Impact of combined alignments on lane departure: A simulator study for mountainous freeways

Lane departures are responsible for many side-swipe, rear-end and single-vehicle run-off-road crashes. There is a dearth of research, however, on how lane departures are impacted by roadway alignments. The objective of this paper is to examine which geometric design characteristics, including road alignment at the current segment and the adjacent segments, have significant influence on lane departure. Lane departure data from a total 30 drivers were collected from a driving simulator study of a four-lane (two lanes in each direction) divided mountainous freeway. Lane departures were classified into lane keeping, lane departure to the left and lane departure to the right for all-alignments (Dataset I), and lane keeping, lane departure to the inside and lane departure to the outside for curves-only (Dataset II). A mixed multinomial logit model for each dataset was employed to examine the contributory factors. This approach allows for the possibility that the estimated model parameters can vary randomly to account for unobserved effects potentially relating to heterogeneous driver behaviors. Fixed parameters that had a significant increase on lane departure were horizontal curvature at the current segment, and the difference (max-min) in horizontal curvature within the 300-m adjacent upstream alignment. Downward slope and upward slope with fixed parameters significantly decreased lane departure. Estimated parameters related to the direction of the curve, driving lane (bordering median or hard shoulder) and driving speed had found to have randomly distributed over the drivers. This indicates that driver behavior is not consistent in the effect of these three variables on lane departure. These results can assist engineers in designing safer mountainous freeways

Impact of data aggregation approaches on the relationships between operating speed and traffic safety

The impact of operating speed on traffic crash occurrence has been a controversial topic in the traffic safety discipline as some studies reported a positive association whereas others indicated a negative relationship between speed and crashes. Two major issues thought to be accountable for such conflicting findings are the application of inappropriate statistical methods and the use of sample datasets with varying levels of aggregation. The main objective of this study is therefore to investigate the impacts of data aggregation schemes on the relationships between operating speed and traffic safety. A total of three aggregation approaches were examined: (1) a segment-based dataset in which crashes are grouped by roadway segment, (2) a scenario-based dataset where crashes are aggregated by traffic operating scenarios, and (3) a disaggregated crash-level dataset consisting of information from individual crashes. The first two aggregation approaches were used in examining the relationships between operating speed and crash frequency using Bayesian random-effects negative binomial models. The third disaggregated crash risk analysis was conducted utilizing Bayesian random-effects logistic regression models. From the modeling results, it has been concluded that the scenario-based approach shared similar findings with those of the disaggregated crash risk analysis approach in which a U-shaped relationship between operating speed and crash occurrence was identified. However, the commonly adopted segment-based aggregation approach revealed a monotonous negative relationship between speed and crash frequency. The implications of the different analyses results and the potential applications of the results on speed management systems have therefore been discussed

How to determine an optimal threshold to classify real-time crash-prone traffic conditions?

One of the proactive approaches in reducing traffic crashes is to identify hazardous traffic conditions that may lead to a traffic crash, known as real-time crash prediction. Threshold selection is one of the essential steps of real-time crash prediction. And it provides the cut-off point for the posterior probability which is used to separate potential crash warnings against normal traffic conditions, after the outcome of the probability of a crash occurring given a specific traffic condition on the basis of crash risk evaluation models. There is however a dearth of research that focuses on how to effectively determine an optimal threshold. And only when discussing the predictive performance of the models, a few studies utilized subjective methods to choose the threshold. The subjective methods cannot automatically identify the optimal thresholds in different traffic and weather conditions in real application. Thus, a theoretical method to select the threshold value is necessary for the sake of avoiding subjective judgments. The purpose of this study is to provide a theoretical method for automatically identifying the optimal threshold. Considering the random effects of variable factors across all roadway segments, the mixed logit model was utilized to develop the crash risk evaluation model and further evaluate the crash risk. Cross-entropy, between-class variance and other theories were employed and investigated to empirically identify the optimal threshold. And K-fold cross-validation was used to validate the performance of proposed threshold selection methods with the help of several evaluation criteria. The results indicate that (i) the mixed logit model can obtain a good performance; (ii) the classification performance of the threshold selected by the minimum cross-entropy method outperforms the other methods according to the criteria. This method can be well-behaved to automatically identify thresholds in crash prediction, by minimizing the cross entropy between the original dataset with continuous probability of a crash occurring and the binarized dataset after using the thresholds to separate potential crash warnings against normal traffic conditions

Speed, speed variation and crash relationships for urban arterials

Speed and speed variation are closely associated with traffic safety. There is, however, a dearth of research on this subject for the case of urban arterials in general, and in the context of developing nations. In downtown Shanghai, the traffic conditions in each direction are very different by time of day, and speed characteristics during peak hours are also greatly different from those during off-peak hours. Considering that traffic demand changes with time and in different directions, arterials in this study were divided into one-way segments by the direction of flow, and time of day was differentiated and controlled for. In terms of data collection, traditional fixed-based methods have been widely used in previous studies, but they fail to capture the spatio-temporal distributions of speed along a road. A new approach is introduced to estimate speed variation by integrating spatio-temporal speed fluctuation of a single vehicle with speed differences between vehicles using taxi-based high frequency GPS data. With this approach, this paper aims to comprehensively establish a relationship between mean speed, speed variation and traffic crashes for the purpose of formulating effective speed management measures, specifically using an urban dataset. From a total of 234 one-way road segments from eight arterials in Shanghai, mean speed, speed variation, geometric design features, traffic volume, and crash data were collected. Because the safety effects of mean speed and speed variation may vary at different segment lengths, arterials with similar signal spacing density were grouped together. To account for potential correlations among these segments, a hierarchical Poisson log-normal model with random effects was developed. Results show that a 1% increase in mean speed on urban arterials was associated with a 0.7% increase in total crashes, and larger speed variation was also associated with increased crash frequency

Developing multivariate time series models to examine the interrelations between police enforcement, traffic violations, and traffic crashes

© 2020 Elsevier Ltd Safer roads and police enforcement are closely associated since the latter directly encourages road users to improve their behavior by complying with basic traffic rules and laws. Understanding the relationships between police enforcement, driving behavior, and traffic safety is a prerequisite for optimizing enforcement strategies. However, there is a dearth of research on the contemporaneous relationships between these three parameters. Using multivariate time series techniques, this study provides an in-depth analysis of contemporaneous relationships and dynamic interactions among police enforcement, traffic violations, and traffic crashes. The amount of police patrol time per day was used as a surrogate measure for police enforcement intensity. A vector autoregressive (VAR) model was first used to examine the influences of exogenous factors including weather conditions and holidays. Based on the findings of the VAR model, a structural vector autoregressive (SVAR) model was developed to determine contemporaneous effects; the Granger causality test was employed to detect any dynamic interactions between the three parameters. The results indicated that traffic crashes and violations had weekly variation and were significantly impacted by holiday and weather conditions, while police patrol time was not impacted. A contemporaneous negative impact of police patrol time was found in traffic crashes: each 1% increase in police patrol time was associated with a 0.15% decrease in contemporaneous crash frequency. The findings from the Granger causality test demonstrated that police patrol time did not Granger-cause traffic crashes, but crashes Granger-caused police patrol time. The significant bidirectional interactions in conditional variances of police enforcement, traffic violations, and traffic crashes further confirm the necessity to analyze the three simultaneously. The findings of this study are expected to assist the relevant traffic authorities in devising policies and strategies such as optimal police patrol scheduling

Examining lane change gap acceptance, duration and impact using naturalistic driving data

Analysis of lane change is important for microsimulation and safety improvement, and can also provide reference for advanced driver assistance systems (ADAS) and connected and autonomous vehicles (CAVs). Yet little research has comprehensively explored lane changing, particularly in China, a site of current CAV testing. This study developed an automatic extraction algorithm to retrieve 5,339 lane change events from the Shanghai Naturalistic Driving Study, and used the data to examine the core lane change components: gap acceptance, duration, and impact on the following vehicle (FV). Multilevel mixed-effects linear models were employed to develop relationships between gap acceptance and duration and the influencing factors; impact was then assessed using speed change rate, brake timestamping, and time-to-collision (TTC). Key results showed that 1) gap acceptance varied by roadway type and motivation, and lead and lag gaps were significantly affected by environmental variables, vehicle type, and kinematic parameters; 2) duration varied from 0.7 s to 16.1 s, significantly affected by variables similar to gap acceptance, but notably, not by motivation; 3) as many as 1 in 5 Chinese FV drivers responded to lane changes with acceleration exceeding 10%; 4) nearly half of FVs braked when they perceived a vehicle’s lane-change intention, and 90% braked before TTC reached 4.7 s; 5) in over 70% of lane changes, the minimum TTC occurred between the initiation and cross-lane points. In addition to advancing the international development of lane-change theory, one of this study’s important applications is that CAVs can be designed to brake during a safer TTC phase

Biodegradable Elastomers with Antioxidant and Retinoid-like Properties

Intimal hyperplasia (IH) is a type of scarring that involves complex pathophysiological responses of the vasculature to injury, including overproliferation and migration of vascular smooth muscle cells (VSMCs), adventitial fibroblasts, and the activation of macrophages. The objective of this research was to develop a biodegradable polymer with intrinsic properties that would combat the cellular processes that contribute to IH. Citric acid, 1,8-octanediol, and all-trans retinoic acid (atRA) were incorporated into a polyester network via a condensation reaction to form the thermoset poly­(1,8-octamethylene-citrate-<i>co</i>-retinate) (POCR). POCR was chemically characterized and assessed for the presence of antioxidant and retinoid-like properties. ^HNMR and ATR-FTIR confirmed the incorporation of atRA into the backbone of the polymer network. POCR was able to scavenge radicals and inhibit lipid peroxidation. The proliferation and migration of vascular smooth muscle cells cultured on POCR were inhibited, whereas endothelial cell proliferation and migration were not. These results are consistent with the biological effects of atRA. These results are the first to demonstrate the synthesis of a polymer with intrinsic antirestenotic properties for potential use in the fabrication of vascular devices such as stents and vascular grafts

Pedestrian safety in an automated driving environment: calibrating and evaluating the responsibility-sensitive safety model

The severity of vehicle–pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle–pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB's Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle–pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle–pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians.</p

Pedestrian crash causation analysis and active safety system calibration

Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers’ own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle–pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.</p

Influence of familiarity with traffic regulations on delivery riders’ e-bike crashes and helmet use: Two mediator ordered logit models

Micro-mobility vehicles such as electric bicycles, or e-bikes, are becoming one of the essential transportation modes in metropolitan areas, and most deliveries in large cities are dependent on them. Due to the e-bike's popularity and vulnerability, e-bike crash occurrence has become a major traffic safety problem in many cities across the world; finding the most important human factors affecting e-bike safety has thus been an important recent issue in traffic safety analysis. Since delivery riders are a key group of e-bike users, and since helmet use plays a crucial role in reducing the severity of a crash, this study conducted a city-wide online survey to analyze the helmet usage of 6,941 delivery riders in Shanghai, China. To determine the in-depth mechanisms influencing helmet use and e-bike crash occurrence, including the direct and indirect effects of the relevant factors, two mediator ordered logistic regression models were employed. The mediator ordered logistic model was compared with the traditional logistic regression model, and was found to be superior for modeling indirect as well as direct influencing factors. Results indicate that riders’ familiarity with traffic regulations (FTR) is an extremely important variable mediating between the independent variables of riders’ educational level and age, and the dependent variables of helmet use and e-bike crashes. Improving riders’ FTR can consequently increase helmet use and decrease crash occurrence. Authorities can apply these findings to develop appropriate countermeasures, particularly in legislation and rider training, to improve e-bike safety