Development of crash frequency models for safety promotion of urban collector streets

The merits for development and application of crash frequency prediction models for safety promotion on any road type, with a focus on urban collector streets, are presented in this article. The city of Yazd, a medium-sized city in the middle of Iran, was selected as a case study and the data required for modelling crash frequencies along five collector streets comprising 31 street sections were collected. Six models including Poisson and negative binomial models and their deviations along with a hybrid artificial neural networks (ANN) model were developed to predict crash frequency along each street section. The overfitting problem was addressed using appropriate sensitivity analysis methods which were also used to identify the input variables with significant impact on the model performance. The results indicated that the developed hybrid ANN model provided the best performance in terms of accuracy and the number of input variables. The application of hybrid ANN model to evaluate the safety impacts of four different strategies, each resembled by one of the input variables of this model, indicated that these models can successfully be used for this purpose.     

Predictability and interpretability of hybrid link-level crash frequency models for urban arterials compared to cluster-based and general negative binomial regression models

Machine learning (ML) techniques have higher prediction accuracy compared to conventional statistical methods for crash frequency modelling. However, their black-box nature limits the interpretability. The objective of this research is to combine both ML and statistical methods to develop hybrid link-level crash frequency models with high predictability and interpretability. For this purpose, M5′ model trees method (M5′) is introduced and applied to classify the crash data and then calibrate a model for each homogenous class. The data for 1134 and 345 randomly selected links on urban arterials in the city of Charlotte, North Carolina was used to develop and validate models, respectively. The outputs from the hybrid approach are compared with the outputs from cluster-based negative binomial regression (NBR) and general NBR models. Findings indicate that M5' has high predictability and is very reliable to interpret the role of different attributes on crash frequency compared to other developed models.     

Factors contributing to motorcycle fatal crashes on National Highways in India

This study analyses fatal crash patterns, and identifies the risk factors contributing to motorcycle versus non-motorcycle fatal crashes using binomial logistic regression on two-, four- and six-lane National Highways (NHs) in India utilizing police fatal crash data. The distribution of victims’ mode by striking vehicles shows that percentage share of striking vehicles (truck) against the victims’ vehicles (motorcycle) is 44%, 52% and 37% on two-lane NH-8, four-lane NH-24 and six-lane NH-1, respectively. Nine explanatory variables pertaining to fatal crash, victim, roadway and environment are considered for the model (using combined data of cited three NHs). The results of the logistic regression model (motorcycle versus non-motorcycle fatal crashes) show that for variable ‘collision type’, likelihood of occurrence of ‘rear-end’, ‘sideswipe’ and ‘head-on’ fatal crashes are 42-times, 35-times and 25-times more than ‘hit pedestrian’ respectively. Similarly, for variable ‘number of vehicle’, likelihood is thrice as ‘single-vehicle’ than ‘two or more vehicles’; and, for variable ‘number of lane’, probability is more on ‘two-lane’ NH-8 than ‘four-lane’ NH-24. Based on the study results, it is recommended to upgrade two-lane (undivided carriageway) to four-lane (divided carriageway) NHs to reduce ‘head-on’ collision.     

Effect of driver,roadway, collision,and vehicle characteristics on crash severity: a conditional logistic regression approach

The aim of the study is to examine the factors that appear to have a higher potential for serious injury or death of drivers in traffic accidents in Turkey, such as collision type, roadway surface, vehicle speed, alcohol/drug use, and restraint use. Driver crash severity is the dependent variable of this study with two categories, fatal and non-fatal. Due to the binary nature of the dependent variable, a conditional logistic regression analysis was found suitable. Of the 16 independent variables obtained from Turkish police accident reports, 11 variables were found most significantly associated with driver crash severity. They are age, education level, restraint use, roadway condition, roadway type, time of day, collision location, collision type, number and direction of vehicles, vehicle speed, and alcohol/drug use. This study found that belted drivers aged 18–25 years involving two vehicles travelling in the same direction, in an urban area, during the daytime, and on an avenue or a street have better chances of survival in traffic accidents.     

Estimating national road crash fatalities using aggregate data

Injuries and fatalities from road traffic crashes have emerged a major public health challenge in Pakistan. Reliable estimates of road crash fatalities (RCF) of a country, is a vital element needed for identification and control of key risk factors, road-safety improvement efforts and prioritizing national health. Reliability of current annual RCF estimates for Pakistan becomes highly questionable due to serious underreporting. This study aimed to predict annual RCF for Pakistan using data from World Health Organization and International Road Federation sources. An ordinary least square (OLS) regression model that relates fatality rate with different explanatory variables was developed. RCF were predicted for Pakistan for year 2012 and 2013, and results were compared with national police reported estimates. Study results indicated that there is serious underreporting of RCF in Pakistan and immediate measures are needed to improve the existing road crash recording and reporting system at the national and subnational levels.     

Consumers’ perspectives on online banking services

The forces of economic change, coupled with advancements in technology, prompt banks to rethink their use of traditional branches and begin forming new partnerships to deliver financial services. The Internet seems to be the new delivery channel in the banking sector. Factors such as the security of personal data or the reliability of a financial institution have been identified by previous studies as the determinants of electronic‐banking adoption. In this paper, a series of new factors, such as the difficulties of using the Internet, are shown to play a crucial role in the consumer’s attitude – adoption or rejection – of this new alternative channel. We examine consumer behaviour by modelling multivariate categorical response data using a generalized linear model. Our choice model is based on the assumption that an individual’s decision to use electronic services depends on a number of explanatory variables, and we try to estimate the factors that affect an individual’s decision to use online services.     

A Bayesian multivariate Poisson regression model of cross-category store brand purchasing behavior

The availability of cross-category transaction data in the retailing industry has enabled the investigation of interdependence in consumer purchase behavior across product categories. In this paper, we develop a multivariate count model to uncover and predict the pattern of cross-category store brand purchasing behavior. The proposed multivariate Poisson regression model, which we estimate using a Bayesian approach, provides flexibility in capturing cross-category correlations for sparse multivariate purchase data associated with infrequently purchased categories or purchasing in retail outlets such as warehouse clubs. We compare the goodness-of-fit of the proposed Poisson regression model with alternate benchmark models using customer purchase records across five product categories from a national warehouse club and find that the proposed model provides a superior fit. We also carry out a profitability analysis to illustrate the use of the model in planning cross-promotions.     

众创空间集聚的连续距离测度及影响因素分析

基于253家浙江省众创空间的微观数据，文章在运用连续距离的测度方法测算众创空间集聚水平的基础上，采用零膨胀负二项回归等方法实证分析众创空间集聚的影响机制。研究发现，众创空间的地理分布存在显著的区域差异，主要分布在杭州和宁波市辖区，选址具有明显的集聚特征。回归结果显示，区域创新能力和生态环境均对众创空间的地理集聚具有促进作用，但两者之间存在明显的替代效应；尽管不能直接促进众创空间的集聚，政策引导与创新能力之间具有较显著的互补效应。据此，文章从培育创新能力和优化创新激励等方面提出了政策建议。     

Exploring the factors influencing pedestrian-vehicle crash severity in Dhaka,Bangladesh

Abstract

Although the rate of road crashes and their severity is relatively higher in developing countries, there is still a lack of research on pedestrian-vehicle crash severity in these contexts, particularly in Bangladesh. Therefore, this study aimed to identify the contributing environmental, road, and vehicular factors that influenced pedestrian—single-vehicle crash severity in Dhaka, a megacity and the capital of Bangladesh. A binary logistic regression model was developed in this study by analyzing a data set of pedestrian—single-vehicle crashes involving casualties in Dhaka from 2010 to 2015. The model identified seven significant factors influencing pedestrian-vehicle crash severity. Significant factors increasing the likelihood of fatal crashes included crashes during adverse weather, dawn/dusk period, night period (where street light was absent), off-peak period, crashes where road divider was unavailable, road geometry was straight and flat, and crashes those were occurred by heavier vehicles. Besides, crashes at three-legged intersections were less likely to be fatal. Both similarities and differences were found among the significant factors influencing pedestrian-vehicle crash severity in Dhaka from the findings of the developed countries. The findings of this study would help transport engineers and planners to design safer roadways for both pedestrians and vehicles.     

Impact of Exchange Rate Uncertainty on Trade Flows: Evidence from Commodity Trade between the United States and the United Kingdom

Previous studies that investigated the impact of exchange rate volatility on the trade flows employed aggregate trade data and standard estimation techniques. They provided mixed results. In this paper we use disaggregated import and export data for 177 commodities traded between the United States and the United Kingdom to investigate whether volatility of the real bilateral dollar–pound exchange rate has any detrimental effect on trade flows at the commodity level. Additionally, we employ the bounds testing approach to cointegration and error‐correction modelling that is suitable for the models used mostly because it does not require pre‐unit‐root testing and variables in the model could be stationary, non‐stationary or a combination of the two. In most trade flow models estimated, we found a negative effect of exchange rate volatility on commodity trade.     

Creating an effective self-managed service climate for frontline service employees

We propose a framework for creating an effective self-managed service climate for frontline service employees with four antecedents – work facilitation, dedication, creativity and variety. Specifically, we examine the role of employee empowerment to mediate the relationship between the proposed antecedents when creating a self-managed service climate. Structural equation modelling was used to examine the anticipated relationships, the research design was cross-sectional. 533 adults, employed in services industries, participated in the study. The antecedents were found to have significant positive direct effects and employee empowerment was found to have a significant partial mediating effect on the four antecedents for creating an effective self-managed service climate. Theoretical and practical implications and future research directions are discussed.     

Post encroachment time threshold identification for right-turn related crashes at unsignalized intersections on intercity highways under mixed traffic

Abstract

Unsignalized intersections in developing countries experience many serious conflicts between cross-traffic due to indiscipline traffic manoeuvrability. Historically, Post Encroachment Time (PET) has gained attention as a proximal indicator to analyze crossing conflicts. However, identifying an appropriate PET threshold to classify critical conflicts for highly heterogeneous traffic scenario is still an unexplored area. Consequently, this study proposes a novel approach of PET threshold identification with proof of application by collecting data from ten intersections located on four-lane intercity highways in the National Capital Region (NCR), India. Both crossing conflicts and right-turn related crash data (for the left-hand drive) are collected. Their correlations are thoroughly studied for each PET threshold using a quantitative technique considering all and individual vehicle categories. Finally, a qualitative analysis is done by ranking the sites based on cumulative PET and related crashes to verify the proposed quantitative technique. A PET threshold of 1?s is obtained from both the techniques which can be used to identify critical conflicts for unsignalized intersections located on four-lane intercity highways. The proposed methodology will serve as an alternative, faster and effective tool to evaluate the hazardousness of unsignalized intersections located on intercity highways under highly heterogeneous traffic condition.     

Cross-category sales promotion effects

We introduce a multivariate binomial logit model measuring cross-category dependence and sales promotion effects of a retail assortment. This model requires as data both the market baskets of individual shoppers and the categories currently promoted in a retail outlet. A special section describes the stepwise procedure used to estimate parameters of this model. Its application is demonstrated analyzing 6147 purchases that were acquired in a medium-sized supermarket. We finally discuss the managerial relevance of this model for sales promotion decisions of retail firms.     

Investigation of contributing factors to extremely severe traffic crashes using survival theory

This study aimed to investigate the contributing factors to serious casualty crashes in China. Crashes with deaths greater than 10 people are defined as serious casualty crashes in China. The serious casualty crash data were collected from 2009 to 2014. The random forest analysis was first conducted to select the candidate variables that affect the risks of serious casualty crashes. The Bayesian random parameters accelerated failure time (AFT) model was then developed to link the probability of the serious casualty crash with road geometric conditions, pavement conditions, environmental characteristics, collision characteristics, vehicle conditions, and driver characteristics. The AFT model estimation results indicate that overload driving, country road, northwest china region, turnover crash, private car, snowy or icy road surface and sight distance conditions have significant fixed effects on the likelihood of serious casualty crashes. In addition to these fixed-parameter variables, freeway, clear weather conditions, coach drivers, and upgrade horizontal curve affect the likelihood of serious casualty crashes with varying magnitude across observations. One of the important findings is that the serious casualty crash likelihood does not always decrease with an increase in the driving experience (number of years driven). Before the inflection point of 7 years, the serious casualty crash likelihood increases as the driving experience grows. The results of this study can help to develop effective countermeasures and policy initiatives for the prevention of serious casualty crashes.     

The effect of natural and built environmental characteristics on pedestrian-vehicle crash severity in Ghana

Despite the benefits of walking as a means of travelling, walking can be quite hazardous. Pedestrian-vehicle crashes remain a major concern in Ghana as they account for the highest percentage of fatalities. The objective of this study is to determine the effect of both natural and built environmental features on pedestrian-vehicle crash severity in Ghana. The study is based on an extensive pedestrian-vehicle crash dataset extracted from the National Road Traffic Accident Database at the Building and Road Research Institute (BRRI) of the Council for Scientific and Industrial Research (CSIR), Ghana. Using a multinomial logit modelling framework, possible determinants of pedestrian-vehicle crash severity were identified. The study found that fatal crashes are likely to occur during unclear weather conditions, on weekends, at night time where there are no lights, on curved and inclined roads, on untarred roads, at mid-blocks and on wider roads. The developed model and its interpretations will make important contributions to road crash analysis and prevention in Ghana with the possibility of extension to other developing countries. These contributing factors could inform policy makers on road design and operational improvements.     

Two‐State Option Pricing: Binomial Models Revisited

This article revisits the topic of two‐state option pricing. It examines the models developed by Cox, Ross, and Rubinstein (1979), Rendleman and Bartter (1979), and Trigeorgis (1991) and presents two alternative binomial models based on the continuous‐time and discrete‐time geometric Brownian motion processes, respectively. This work generalizes the standard binomial approach, incorporating the main existing models as particular cases. The proposed models are straightforward and flexible, accommodate any drift condition, and afford additional insights into binomial trees and lattice models in general. Furthermore, the alternative parameterizations are free of the negative aspects associated with the Cox, Ross, and Rubinstein model. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:987–1001, 2001     

The impact of affective and cognitive antecedents on negative word-of-mouth intentions

Negative word-of-mouth (WOM), defined as derogatory information disseminated from person to person and aimed at defaming a product, highlighting a product complaint, and/or highlighting unsatisfactory service experiences, influences consumer behavior and can harm a company’s image. The present study was conducted to ascertain the impact of affective antecedents (negative emotions) and cognitive antecedents (distrust) in the intent of negative WOM. A proposed theoretical model was employed to analyze the effect that perceived injustice had on negative WOM and how this effect was mediated by emotional and cognitive aspects. The research was a pre-experimental design – one treatment leading to one observation – with 252 students. Through an OLS regression and mediation analyses, we found an indirect link between perceived injustice and negative WOM intent, which was mediated by negative emotions and distrust. Contributing to the field of marketing studies, this paper fills an investigative gap regarding affective and cognitive antecedents as mediators of negative WOM. The results suggest that marketing managers should create mechanisms to allow consumers to express their negative emotions and rebuild their trust in order to avoid the spread of negative WOM.