Evaluating Influence of Neighboring Structures on Spatial Crash Frequency Modeling and Site-Ranking Performance

2017 ◽  
Vol 2659 (1) ◽  
pp. 117-126 ◽  
Author(s):  
Gurdiljot Singh Gill ◽  
Wen Cheng ◽  
Meiquan Xie ◽  
Tom Vo ◽  
Xudong Jia ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Random Effect ◽  
Model Fitting ◽  
Poor Performance ◽  
Spatial Proximity ◽  
Crash Prediction ◽  
Ranking Performance ◽  
Site Ranking

Many neighborhood weight matrices have been adopted for modeling crash spatial heterogeneity. However, there has been little evaluation of their influence on crash prediction modeling performance. This study investigated 17 spatial-proximity matrices for development of spatial crash prediction models and site ranking with county-level data in California. Of the group of matrices being evaluated, traffic exposure–weighted and population-weighted distance-based matrices were first proposed in the traffic safety field. Bayesian spatial analysis was conducted with a combination of a first-order autoregressive error process and time trend for crashes to address the serial correlation of crashes in successive years. Two diagnostic measures were used for assessment of goodness of fit and complexity of models, and seven evaluation criteria were employed to assess the benefits associated with better-fitting models in site ranking. The results showed that modeling performance improved with an increase in number of neighbors considered in the weight matrix. However, a larger number of neighbors also led to greater variability of modeling performance. Specifically, Queen-2 and Decay-50 models proved to be superior among the adjacency- and distance-based models, respectively. The significance of incorporating spatial correlations was highlighted by the consistently poor performance of the base model, which included only the heterogeneity random effect. Finally, model-fitting performance seems to be strongly correlated with site-ranking performance. The models with closer goodness of fit tend to yield more consistent ranking results.

scholarly journals Modeling Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia

2018 ◽  
Author(s):  
Fedy Ouni ◽  
Mounir Belloumi
Keyword(s):  
Goodness Of Fit ◽  
Negative Binomial ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Surface Condition ◽  
Random Effect ◽  
Negative Binomial Model ◽  
Empirical Modeling ◽  
Binomial Model ◽  
Model Average

The purpose of the present study is to explore the linkage between Hazardous Road Locations-based crash counts and a variety of geometric characteristics, roadway characteristics, traffic flow characteristics and spatial features in the region of Sousse, Tunisia. For this purpose, collision data was collected from at 52 hazardous road sections including 1397 crash records for a 11-year monitoring period from January 1, 2004 to December 31, 2014 obtained from National Observatory for Information, Training, Documentation and Studies on Road Safety in Tunisia (NOITDRS). The matrix of Pearson correlation was used in order to avoid inclusion of both variables, which were highly correlated. Both the Random Effects Negative Binomial model and the Negative Binomial model were estimated. To evaluate the models, the Random Effect Negative Binomial model improves the goodness-of-fit compared to the Negative Binomial model. Average Daily Traffic volume, Curved alignment, Presence of public lighting, Visibility, Number of lane, Presence of vertical/horizontal sign, Presence of rural segment, Presence of drainage system, Roadway surface condition, Presence of paved shoulder and presence of major road were found as significant variables influencing accident occurrences. Overall, the current research contributes to the literature from empirical, modeling methodological standpoints since it was the first study conducted in Tunisia to use crash prediction models for hazardous road locations, and that portrays Tunisian reality. The research findings present advantageous insights on hazardous road locations in the region of Sousse, Tunisia and present useful planning tools for public authorities in Tunisia.

scholarly journals Investigation of surrogate measures for safety assessment of two-way stop controlled intersections.

2021 ◽  
Author(s):  
Adrian Lorion
Keyword(s):  
Goodness Of Fit ◽  
Prediction Models ◽  
Traffic Volume ◽  
Left Turn ◽  
Crash Prediction ◽  
Greater Toronto Area ◽  
Crash Prediction Models ◽  
Surrogate Measures ◽  
Surrogate Safety Measures ◽  
Volume Intersection

Crash prediction models used to estimate safety at intersections, road, and highway segments are traditionally developed using various traffic volume measures. There are issues with this approach and surrogate safety measures such as conflicts and delays can overcome them. This study investigates the relationships between crash frequencies and traffic volume, intersection delay, and conflicts to explore the viability of these models for estimating safety at two-way stop controlled intersections. The database used includes 78 three leg and 55 four leg intersections within the Greater Toronto Area. Crash prediction models were developed and evaluated based on goodness-of-fit measures and CURE plots. Two conflict estimation techniques are compared in order to determine which is best suited for two-way stop controlled intersection simulations. This study also investigates the use of the models for estimating the safety impact of implementing a left turn lane on a major approach of a three leg intersection.

Traffic Safety Implications of Travel Demand Management Policies

2014 ◽  
pp. 115-140
Author(s):  
Ali Pirdavani ◽  
Tom Bellemans ◽  
Tom Brijs ◽  
Bruno Kochan ◽  
Geert Wets
Keyword(s):  
Spatial Correlation ◽  
Traffic Safety ◽  
Travel Demand ◽  
Prediction Models ◽  
Demand Management ◽  
Primary Objective ◽  
Travel Demand Management ◽  
Crash Prediction ◽  
Crash Prediction Models ◽  
Crash Types

Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system’s effectiveness by changing travel behavior. Although the primary objective to implement such TDM strategies is not to improve traffic safety, their impact on traffic safety should not be neglected. The main purpose of this study is to investigate differences in the traffic safety consequences of two TDM scenarios: a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) and a teleworking scenario (i.e. 5% of the working population engages in teleworking). Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models that are used to evaluate such strategies should also be developed at an aggregate level. Moreover, given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, Zonal Crash Prediction Models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the Number Of Crashes (NOCs) (including fatal, severe, and slight injury crashes recorded between 2004 and 2007) and a set of explanatory variables. Different exposure, network, and socio-demographic variables of 2200 traffic analysis zones in Flanders, Belgium, are considered as predictors of crashes. An activity-based transportation model is adopted to produce exposure metrics. This enables a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this chapter, several ZCPMs with different severity levels and crash types are developed to predict the NOCs. The results show considerable traffic safety benefits of conducting both TDM scenarios at an average level. However, there are certain differences when considering changes in NOCs by different crash types.

scholarly journals COMPARISON OF ADJACENCY AND DISTANCE-BASED APPROACHES FOR SPATIAL ANALYSIS OF MULTIMODAL TRAFFIC CRASH DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1157-1161 ◽  
Author(s):  
G. Gill ◽  
T. Sakrani ◽  
W. Cheng ◽  
J. Zhou
Keyword(s):  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Influential Factors ◽  
Simultaneous Estimation ◽  
Spatial Correlations ◽  
Crash Prediction ◽  
Traffic Crash ◽  
Crash Data ◽  
Crash Prediction Models

Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among crash data at macro-level (County). Multivariate Full Bayesian crash prediction models were developed using Decay-50 (distance-based) and Queen-1 (adjacency-based) weight matrices for simultaneous estimation crash counts of four different modes: vehicle, motorcycle, bike, and pedestrian. The goodness-of-fit and different criteria for accuracy at prediction of crash count reveled the superiority of Decay-50 over Queen-1. Decay-50 was essentially different from Queen-1 with the selection of neighbors and more robust spatial weight structure which rendered the flexibility to accommodate the spatially correlated crash data. The consistently better performance of Decay-50 at prediction accuracy further bolstered its superiority. Although the data collection efforts to gather centroid distance among counties for Decay-50 may appear to be a downside, but the model has a significant edge to fit the crash data without losing the simplicity of computation of estimated crash count.

Assessment of the Effects of Volume Completeness and Spatial and Temporal Correlation on Hourly Freeway Crash Prediction Models

2020 ◽  
Vol 2674 (9) ◽  
pp. 1097-1109
Author(s):  
Nancy Dutta ◽  
Michael D. Fontaine
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Prediction Models ◽  
Data Availability ◽  
Volume Data ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Crash Prediction ◽  
Hourly Data ◽  
Crash Prediction Models

Traditional traffic safety analyses of crash frequency usually use highly aggregated cross-sectional data and ignore the time-varying nature of some critical factors. This research used 7 years of hourly data from 110 rural four-lane segments and 80 urban six-lane segments to develop hourly level crash prediction models and contrasted them with traditional annual average daily traffic (AADT)-based models. To account for the overdispersion of data and unobserved heterogeneity, generalized linear mixed-effect models were contrasted with negative binomial models. The models used average hourly volume as a measure of exposure, and the quantity of volume data available for the sites ranged from continuous counts to locations where only a couple of weeks of data were available every other year (short counts). While developing disaggregated models, the difference in data availability from these sources can be a potential source of error, so evaluating the change in performance of prediction models with changes in volume data availability was examined. The results showed that the best models include a combination of average hourly volume, selected geometric variables, and speed related parameters. Hourly models that included speed parameters consistently outperformed AADT models. Further investigation revealed that the positive effect of using a more inclusive and larger dataset was larger than the effect of accounting for data correlation. This showed that using short count stations as a data source does not diminish the quality of the developed models, thus indicating that these methods could be applied broadly across agencies, even when volume data is relatively sparse.

Traffic Safety Implications of Travel Demand Management Policies

2015 ◽  
pp. 1082-1107 ◽  
Author(s):  
Ali Pirdavani ◽  
Tom Bellemans ◽  
Tom Brijs ◽  
Bruno Kochan ◽  
Geert Wets
Keyword(s):  
Spatial Correlation ◽  
Traffic Safety ◽  
Travel Demand ◽  
Prediction Models ◽  
Demand Management ◽  
Primary Objective ◽  
Travel Demand Management ◽  
Crash Prediction ◽  
Crash Prediction Models ◽  
Crash Types

Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system's effectiveness by changing travel behavior. Although the primary objective to implement such TDM strategies is not to improve traffic safety, their impact on traffic safety should not be neglected. The main purpose of this study is to investigate differences in the traffic safety consequences of two TDM scenarios: a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) and a teleworking scenario (i.e. 5% of the working population engages in teleworking). Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models that are used to evaluate such strategies should also be developed at an aggregate level. Moreover, given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, Zonal Crash Prediction Models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the Number Of Crashes (NOCs) (including fatal, severe, and slight injury crashes recorded between 2004 and 2007) and a set of explanatory variables. Different exposure, network, and socio-demographic variables of 2200 traffic analysis zones in Flanders, Belgium, are considered as predictors of crashes. An activity-based transportation model is adopted to produce exposure metrics. This enables a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this chapter, several ZCPMs with different severity levels and crash types are developed to predict the NOCs. The results show considerable traffic safety benefits of conducting both TDM scenarios at an average level. However, there are certain differences when considering changes in NOCs by different crash types.

scholarly journals Practical Minimum Sample Size for Road Crash Time-Series Prediction Models

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fady M. A. Hassouna ◽  
Khaled Al-Sahili
Keyword(s):  
Sample Size ◽  
Traffic Safety ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Sample Sizes ◽  
Crash Prediction ◽  
Crash Data ◽  
Road Crashes ◽  
Stable Conditions ◽  
Road Crash

Road crashes are problems facing the transportation sector. Crash data in many countries are available only for the past 10 to 20 years, which makes it difficult to determine whether the data are sufficient to establish reasonable and accurate prediction rates. In this study, the effect of sample size (number of years used to develop a prediction model) on the crash prediction accuracy using Autoregressive integrated moving average (ARIMA) method was investigated using crash data for years 1971–2015. Based on the availability of annual crash records, road crash data for four selected countries (Denmark, Turkey, Germany, and Israel) were used to develop the crash prediction models based on different sample sizes (45, 35, 25, and 15 years). Then, crash data for 2016 and 2017 were used to verify the accuracy of the developed models. Furthermore, crash data for Palestine were used to test the validity of the results. The used data included fatality, injury, and property damage crashes. The results showed similar trends in the models’ prediction accuracy for all four countries when predicting road crashes for year 2016. Decreasing the sample sizes led to less prediction accuracy up to a sample size of 25; then, the accuracy increased for the 15-year sample size. Whereas there was no specific trend in the prediction accuracy for year 2017, a higher range of prediction error was also obtained. It is concluded that the prediction accuracy would vary based on the varying socioeconomic, traffic safety programs and development conditions of the country over the study years. For countries with steady and stable conditions, modeling using larger sample sizes would yield higher accuracy models with higher prediction capabilities. As for countries with less steady and stable conditions, modeling using smaller sample sizes (15 years, for example) would lead to high accuracy models with good prediction capabilities. Therefore, it is recommended that the socioeconomic and traffic safety program status of the country is considered before selecting the practical minimum sample size that would give an acceptable prediction accuracy, therefore saving efforts and time spent in collecting data (more is not always better). Moreover, based on the data analysis results, long-term ARIMA prediction models should be used with caution.

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