Evaluation of Safety in Horizontal Curves of Roads Using a Multi-Body Dynamic Simulation Process

A review of truck driver persona construction for safety management

The trucking industry urgently requires comprehensive methods to evaluate driver safety, given the high incidence of serious traffic accidents involving trucks. The concept of a "truck driver persona" emerges as a crucial tool in enhancing driver safety and enabling precise management of road transportation safety. Currently, the road transport sector is only beginning to adopt the user persona approach, and thus the development of such personas for road transport remains an exploratory endeavor. This paper delves into three key aspects: identifying safety risk characteristic parameters, exploring methods for constructing personas and designing safety management interventions. Initially, bibliometric methods are employed to analyze safety risk factors across five domains: truck drivers, vehicles, roads, the environment, and management. This analysis provides the variables necessary to develop personas for road transportation drivers. Existing methods for constructing user personas are then reviewed, with a particular focus on their application in the context of road transportation. Integrating contemporary ideas in persona creation, we propose a framework for developing safety risk personas specific to road transportation drivers. These personas are intended to inform and guide safety management interventions. Moreover, the four stages of driver post-evaluation are integrated into the persona development process, outlining tailored safety management interventions for each stage: pre-post, pre-transit, in-transit, and on-post. These interventions are designed to be orderly and finely tuned. Lastly, we offer optimization recommendations and suggest future research directions based on safety risk factors, persona construction, and safety management interventions. Overall, this paper presents a safety management-oriented research technology system for constructing safety risk personas for truck drivers. We argue that improving the design of the persona index system, driven by big data, and encompassing the entire driver duty cycle-from pre-post to on-post-will significantly enhance truck driver safety. This represents a vital direction for future development in the field.

Enhancing highway Loop Safety Level through proactive risk-based assessment of geometric configuration using lateral acceleration

OBJECTIVE

Loop ramps are complex due to their combination of horizontal curves and vertical alignments. Analyzing driving behavior and measuring safety levels can provide insights for designers, helping to improve the performance and alignment of design assumptions with actual driving behavior on loops. Therefore, the primary objective of this research is to explore the safety, performance and geometric configuration of the main body and general shape of free-flow loop ramps in the free-following mode.

METHODS

The study uses data from a UAV to investigate vehicle demand behavior. Maximum lateral acceleration (ay,i) in loops is used as a Surrogate Safety Measure (SSM), along with a new parameter, the a/b ratio, to determine the general shape of loop bodies. The study presents the Loop Safety Level (LSL), an approach for proactive risk analysis and ranking that relies on threshold lateral acceleration (at), 85th percentile maximum lateral acceleration (a y,max,85%), and crash analysis.

RESULTS

A higher LSL value points to a more critical safety concern regarding the loop's shape in relation to lateral acceleration caused by driving behaviors. Comparing crash statistics with lateral acceleration results enables the LSL to provide appropriate safety ratings and diagnose loop segment safety. A prediction model for maximum lateral acceleration, based on loop geometry, demonstrates a good fit (R2=0.88) between observed and predicted data.

CONCLUSIONS

The study enhances understanding of safety considerations in geometric configuration and general shape enhancement of loops during the design process.

Why Do Drivers' Collision Avoidance Maneuvers Tend to Cause SUVs to Sideslip or Rollover on Horizontal Curve and Grade Combinations?-An Analysis of the Causes Based on a Modified Multibody Dynamics Model

The extent to which drivers' collision avoidance maneuvers affect the safety margins of sideslip and rollover is not captured by road geometric design theory. To quantify the effects of drivers' collision avoidance maneuvers on the safety margins of sport utility vehicles (SUVs) on horizontal curve and grade combinations, a modified 8-degree-of-freedom multibody model based on SUVs was developed. The model was then used to calculate the design safety margins of sideslip and rollover for steady states and the actual safety margins for collision avoidance maneuvers. Subsequently, the design safety margin reduction rate (the difference between the design and actual safety margins divided by the design safety margin) was calculated and used to assess the safety margins. The results showed that the safety margins of SUVs were significantly reduced by braking, lane changing, and lane changing with braking. The marginal effects indicated that the greater the deceleration and the shorter the lane change duration, the greater the effect on the safety margins, particularly the sideslip safety margin. Furthermore, when the SUV was driven at 80 km·h^-1 on grades with a horizontal curve radius of 270 m and 400 m, the sideslip safety margin with emergency braking (deceleration over -4.5 m·s^-2) was reduced by 71% and 21%, and the rollover safety margin was reduced by 11% and 5%, respectively. Under these conditions, an emergency lane change (lane change duration less than 2 s) caused the SUV to sideslip and reduced the rollover safety margin by 47% (curve radius 270 m) and 45% (curve radius 400 m). Therefore, drivers' collision avoidance maneuvers are a factor that cannot be neglected in alignment design.

Semi-Automatic Extraction of Geometric Elements of Curved Ramps from Google Earth Images

Generating and updating roadway geometric elements from aerial images is necessary for multiple geospatial information system purposes, which have been addressed through various approaches. However, most existing methods cannot deal with challenges such as differently curved ramp characteristics, whereas measurements of geometric elements are still of low effectiveness and accuracy. This paper presents a new method for the semi-automatic extraction of horizontal parameters of curved highway ramps using Google Earth images. The proposed method first determines a road centerline manually using a graphics editor software; the file is then saved and processed with a program that analyzes and splits the centerline into its basic components. After that, the curvature analysis and linear fitting methods are integrated for automatic PC and PT determination. Finally, at the post-processing stage, the radii of the curves are computed automatically using the least-squares method. The proposed method was tested on four highway ramps and validated by comparison with the obtained design plans. Results show that the proposed method successfully detected the curves’ PC/PT and measured their radii with a high degree of accuracy.

Application of Analytical Hierarchy Process for Structural Health Monitoring and Prioritizing Concrete Bridges in Iran

This paper proposes a method for monitoring the structural health of concrete bridges in Iran. In this method, the bridge condition index (BCI) of bridges is determined by the analytical hierarchy process (AHP). BCI constitutes eight indices that are scored based on the experts’ views, including structural, hydrology and climate, safety, load impact, geotechnical and seismicity, strategic importance, facilities, and traffic and pavement. Experts’ views were analyzed by Expert Choice software, and the relative importance (weight) of all eight indices were determined using AHP. Moreover, the scores of indices for various conditions were extracted from experts’ standpoints. BCI defines as the sum of weighted scores of indices. Bridge inspectors can examine the bridge, determine the scores of indices, and compute BCI. Higher values of BCI indicate better conditions. Therefore, bridges with lower BCI take priority in maintenance activities. As the case studies, the authors selected five bridges in Iran. Successful implementation of the proposed method for these case studies verified that this method can be applied as an easy-to-use optimization tool in health monitoring and prioritizing programs.

Ranking Road Sections Based on MCDM Model: New Improved Fuzzy SWARA (IMF SWARA)

Traffic management is a significantly difficult and demanding task. It is necessary to know the main parameters of road networks in order to adequately meet traffic management requirements. Through this paper, an integrated fuzzy model for ranking road sections based on four inputs and four outputs was developed. The goal was to determine the safety degree of the observed road sections by the methodology developed. The greatest contribution of the paper is reflected in the development of the improved fuzzy step-wise weight assessment ratio analysis (IMF SWARA) method and integration with the fuzzy measurement alternatives and ranking according to the compromise solution (fuzzy MARCOS) method. First, the data envelopment analysis (DEA) model was applied, showing that three road sections have a high traffic risk. After that, IMF SWARA was applied to determine the values of the weight coefficients of the criteria, and the fuzzy MARCOS method was used for the final ranking of the sections. The obtained results were verified through a three-phase sensitivity analysis with an emphasis on forming 40 new scenarios in which input values were simulated. The stability of the model was proven in all phases of sensitivity analysis.