffic conflict prediction

The use of traffic conflicts in road safety evaluation is gaining considerable popularity as it plays a vital role in developing a proactive safety management strategy and allowing for real-time safety analysis. This study proposes an integrated approach that combines a machine learning (ML) algorithm and a Bayesian spatial Poisson (BSP) model to conduct large-scale real-time traffic conflict prediction by considering traffic states as the explanatory variables. Traffic conflicts are measured by two indicators, the Time to Collision (TTC) and the Post-Encroachment Time (PET). Based on both TTC and PET, traffic conflict severity is classified into five categories. For each conflict severity category, a binary variable (conflict occurrence) and a count variable (conflict frequency) are developed, respectively. In addition to conflict variables, traffic state parameters are extracted from a large-scale high-resolution trajectory dataset. The traffic parameters include volume, density, speed, and the corresponding space-based and space-time-based measures within a 30-second interval. Eight ML-based classifiers are applied to predict conflict occurrence, and the best classifier is selected. A binary logistic regression is developed to explore the potential linkages between traffic states and conflict occurrence. As well, a resampling technique Borderline-SMOTE is used to mitigate the sparsity caused by the predefined short interval. The BSP model is utilized to predict the specific number of conflicts. Further, the BSP model can also explain the relationship between traffic states and conflict frequency, and thus the significant influencing traffic states are identified. The results show that random forest outperforms the other MLs in terms of conflict occurrence prediction accuracy. Further, the proposed integrated approach achieves a high performance of conflict frequency prediction with RMSE values of 0.1384 ∼ 0.1699, MAPE values of 9.25% ∼ 36.99%, and MAE values of 0.0087 ∼ 0.6398. The finding emphasizes the need for separately predicting the occurrence and frequency of conflicts with different severities.

Risk exposure factors influencing the frequency of road crashes during the COVID-19 pandemic in Ciudad Juarez, Mexico. A negative binomial spatial regression model

The article aims to investigate the influence of risk exposure factors on the frequency of road crashes from January to August 2020 in Ciudad Juarez, Mexico. It is a longitudinal study with four data sets: road crashes, population and housing census, location of economic activities, and road network information. Specifically, this study investigates the relationship between exposure factors - demographics, main roads and land use - and road crashes. A mixed method analysis was employed, (1) spatial analysis using GIS techniques; and (2) a negative binomial spatial regression model. The results showed a strong spatial dependence (0.274; p-value 0.00) of road crashes in the census tracts, and this effect was statistically significant (0.007) in the spatial regression model. In the model, a high probability (<0.05) of road crashes in the census tracts was found with the population aged 15 to 65 years, the length of main roads and the level of road coverage (Engel index), land uses with economic activities of an industrial and commercial character. The findings of this study successfully capture the social, economic, and urban conditions during the January-August 2020 period in the context of the COVID-19 pandemic. This new knowledge could help create preventive plans and policies to address the frequency of road crashes.

Cluster analysis of the spatial distribution of pedestrian deaths and injuries by parishes in Kampala city, Uganda

Studies on pedestrian deaths and injuries at the urban level in Africa mostly provide overall aggregated figures and do not examine variation in the sub-urban units. Using cluster analysis, this study sought to determine if the observed pattern in the distribution of pedestrian injuries and deaths among parishes in Kampala city is significant. Pedestrian crash data from 2015 to 2019 were collected from the Uganda Traffic Police database. Serious and fatal pedestrian injury rates were mapped by parish using ArcMap and cluster analyses conducted. Results from spatial autocorrelation (Moran's Index of 0.18 and 0.17 for fatal and serious injury rates respectively) showed that the distributions were clustered within parishes crossed by highways and located in the inner city respectively. Z-scores of 3.32 (p < 0.01) for serious injury rates and 3.71 (p < 0.01) for fatal injury rates indicated that the clustering was not random. This study's main contribution was providing a detailed spatial distribution of pedestrian fatal and serious injury rates for Kampala; a city in a low developing country in Africa at the micro-scale of a parish. This foundational exploratory paper formed the first step of a broader study examining built environment factors explaining this pattern.

Do footbridge and underpass improve pedestrian safety? A Hong Kong case study using three-dimensional digital map of pedestrian network

Hong Kong is a compact city with high activity and travel intensity. In the past decades, many footbridges and underpasses were installed to reduce the pedestrian-vehicle conflicts on urban roads. However, it is rare that the effects of configuration of pedestrian network on pedestrian crashes are investigated. In Hong Kong, many footbridges and underpasses are connected to major transport hubs and commercial building development and become parts of giant elevated and underground walkway systems. It is challenging to characterize such a complicated pedestrian network. In this study, a three-dimensional digital map is applied to estimate the connectivity and accessibility of pedestrian network, and measure the relationship between pedestrian network characteristics and pedestrian safety at the macroscopic level. Hence, the effects of footbridge and underpass on pedestrian safety are examined. For example, comprehensive built environment, pedestrian network, traffic, and crash data are aggregated to 379 grids (0.5 km × 0.5 km). Then, multivariate Poisson lognormal regression approach is applied to model fatal and severe injury (FSI) and slight injury pedestrian crashes, with which the effects of unobserved heterogeneity, spatial correlation, and correlation between crash counts are accounted. Results indicate that population density, traffic volume, walking trip, footpath density, node density, number of vertices per footpath segment, bus stop, metro exit, residential area, commercial area, and government and utility area are positively associated with pedestrian crashes. In contrast, average gradient, accessibility of footbridge, accessibility of underpass, and number of crossings per road segment are negatively associated with pedestrian crashes. In other word, pedestrian safety would be improved when footbridge and underpass are more accessible. Findings have implications for the design and planning of pedestrian network to promote walkability and improve pedestrian safety.

Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression

Previous researches have demonstrated that traffic crashes in urban areas are geographical events and strongly linked to local characteristics such as road network and land attributes. However, with a significant emphasis on moving-vehicle crashes, the spatial pattern of fixed-object crashes is unclear so far. The difference between these two types of crashes, and whether existing spatial tools such as geographically weighted regression can interpretate the occurrence mode have not been investigated before. To fill this gap, this paper focuses on understanding the spatial features and occurrence of these two types of crash, i.e., moving-vehicle and fixed-object on the city level. Crash data from Dalian, China were aggregated into subdistricts and calibrated with multi-scale geographically weighted regression (MGWR) models. A noticeable but similar clustering pattern was revealed in both types, with spatial overlap of their accident-prone regions. The spatial influence of explanatory variables (road network, geographic, demographic, socio-economic, and land-use variables) was also found mostly similar in both types of crashes. However, fixed-object crash in downtown is more affected by node count, while POI entrance/exit count, especially those in areas with more industrial zones tend to significantly reduce crash risk. In both types of crashes, terrain slope rather than elevation is found to mitigate the crash risk, especially in the downtown area. Compared to traditional Geographically Weighted Regression (GWR) with a fixed bandwidth, the improvement in modeling performance using MGWR highlights the reasonability and benefits to consider the influence scale of each contributing factor in urban spatial analysis of traffic collisions. This study could help transportation authorities identify high-risk regions, understand their contributing factors and take precautions for improving the local traffic safety.

One-year impact of a multicomponent, street-level design intervention in Mexico City on pedestrian crashes: a quasi-experimental study

BACKGROUND

Mexico City implemented the Pasos Seguros programme to prevent pedestrian injuries and deaths at dangerous road intersections, which included street-level design changes, such as visible pedestrian crossings, sidewalk widening, refuge islands, lane reductions, pedestrian signals and adjustment of traffic light timing at these intersections. Few studies in low and middle-income countries (LMICs) have evaluated the effect of such interventions on pedestrian safety.

AIM

Assess the effectiveness of the Pasos Seguros programme at reducing total, injury and fatal pedestrian-motor vehicle crashes.

METHODS

Two-group quasi-experimental design. Monthly pedestrian crashes were obtained from the road incident database from Mexico City's Citizen Contact Center. The programme's effectiveness was evaluated by comparing 12 months preintervention to 12 months postintervention implementation using a negative binomial regression with random intercept with a difference-in-difference estimation. A qualitative comparative analysis was used to find the configuration of intersection characteristics and programme components associated with a decrease in pedestrian crashes.

RESULTS

Total pedestrian crashes were reduced by 21% (RR 0.79; 95% CI 0.62 to 0.99) after implementation of Pasos Seguros programme. This reduction was observed for pedestrian injury crashes (RR 0.79; 95% CI 0.62 to 1.00) and for fatal crashes (RR 0.61; 95% CI 0.13 to 2.92) although not statistically significant for the latter. A decrease in pedestrian crashes was found at the most complex intersections where more of the programme components was implemented.

CONCLUSION

The Pasos Seguros programme successfully decreased total and injury pedestrian crashes. Similar interventions may improve walking safety in other LMIC cities.

Macroscopic Spatial Analysis of the Impact of Socioeconomic, Land Use and Mobility Factors on the Frequency of Traffic Accidents in Bogotá

The urban structure of a city, defined by its inhabitants, daily movements, and land use, has become an environmental factor of interest that is related to traffic accidents. Traditionally, macro modeling is usually implemented using spatial econometric methods; however, techniques such as support vector regression have proven to be efficient in identifying the relationships between factors at the zonal level and the frequency associated with these events when considering the autocorrelation between spatial units. As a result of this, the main objective of this study was to evaluate the impact of socioeconomical, land use, and mobility variables on the frequency of traffic accidents through the analysis of area data using spatial and vector support regression models. The spatial weighting matrix term was incorporated into the support vector regression models to compare the results against those that ignore it. The urban land of Bogotá, disaggregated into the territorial units of mobility analysis, was delimited as a study area. Two response variables were used: the traffic accidents index on the road perimeter and the traffic accidents index with deaths on the road perimeter, to analyze the total number of traffic accidents and the deaths caused by them. The results indicated that the rate of trips per person by taxi and motorcycle had the greatest impact on the increase in total accidents and deaths caused by them. Support vector regression models that incorporate the spatial structure allowed the modeling of the spatial dependency between spatial units with a better fit than the spatial regression models.

The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach

INTRODUCTION

Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies.

METHOD

This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance.

RESULTS

This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area.

CONCLUSION

These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces.

PRACTICAL APPLICATIONS

The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.

Effect of urban street trees on pedestrian safety: A micro-level pedestrian casualty model using multivariate Bayesian spatial approach

In the past decades, trees were considered roadside hazard. Street trees were removed to provide clear zone and improve roadside safety. Nowadays, street trees are considered to play an important role in urban design. Also, street tree is considered a traffic calming measure. Studies have examined the effects of urban street trees on driver perception, driving behaviour, and general road safety. However, it is rare that the relationship between urban street trees and pedestrian safety is investigated. In this study, a micro-level frequency model is established to evaluate the effects of tree density and tree canopy cover on pedestrian injuries, accounting for pedestrian crash exposure based on comprehensive pedestrian count data from a state in Australia, Melbourne. In addition, effects of road geometry, traffic characteristics, and temporal distribution are also considered. Furthermore, effects of spatial dependency and correlation between pedestrian casualty counts of different injury severity levels are accounted using a multivariate Bayesian spatial approach. Results indicate that road width, bus stop, tram station, on-street parking, and 85th percentile speed are positively associated with pedestrian casualty. In contrast, pedestrian casualty decreases when there is a pedestrian crosswalk and increases in tree density and canopy. Also, time variation in pedestrian injury risk is significant. To sum up, urban street trees should have favorable effect on pedestrian safety. Findings are indicative to optimal policy strategies that can enhance the walking environment and overall pedestrian safety. Therefore, sustainable urban and transport development can be promoted.

The Use of Macro-Level Safety Performance Functions for Province-Wide Road Safety Management

Safety Performance Functions (SPFs) play a key role in identifying hotspots. Most SPFs were built at the micro-level, such as for road intersections or segments. On the other hand, in case of regional transportation planning, it may be useful to estimate SPFs at the macro-level (e.g., counties, cities, or towns) to determine ad hoc intervention prioritizations. Hence, the final aim of this study is to develop a predictive framework, supported by macro-level SPFs, to estimate crash frequencies, and consequently possible priority areas for interventions. At a province-wide level. The applicability of macro-level SPFs is investigated and tested thanks to the database retrieved in the context of a province-wide Sustainable Urban Mobility Plan (Bari, Italy). Starting from this database, the macro-areas of analysis were carved out by clustering cities and towns into census macro-zones, highlighting the potential need for safety interventions, according to different safety performance indicators (fatal + injury, fatal, pedestrian and bicycle crashes) and using basic predictors divided into geographic variables and road network-related factors. Safety performance indicators were differentiated into rural and urban, thus obtaining a set of 4 × 2 dependent variables. Then they were linked to the dependent variables by means of Negative Binomial (NB) count data models. The results show different trends for the urban and rural contexts. In the urban environment, where crashes are more frequent but less severe according to the available dataset, the increase in both population and area width leads to increasing crashes, while the increase in both road length and mean elevation are generally related to a decrease in crash occurrence. In the rural environment, the increase in population density, which was not considered in the urban context, strongly influences crash occurrence, especially leading to an increase in pedestrian and bicyclist fatal + injury crashes. The increase in the rural network length (excluding freeways) is generally related to a greater number of crashes as well. The application of this framework aims to reveal useful implications for planners and administrators who must select areas of intervention for safety purposes. Two examples of practical applications of this framework, related to safety-based infrastructural planning, are provided in this study.

Exploring the impacts of street layout on the frequency of pedestrian crashes: A micro-level study

INTRODUCTION

Pedestrian safety has become a critical issue since walking is increasingly promoted as a sustainable transport mode. However, pedestrians are vulnerable to severe injury and mortality in road crashes. Therefore, it is important to understand the factors that affect the safety of pedestrians. This paper investigates the impacts of street layout on the frequency of pedestrian crashes by examining the interactive pattern of built environment, crossing facilities, and road characteristics.

METHOD

A surrogate exposure variable of pedestrian crashes at the road-segment level is proposed by considering the locations of crossing facilities, distribution of points of interest (POIs), road characteristics, and pedestrian activities. A network-based kernel density technique is used to identify the pedestrian crash risk at the road segment level. Bayesian spatial models based on different exposure variables are employed and compared.

RESULTS

The results suggest that models using the surrogate exposure of pedestrian crashes provide better model fit than the ones simply using the density of pedestrians. It is also found that the presence of POIs is related to a higher risk of pedestrian-vehicle crash. In addition, a significantly higher number of pedestrian crashes are found to occur on segments with more bus stops and metro stations. Results also show that the longer the distance between the crossing facilities and road segments, the more pedestrian crashes are observed.

CONCLUSIONS

The proposed aggregated indicator can provide more efficient exposure and higher prediction accuracy than the density of pedestrians. Besides, the POIs, crossing facilities, and road types were all significantly related to pedestrian crashes.

PRACTICAL APPLICATIONS

Our results suggest that the locations of POIs and transport facilities should be planned in a way that can decrease the number of road crossed or guide pedestrians to take safe crossing path.

Driving behavior at midblock crosswalks with Rectangular Rapid Flashing Beacons: Hidden Markov model approach using naturalistic data

Pedestrian fatalities have increased in the U.S. with the largest increase being observed on urban arterials and away from intersections. Rectangular Rapid Flashing Beacon (RRFB) has been widely implemented as a safety countermeasure to improve driver awareness and visibility of pedestrians, especially for midblock crosswalks. Studies show that drivers are more likely to yield to pedestrians at crosswalks with an RRFB. These studies are often based on a binary outcome of whether or not drivers yield to pedestrians. Nevertheless, it is also important to consider the drivers' deceleration behavior as a dynamic process at these crosswalks and the impact of pedestrians being present or not. Understanding this dynamic behavior and the related circumstances can provide information on the design of alerting systems that help drivers make more appropriate decisions at these crosswalks to avoid a vehicle-pedestrian crash. This study examined this research topic using Hidden Markov Models (HMMs) and data from a naturalistic study. More specifically, four HMMs were applied to the naturalistic brake and jerk data from the Safety Pilot Model Deployment (SPMD) program given drivers' intention to slow down, the RRFB activation status, and the presence of pedestrians. The time-based data sequence was converted to distance-based through a moving window to enhance result comparison and interpretation. Grid-search was used to select the best moving window parameters and the optimal number of hidden states. This study confirmed the high compliance at an activated RRFB when pedestrians were present. Even without pedestrians, one in five traversals showed drivers slowing down to less than 8.94 m/s (20 mph) within 35 m of the crosswalk. Model results further indicate that drivers started braking as far back as 180 m before the crosswalk and stopped braking from 70 m before the crosswalk at an activated RRFB without pedestrians. When there were pedestrians, drivers would start braking 20 to 30 m later but would brake more firmly and for longer. Finally, drivers were not likely to brake or decelerate when RRFB was off and no pedestrians were present.

A context-aware driver model for determining recommended speed in blind intersection situations

The near-miss events involving vulnerable road users can lead to serious accidents. Safe and careful expert drivers perform a hazard-anticipatory driving and they will naturally seek to reduce the uncertainty by attempting to fit their current driving context into a pre-existing category they have already developed, that is, predicting what can happen. In this study, our target situation consists of a cyclist attempting a road crossing at a blind spot. This study aims at developing a context-aware driver model for determining the recommended driving speed at blind intersections based on the analysis of near-miss-incidence database, which includes the data on driver behavior and road environmental factors just before the near-miss. First, we extracted the drive-recorder data using the management tool provided in the database. Second, risk, which is defined as the time margin for drivers to perform evasive actions to avoid a crash, was quantified for the extracted data using the safety-cushion time. The safety-cushion time can be observed as a result of the driver's adjustment to the vehicle velocity depending on the given road environment. One of the key aspects in developing the context-aware driver model is to categorize the extracted near-miss data into two levels based on the risk quantifications: low- and high-risk events. The low- and high-risk events were regarded as a result of the driver's appropriate adjustment of, and inability or failure to adjust the vehicle velocity depending on the given road environment, respectively. Third, based on a multiple linear regression analysis with low-risk event dataset, we constructed a context-aware driver model to produce the recommended vehicle speed depending on the given road environment. The road environment variables, determined by stepwise regression, were identified as factors that reduced or increased the vehicle velocity at blind intersections, and were incorporated into the model as predictors. Furthermore, we quantitatively visualized drivers setting the baseline for speed adjustment and increasing or decreasing the speed according to the given road environment context. Fourth, the model validation demonstrated a coefficient of determination (R²) of 0.20, and a mean absolute error (MAE) of 6.54 km/h on average in the 5-fold cross-validation. Finally, to investigate the effectiveness of the constructed driver model on safety performance, we used the dataset of high-risk events as test data. Theoretically, the constructed driver model guided the drivers to drive the vehicle at the recommended speed, and thus convert more than half of the high-risk events into low-risk events. These results indicate that the context-aware driver model is feasible to be used to adjust the approaching speed at blind intersections in accordance with the road environment factors.

Investigating the fatal pedestrian crash occurrence in urban setup in a developing country using multiple-risk source model

Pedestrian fatalities and injuries are a major public health burden in developing countries. In the safety literature, pedestrian crashes have been modelled predominately using single equation regression models, assuming a single underlying source of risk factors. In contrast, the fatal pedestrian crash counts at a site may be an outcome of multiple sources of risk factors, such as poor road infrastructure, land use type, traffic exposures, and operational parameters, site-specific socio-demographic characteristics, as well as pedestrians' poor risk perception and dangerous crossing behavior, which may be influenced by poor road infrastructure and lack of information, etc. However, these multiple sources are generally overlooked in traditional single equation crash prediction models. In this background, this study postulates, and demonstrates empirically, that the total fatal pedestrian crash counts at the urban road network level may arise from multiple simultaneous and interdependent sources of risk factors, rather than one. Each of these sources may distinctively contribute to the total observed crash count. Intersection-level crash data obtained from the "Kolkata Police", India, is utilized to demonstrate the present modelling methodology. The three-components mixture model and a joint econometric model are developed to predict fatal pedestrian crashes. The study outcomes indicate that the multiple-source risk models perform significantly better than the single equation regression model in terms of prediction ability and goodness-of-fit measures. Moreover, while the single equation model predicts total fatal crash counts for individual sites, the multiple risk source model predicts crash count proportions contributed by each source of risk factors and predicts crashes by a particular source.

Using bus critical driving events as surrogate safety measures for pedestrian and bicycle crashes based on GPS trajectory data

Pedestrian and bicycle safety is a key component in traffic safety studies. Various studies were conducted to address pedestrian and bicycle safety issues for intersections, road segments, etc. However, only a few studies investigated pedestrian and bicycle safety for bus stops, which usually have a relatively larger volume of pedestrians and bicyclists. Moreover, traditional reactive safety approaches require a significant number of historical crashes, while pedestrian and bicycle crashes are usually rare events. Alternatively, surrogate safety measures could proactively evaluate traffic safety status when crash data are rare or unavailable. This paper utilized critical bus driving events extracted from GPS trajectory data as pedestrian and bicycle surrogate safety measures for bus stops. A city-wide trajectory data from Orlando, Florida was used, which contains around 300 buses, 6,700,000 GPS records, and 1300 bus stops. Three critical driving events were identified based on the buses' acceleration rates and stop time; hard acceleration, hard deceleration, and long stop. The relationships between critical driving events and crashes were examined using Spearman's rank correlation coefficient. All three events were positively correlated with pedestrian and bicycle crashes. Long stop event has the highest correlation coefficient, followed by hard acceleration and hard deceleration. A Bayesian negative binomial model incorporating spatial correlation (Bayesian NB-CAR) was built to estimate the pedestrian and bicycle crash frequency using the generated events. The results were consistent with the correlation estimation. For example, hard acceleration and long stop events were both positively related to pedestrian and bicycle crashes. Moreover, model evaluation results indicated that the proposed Bayesian NB-CAR outperformed the standard Bayesian negative binomial model with lower Watanabe-Akaike Information Criterion (WAIC) and Deviance Information Criteria (DIC) values. In conclusion, this paper suggests the use of critical bus driving events as surrogate safety measures for pedestrian and bicycle crashes, which could be implemented in a proactive traffic safety management system.

A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics

Road safety is a major public health issue, with road crashes accounting for one-fourth of all documented injuries. In these crashes, pedestrians are more vulnerable to fatal and/or severe injuries than car occupants. Therefore, it is necessary to have a better understanding of the relationship between pedestrian crashes and possible influencing factors, including road environment, traffic conditions, and population characteristics. In conventional studies, separate prediction models were established for pedestrian crashes and other crash types, which could have ignored possible correlations among the different crash types. Additionally, these influencing factors can contribute to pedestrian crashes in two manners, i.e., contributing to crash occurrence and propensity of pedestrian involvement. Furthermore, extensive pedestrian count data were generally not available, affecting the estimation of pedestrian crash exposure. In this study, a joint probability model is adopted for the simultaneous modeling of crash occurrence and pedestrian involvement in crashes; effects of possible influencing factors, including land use, road networks, traffic flow, population demographics and socioeconomics, public transport facilities, and trip attraction attributes, are considered. Additionally, trip generation and pedestrian activity data, based on a comprehensive household travel survey, are used to determine pedestrian crash exposure. Markov chain Monte Carlo full Bayesian approach is then applied to estimate the parameters. Results indicate that crash occurrence is correlated to traffic flow, number of non-signalized intersections, and points of interest such as restaurants and hotels. By contrast, population age, ethnicity, education, household size, road density, and number of public transit stations could affect the propensity of pedestrian involvement in crashes. These findings indicate that better design and planning of built environments are necessary for safe and efficient access for pedestrians and for the long-term improvement of walkability in a high-density city such as Hong Kong.

Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016

Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China.

Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes

BACKGROUND

Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods.

METHODS

Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared.

RESULTS

Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes.

CONCLUSIONS

The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.

Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection

Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

Spatial relationships between alcohol outlet densities and drunk driving crashes: An empirical study of Tianjin in China

INTRODUCTION

Numerous studies have demonstrated the close relationship between alcohol availability and alcohol-related crashes. However, there is still a lack of spatial empirical analysis regarding this relationship, particularly in large cities of developing countries. Differences in alcohol outlets and drinking patterns in these cities may lead to quite different patterns of crash outcomes.

METHOD

3356 alcohol-related crashes were collected from the blood-alcohol test report of a forensic institution in Tianjin, China. Density of alcohol outlets such as retail locations, entertainment venues, restaurants, hotels, and companies were extracted based on 2114 Traffic Analysis Zones (TAZ) together with the residential and demographic characteristics. After applying the exploratory spatial data analysis, this research developed and compared the traditional Ordinary Least Square model (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM) and Spatial Durbin Model (SDM) to explore spatial effects of all the variables.

RESULTS

The results of incremental spatial autocorrelation show that the most significant distance threshold of alcohol-related roadway traffic crashes is 3 km. The SDM is found to be the optimal spatial model to characterize the relationship between alcohol outlets and crashes. The number of alcohol-involved traffic crashes is positively related to population density and retail density, but negatively related to the company density, hotel density, and residential density within the same TAZ. Meanwhile, dense population and hotels have reverse spillover effects in adjacent zones.

CONCLUSIONS

The significant spatial direct effect and spillover effect of alcohol outlet densities on drunk driving crashes should not be neglected. These findings could help improve transportation planning, traffic law enforcement and traffic management for large cities in developing countries.

An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor

PROBLEM

Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle-pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events.

METHOD

This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification.

RESULTS

The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation.

A review of spatial approaches in road safety

Spatial analyses of crashes have been adopted in road safety for decades in order to determine how crashes are affected by neighboring locations, how the influence of parameters varies spatially and which locations warrant interventions more urgently. The aim of the present research is to critically review the existing literature on different spatial approaches through which researchers handle the dimension of space in its various aspects in their studies and analyses. Specifically, the use of different areal unit levels in spatial road safety studies is investigated, different modelling approaches are discussed, and the corresponding study design characteristics are summarized in respective tables including traffic, road environment and area parameters and spatial aggregation approaches. Developments in famous issues in spatial analysis such as the boundary problem, the modifiable areal unit problem and spatial proximity structures are also discussed. Studies focusing on spatially analyzing vulnerable road users are reviewed as well. Regarding spatial models, the application, advantages and disadvantages of various functional/econometric approaches, Bayesian models and machine learning methods are discussed. Based on the reviewed studies, present challenges and future research directions are determined.

Evaluation of the factors influencing the housing safety awareness of residents in Shanghai

Shanghai has experienced rapid urbanization and has a serious housing aging problem. The situation of urban housing safety management needs to be strengthened. However, in China, housing safety management (HSM) is just in its beginning stage and it lacks thorough research. Housing safety awareness is one of the most significant aspects of housing safety management. Therefore, in order to investigate the housing safety awareness of Shanghai residents, this paper investigates the safety attitudes of residents living in housing of different ages using consulting questionnaires and Statistical Package for Social Science (SPSS) software. The results show that in Shanghai, the residents lack an understanding of housing management law, policy, and awareness of safety use and have low willingness to buy commercial insurance. Based on these results, the factors that affect the safety awareness of Shanghai residents are summarized as follows: (1) asymmetric information; (2) assessment of the safety status of the premises; and (3) differences in house users.

Macro-level accident modeling in Novi Sad: A spatial regression approach

In this study, a macroscopic analysis was conducted in order to identify the factors which have an effect on traffic accidents in traffic analysis zones. The factors that impact accidents vary according to the characteristics of the observed area, which in turn leads to a discrepancy between research and practice. The total number of accidents was observed in this paper, along with the number of motorized and non-motorized mode accidents within a three-year period in the city of Novi Sad. The models used for this analysis were spatial predictive models comprised of the classical predictive space model, spatial lag model and spatial error model. The spatial lag model showed the best performances concerning the total number of accidents and number of motorized mode accidents, whereas the spatial error model was prominent within the number of non-motorized mode accidents. The results found that increasing Daily Vehicle-Kilometers Traveled, parking spaces, 5-legged intersections and signalized intersections increased all types of accidents. The other demographic, traffic, road and environment characteristics showed that they had a different effect on the observed types of accidents. The results of this research can be benefitial to reserachers who deal with traffic engineering, space planning as well as making decisions with the aim of preparing countermeasures necessary for road safety improvement in the analysed area.

Exposure to pedestrian crash based on household survey data: Effect of trip purpose

Pedestrian are vulnerable to severe injury and mortality in the road crashes. Understanding the essence of the pedestrian crash is important to the development of effective safety countermeasures and improvement of social well-being. It is necessary to measure the exposure for the quantification of pedestrian crash risk. The primary goals of this study are to explore the efficient exposure measure for pedestrian crash, and identify the possible factors contributing to the incidence of pedestrian crash. In this study, amount of travel was estimated based on the Travel Characteristic Survey (TCS) data in 2011, and the crash data were obtained from the Transport Information System (TIS) of the Hong Kong Transport Department during the period from 2011 to 2015. Total population, walking frequency and walking time were adopted to represent the pedestrian exposure to road crash. The effect of trip purpose on pedestrian crash was evaluated by disaggregating the pedestrian exposure proxies by purpose. Three random-parameter negative binomial regression models were developed to compare the performances of the three pedestrian exposure proxies. It was found that the model in which walking frequency was used as the exposure proxy provided the best goodness-of-fit. Frequency of walking back home, among other trip purposes, was the most sensitive to the increase in pedestrian crash risk. Additionally, increase in the frequency of pedestrian crash was correlated to the increases in the proportions of children and elderly people. Furthermore, household size, median household income, road density, number of non-signalized intersection as well as number of zebra crossings also significantly affected the pedestrian crash frequency. Findings of this study should be indicative to the development and implementation of effective traffic control and management measures that can improve the pedestrian safety in the long run.

Macro-level traffic safety analysis in Shanghai, China

Continuing rapid growth in Shanghai, China, requires traffic safety to be considered at the earliest possible stage of transport planning. Macro-level traffic safety studies have been carried out extensively in many countries, but to date, few have been conducted in China. This study developed a macro-level safety model for 263 traffic analysis zones (TAZs) within the urban area of Shanghai in order to examine the relationship between traffic crash frequency and road network, traffic, socio-economic characteristics, and land use features. To account for the spatial correlations among TAZs, a Bayesian conditional autoregressive negative binomial model was estimated, linking crash frequencies in each TAZ to several independent variables. Modeling results showed that higher crash frequencies are associated with greater populations, road densities, total length of major and minor arterials, trip frequencies, and with shorter intersection spacing. The results from this study can help transportation planners and managers identify the crash contributing factors, and can lead to the development of improved safety planning and management.

Investigating proximity of crash locations to aging pedestrian residences

Many campaigns promote walking for recreation, work, and general-purpose trips for health and environmental benefits. This study investigated factors that influence the occurrence of crashes involving elderly pedestrians in relation to where they reside. Using actual pedestrian residential addresses, a Google integrated GIS-based method was developed for estimating distances from crash locations to pedestrian residences. A generalized linear mixed model (GLMM) was used to evaluate the effect of factors associated with residences, such as age group, roadway features, and demographic characteristics on the proximity of crash locations. Results indicated that the proximity of crash locations to pedestrian residences is influenced by the pedestrian age, gender, roadway traffic volume, seasons of the year, and pedestrian residence demographic characteristics. The findings of this study can be used by transportation agencies to develop plans that enhance aging pedestrian safety and improve livability.

Traffic crash analysis with point-of-interest spatial clustering

This paper presents a spatial clustering method for macro-level traffic crash analysis based on open source point-of-interest (POI) data. Traffic crashes are discrete and non-negative events for short-time evaluation but can be spatially correlated with long-term macro-level estimation. Thus, the method requires the evaluation of parameters that reflect spatial properties and correlation to identify the distribution of traffic crash frequency. A POI database from an open source website is used to describe the specific land use factors which spatially correlate to macro level traffic crash distribution. This paper proposes a method using kernel density estimation (KDE) with spatial clustering to evaluate POI data for land use features and estimates a simple regression model and two spatial regression models for Suzhou Industrial Park (SIP), China. The performance of spatial regression models proves that the spatial clustering method can explain the macro distribution of traffic crashes effectively using POI data. The results show that residential density, and bank and hospital POIs have significant positive impacts on traffic crashes, whereas, stores, restaurants, and entertainment venues are found to be irrelevant for traffic crashes, which indicate densely populated areas for public services may enhance traffic risks.

Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure

This study intended to identify the potential factors contributing to the occurrence of pedestrian crashes at signalized intersections in a densely populated city, based on a comprehensive dataset of 898 pedestrian crashes at 262 signalized intersections during 2010-2012 in Hong Kong. The detailed geometric design, traffic characteristics, signal control, built environment, along with the vehicle and pedestrian volumes were elaborately collected. A Bayesian measurement errors model was introduced as an alternative method to explicitly account for the uncertainties in volume data. To highlight the role played by exposure, models with and without pedestrian volume were estimated and compared. The results indicated that the omission of pedestrian volume in pedestrian crash frequency models would lead to reduced goodness-of-fit, biased parameter estimates, and incorrect inferences. Our empirical analysis demonstrated the existence of moderate uncertainties in pedestrian and vehicle volumes. Six variables were found to have a significant association with the number of pedestrian crashes at signalized intersections. The number of crossing pedestrians, the number of passing vehicles, the presence of curb parking, and the presence of ground-floor shops were positively related with pedestrian crash frequency, whereas the presence of playgrounds near intersections had a negative effect on pedestrian crash occurrences. Specifically, the presence of exclusive pedestrian signals for all crosswalks was found to significantly reduce the risk of pedestrian crashes by 43%. The present study is expected to shed more light on a deeper understanding of the environmental determinants of pedestrian crashes.

An empirical evaluation of multivariate spatial crash frequency models

Many studies have employed spatial, temporal, or a combination of both specifications for analysis of roadway crashes at different spatial levels. However, there is lack of a comprehensive study which compares the crash estimation performance of different spatial weight matrices and their combination with various temporal treatments. The current study fills the research gap by comparing different Full Bayesian (FB) multivariate spatiotemporal crash models. The pedestrian and bicyclist crash data across an eight-year period for 58 counties in California were used as a case study. Three groups of models were developed based on temporal treatment, where each group comprised of 17 models differing on the basis of different adjacency- and distance-based spatial weight matrices. The first group of multivariate models incorporated only unstructured random error term and spatially structured conditional autoregressive (CAR) term. The second group built upon the former and introduced a linear time trend to develop a spatiotemporal model, while the third group allowed the interaction of space and time. The predictive performance of the alternate models across and within groups was assessed by employing several evaluation criteria. The modeling results demonstrated the robustness of models based on the similar signs and closeness of coefficients for the posterior estimates of parameters. For overall model comparison, the pure-distance model D0.5 demonstrated the best performance for different evaluation criteria based on training and test errors across three groups. The variability in performance of other distance models suggested that caution must be exercised for the choice of exponents. The correlation analysis revealed the presence of positive correlations among the criteria based on training errors, as well as with cross-validation. However, a very strong positive correlation was observed between the criteria based on effective number of parameters and posterior deviance, indicating that an increased number of parameters may not lead to improved model fit. This finding reinforced the importance of selecting the optimum weight matrix for spatial correlation as a more complex structure may not lead to expected advantages at model performance. For comparison among three groups of different temporal treatments, the third group demonstrated the best performance and conveyed the benefits of incorporating the spatial and temporal interaction. The results from ANOVA (analysis of variance) and HSD (Honest Significant Differences) tests also established the existence of statistical differences for the superiority of space-time interactions models. However, the box and whisker plots demonstrated high variability among the models of the third group, suggesting that some models may not benefit from interaction term. For comparison among adjacency- and distance-based models, the distance-based models were mostly observed to be superior. However, the greater variability of model performance associated with distance-based models suggested for careful consideration during their selection. Additionally, it is important to note that the results observed in this study are specific to the county-level crash data of California. As such, the study does not recommend generalization of the results for extension to other spatial levels of roadway network, and readers and future research studies are advised to exercise caution before implementing the models.

A Bayesian multivariate hierarchical spatial joint model for predicting crash counts by crash type at intersections and segments along corridors

The safety and operational improvements of corridors have been the focus of many studies since they carry most traffic on the road network. Estimating a crash prediction model for total crash counts identifies the crash risk factors that are associated with crash counts at a specific type of road entity. However, this may not reveal useful information to detect the road problems and implement effective countermeasures. Therefore, investigating the contributing factors for crash counts by different types is of great importance. This study aims to provide a good understanding of the contributing factors to crash counts by different types at intersections and roadway segments along corridors. Data from 255 signalized intersections and 220 roadway segments along 20 corridors have been used for this study. The investigated crash types include same direction, angle and turning, opposite direction, non-motorized, single vehicle, and other multi-vehicle crashes. Two models have been estimated, which are multivariate hierarchical Poisson-lognormal (HPLN) spatial joint model and univariate HPLN spatial joint model. The significant variables include exposure measures and some geometric design variables at intersection, roadway segment, and corridor levels. The results revealed that the multivariate HPLN spatial joint model outperforms the univariate HPLN spatial joint model. Also, the correlations among crash counts of most types exist at individual road entity and between adjacent entities. Additionally, the significant explanatory variables are different across crash types, and the magnitude of the parameter estimates for the same independent variable is different across crash types. The results emphasize the need for estimating crash counts by type in a multivariate form to better detect the problems and provide appropriate countermeasures.

Correlates of fatality risk of vulnerable road users in Delhi

Pedestrians, cyclists, and users of motorised two-wheelers account for more than 85% of all the road fatality victims in Delhi. The three categories are often referred to as vulnerable road users (VRUs). Using Bayesian hierarchical approach with a Poisson-lognormal regression model, we present spatial analysis of road fatalities of VRUs with wards as areal units. The model accounts for spatially uncorrelated as well as correlated error. The explanatory variables include demographic factors, traffic characteristics, as well as built environment features. We found that fatality risk has a negative association with socio-economic status (literacy rate), population density, and number of roundabouts, and has a positive association with percentage of population as workers, number of bus stops, number of flyovers (grade separators), and vehicle kilometers travelled. The negative effect of roundabouts, though statistically insignificant, is in accordance with their speed calming effects for which they have been used to replace signalised junctions in various parts of the world. Fatality risk is 80% higher at the density of 50 persons per hectare (pph) than at overall city-wide density of 250 pph. The presence of a flyover increases the relative risk by 15% compared to no flyover. Future studies should investigate the causal mechanism through which denser neighborhoods become safer. Given the risk posed by flyovers, their use as congestion mitigation measure should be discontinued within urban areas.

Exploring the safety in numbers effect for vulnerable road users on a macroscopic scale

A "Safety in Numbers" effect for a certain group of road users is present if the number of crashes increases at a lower rate than the number of road users. The existence of this effect has been invoked to justify investments in multimodal transportation improvements in order to create more sustainable urban transportation systems by encouraging walking, biking, and transit ridership. The goal of this paper is to explore safety in numbers effect for cyclists and pedestrians in areas with different levels of access to multimodal infrastructure. Data from Chicago served to estimate the expected number of crashes on the census tract level by applying Generalized Additive Models (GAM) to capture spatial dependence in crash data. Measures of trip generation, multimodal infrastructure, network connectivity and completeness, and accessibility were used to model travel exposure in terms of activity, number of trips, trip length, travel opportunities, and conflicts. The results show that a safety in numbers effect exists on a macroscopic level for motor vehicles, pedestrians, and bicyclists.

Evaluating the impact of connectivity, continuity, and topography of sidewalk network on pedestrian safety

With the increasing demand for sustainability, walking is being encouraged as one of the main active modes of transportation. However, pedestrians are vulnerable to severe injuries when involved in crashes which can discourage road users from walking. Therefore, studying factors that affect the safety of pedestrians is important. This paper investigates the relationship between pedestrian-motorist crashes and various sidewalk network indicators in the city of Vancouver. The goal is to assess the impact of network connectivity, directness, and topography on pedestrian safety using macro-level collision prediction models. The models were developed using generalized linear regression and full Bayesian techniques. Both walking trips and vehicle kilometers travelled were used as the main traffic exposure variables in the models. The safety models supported the safety in numbers hypothesis showing a non-linear positive association between pedestrian-motorist crashes and the increase in walking trips and vehicle traffic. The model results also suggested that higher continuity, linearity, coverage, and slope of sidewalk networks were associated with lower crash occurrence. However, network connectivity was associated with higher crash occurrence. The spatial effects were accounted for in the full Bayes models and were found significant. The models provide insights about the factors that influence pedestrian safety and the spatial variability of pedestrian crashes within a city, which can be useful for the planning of pedestrian networks.

Geographically weighted negative binomial regression applied to zonal level safety performance models

Generalized Linear Models (GLM) with negative binomial distribution for errors, have been widely used to estimate safety at the level of transportation planning. The limited ability of this technique to take spatial effects into account can be overcome through the use of local models from spatial regression techniques, such as Geographically Weighted Poisson Regression (GWPR). Although GWPR is a system that deals with spatial dependency and heterogeneity and has already been used in some road safety studies at the planning level, it fails to account for the possible overdispersion that can be found in the observations on road-traffic crashes. Two approaches were adopted for the Geographically Weighted Negative Binomial Regression (GWNBR) model to allow discrete data to be modeled in a non-stationary form and to take note of the overdispersion of the data: the first examines the constant overdispersion for all the traffic zones and the second includes the variable for each spatial unit. This research conducts a comparative analysis between non-spatial global crash prediction models and spatial local GWPR and GWNBR at the level of traffic zones in Fortaleza/Brazil. A geographic database of 126 traffic zones was compiled from the available data on exposure, network characteristics, socioeconomic factors and land use. The models were calibrated by using the frequency of injury crashes as a dependent variable and the results showed that GWPR and GWNBR achieved a better performance than GLM for the average residuals and likelihood as well as reducing the spatial autocorrelation of the residuals, and the GWNBR model was more able to capture the spatial heterogeneity of the crash frequency.

The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.