Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling

Estimating the effectiveness of marked sidewalks: An application of the spatial causality approach

Various safety enhancements and policies have been proposed to enhance pedestrian safety and minimize vehicle-pedestrian accidents. A relatively recent approach involves marked sidewalks delineated by painted pathways, particularly in Asia's crowded urban centers, offering a cost-effective and space-efficient alternative to traditional paved sidewalks. While this measure has garnered interest, few studies have rigorously evaluated its effectiveness. Current before-after studies often use correlation-based approaches like regression, lacking effective consideration of causal relationships and confounding variables. Moreover, spatial heterogeneity in crash data is frequently overlooked during causal inference analyses, potentially leading to inaccurate estimations. This study introduces a geographically weighted difference-in-difference (GWDID) method to address these gaps and estimate the safety impact of marked sidewalks. This approach considers spatial heterogeneity within the dataset in the spatial causal inference framework, providing a more nuanced understanding of the intervention's effects. The simplicity of the modeling process makes it applicable to various study designs relying solely on pre- and post-exposure outcome measurements. Conventional DIDs and Spatial Lag-DID models were used for comparison. The dataset we utilized included a total of 13,641 pedestrian crashes across Taipei City, Taiwan. Then the crash point data was transformed into continuous probability values to determine the crash risk on each road segment using network kernel density estimation (NKDE). The treatment group comprised 1,407 road segments with marked sidewalks, while the control group comprised 3,097 segments with similar road widths. The pre-development program period was in 2017, and the post-development period was in 2020. Results showed that the GWDID model outperformed the spatial lag DID and traditional DID models. As a local causality model, it illustrated spatial heterogeneity in installing marked sidewalks. The program significantly reduced pedestrian crash risk in 43% of the total road segments in the treatment group. The coefficient distribution map revealed a range from -22.327 to 2.600, with over 95% of the area yielding negative values, indicating reduced crash risk after installing marked sidewalks. Notably, the impact of crash risk reduction increased from rural to urban areas, emphasizing the importance of considering spatial heterogeneity in transportation safety policy assessments.

The impact of target speed on pedestrian, bike, and speeding crash frequencies

This research aims to investigate the influence of adopting the target speed concept on different types of crashes including pedestrian, bike, and speeding-related crashes. The Target speed is the highest speed that vehicles should operate on a roadway segment in a specific context. Based on the reviewed literature, this is the first study to investigate the relationship between target speed and crash frequency. Hence, big data including probe-vehicle data, traffic characteristics, geometric features, and land use attributes were utilized to develop crash prediction models. The main contributions of this research are to quantify the impacts of target speed on traffic safety considering context categories and to conclude the potential recommendations to lower different types of crashes. The 85th percentile speed was calculated and utilized in the developed models. Three crash prediction models were developed for pedestrian, bike, and speeding-related crashes. They were used in the analysis to quantify the impact of adopting target speed on different crash types. The results showed a significant reduction in the three crash types when using the target speed. Most of the improvements took place in three context categories: C3C: Suburban Commercial Segments, C3R: Suburban Residential Segments, and C4: Urban General Segments. Hence, this research recommends adopting target speed specifically in urban and suburban areas. Further, it suggests considering some measures to lower vulnerable road users' and speeding-related crashes. Following the recommendations of this research would help to reduce different types of crash frequency, hence, improving the mobility and safety for all users in different context classifications.

Changes in traffic crash patterns: Before and after the outbreak of COVID-19 in Florida

In the twentieth year of the twenty-first century, humanity is facing an unprecedented global crisis owing to the COVID-19 pandemic. It has brought about drastic changes in the way we live and work, as well as the way we move from one place to another, namely transportation. Previous studies have preliminarily found that mobility, travel behavior, and road traffic safety status experienced great changes after the outbreak of the COVID-19. The objective of this study is to explore how crash patterns have changed, as well as the contributing factors of such changes and the heterogeneity between counties in Florida. Thus, data of COVID-19 cases, crash, socioeconomic factors, and traffic volume of 2019 and 2020 are collected. Preliminary analyses show a considerable reduction from March to June. Substantial changes are shown in the proportions of crashes by time of occurrence and injury severity. Two types of statistical models are developed to identify factors of (1) changes in the percentages of crashes by type and (2) the numbers of crashes by type. The developed models reveal various demographic, socioeconomic, and travel factors. After controlling other factors, the total numbers of crashes are 14% lower after the outbreak. The most significant reductions are observed in peak-hour (22%), while no significant change is found in fatal crashes. The results show that the number of crashes has significantly decreased even after controlling the traffic volume, but some crash types (e.g., fatal) did not show a significant reduction. The findings are expected to provide some insights into better transportation planning and management to ensure traffic safety in a possible future epidemic.

Understanding the impact of vehicle dynamics, geometric and non-geometric roadway attributes on surrogate safety measure using connected vehicle data

Traditional safety research mostly relies on accident data to analyze the precedents to a crash. Alternatively, surrogate safety measures have the potential to proactively evaluate safety events. The era of connected vehicles and smart sensing has brought about tremendous innovations in safety research. GPS data from such vehicles form a useful case of big data analytics where surrogate safety measures have largely been unexplored. In this paper, we propose time to collision estimation from connected vehicle GPS data. The vehicle dynamics such as speed, acceleration, yaw rate, etc. are then coupled with geometric and non-geometric roadway attributes to understand the contributing factors for a traffic conflict. The dataset contains 2,568,421 GPS points from 14,753 unique journeys. 1:4 ratio of conflict to non-conflict events was used to select 15,258 samples with 28 independent vehicle dynamics, geometric, and non-geometric variables. Binary logit model was used to investigate the relationship of these variables with conflicts. Model results showed that out of 28 independent variables, 6 independent variables and 7 interaction variables were found significant. The results showed some interesting and unique relations of these variables with conflicts. Based on these significant variables, k-means clustering was performed to understand the threshold for the significant values for which the number of conflicts is significantly increased. Results from k-means clustering and two sample binomial proportion t-tests revealed that when absolute acceleration crossed 0.8 m/s², conflict probability increased by 8 percentage points.​ Moreover, when the yaw rate crossed 8 degrees/s, the conflict probability doubled. Besides, vehicles traveling at more than 140% of the recommended speed limit increased conflict probability by 7 percentage points.

A reliability-based approach to evaluate the lateral safety of truck platoon under extreme weather conditions

The truck platoon is one of the most promising connected and autonomous vehicle (CAV) technologies that can reduce fuel consumption and emission, enhance traffic safety, and increase roadway capacity. It is predicted to become mainstream in the next decade. Therefore, it is imperative to fully investigate the safety issues of the truck platoon before its large-scale deployment. However, studies on the lateral safety of the truck platoon under extreme weather, especially crosswinds are still lacking. To fill such a research gap, the current study contributes to the literature by proposing a reliability-based framework to evaluate the safety of the truck platoon regarding incursion into neighboring lanes due to extreme weather, especially crosswinds. The proposed approach involved three main steps: (1) the computational fluid dynamics (CFD) simulation of the aerodynamics of the truck platoon; (2) the truck platoon driving simulation under crosswind; and (3) the advanced response surface model development and the reliability analysis. Four main factors regarding lateral safety of the platoon were considered: wind speed, road friction coefficients, driving speed, and inter-vehicle spacing. The maximum lateral displacement (MLD) was chosen as a measure of lateral safety. The results showed that there was a significant difference between the aerodynamics of a single truck and that of the truck platoon vehicles and the inter-vehicle spacing between trucks within the truck platoon barely influenced the MLD. The MLD was largest for the leading truck as compared to those of the following trucks. The inter-vehicle spacing didn't have a significant influence on MLD when the inter-vehicle spacing is shorter than 1.5 times of the truck length, while the other factors impacted the MLD significantly. In addition, the support vector regression with the radial basis function outperformed the other response surface functions. Based on reliability analysis, the risk level of the truck platoon was quantified using the safety index, and the impact of contributing factors towards the safety index of the truck platoon was also evaluated. This study confirmed that the proposed framework could be applied to evaluate the lateral safety of the truck platoon. The findings provide important practical implications for the decision-making of transportation management agencies and tailored countermeasures in the CAV) environment.

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.

Predicting Pedestrian Crashes in Texas’ Intersections and Midblock Segments

This study analyzes pedestrian crash counts at more than one million intersections and midblock segments using Texas police reports over ten years. Developing large-scale micro-level analyses is challenging due to the lack of geographic information and characterization at a statewide scale. Therefore, key contributions include methods for obtaining many points and related variables across a vast network while controlling for traffic control variables (signalized intersections), highway design details, traffic attributes, and land use information across multiple sources. The analytical framework includes a method to estimate the intersection and midblock segments’ geometry and characteristics, data processing of historical pedestrian crashes and mapping to the estimated geometry, and the development of predictive models. A negative binomial model for crash counts across the state of Texas and within the city of Austin suggests that signalized intersections, arterial roads, more lanes, narrower or non-existent medians, and wider lanes coincide with higher crash rates per vehicle-mile traveled (VMT) and per walk-mile traveled. The analysis suggests that daily VMT increases the likelihood of pedestrian crashes, and midblock segments are more vulnerable than intersections, where one standard deviation increase in VMT caused an increase in crashes at intersections and midblock sections of 52% and 187%, respectively. Furthermore, the number of intersection crashes in Austin is higher than in the rest of Texas, but the number of midblock crashes is lower. Analysis of the Austin area suggests that the central business district location is critical, with midblock crashes being more sensitive (240%) in this area than intersection (78%) crashes. Moreover, a significant inequity was found in the area: an increase of USD 41,000 in average household income leads to a reduction of 32% (intersections) and 39% (midblock) in pedestrian crash rates.

Exposure based geographic analysis mode for estimating the expected pedestrian crash frequency in urban traffic zones; case study of Tehran

Predicting pedestrian crashes on urban roads is one of the most important issues related to urban traffic safety. Due to the lack of spatial correlation and instability in the crash data, the statistical reliability of Empirical Bayesian method in the combination of the observed and predicted crash frequency is questionable. In this study, an EB model has been developed to estimate the expected frequency of pedestrian crashes in urban areas using the over-dispersion parameter taking into account the spatial correlation of crash data. The objective of this study is to estimate the expected geographical frequency of pedestrian crashes using the Empirical Bayesian (EB) approach using weighted geographical regression models for pedestrian crashes in Tehran. For doing so, four models of geographic weighted Poisson regression (GWPR), geographic weighted zero-inflated Poisson regression (GWZIPR), geographic weighted Negative Binomial regression (GWNBR) and the geographic weighted zero-inflated Negative Binomial regression (GWZINBR) have been used. In this study, the areas analyzed for the development of the EB model based on pedestrian exposure variables include traffic analysis zones (TAZs). Finally, the EB model was extended to the Geographic Empirical Bayesian (Ge-EB) model. The results showed that GWZIPR and GWZINBR models make more accurate predictions. These models had the lowest values of Akaike Information Criterion (AIC), the lowest values of Cross Validation and the lowest values of Root Mean Square Error (RMSE). The Moran and Variance Inflated Factor (VIF) indices were also within acceptable limits. The weighted negative binomial distribution could moderate the amount of heterogeneity of crash data to some extent. This study has shown the dispersion and density of pedestrian crashes without having the volume of pedestrians and thus can be done by taking safety measures in places prone to pedestrian crashes.

Vulnerable road users' crash hotspot identification on multi-lane arterial roads using estimated exposure and considering context classification

This research develops safety performance functions and identifies the crash hotspots based on estimated vulnerable road users' exposure at intersections and along the roadway segments. The study utilized big data including Automated Traffic Signal Performance Measures (ATSPM) data, crowdsourced data (Strava), Closed Circuit Television (CCTV) surveillance camera videos, crash data, traffic information, roadway features, land use attributes, and socio-demographic characteristics. It comprises an extensive comparison between a wide array of statistical and machine learning models that were developed to estimate pedestrian and bike exposure. The results indicated that the XGBoost approach was the best to estimate vulnerable road users' exposure at intersections as well as bike exposure along the roadway segments. Afterwards, the estimated exposure was utilized as input variables to develop crash prediction models that relate different crash types to potential explanatory variables. Negative Binomial approach was followed to develop crash prediction models to be consistent with the Highway Safety Manual. The results show that the exposure variables (i.e., AADT, bike exposure, and the interaction between them) have significant influences on the two types of crashes (i.e., crashes of vulnerable road users at intersections and bike crashes along the segments). Further, the results indicated that the context classification is significantly related to crashes. Based on the developed models, the PSIs were calculated and the hotspots were identified for the two crash types. It was found that hotspots were more likely to be located near the city of Orlando. Coastal roadways were classified as cold categories regarding bike crashes. Further, C4 roadway segments were found to be significantly related to the increase of vulnerable road users' crashes at intersections and bike crashes along the segments.

The Effectiveness of Selected Devices to Reduce the Speed of Vehicles on Pedestrian Crossings

Accidents involving pedestrians often result in serious injury or death. The main goal of this conducted research is to evaluate selected devices that will help reduce the speed of vehicles on pedestrian crossings. Many devices from a group of “speed control measures” and “mid block tools” (refugee islands, speed tables, and raised pedestrian crossings) are examined to find the most effective ones. In our research, the range of reduction of a vehicle’s speed is used as a main measure of effectiveness, but a wider statistical analysis was conducted as well. One of the results of the research is the identification of three categories of devices referred to as high effectives (good), medium effectives (intermediate), and low or lack of effectives (bad). The content of the paper starts by highlighting the reasons to reduce the vehicle’s speed on pedestrian crossings (as an introduction). Next, we present the description of devices used to reduce the vehicle’s speed with a presentation of the research of their effectiveness. The studies that have been conducted are described in the following chapters: first, the characteristic of method and location, second, with discussion, the results of research and identification of the three categories of devices. The paper is then summarized by conclusions and comments. The research only covered the issues of road traffic engineering. The research was made in Poland, but the conclusions could be useful worldwide due to similar traffic rules and technical solutions.

Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.

SafetyCube: Framework for potential pedestrian risk analysis using multi-dimensional OLAP

In the past decade, the number of road traffic accidents and fatalities has remained about the same level. One of strategies to protect vulnerable road users (VRUs) is to analyze the factors that cause traffic accident and then to deploy safety facilities. However, most traffic safety systems currently in operation rely on historical data, which is post-facto approach. Thus, it is necessary to prevent accident in advance and to respond in proactive manner before the accident. In this study, we propose a framework for potential pedestrian risk analysis using a multi-dimensional on-line analytical processing (OLAP), called SafetyCube, which enables decision-makers to understand the situations by scrutinizing interactive behaviors between vehicle and pedestrian. First, we collect the behavioral features of traffic-related objects (e.g., vehicles and pedestrians) extracted from closed circuit televisions (CCTVs) deployed on crosswalks throughout the overall urban, and accumulate them in a data warehouse over an extended period in order to construct a data cube model. Then, we conduct comprehensive analyses in multi-dimensional perspective using OLAP operations by varying the abstraction levels. Our analytical experiments are based on three scenarios, and the results show that the vehicle's movement patterns before entering the crosswalk, patterns of changes in speed of vehicles approaching to pedestrians, and so on. Through these results from the proposed analytical system, decision-makers can gain a better understanding of how the vehicles and pedestrians behave near the crosswalk by visualizing their interactions. Further, these insights would be reflected to improve the road environment safer. In order to validate the feasibility and applicability of the proposed system, we apply it to various crosswalks in Osan city, South Korea.

Role of exposure in bicycle safety analysis: Effect of cycle path choice

Despite the recognized environmental and health benefits of cycling, bicyclists are vulnerable to severe injuries and mortalities in the road crashes. Therefore, it is of paramount importance to identify the possible factors that may affect the bicycle crash risk. However, reliable estimates of bicycle exposure are often not available for the safety risk evaluation of different entities. The objective of this study is to advance the estimation of exposure in the bicycle safety analysis, using the detailed origin-destination data of each trip of the London public bicycle rental system. Two approaches including shortest path method (SPM) and weighted shortest path method (WSPM) are proposed to model the bicycle path choice and to estimate the bicycle distance traveled (BDT). Then, the bicycle crash frequency models that adopt BDTs as the exposure estimated using SPM and three WSPMs are developed. Three exposure measures including bicycle trips, bicycle time traveled (BTT), and BDT are assessed. Results indicate that the bicycle crash frequency models that incorporate the BDTs using WSPM have superior model fit. Moreover, the bicycle crash frequency model that incorporate the BDTs as the exposure outperforms those that incorporate the bicycle trips and BTT as the exposures. Findings of current study are indicative to the development of bicycle crash frequency model. Moreover, it should enhance the understanding on the roles of environmental, traffic and bicyclist factors in bicycle crash risk, based on appropriate estimates of bicycle exposures. Therefore, it should be useful to the transport planners and engineers for the development of bicycle infrastructures that can improve the overall bicycle safety in the long run.

Towards routine, city-scale accessibility metrics: Graph theoretic interpretations of pedestrian access using personalized pedestrian network analysis

A wide range of analytical methods applied to urban systems address the modeling of pedestrian behavior. These include methods for multimodal trip service areas, access to businesses and public services, diverse metrics of "walkability", and the interpretation of location data. Infrastructure performance metrics in particular are an increasingly important means by which to understand and provide services to an urbanizing population. In contrast to traditional one-size-fits all analyses of street networks, as more detailed pedestrian-specific transportation network data becomes available, the opportunity arises to model the pedestrian network in terms of individual experiences. Here, we present a formalized and city-scale framework, personalized pedestrian network analysis (PPNA), for embedding and retrieving pedestrian experiences. PPNA enables evaluation of new, detailed, and open pedestrian transportation network data using a quantitative parameterization of a pedestrian's preferences and requirements, producing one or more weighted network(s) that provide a basis for posing varied urban pedestrian experience research questions, with four approaches provided as examples. We introduce normalized sidewalk reach (NSR), a walkshed-based metric of individual pedestrian access to the sidewalk network, and sidewalk reach quotient (SRQ), an estimate of inequity based on comparing the normalized sidewalk reach values for different pedestrian profiles at the same location. Next, we investigate a higher-order and combinatorial research question that enumerates pedestrian network-based amenity access between pedestrians. Finally, we present city-scale betweenness centrality calculations between unique pedestrian experiences, highlighting disagreement between pedestrians on the "importance" of various pedestrian network corridors. Taken together, this framework and examples represent a significant emerging opportunity to promote the embedding of more explicit and inclusive hypotheses of pedestrian experience into research on urban pedestrian accessibility, multimodal transportation modeling, urban network analysis, and a broader range of research questions.

The effect of human mobility and control measures on traffic safety during COVID-19 pandemic

As mobile device location data become increasingly available, new analyses are revealing the significant changes of mobility pattern when an unplanned event happened. With different control policies from local and state government, the COVID-19 outbreak has dramatically changed mobility behavior in affected cities. This study has been investigating the impact of COVID-19 on the number of people involved in crashes accounting for the intensity of different control measures using Negative Binomial (NB) method. Based on a comprehensive dataset of people involved in crashes aggregated in New York City during January 1, 2020 to May 24, 2020, people involved in crashes with respect to travel behavior, traffic characteristics and socio-demographic characteristics are found. The results show that the average person miles traveled on the main traffic mode per person per day, percentage of work trip have positive effect on person involved in crashes. On the contrary, unemployment rate and inflation rate have negative effects on person involved in crashes. Interestingly, different level of control policies during COVID-19 outbreak are closely associated with safety awareness, driving and travel behavior, and thus has an indirect influence on the frequency of crashes. Comparing to other three control policies including emergence declare, limits on mass gatherings, and ban on all nonessential gathering, the negative relationship between stay-at-home policy implemented in New York City from March 20, 2020 and the number of people involved crashes is found in our study.

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.

Effects of state-led suburbanization on traffic crash density in China: Evidence from the Chengdu City Proper

Road crashes have become a leading cause of death in China. Although enormous efforts have been exerted to determine the factors that affect individual crash incidents, neighborhood-level crash incidence in Chinese cities has not been sufficiently analyzed. This study fills this gap by quantifying the effects of built environment factors on neighborhood-level automobile-involved crash density (NACD) in urban China and identifying its mediators and mediating effects. In American suburbs, urban sprawl is widely recognized to render neighborhoods unsafe for residence, thus leading to a high crash incidence. This study compares the characteristics of built environments between inner-city neighborhoods and the new neighborhoods that have been developed through China's state-led suburbanization since 2008 to reveal how this suburbanization provides a safer neighborhood environment. A structural equation model is used to examine the relationships among suburbanization, built environment factors, and NACD in the city proper of Chengdu, the largest metropolis in southwest China. Thus, this study contributes new empirical evidence to the debates over urban designs that are safest for traffic. Moreover, this study enriches our understanding of different sociospatial consequences between American-style urban sprawl and China's state-led suburbanization.

The adaptability and challenges of autonomous vehicles to pedestrians in urban China

China is the world's largest automotive market and is ambitious for autonomous vehicles (AVs) development. As one of the key goals of AVs, pedestrian safety is an important issue in China. Despite the rapid development of driverless technologies in recent years, there is a lack of researches on the adaptability of AVs to pedestrians. To fill the gap, this study would discuss the adaptability of current driverless technologies to China urban pedestrians by reviewing the latest researches. The paper firstly analyzed typical Chinese pedestrian behaviors and summarized the safety demands of pedestrians for AVs through articles and open database data, which are worked as the evaluation criteria. Then, corresponding driverless technologies are carefully reviewed. Finally, the adaptability would be given combining the above analyses. Our review found that autonomous vehicles have trouble in the occluded pedestrian environment and Chinese pedestrians do not accept AVs well. And more explorations should be conducted on standard human-machine interaction, interaction information overload avoidance, occluded pedestrians detection and nation-based receptivity research. The conclusions are very useful for motor corporations and driverless car researchers to place more attention on the complexity of the Chinese pedestrian environment, for transportation experts to protect pedestrian safety in the context of AVs, and for governors to think about making new pedestrians policies to welcome the upcoming driverless cars.

A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections

Reducing nonmotorized crashes requires a profound understanding of the causes and consequences of the crashes at the facility level. Generally, existing literature on bicyclists and pedestrian crash models suffers from two distinct problems: lack of exposure/volume data and inadequacy in capturing potential correlations across various crash aspects. To develop a robust framework for pedestrian crash analysis, this research employed a multivariate model across multiple pedestrian crash severities incorporating a crucial piece of information: pedestrian exposure. A multivariate spatial (conditional autoregressive) Poisson-lognormal model in a Bayesian framework was developed to examine the significant factors influencing the fatal, incapacitating injury (or suspected serious injury), and non-incapacitating injury pedestrian crashes at 409 signalized intersections in the Austin area. Various explanatory variables were used to examine the pedestrian crashes, including traffic characteristics, road geometry, built environment features, and pedestrian exposure volume at intersections, which was estimated through a direct demand model as part of the study. Model results revealed valuable insights. The superior performance of the multivariate model over the univariate model emphasized the need to jointly model multiple pedestrian crash severities. The results showed the significant positive influence of speed limit on fatal pedestrian crashes and revealed that both incapacitating and non-incapacitating injury crashes increase with increasing motorized traffic volume. Bus stop presence was found to have a negative influence on incapacitating injury crashes and a positive influence on non-incapacitating injury crashes. Moreover, the pedestrian volume at intersections positively influences non-incapacitating injury crashes. The difference in influence across crash types warrants careful and focused policy design of intersections to reduce pedestrian crashes of all severity types.

Development of systemwide pedestrian safety performance function using stratified random sampling and a proxy measure of pedestrian exposure

The lack of pedestrian counts at a systemwide level prompts the need to find other innovative ways of assessing pedestrian traffic crash risks using proxy measures of exposure. This study aims to formulate the methodology for developing pedestrian safety performance functions (SPF) using the proxy measure of pedestrian exposure and stratified random sampling. The case study was all urban intersections in Michigan State that comprise of collector and arterial roads. The stratified random sampling strategy was deployed to select the sample which is representative of all urban intersections in the state of Michigan. Factor analysis was used to develop a proxy measure of pedestrian exposure at urban intersections using a walkability measure (walk score), among other factors. The performances of various count models were compared using the goodness of fit measures based on the Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and Vuong test. The final pedestrian SPFs was formulated using the Zero-Inflated Poisson (ZIP) model with AADT at a major approach, AADT at the minor approach, and a proxy measure of pedestrian exposure. The proposed methodology in this study can benefit transportation agencies that have embarked on systemwide planning of pedestrian facilities to improve the safety of pedestrians but lack systemwide analytical tools and pedestrian counts to make data-driven decisions.

Systemic approach to improve safety of urban unsignalized intersections: Development and validation of a Safety Index

Methods based on crash data analysis are effective in identifying intersections with a potential for safety improvement. However, it is well recognized that crash data suffer from several shortcomings and that there are clues to safety other than crash occurrence. The systemic approach is an alternative method to address safety issues. In this approach, a transportation agency is able to identify priority locations based on the presence of risk factors rather than actual crashes. To promote wider use of the systemic safety approach, this paper aims at developing and validating a procedure to rank unsignalised urban intersections for safety improvement based on the evaluation of risk factors by road safety inspections. The procedure assesses a Safety Index (SI) that measures the safety performance of unsignalised urban intersections. The SI is formulated by combining two components of risk: the exposure of road users to road hazards (Exposure) and the risk factors, which increase the probability of involvement in crashes (Risk Index). The SI is made of two elements, one related to vehicles and one to pedestrians. Twenty-three detailed safety issues and ten general safety issues are assessed to compute the vehicle Risk Index and the pedestrian Risk Index. Safety issues were selected considering that they are common issues and that effective remedial measures exist and have already proven their effectiveness. Finally, criteria for identifying and ranking safety issues were defined. The SI has two main practical applications. High risk intersections, where safety measures that can reduce crash frequency exist, can be identified and ranked by the SI. Specific safety issues, that give more contribution to unsafety, are pointed out in order to give indication about more appropriate safety measures according to the systemic safety approach. The procedure was validated with a sample of eighty-nine urban intersections located in Orange County (Florida, U.S.). For these intersections, the SI scores, the Empirical Bayes (EB) safety estimates, and the potential for improvement (PFI) were compared. The correlation between the SI scores and the EB estimates was highly significant both for vehicles (R² = 0.66) and pedestrians (R² = 0.58) as well as for the total crashes (R² = 0.68). The results from the Spearman's rank-correlation analysis provided further validation for the SI indicating that ranking from the SI and the EB estimates agree at the 99.9% confidence level for vehicles (ρ_s = 0.78), pedestrians (ρ_s = 0.93), and total (ρ_s = 0.93).

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.