Transportation Safety Planning Research Articles

How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.

Mitigating Bias in Big Data for Transportation

Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

Modeling spatial nonstationary and overdispersed crash data: Development and comparative analysis of global and geographically weighted regression models applied to macrolevel injury crash data

There is a wide body of research, especially in developed countries case studies, conducted to quantify safety impacts of transportation strategies. These models can be broadly divided into global and local types. Contrary to consistency assumption of global parameters, parameters’ variation over the space, which stems from heterogeneity, can be considered by the local models. Geographically weighted regression (GWR) is a proper technique which is employed to locally estimate the parameters. Geographically weighted Poisson regression (GWPR) is one of the most prevailing count models to be exercised in transportation safety planning. Overdispersion as another consequence of heterogeneity, however, is not addressed by GWPR. Therefore, this study is centered on three targets. First, crash models are developed in a developing country case study. Second, overdispersed and spatial nonstationary data is scrutinized by applying and investigating the performance of geographically weighted negative binomial regression with two aspects of global and spatially varying overdispersion parameters (i.e., GWNBRg and GWNBR). Also, the explanatory performance and prediction accuracy are compared with GWPR, traditional Poisson and negative binomial regressions as two widely used classes of the global models. Finally, the fluctuating safety effects of spatial-unit-related variables at the macrolevel are examined. To this end, geographically coded data on injury crashes, road characteristics, socioeconomic and demographic factors, public transit and land use were collected for 253 traffic zones as the macrolevel spatial units from Mashhad city. The findings indicated that GWR family outperforms traditional regressions in capturing spatial heterogeneity when goodness of fit criteria is compared. Also, GWNBR revealed better performance in terms of AICc and lesser sensitivity to extreme values by developing smoother kernel function of geographical weights. Based on our findings, however, GWNBRs cannot predict other data sets as well as GWPR and researchers are recommended to use GWPR rather than GWNBR. The results also demonstrated that major arterials, ramps, number of intersections, bus stops and employees as well as commercial, residential, and agricultural land uses have considerable impact on injury crashes. In addition, implications regarding the detection of multicollinearity among explanatory variables based on condition index were noted.

Applying a deep learning approach for transportation safety planning by using high-resolution transportation and land use data

Analytical Transportation Safety Planning (TSP) is an important concept for integrating and improving both planning and safety and achieving better policies and decision making. In the recent decade, considerable efforts have been devoted to providing better prediction results with the consideration of zonal systems, mathematical methods, input variables, etc. In previous studies, transportation and land use data have been widely used as input to predict crashes. Meanwhile, the previous studies required all input variables to be aggregated at the zonal level. With the aggregation process, the collected data fell into low resolution and lost details, which may introduce low accuracy and even biases. The primary objective of this study is to validate the viability of applying a deep learning approach to predict crashes for TSP with the high-resolution data. A framework of collecting high-resolution data is first introduced. Then, a deep learning architecture of a convolutional neural network (CNN) is adopted to predict traffic crashes. To validate the proposed method, an empirical study is conducted and the proposed method is compared with three counterparts: two statistical models (i.e., negative binomial model and spatial Poisson lognormal model) and a traditional machine learning model (i.e., artificial neural network) using low-resolution data (i.e., data that are aggregated based on zones). The results indicate that the proposed deep learning method with high-resolution data could provide significantly higher prediction accuracy than the three conventional models using low-resolution data, which validates the concept of using the deep learning approach with detailed data for traffic crash prediction. It is expected that the deep learning approach for traffic crash prediction in this study could provide new and valuable insights into the future directions of transportation safety planning.

Transportation Safety Planning Approach for Pedestrians: An Integrated Framework of Modeling Walking Duration and Pedestrian Fatalities

Multiple approaches have been proposed to take traffic safety into consideration in long-term transportation plans, referred to as transportation safety planning. Some early studies used trip generation data as the explanatory variables for their macro-level crash safety performance functions, or crash prediction models. However, no study to date has attempted to integrate walking exposure and pedestrian safety at the modeling stage. Thus, a novel methodological framework for integrating the analyses of walking exposure and pedestrian crashes is proposed toward better transportation safety planning for pedestrians. In comparison with walking trips and walking miles, walking hours was identified as the best walking exposure variable by a preliminary analysis. Thus, an integrated modeling structure with walking hours as its exposure variable was developed. The modeling results indicate that climate conditions, population, and car usage patterns affect walking hours, and predicted walking hours, climate conditions, percentage of mid-elderly (64–75 years), proportions of minority race/ethnicity, and percent of tertiary industry occupations have significant effects on pedestrian fatalities. In addition, the integrated modeling framework was compared with non-integrated ones, and the results indicate that the integrated framework outperforms its counterparts in relation to deviance information criterion. The proposed approach and the findings from this study are expected to provide useful insights not only to researchers but also to policy makers and practitioners in the fields of transportation planning and traffic safety.

Integrated Modeling Approach for Non-Motorized Mode Trips and Fatal Crashes in the Framework of Transportation Safety Planning

In recent decades, considerable efforts have been made to incorporate traffic safety into long-term transportation plans (LTTPs), a process which is often termed transportation safety planning (TSP). Although some researchers have attempted to integrate transportation plans and safety by adopting transportation planning data (e.g., trip generation) for estimating traffic crash frequency at the macroscopic level, no studies have attempted to develop trip and safety models in one structure simultaneously. A Bayesian integrated multivariate modeling approach is suggested for estimating trips and crashes of non-motorized modes (i.e., walking and cycling). American Housing Survey (AHS) data were collected from the U.S. Census Bureau and were used for the proposed approach. In the first part of the proposed model, the probabilities of choosing walking and cycling modes were estimated, and the estimated probabilities were converted to trips by multiplying the number of sampled households. In the second part, the estimated trips were fed into crash prediction models (or safety performance functions) as an exposure variable. The modeling result revealed many contributing factors for pedestrian/bicycle trips and crashes. Possible shared unobserved features between pedestrian and bicycle trips, and between pedestrian and bicycle crashes, were accounted for by adopting a multivariate structure. In addition, it was found that the crash models with the estimated exposures outperform those with the observed exposures. It is expected that the integrated modeling approach for trips and crashes in this study will provide great insights into the future directions of TSP.

Comparative analysis of zonal systems for macro-level crash modeling

IntroductionMacro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). MethodPoisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) are developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposes a method to compare the modeling performance of the three types of geographic units at different spatial configurations through a grid based framework. Specifically, the study region is partitioned to grids of various sizes and the model prediction accuracy of the various macro models is considered within these grids of various sizes. ResultsThese model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperform the ones that do not consider it. ConclusionsBased on the modeling results and motivation for developing the different zonal systems, it is recommended using CTs for socio-demographic data collection, employing TAZs for transportation demand forecasting, and adopting TADs for transportation safety planning. Practical ApplicationsThe findings from this study can help practitioners select appropriate zonal systems for traffic crash modeling, which leads to develop more efficient policies to enhance transportation safety.

Geospatially Enabled Database for Analyzing Traffic Injuries in San Francisco, California

The collection and the analysis of transportation and health data, especially in geospatial terms, can be a complex process. TransBASESF.org is a geospatially enabled analytics database developed by the San Francisco, California, Department of Public Health and used to analyze, evaluate, monitor, and communicate transportation safety issues in San Francisco. The goal of TransBASESF.org is to serve as the central data repository for public health–related transportation data, to be a free and open data resource for the general public to use, and to support interagency collaboration, data standards, and data sharing in the City and County of San Francisco. The TransBASESF.org database currently includes more than 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. The TransBASESF.org database originated as a tool developed to facilitate health impact assessments and understand better the geographic distribution and characteristics of pedestrian injuries. The scope and the role have continued to evolve, and the database now plays a key role in the implementation of San Francisco’s Vision Zero policy, an effort to reduce all modes of transportation-related fatalities to zero by 2024. The purpose of this paper is to give an overview of the TransBASESF.org system, including its origin, development, content, technical specifications, and examples of applications to date in informing transportation safety planning and policy in San Francisco.

Modeling Zonal Traffic Accident Counts with the Regression under Zero-adjusted Inverse Gaussian Distribution

Statistical analyses on crash counts/rates are important steps for transportation safety planning and evaluations of certain transportation facilities. Even there are various types of regression models in connecting between the response variable and characteristics of transportation facilities in relevance with considerations of different aspects of the frequency distribution of crashes, due to the complicated data generating process, past models still lack sufficient precision for accounting for several issues of crash counts especially the widely mentioned problem of excessive zero counts of accidents. Towards to this end, this study seeks to provide an alternative approach for predicating and inferring the response variable through a set of explanatory variables. As the crash rates fundamentally follow a mixed distribution such that non-zero probability will be associated with zero point in the domain, the zero-adjusted inverse Gaussian regression is introduced to overcome these issues as well as provide new thoughts in this field. Based on an observed dataset of accidents aggregated for the level of traffic analysis zone (TAZ), the regression model is developed and estimated using the principle of maximum likelihood estimation. The results discover that several attributes related to the zonal social economic, average traffic condition as well as roadway geometric design characteristics are statistically significant. Under such empirical analysis, the zero-adjusted inverse Gaussian distribution is appropriate to reflect the distributional characteristics of crash rates and provide a new direction of approaches in the field of crash data analyses.

Crash Prediction and Risk Evaluation Based on Traffic Analysis Zones

Traffic safety evaluation for traffic analysis zones (TAZs) plays an important role in transportation safety planning and long-range transportation plan development. This paper aims to present a comprehensive analysis of zonal safety evaluation. First, several criteria are proposed to measure the crash risk at zonal level. Then these criteria are integrated into one measure-average hazard index (AHI), which is used to identify unsafe zones. In addition, the study develops a negative binomial regression model to statistically estimate significant factors for the unsafe zones. The model results indicate that the zonal crash frequency can be associated with several social-economic, demographic, and transportation system factors. The impact of these significant factors on zonal crash is also discussed. The finding of this study suggests that safety evaluation and estimation might benefit engineers and decision makers in identifying high crash locations for potential safety improvements.

Analysis and assessment of accident characteristics: case study of Dhofar Governorate, Sultanate of Oman

Traffic accidents have serious public health and economic concern all over the world especially in developing countries. Oman had registered as the highest road accident death rate in the GCC and third highest in the Eastern Mediterranean region. The analysis of accident characteristics is one of the most significant aspects that engineers rely on during the design of road networks and/or the safety performance evaluation of roadway systems. The main objective of this paper is to perform several statistical analyses for traffic accidents characteristic at the road network in Dhofar, Sultanate of Oman. Such analyses will help to explore the scale and main characteristics of traffic accidents for specific network system or location as well as identifying the hazardous locations and the most important variables that may be considered in building Accident Prediction Models (APMs). In this study a new methodology for recording and analyzing accidents data was established. Different types of accident and accidents' causes were defined and analyzed in this study. Total of 5825 accidents was collected from Royal Oman Police (ROP) during 2007 to 2010. Statistical analysis was carried out using accident frequencies and accident rates. The results showed that Collision between vehicles has the highest ratio among the accident with 58% followed by crashing with stationary with 21%. Approximately 47% of total accidents occur at intersections, 90% of them occurred at un-signalized intersections (roundabout and T-intersections). About 68% of fatal accidents occur at road links. The sideswipe accidents have the highest ratio among accident types. The accident rates for roundabouts (0.61 accidents per million entering vehicles) are higher than the T-intersection rates (0.48 accidents per million entering vehicles). It seems that traffic volume and drivers' characteristics, road network types, weather and time were the major factors affecting the number of accidents. The results of the study may have significant consideration for transportation and road safety planning in Sultanate of Oman and other countries in the Middle East area, especially Gulf countries with similar conditions. Language: en

Evaluating Spatial-Proximity Structures in Crash Prediction Models at the Level of Traffic Analysis Zones

Zonal crash prediction that considers cross-zonal spatial correlation is a frontline research topic, especially in the context of transportation safety planning. This study presents an evaluation of crash prediction models at the level of traffic analysis zones with four types of spatial-proximity structures: 0–1 first-order adjacency, common-boundary length, geometry-centroid distance, and crash-weighted centroid distance. Bayesian spatial analysis with conditional autoregressive priors was successfully applied, and Hillsborough data were used to compare the model-fitting and predictive performance associated with the proposed models. The results confirmed the extensive existence of cross-zonal spatial correlation in crash occurrence. The best predictive capability, relatively, was associated with the model that considered proximity of neighboring zones by weighing their common-boundary lengths. Furthermore, full consideration of all possible spatial correlations for all zones significantly increased model complexity, which probably resulted in reduced predictive performance.

Geographical unit based analysis in the context of transportation safety planning

A wide array of spatial units has been explored in macro-level modeling. With the advancement of Geographic Information System (GIS) analysts are able to analyze crashes for various geographical units. However, a clear guideline on which geographic entity should be chosen is not present. Macro level safety analysis is at the core of transportation safety planning (TSP) which in turn is a key in many aspects of policy and decision making of safety investments. The preference of spatial unit can vary with the dependent variable of the model. Or, for a specific dependent variable, models may be invariant to multiple spatial units by producing a similar goodness-of-fits. In this study three different crash models were investigated for traffic analysis zones (TAZs), block groups (BGs) and census tracts (CTs) of two counties in Florida. The models were developed for the total crashes, severe crashes and pedestrian crashes in this region. The primary objective of the study was to explore and investigate the effect of zonal variation (scale and zoning) on these specific types of crash models. These models were developed based on various roadway characteristics and census variables (e.g., land use, socio-economic, etc.).It was found that the significance of explanatory variables is not consistent among models based on different zoning systems. Although the difference in variable significance across geographic units was found, the results also show that the sign of the coefficients are reasonable and explainable in all models.Key findings of this study are, first, signs of coefficients are consistent if these variables are significant in models with same response variables, even if geographic units are different. Second, the number of significant variables is affected by response variables and also geographic units.Admittedly, TAZs are now the only traffic related zone system, thus TAZs are being widely used by transportation planners and frequently utilized in research related to macroscopic crash analysis. Nevertheless, considering that TAZs are not delineated for traffic crash analysis but they were designed for the long range transportation plans, TAZs might not be the optimal zone system for traffic crash modeling at the macroscopic level. Therefore, it recommended that other zone systems be explored for crash analysis as well.

Gasoline Prices and Traffic Crashes in Alabama, 1999–2009

Objective: The price of gasoline has been found to be negatively associated with traffic crashes in a limited number of studies. However, most of the studies have focused either on fatal crashes only or on all crashes but measured over a very short time period. In this study, we examine gasoline price effects on all traffic crashes by demographic groups in the state of Alabama from 1999 to 2009. Methods: Using negative binomial regression techniques to examine monthly data from 1999 to 2009 in the state of Alabama, we estimate the effects of changes in gasoline price on changes in automobile crashes. We also examine how these effects differ by age group (16–20, 21–25, 26–30, 31–64, and 65+), gender (male and female), and race/ethnicity (non-Hispanic white, non-Hispanic black, and Hispanic). Results: The results show that gasoline prices have both short-term and long-term effects on reducing total traffic crashes and crashes of each age, gender, and race/ethnicity group (except Hispanic due to data limitations). The short-term and long-term effects are not statistically different for each individual demographic group. Gasoline prices have a stronger effect in reducing crashes involving drivers aged 16 to 20 than crashes involving drivers aged 31 to 64 and 65+ in the short term; the effects, however, are not statistically different across other demographic groups. Conclusions: Although gasoline price increases are not favored, our findings show that gasoline price increases (or decreases) are associated with reductions (or increases) in the incidence of traffic crashes. If gasoline prices had remained at the 1999 level of $1.41 from 1999 to 2009, applying the estimated elasticities would result in a predicted increase in total crashes of 169,492 (or 11.3%) from the actual number of crashes. If decision makers wish to reduce traffic crashes, increasing gasoline taxes is a possible option—however, doing so would increase travel costs and lead to equity concerns. These findings may help to shape transportation safety planning and policy making.

Safety Evaluation for Signalized Intersections Using GIS Techniques and Statistic Analysis

This paper presents a systematic GIS-based method to identify and evaluate signalized intersections for purposes of transportation safety planning. The intersections were analyzed and compared based on the crash average hazard index which is the average of five safety measures: total number of crashes, total number of injury and fatal crashes, total crash exposure rate, injury/fatal exposure rate, and weighted hazard index (WHI). The GIS technique along with statistics analysis is utilized to estimate the signalized intersection’s risk distribution in terms of the averaged hazard index. It results in priority ranking of signalized intersections from the most dangerous to the least dangerous hazard levels. The method and results can assist engineers, planners and decision-makers in identifying critical intersections with potential for safety improvement.