A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels

An analysis of crashes occurring in 252 unidirectional Italian motorway tunnels over a 4-year monitoring period is provided to identify the main causes of crashes in tunnels. In this paper, we propose a full Bayesian bivariate Poisson lognormal hierarchical model with correlated parameters for the joint analysis of crashes of two levels of severity, namely severe (including fatality and injury accidents only) and non-severe (property damage only), providing better insight on the available data with respect to an analysis based on severe and non-severe independent univariate models. In particular, the proposed model shows that for both of severity levels the crash frequency increases with some parameters: the average annual daily traffic per lane, the tunnel length, and the percentage of trucks, while the presence of the sidewalk provides a reduction in severe accidents. Also the presence of the third lane induces a reduction in severe accidents. Moreover, a reduction in the crash frequency of the two crash-types over years is present. The correlation between the parameters might offer additional insights into how some combinations can affect safety in tunnels. The results are critically discussed by highlighting strength and weakness of the proposed methodology.

Bayesian Nonparametric Approach to Average Annual Daily Traffic Estimation for Bridges

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

Integrating Economic and Utility Concepts for a Comprehensive Bridge Valuation Model

Current approaches in bridge valuation mostly focus on engineering construction costs while neglecting other values generated by the bridge's functionality and utilization, along with other indirect economic values of a bridge. Such limitations prevent public agencies from capturing the true asset value of their bridges to make informed management decisions. The objective of this paper is to develop a comprehensive bridge asset valuation methodology that is based on the utility theory concept to better represent the asset value of a bridge. Various factors featuring the bridge asset value are characterized and considered in the model including physical condition, safety, mobility, average annual daily traffic (AADT), direct time savings, and so on. These factors are categorized into economic value and utility-based value and quantified with specific performance measures. The applicability of the proposed methodology is demonstrated through a case study using five real-world bridges in Austin, Texas. The results show that the integrated, comprehensive model is able to effectively evaluate the overall asset value of a bridge. Finally, the paper discusses how public agencies can take advantage of the evaluation results to better understand bridge asset value and make more insightful decisions in bridge management practice.

A Model for Pavement Characteristic Deflection for Rural Roads in Himachal Pradesh

Structural Evaluation of pavements is essential to assess the structural strength of different layers of pavement. It also helps in determining the remaining life of a pavement and the thickness of overlay required. Surface Deflection is the structural response that is easy to measure and hence, commonly used parameter in structural evaluation. In the present study, an attempt has been made to develop a relationship between surface deflection and various structural parameters of pavements selected on low volume flexible rural roads in Himachal Pradesh. Benkelman Beam has been used to determine characteristic deflection on selected 12 rural road stretches in Shimla and Solan district of Himachal Pradesh. Because the conduction of Benkelman Beam Deflection (BBD) test is costly and difficult to carry out in the region of Himachal Pradesh due to hilly and narrow rural roads leading to disruption of traffic, hence, models have been developed to predict surface deflection value using Soaked CBR, Un-soaked CBR, Average Annual Daily Traffic (AADT) and Age of pavement from last overlay (in years). Another model has also been developed to estimate surface deflection using K-value, AADT and age of pavement. Multiple models have been developed using linear regression model. The various developed models have been studied, compared and best model is suggested supporting better coefficient of determination value and root mean square error (RMSE).

Predicting deer–vehicle collision risk across Victoria, Australia

The risk of deer–vehicle collisions (DVCs) is increasing in south-east Australia as populations of introduced deer expand rapidly. There are no investigations of the spatial and temporal patterns of DVC or predictions of where such collisions are most likely to occur. Here, we use an analytical framework to model deer distribution and vehicle movements in order to predict DVC risk across the State of Victoria. We modelled the occurrence of deer using existing occurrence records and geographic climatic variables. We estimated patterns of vehicular movements from records of average annual daily traffic and speeds. Given the low number of DVCs reported in Victoria, we used a generalised linear regression model fitted to DVCs in California, USA. The fitted model coefficients suggested high collision risk on road segments with high predicted deer occurrence, moderate traffic volume and high traffic speed. We used the California deer model to predict collision risk on Victorian roads and validated the predictions with two independent datasets of DVC records from Victoria. The California deer model performed well when comparing predictions of collision risk to the independent DVC datasets and generated plausible DVC risk predictions across the State of Victoria.

What Level of Tourism Traffic Should be planned for in North Carolina’s Major Tourism Areas

Aims: The purpose of this research is to provide the North Carolina Department of Transportation (NCDOT) with an execution strategy for using traffic counts in high tourism areas to aid in the development of Comprehensive Transportation Plans (CTPs). Due to the high variability of traffic counts in these localities, it is arbitrary to apply the typical weekday traffic count as the reference metric for developing the CTPs for these areas. Methods: A literature review and assessment of best practices, forecasting models, and implementation strategies are provided. The first and primary recommendation with respect to Average Annual Daily Traffic (AADT) calculations and planning is to incorporate peak-usage and directionality; whether it be hourly or monthly. Urban areas will have AADT values similar to the design value. However, seasonal areas, such as tourist locations, will have significant differences between the design value and the AADT. Results: While other states (notably Nevada and Florida) have incorporated peak-hour usage ratios into their planning forecasts, the recommendation in this report suggests using an average of the two busiest months (as shown in the case studies) when peak-hour usage rates are unknown. Conclusion: The primary recommendations should be addressed tactically (i.e., 3-5 years), and phased-in as resources are available. Other recommendations should be addressed strategically (i.e., 5-10 years), and phased-in as resources are available. Future work, including simulation modeling could be completed to test different levels of funding and to compare different approaches.

Comparison of Different Road Segmentation Methods

In road safety, the process of organizing road infrastructurenetwork data into homogenous entities is called segmentation.Segmenting a road network is considered thefirst and most important step in developing a safety performancefunction (SPF). This article aims to study the benefitof a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.

Safety Evaluation of Freight Intermodal Connectors in Tennessee State

This paper evaluates the traffic safety along freight intermodal connectors (FICs), which are also known as “first mile/last mile roadways,” connecting facilities that link freight-intensive land uses to main freight routes. Using Tennessee’s FICs as a case study, the paper digests the safety with reference to crash frequency, crash rates, and statistical significance of attributing traffic and geometric factors. It was found that connectors leading to pipeline terminals have high crash rates (almost double) compared with other type of terminals, whereas port terminal connectors have the lowest safety problem indices. The study established correlative contributing causes of crash frequencies and rates along FICs that included average annual daily traffic, lanes, shoulders, access, and median types. Traffic signal density was found to strongly and significantly affect the probability of crashes, together with the presence of a two-way left-turn lane (TWLTL), which surprisingly tends to decrease the probability of crashes along these connectors. The presence of shoulders along intermodal connectors was found to help reduce the probability of crashes, whereas the presence of curb and gutter tends to increase crash frequency. Analysis indicated that most of the FICs with high crash rates were also operating at a lower traffic operations level of service (LOS), especially for critical movements toward freight facilities because of high truck volumes.