The red-light running behavior of electric bike riders and cyclists at urban intersections in China: An observational study

Electric bikes and regular bicycles play an important role in the urban transportation system of China. Red-light running is a type of highly dangerous behavior of two-wheeled riders. The main purpose of this study was to investigate the rate, associated factors, and behavior characteristics of two-wheelers’ red-light running in China. A field observational study was conducted using two synchronized video cameras at three signalized intersections in Beijing. A total of 451 two-wheelers facing a red light (222 e-bike riders and 229 cyclists) were observed and analyzed. The results showed that 56% of the two-wheelers crossed the intersection against a red light. Age was found to be a significant variable for predicting red-light runners, with the young and middle-aged riders being more likely than the old ones to run against a red light. The logistic regression analysis also indicated that the probability of a rider running a red light was higher when she or he was alone, when there were fewer riders waiting, and when there were riders already crossing on red. Further analysis of crossing behavior revealed that the majority of red-light running occurred in the early and late stages of a red-light cycle. Two-wheelers’ crossing behavior was categorized into three distinct types: law-obeying (44%), risk-taking (31%) and opportunistic (25%). Males were more likely to act in a risk-taking manner than females, and so were the young and middle-aged riders than the old ones. These findings provide valuable insights in understanding two-wheelers’ red-light running behaviors, and their implications in improving road safety were discussed.     

An accelerated failure time model for investigating pedestrian crossing behavior and waiting times at signalized intersections

The waiting process is crucial to pedestrians in the street-crossing behavior. Once pedestrians terminate their waiting behavior during the red light period, they would cross against the red light and put themselves in danger. A joint hazard-based duration model is developed to investigate the effect of various covariates on pedestrian crossing behavior and to estimate pedestrian waiting times at signalized intersections. A total of 1181 pedestrians approaching the intersections during red light periods were observed in Beijing, China. Pedestrian crossing behaviors are classified into immediate crossing behavior and waiting behavior. The probability and effect of various covariates for pedestrians’ immediate crossing behavior are identified by a logit model. Four accelerated failure time duration models based on the exponential, Weibull, lognormal and log-logistic distributions are proposed to examine the significant risk factors affecting duration times for pedestrians’ waiting behavior. A joint duration model is developed to estimate pedestrian waiting times. Moreover, unobserved heterogeneity is considered in the proposed model. The results indicate that the Weibull AFT model with shared frailty is appropriate for modelling pedestrian waiting durations. Failure to account for heterogeneity would significantly underestimate the effects of covariates on waiting duration times. The proposed model provides a better understanding of pedestrian crossing behavior and more accurate estimation of pedestrian waiting times. It may be applicable in traffic system analysis in developing countries with high flow of mixed traffic.     

The effects of sunshields on red light running behavior of cyclists and electric bike riders

Bicycles held an important position in transportation of China and other developing countries. As accidents rate involving electronic and regular bicycles is increasing, the severity of the bicycle safety problem should be paid more attention to. The current research explored the effect of sunshields (a kind of affordable traffic facility built on stop line of non-motor vehicle lanes (According to National Standard in China, e-bikes share the non-motor vehicle lane with regular bikes.) which was undertaken to avoid riders suffering from sunlight and high temperature) on diminishing red light running behavior of cyclists and e-bike riders. An observational study of 2477 riders was conducted to record and analyze their crossing behaviors at two sites across the city of Hangzhou, China. Results from logistic regression and analysis of variance indicated a significant effect of sunshield on reducing red light infringement rate both on sunny and cloudy days, while this effect of sunshield was larger on sunny days than on cloudy days based on further analysis. The effect of intersection type in logistic regression showed that riders were 1.376 times more likely to run through a red light upon approaching the intersection without sunshields compared to with sunshields in general. The results of MANCOVA further confirmed that rates of running behaviors against red lights were significantly lower at the intersections with a sunshield than at intersections without sunshields when other factors including traffic flow were statistically controlled. To sum up, it is concluded that sunshields installed at intersections can reduce the likelihood of red light infringement of cyclists and e-bike riders on both sunny and cloudy days. For those areas or countries with a torrid climate, sunshield might be a recommended facility which offers an affordable way to improve the safety of cyclists and e-bike riders at intersections. Limitations of the current sunshield design and current study are also discussed.     

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).     

Mobile phone use among motorcyclists and electric bike riders: A case study of Hanoi,Vietnam

Motorcyclist injuries and fatalities are a major concern of many developing countries. In Vietnam, motorcycles are involved in more than 70% of all road traffic crashes. This paper aims to explore the prevalence and factors associated with mobile phone use among motorcyclists and electric bike riders, using a case study of Hanoi, Vietnam. A cross-sectional observation survey was undertaken at 12 sites, in which each site was surveyed during a two-hour peak period from 16:30 to 18:30 for two weekdays and one weekend day. A total of 26,360 riders were observed, consisting of 24,759 motorcyclists and 1601 electric bike riders. The overall prevalence of mobile phone use while riding was 8.4% (95% CI: 8.06–8.74%) with calling having higher prevalence than screen operation: 4.64% (95% CI: 4.39–4.90%) vs. 3.76% (95% CI: 3.52–3.99%) respectively. Moreover, the prevalence of mobile phone use was higher among motorcyclists than electric bike riders: 8.66% (95%CI: 8.30–9.01%) vs. 4.43% (95% CI: 3.40–5.47%) respectively. Logistic regression analyses revealed that mobile phone use while riding was associated with vehicle type, age, gender, riding alone, weather, day of week, proximity to city centre, number of lanes, separate car lanes, red traffic light duration, and police presence. Combining greater enforcement of existing legislations with extensive education and publicity programs is recommended to reduce potential deaths and injuries related to the use of mobile phones while riding.     

Development of an accident duration prediction model on the Korean Freeway Systems

Since duration prediction is one of the most important steps in an accident management process, there have been several approaches developed for modeling accident duration. This paper presents a model for the purpose of accident duration prediction based on accurately recorded and large accident dataset from the Korean Freeway Systems. To develop the duration prediction model, this study utilizes the log-logistic accelerated failure time (AFT) metric model and a 2-year accident duration dataset from 2006 to 2007. Specifically, the 2006 dataset is utilized to develop the prediction model and then, the 2007 dataset was employed to test the temporal transferability of the 2006 model. Although the duration prediction model has limitations such as large prediction error due to the individual differences of the accident treatment teams in terms of clearing similar accidents, the results from the 2006 model yielded a reasonable prediction based on the mean absolute percentage error (MAPE) scale. Additionally, the results of the statistical test for temporal transferability indicated that the estimated parameters in the duration prediction model are stable over time. Thus, this temporal stability suggests that the model may have potential to be used as a basis for making rational diversion and dispatching decisions in the event of an accident. Ultimately, such information will beneficially help in mitigating traffic congestion due to accidents.     

Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections

Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations.     

A parametric duration model of the reaction times of drivers distracted by mobile phone conversations

The use of mobile phones while driving is more prevalent among young drivers—a less experienced cohort with elevated crash risk. The objective of this study was to examine and better understand the reaction times of young drivers to a traffic event originating in their peripheral vision whilst engaged in a mobile phone conversation. The CARRS-Q advanced driving simulator was used to test a sample of young drivers on various simulated driving tasks, including an event that originated within the driver's peripheral vision, whereby a pedestrian enters a zebra crossing from a sidewalk. Thirty-two licensed drivers drove the simulator in three phone conditions: baseline (no phone conversation), hands-free and handheld. In addition to driving the simulator each participant completed questionnaires related to driver demographics, driving history, usage of mobile phones while driving, and general mobile phone usage history. The participants were 21–26 years old and split evenly by gender. Drivers’ reaction times to a pedestrian in the zebra crossing were modelled using a parametric accelerated failure time (AFT) duration model with a Weibull distribution. Also tested where two different model specifications to account for the structured heterogeneity arising from the repeated measures experimental design. The Weibull AFT model with gamma heterogeneity was found to be the best fitting model and identified four significant variables influencing the reaction times, including phone condition, driver's age, license type (provisional license holder or not), and self-reported frequency of usage of handheld phones while driving. The reaction times of drivers were more than 40% longer in the distracted condition compared to baseline (not distracted). Moreover, the impairment of reaction times due to mobile phone conversations was almost double for provisional compared to open license holders. A reduction in the ability to detect traffic events in the periphery whilst distracted presents a significant and measurable safety concern that will undoubtedly persist unless mitigated.     

淹没式MBR去除有机物动力学模型及其应用

针对淹没式MBR的工艺特点,以反应器内物料衡算为基础,建立了有机物去除的动力学模型——ROM模型,得出淹没式MBR对有机底物的去除速率Uy不仅与进、出水中的有机底物质量浓度S0、Sc和水力停留对间tH有关,还与膜组件去除的有机底物质量浓度Sm、污泥停留时间ts有关．利用ROM模型,可以预测反应器内活性污泥浓度X,确定污泥停留时间ts和不排泥条件下反应器内的最大活性污泥浓度Xmax,试验中以啤酒厂废水为例,对模型进行了验证,试验结果与模型计算结果基本一致．     

A compartment based population balance model for the prediction of steady and induction granule growth behavior in high shear wet granulation

This paper presents a predictive modeling approach of the high shear wet granulation process, quantifying the difference between the steady and induction granule growth behavior. The spatial heterogeneity in liquid binder distribution and shear rate is simulated using a compartmental population balance model. The granulator is divided into two compartments based on particle motion, which consists of a circulation compartment, and an impeller compartment. In the circulation compartment, a viscous dissipation dependent coalescence kernel is adapted for the aggregation process. In the impeller compartment a shear rate dependent aggregation kernel is implemented. The model was calibrated and validated using the dynamic evolution of granule mean size (d₅₀). The granulation dynamics are studied with respect to change in impeller speed, liquid to solid ratio, wet massing time, initial porosity, and binder viscosity. The transition from induction growth to steady growth regime with changing process conditions is demonstrated using the model. It is observed that the model captures the effect of process parameters and spatial heterogeneity on the dynamic evolution of d₅₀.