Investigating safety and liability of autonomous vehicles:Bayesian random parameter ordered probit model analysis

Purpose–This study aims to investigate the safety and liability of autonomous vehicles(AVs),and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.Design/methodology/approach–The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations.The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs,respectively,as well as accommodating the heterogeneity issue simultaneously.Findings–The findings show that day,location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.Originality/value–The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

Vegetable production under COVID-19 pandemic in China: An analysis based on the data of 526 households

The COVID-19 pandemic had an enormous impact on the vegetable supply chain in China.Effective evaluation of the pandemic’s influences on vegetable production is vital for policy settings to enhance the security of vegetable supply.Based on first-hand data from 526 households,we explored regional differences in different types of loss and potential factors affecting the severity farmer households suffered during the pandemic.The results underline that sales contraction and price volatility in the context of interruption of supply chain dominate the total losses during the pandemic.Such losses differ across provinces and are more substantial in provinces with stricter confinement measures.Farmer households’participation in local market and modern marketing methods helps mitigate the negative effects of the COVID-19 shock,while labor hiring and facilities adoption in production widen the losses due to the shortage in the workforce.In the future,the vegetable industry practitioners and relevant government departments should work together to coordinate the development of short and long supply chains and strengthen the stability and security of the vegetable supply chain.

Intersection Related Crash Injuries: A Study on Factors Contributing to Injury Severity among Younger and Older Drivers in Summer and Winter

Older drivers and younger drivers are affected differently both in summer and winter. Different factors affect each level of severity differently;some factors affect a particular level of injury severity differently from when the same factor is analyzed for another injury severity. The goal of this study is to identify the factors that contribute to injury severity among older drivers (65+) and young drivers (16 - 25) considering two seasons namely, summer and winter at intersections. Binary ordered probit models were used to develop four models to identify the contributing factors, two models for each season, namely winter and summer. A statistical t-test has been done to identify the statistically significant variables @ 90% confidence interval. Based on the developed models, in summer, three contributing factors, driving too fast condition, rear-end crashes, and followed too close are associated with younger drivers injury severity, while two contributing factors, rear-end crashes and followed too close are associated with older drivers injury severity. In winter, five factors, made an improper turn, E Failed to Yield Right-of-Way from Traffic Signal, rear end (front to rear), gender like male and lighting condition like dark and dusk light condition, are associated with younger drivers injury severity, while three factors such as made improper turn, rear-end crashes, and followed too close are associated with older drivers injury severity. Contributing factors in summer are the same for both younger and older drivers, but different in winter for both younger and older drivers. This indicates that older drivers and younger drivers are affected differently both in summer and winter.

Analysis and O-D Demand Estimation of a Public Bike-Sharing Program in Las Vegas

Bike-share systems are an effective way of mitigating congestion on the road. In addition, bike-share systems have been built in universities to serve for trips to work/commuting as well as the trips on campus. In Las Vegas, a bike-share system was proposed at the University of Nevada, Las Vegas. This study analyzed factors that influence the usage of bike-share program and estimated the origin-destination demand. To achieve these objectives, first, a literature review was conducted on university bike-sharing systems in the U.S. and abroad. Then, a survey with a questionnaire was distributed to UNLV to obtain the users’ preferences to the locations of the proposed bike-share stations and their likelihood and frequency to use the bike-share program. In total, 241 faculty, staff, and students responded to the survey. About 50% of those participating in the survey expressed willingness to use the bike-share system for commuting and 60% said they are willing to use bike share for on-campus travel. Commuting and on-campus travel are two different types of travel, and the factors to determine whether an individual would use the bike-share system are quite different for each. It was estimated that there would be 3450 members for a bike-share program at UNLV, each making bicycle trips with varying frequencies, producing 1966 trips per day.

A copula-based approach to accommodate intra-household interaction in workers' daily maintenance activity stop generation modeling

A new stop frequency model was developed to predict the daily number of maintenance activity stops made by individual household heads during a typical weekday. This new model was based on the modification of a conventional multivariate ordered probit(MOP) model by maintaining the probit assumption for the marginal distributions while introducing nonnormal dependence among the error terms using copula functions. Therefore, the copulabased MOP model would relieve the restriction of imposing joint normality on the error terms in the conventional MOP model. The new MOP model would not only account for the intrahousehold interactions in stop-making decisions, but also allow the best functional form to be determined for representing dependencies among household heads. Using the New York Metropolitan Transportation Council’s 2010/2011 regional household travel survey data, the copula-based MOP model was employed to examine stop-making behavior for individual household heads residing in New York City and its adjacent counties in Mid-Hudson Valley and New Jersey. Empirical results provided useful insights into the observed effects of sociodemographics, land use density, transportation service, and work schedule together with potential unobserved common effects on the inter-relatedness of spousal stop-making decisions at the household level. The results show that the MOP model with a Clayton copula structure provides the best data fits and there is a very strong positive dependence among error terms of stop-making equations. Furthermore, the dependence among the maintenance activity propensities of household heads is asymmetric, with a stronger tendency of household heads to simultaneously have low maintenance activity levels than to simultaneously have high maintenance activity levels.

Exploring the factors affecting electric bicycle riders'working conditions and crash involvement in Ningbo,China

The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The primary objective of this study is to explore the factors affecting delivery e-bike riders’stressful work pressure and crash involvement in China.Data were collected by a questionnaire survey administered in the city of Ningbo,China.A bivariate ordered probit(BOP)model was developed to simultaneously examine the factors associated with both the working conditions of delivery e-bike riders and their involvement in crashes.The marginal effects for the contributory factors were calculated to quantify their impacts on the outcomes.The results showed that the BOP model can account for commonly unobserved characteristics of the working conditions and crash involvement of delivery e-bike riders.The BOP model results showed that the stressful working conditions of delivery e-bike riders were affected by the number of orders and delivery time and rider age and risky riding behaviors.Delivery rider involvement in crashes was affected by the number of orders,strength of the punishment for traffic violations,and familiarity with traffic regulations.It was also found that stressful working conditions and crash involvement were strongly and positively correlated.The findings of this study can enhance our understanding of the factors that affect the working conditions and delivery rider crash involvement.Based on the results,some suggestions regarding public policy,risky riding behaviors,safety promotion,and stronger corporate governance rules were discussed to increase the targeted safety-related interventions for delivery ebike riders in Ningbo,China.

Investigating crash injury severity at unsignalized intersections in Heilongjiang Province,China

Many studies suggest that more crashes occur due to mixed traffic flow at unsignalized intersections. However, very little is known about the injury severity of these crashes. The objective of this study is therefore to investigate how contributory factors affect crash injury severity at unsignalized intersections. The dataset used for this analysis derived from police crash reports from Dec. 2006 to Apr. 2009 in Heilongjiang Province, China. An ordered probit model was developed to predict the probability that the injury severity of a crash will be one of four levels ： no injury, slight injury, severe injury, and fatal injury. The injury severity of a crash was evaluated in terms of the most severe injury sustained by any person involved in the crash. Results from the present study showed that different factors had varying effects on crash injury severity. Factors found to result in the increased probability of serious injuries include adverse weather, sideswiping with pedestrians on poor surface, the interaction of rear-ends and the third-class highway, winter night without illumination, and the interaction between traffic signs or markings and the third-class highway. Although there are some limitations in the current study, this study provides more insights into crash injury severity at unsignalized intersections.

Injury severity analysis of electric bike crashes in Changsha, Hunan Province: taking different lighting conditions into consideration

With the increasing use of electric bikes, electric bike crashes occur frequently. Analysing the influencing factors of electric bikecrashes is an effective way to reduce mortality and improve road safety. In this paper, spatial analysis is performed by geographicinformation system (GIS) to present the hot spots of electric bike crashes during daylight and darkness in Changsha, Hunan Province,China. Based on the Ordered Probit (OP) model, we studied the risk factors that led to different severities of electric bike crashes.The results show that the main influencing variables include age, illegal behaviour, collision type and road factors. During daylightand darkness, elderly electric bike riders over the age of 65 years have a higher probability of fatal crashes. Not following trafficsignals and reverse driving are significantly related to the severity of riders’ injuries. In darkness, frontal collisions are significantfactors causing rider injury. In daylight, more serious crashes will be caused in bend and slope road segments. In darkness, roadswith no physically separated bicycle lanes increases the risk of riders being injured. Electric bike crashes are mainly concentratedin the commercial, public service and residential areas in the main urban area. In suburbs at darkness, electric bike riders are morelikely to be seriously injured. Adding protectionmeasures, such as improved lighting, non-motorized lane facilities and interventionstargeting illegal behaviour in the hot spot areas can effectively reduce the number of electric bike crashes in complex traffic.

The effect of ride experience on changing opinions toward autonomous vehicle safety

Autonomous vehicles(AVs)are a promising emerging technology that is likely to be widely deployed in the near future.People's perception on AV safety is critical to the pace and success of deploying the AV technology.Existing studies found that people's perceptions on emerging technologies might change as additional information was provided.To investigate this phenomenon in the AV technology context,this paper conducted real-world AV experiments and collected factors that may associate with people's initial opinions without any AV riding experience and opinion change after a successful AV ride.A number of ordered probit and binary probit models considering data heterogeneity were employed to estimate the impact of these factors on people's initial opinions and opinion change.The study found that people's initial opinions toward AV safety are significantly associated with people's age,personal income,monthly fuel cost,education experience,and previous AV experience.Further,the factors dominating people's opinion change after a successful AV ride include people's age,personal income,monthly fuel cost,daily commute time,driving alone indicator,willingness to pay for AV technology,and previous AV experience.These results provide important references for future implementations of the AV technology.Additionally,based on the inconsistent effects for variables across different models,suggestions for future transportation survey designs are provided.

Factors Affecting Vulnerability of Ready-Made Garment Factory Buildings in Bangladesh: An Assessment Under Vertical and Earthquake Loads

The importance of workplace safety in the ready-made garment(RMG) industry in Bangladesh came to the forefront after a series of disastrous events in recent years. In order to reduce the loss of lives and to ensure sustainable development, an in-depth understanding of the determining factors governing structural vulnerability in the RMG industry is needed. This research explores the key factors influencing the vulnerability of factory buildings under both vertical and earthquake loads. For this purpose,an ordered probit model was applied to 3746 RMG factory buildings to determine the key factors that influenced their vertical load vulnerability. A smaller subset of the original sample, 478 buildings, was examined by the same modeling method in greater detail to assess the key factors that influenced their earthquake load vulnerability. This research reveals that column capacity, structural system,and construction materials are the most influential factors for both types of vulnerabilities. Among other factors, soil liquefaction and irregular internal frame affect earthquake load vulnerability significantly. These findings are expected to enable factory owners and designers to better weigh the appropriate vulnerability factors in order to make informed decision that increase workplace safety. Theresearch findings will also help the designated authorities to conduct successful inspections of factory buildings and take actions that reduce vulnerability to both vertical and earthquake loads.

Injury severities from heavy vehicle accidents:An exploratory empirical analysis

Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.