Safety Analysis of Motorcycle Crashes in Seoul Metropolitan Area, South Korea: An Application of Nonlinear Optimal Scaling Methods

Reducing traffic violations in the online food delivery industry-A case study in Xi'an City, China

Online food delivery (OFD) is one of the top industries in the Online-to-offline (O2O) commerce sector. Deliverymen need to complete a large number of delivery orders in limited default time every day, which cause high working stress to them. Therefore, a high level of traffic violations and crashes by deliverymen and corresponding negative impact on public safety are observed. To reduce traffic violations by deliverymen and resulting crashes, a hierarchical online food delivery framework is proposed, which is based on data from questionnaire surveys conducted in Xi'an City, China. The study includes the analysis of the root cause correlated with traffic violations during online food delivery as part of an empirical study on the priority delivery fee by applying a conditional price sensitivity measurement (PSM) model. The feasibility and rationality of the framework are further investigated by using cross analysis of urban dwellers' occupation, income, and commuting cost. The results identify that, through rationally shunting the demand of online food delivery, prolonging the default delivery duration, and providing diversified delivery services, the proposed hierarchical online food delivery mechanism is able to relieve the stress of deliverymen during peak hours of food requests. This reduces the willingness of deliverymen to engage in traffic violations, and other risky behaviors during food delivery trips. All of which facilitate high-quality and timely online food delivery service while enabling improved safety of deliverymen and others as part of enhanced public safety and health.

Consumers attitudes toward animal welfare friendly produce: An island of Ireland study

Public concern has arisen between increased productivity in animal production systems and farm animal welfare (FAW). Consumers demand ethical production systems and tend to purchase products that reflect their animal welfare concerns. They are becoming increasingly judicious, with growing expectations around animal produce. Nonetheless, positive attitudes concerning FAW do not always correspond to purchasing behavior, where higher concern is not related to higher purchasing frequency. The study seeks to determine if individuals behave differently in their dual roles as citizens and consumers, expressing preferences for FAW but ultimately failing to act on such preferences when making purchasing decisions. Theoretically, the research uses elements of the COM-B model to investigate the importance of capability, opportunity, and motivation for consumers. The study is conducted on an island of Ireland basis and investigates the perceptions and purchasing frequency of Irish and Northern Irish consumer’s (n = 972). Firstly, attitudes towards purchasing animal welfare products are elicited using Categorical Principal Component Analysis (CATPCA). Cluster analysis subsequently defines three distinct typologies based upon the final cluster centers attained from CATPCA. The largest cluster is characterized as being Indifferent (n = 672) and differentiated by relatively low concern for animal welfare. Despite this, their purchasing frequency of animal welfare products is monthly. The other two clusters, The Engaged and The Struggling, are similar in size and share the same concern for animal welfare but are polarized in their motivation, capability, and opportunity to purchase animal welfare friendly products. The Struggling typology act as citizens rather than consumers; while they are highly concerned for welfare they have significantly lower purchasing frequency even compared to The Indifferent. Finally, Non-Linear Canonical Correlation Analysis (NLCCA) is used to reveal and visually demonstrate the structure of the whole dataset. The analysis demonstrates that concern for animal welfare is not associated with purchase frequency of animal welfare friendly produce when contextual factors are not conducive to doing so. The findings are relevant to a broad audience including industry, policymakers, and academics.

Characteristics of crashes and injuries on delivery motorcycles: A retrospective cohort study

Objectives: The food delivery market is growing rapidly. As most delivery riders use motorcycles, motorcycle crashes will increase along with the growing delivery market size. This study aimed at examining the proportions of motorcycle crashes and characteristics of injuries incurred while using motorcycles for occupational purposes.Methods: This retrospective analysis included motorcycle crash patients aged 16 years or older, who were treated in 23 emergency rooms in Korea, between 2014 and 2018. Patients were divided into two groups: delivery riders (delivery group) and others (nondelivery group). Crash and injury characteristics were compared between the two groups. In addition, trends of patients in the delivery group were compared from 2014 to 2018.Results: This study examined 26,982 motorcycle crash patients, including 3894 (14.43%) patients in the delivery group and 23,088 (85.57%) in the nondelivery group. The number of patients in the delivery group increased drastically from 583 in 2014 to 1029 in 2018, whereas the number of patients in the nondelivery group did not considerably increase (4411 in 2014 and 4462 in 2018). The delivery group had a higher proportion of crashes caused by collisions with cars or other motorcycles (p < 0.001); however, injury severity was lower. The delivery group had a lower proportion of head and face injuries but a higher proportion of extremity injuries. Furthermore, 39.9% of all crashes in this group occurred between 17:00 and 21:00. Over time, there were neither any changes in the injury severities, nor any changes in the characteristics of the delivery group, with the exception of increases in both the proportion of motorist insurance and the proportion of wearing a helmet.Conclusions: The results indicated differences in characteristics between delivery motorcycle crashes and other motorcycle crashes. Although delivery motorcycle crash severity was low compared to other motorcycle crashes, the number of patients increased significantly. Therefore, to prevent crashes, it is necessary to improve the working environment. In addition, to prevent the extremity injuries of delivery riders, the policy of wearing extremity protective gears should be considered.

Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fatalities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis' framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotemporal association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2-26, 27-57, 58-105, 106-192, and 193-364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2-26 m) class concentrated the most intense level of spatiotemporal association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions.

Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors

INTRODUCTION

Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data.

RESULTS

The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.