Effects of speed-control measures on the safety of unsignalized midblock street crossings in China

<p><b>Objective</b>: The primary objective of this study was to evaluate the effects of different speed-control measures on the safety of unsignalized midblock street crossings.</p> <p><b>Methods</b>: In China, it is quite difficult to obtain traffic crash and conflict data for pedestrians using such crossings, mainly due to the lack of traffic data management and organizational issues. In light of this, the proposed method did not rely on such data, but considered vehicle speed, which is a leading contributing factor of pedestrian safety at mid blocks. To evaluate the speed reduction effects at different locations, the research team utilized the following methods in this study: (1) testing speed differences—on the basis of the collected data, statistical analysis is conducted to test the speed differences between upstream and crosswalk, upstream and downstream, and downstream and crosswalk; and (2) mean distribution deviation—this value is calculated by taking the difference in cumulative speed distributions for the two different samples just mentioned. In order to better understand the variation of speed reduction effects at different distances from speed-control facilities, data were collected from six types of speed-control measures with a visual range of 60 m.</p> <p><b>Results</b>: The results showed that speed humps, transverse rumble strips, and speed bumps were effective in reducing vehicle speeds. Among them speed humps performed the best, with reductions of 21.1% and 20.0% from upstream location (25.01 km/h) and downstream location (24.66 km/h) to pedestrian crosswalk (19.73 km/h), respectively. By contrast, the speed reduction effects were minimal for stop and yield signs, flashing yellow lights, and crossings without treatment.</p> <p><b>Conclusions</b>: Consequently, in order to reduce vehicle speeds and improve pedestrian safety at mid blocks, several speed-control measures such as speed humps, speed bumps, and transverse rumble strips are recommended to be deployed in the vicinity of pedestrian crosswalks.</p

Evaluating the influence of road lighting on traffic safety at accesses using an artificial neural network

<p><b>Objectives:</b> This article focuses on the effect of road lighting on road safety at accesses to quantitatively analyze the relationship between road lighting and road safety.</p> <p><b>Methods:</b> An artificial neural network (ANN) was applied in this study. This method is one of the most popular machine learning methods and does not require any predefined assumptions. This method was applied using field data collected from 10 road segments in Nanjing, Jiangsu Province, China.</p> <p><b>Results:</b> The results show that the impact of road lighting on road safety at accesses is significant. In addition, road lighting has a greater influence when vehicle speeds are higher or the number of lanes is greater. A threshold illuminance was also found, and the results show that the safety level at accesses will become stable when reaching this value.</p> <p><b>Conclusions:</b> Improved illuminance can decrease the speed variation among vehicles and improve safety levels. In addition, high-grade roads need better illuminance at accesses. A threshold value can also be obtained based on related variables and used to develop scientific guidelines for traffic management organizations.</p

Modeling level-of-safety for bus stops in China

<p><b>Objective:</b> Safety performance at bus stops is generally evaluated by using historical traffic crash data or traffic conflict data. However, in China, it is quite difficult to obtain such data mainly due to the lack of traffic data management and organizational issues. In light of this, the primary objective of this study is to develop a quantitative approach to evaluate bus stop safety performance.</p> <p><b>Methods:</b> The concept of level-of-safety for bus stops is introduced and corresponding models are proposed to quantify safety levels, which consider conflict points, traffic factors, geometric characteristics, traffic signs and markings, pavement conditions, and lighting conditions. Principal component analysis and <i>k</i>-means clustering methods were used to model and quantify safety levels for bus stops.</p> <p><b>Results:</b> A case study was conducted to show the applicability of the proposed model with data collected from 46 samples for the 7 most common types of bus stops in China, using 32 of the samples for modeling and 14 samples for illustration. Based on the case study, 6 levels of safety for bus stops were defined. Finally, a linear regression analysis between safety levels and the number of traffic conflicts showed that they had a strong relationship (<i>R</i>² value of 0.908).</p> <p><b>Conclusions:</b> The results indicated that the method was well validated and could be practically used for the analysis and evaluation of bus stop safety in China. The proposed model was relatively easy to implement without the requirement of traffic crash data and/or traffic conflict data. In addition, with the proposed method, it was feasible to evaluate countermeasures to improve bus stop safety (e.g., exclusive bus lanes).</p

A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

<p><b>Objective</b>: This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving.</p> <p><b>Method</b>: Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual–manual task) and distracted (poststarting of visual–manual task) driving periods. Average relative spectral power in a low frequency range (0–0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses.</p> <p><b>Results</b>: Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual–manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving.</p> <p><b>Conclusions</b>: This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses.</p