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.     

Marketing and business intelligence with help of ant colony algorithm

Recently, there is increasing need of banks for targeting and acquiring new customers, for fraud detection in real time and for segmentation products through analysis of the customers. Doing it, they can serve their customers better, and can increase the effectiveness of the company. For this purpose, various data mining methods are used which enable extraction of interesting, nontrivial, implicit, previously unknown, and potentially useful patterns or knowledge from huge amounts of data. Traditional data mining methods include classification rule tasks, for their solution there are a number of methods. Among them can be mentioned, for example, Random forest algorithm or C4.5 algorithm. However, accuracy of these methods significantly reduces in the event that some data in databases is missing. These methods are always not optimal for very large databases. The aim of our work is to verify a possible solution of these problems by using the algorithm based on artificial ant colonies. This algorithm was successful in other areas. Therefore, we tested its applicability and accuracy in marketing and business intelligence and compared it with so far used methods. The experimental results showed that the presented algorithm is very effective, robust, and suitable for processing of very large files. It was also found that this algorithm overcomes the previously used algorithms in accuracy. Algorithm is easily implementable on different platforms and can be recommended for using in banking and business intelligence.     

Nano-economics,spinoffs, and the wealth of regions

The historical evolution of four prominent industry clusters is compared: automobiles in Detroit, Michigan, tires in Akron, Ohio, semiconductors in Silicon Valley, California, and cotton garments in Dhaka, Bangladesh. Detailed data are collected concerning the intellectual and geographic origins of entrants into the clusters and other regions to probe the mechanisms underlying geographic clustering. The main mechanism at work in the four clusters involves employees leaving established firms to found their own firms or shape new entrants in their industry. Questions and policy implications related to the spinoff mechanism and the mobility of employees are discussed.     

Underpinning the benefits of green banking: A comparative study between Islamic and conventional banks in Bangladesh

This study aims to compare environmental motives and performance of conventional and Islamic banks in Bangladesh. Green compliance index was developed based on the Bangladesh Bank (the central bank of Bangladesh) guidelines whereas information regarding governance variables is collected from the annual reports of 9 Islamic and 31 conventional banks. Results show Islamic banks are more environmentally friendly compared to their conventional counterparts. Board size is negatively related to green compliance whereas board independence and auditor's type do not have any significant influence on green compliance for both clusters of banks. Compliance with green banking policies enhances the reputation for Islamic banks and accountability and profitability for conventional banks. Results of this study provide useful information for regulatory authorities to formulate policies that are conducive to enhance bank's environmental performance.     

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.     

The role of training in reducing poverty: the case of agricultural workers receiving microcredit in Bangladesh

The policy of providing microcredit and skill training to poor agricultural workers in developing countries is well‐established. In this study, an attempt has been made to assess the effectiveness of the training part of that policy. BRAC (formerly the Bangladesh Rural Advancement Committee), the largest non‐governmental organization in Bangladesh, is making efforts to alleviate poverty by providing microcredit and relevant training to landless and marginal borrowers, who have less than 1.5 acres of land, in order to enhance their standard of living. The study aims to assess the effects of BRAC's training programs on the recipients' total household expenditure. Primary data were collected from female borrowers who participated in the microcredit programs of BRAC and these data reflect the key role women play in agricultural work and the lack of research on their status. The study shows that the training provided to the borrowers failed to create any significant impact on their household expenditure because only a small percentage of borrowers received training and the duration of the training was limited (on average three days per person). Moreover, many of those who received training experienced only a modest income effect. The findings are important not just for the practical purpose of influencing policies for reducing poverty in Bangladesh but also for their contribution to the understanding of the effectiveness of training. The existing literature is concerned mainly with the effectiveness of training in the context of business and other organizations in developed countries. Research on the effectiveness of training in developing countries, particularly in alleviating poverty, is less evident. Moreover, the present research is also exceptional as it focuses only on female trainees.     

Fatality rate of pedestrians and fatal crash involvement rate of drivers in pedestrian crashes: a case study of Iran

The aim of this study was to uncover patterns of pedestrian crashes. In the first stage, 34,178 pedestrian-involved crashes occurred in Iran during a four-year period were grouped into homogeneous clusters using a clustering analysis. Next, some in-cluster and inter-cluster crash patterns were analysed. The clustering analysis yielded six pedestrian crash groups. Car/van/pickup crashes on rural roads as well as heavy vehicle crashes were found to be less frequent but more likely to be fatal compared to other crash clusters. In addition, after controlling for crash frequency in each cluster, it was found that the fatality rate of each pedestrian age group as well as the fatal crash involvement rate of each driver age group varies across the six clusters. Results of present study has some policy implications including, promoting pedestrian safety training sessions for heavy vehicle drivers, imposing limitations over elderly heavy vehicle drivers, reinforcing penalties toward under 19 drivers and motorcyclists. In addition, road safety campaigns in rural areas may be promoted to inform people about the higher fatality rate of pedestrians on rural roads. The crash patterns uncovered in this study might also be useful for prioritizing future pedestrian safety research areas.     

All That Glitters is Not Gold: Digging Beneath the Surface of Data Mining

This article develops a more comprehensive understanding of data mining by examining the application of this technology in the marketplace. In addition to exploring the technological issues that arise from the use of these applications, we address some of the social concerns that are too often ignored.As more firms shift more of their business activities to the Web, increasingly more information about consumers and potential customers is being captured in Web server logs. Sophisticated analytic and data mining software tools enable firms to use the data contained in these logs to develop and implement a complex relationship management strategy. Although this new trend in marketing strategy is based on the old idea of relating to customers as individuals, customer relationship management actually rests on segmenting consumers into groups based on profiles developed through a firm's data mining activities. Individuals whose profiles suggest that they are likely to provide a high lifetime value to the firm are served content that will vary from that which is served to consumers with less attractive profiles.Social costs may be imposed on society when objectively rational business decisions involving data mining and consumer profiles are made. The ensuing discussion examines the ways in which data mining and the use of consumer profiles may exclude classes of consumers from full participation in the marketplace, and may limit their access to information essential to their full participation as citizens in the public sphere. We suggest more ethically sensitive alternatives to the unfettered use of data mining.     

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.     

环境管理体系认证能够抑制股价崩盘风险吗？

近年来，随着环境政策日趋严格，环境负面消息累积导致的股价崩盘风险不断提高。文章基于2007-2019年中国沪深两市A股重污染企业的经验数据，探讨了环境管理体系认证对股价崩盘风险的影响。研究发现，环境管理体系认证能够显著抑制股价崩盘风险，且当媒体关注越高、分析师关注越高时，环境管理体系认证对股价崩盘风险的抑制作用更强。进一步探讨影响机制发现，环境管理体系认证主要通过治理机制和信息机制来影响股价崩盘风险，换言之，环境管理体系认证能够通过提高公司环境绩效和公司信息透明度来抑制股价崩盘风险。研究结论揭示了环境管理体系认证具有资本市场有效性，丰富了股价崩盘风险的影响因素研究，对于政府完善环境管理体系认证制度以提高资本市场稳定性、企业优化战略决策以提升企业价值具有重要的借鉴意义。     

Changing consumption patterns in rural Bangladesh

Bangladesh is one of the most densely populated countries in the world. Despite rapid urbanization, more than 75% of the people still live in rural areas. The density of the rural population is also one of the highest in the world. Being a poor and low‐income country, however, Bangladesh's main challenge is to eradicate poverty by increasing equitable income. The gross domestic product of Bangladesh is growing steadily, and the country has outperformed many low‐income countries in terms of several social indicators. Bangladesh has achieved the Millennium Development Goal of eliminating gender disparity in primary and secondary school enrolment. A sharp decline in child and infant mortality rates, increased per capita income, and improved food security have placed Bangladesh on the track to achieving the status of a middle‐income country in the near future. All these developments have influenced the consumption patterns of the country. This study explored the consumption scenario of rural Bangladesh. Data were collected through direct observations and semi‐structured interviews. Information regarding the consumption of food, clothing, housing, education and medical facilities were collected, and qualitative methods were applied to the data analysis. The findings of this study suggested that the consumption patterns of rural Bangladeshis are changing over time along with economic and social development.     

Under-reporting of road traffic crash data in Ghana

Having reliable estimates of the shortfalls in road traffic crash data is an important prerequisite for setting more realistic targets for crash/casualty reduction programmes and for a better appreciation of the socio-economic significance of road traffic crashes. This study was carried out to establish realistic estimates of the overall shortfall (under-reporting) in the official crash statistics in Ghana over an eight-year period (1997–2004). Surveys were conducted at hospitals and among drivers to generate relevant alternative data which were then matched against records in police crash data files and the official database. Overall shortfalls came from two sources, namely, ‘non-reporting’ and ‘under-recording’. The results show that the level of non-reporting varied significantly with the severity of the crash from about 57% for property damage crashes through 8% for serious injury crashes to 0% for fatal crashes. Crashes involving cyclists and motorcyclists were also substantially non-reported. Under-recording on the other hand declined significantly over the period from an average of 37% in 1997–1998 to 27% in 2003–2004. Thus, the official statistics of road traffic crashes in Ghana are subject to significant shortfalls that need to be accounted for. Correction factors have therefore been suggested for adjusting the official data.     

A taxonomy of prestige-seeking university students: strategic insights for higher education

This study explores the importance of psychographic characteristics as potential segmentation bases in the higher education sector. In particular, we develop a taxonomy of university students based on their achievement orientation and prestige sensitivity. The study analyses the survey data obtained from 948 respondents using cluster analyses and multiple analysis of variance (MANOVA), indicating interesting findings. Three distinct clusters emerge, namely Strivers, Modest Achievers and Prestige-seeking Innovators. Findings reveal that Prestige-seeking Innovators have a more positive attitude towards the university, whereas Strivers have the strongest sense of regret over their decision to enrol at their current university and would seize the opportunity to enrol in a more prestigious university. The taxonomy is highly relevant to marketers of higher education institutions as it gives insights into potential bases for segmentation, positioning and communication strategies targeting the specific characteristics of each segment.     

Interactive Mining of Strong Friends from Social Networks and Its Applications in E-Commerce

Social networks are generally made of individuals who are linked by some types of interdependencies such as friendship. Most individuals in social networks have many linkages in terms of friends, connections, and/or followers. Among these linkages, some of them are stronger than others. For instance, some friends may be acquaintances of an individual, whereas others may be friends who care about him or her (e.g., who frequently post on his or her wall). In this study, we integrate data mining with social computing to form a social network mining algorithm, which helps the individual distinguish these strong friends from a large number of friends in a specific portion of the social networks in which he or she is interested. Moreover, our mining algorithm allows the individual to interactively change his or her mining parameters. Furthermore, we discuss applications of our social mining algorithm to organizational computing and e-commerce     

Assessing new empirical industrial organization (NEIO) methods: The cases of five industries

The methods proposed in the new empirical industrial organization (NEIO) literature have made significant contributions to our understanding of competitive behavior. However, these methods have yet to be compared with each other for their performance in explaining and diagnosing competitive market conduct. This inter-method comparison is important because conclusions about competitive behavior based on these methods have significant strategic as well as policy implications for firms. Our objective in this paper is to examine the performance of these different NEIO methods in terms of their discriminatory power, ability to identify strategic variables, and robustness in estimation. For empirical demonstration, we use data from diverse industries such as microprocessors, personal computers, facial tissue, disposable diapers and automobiles. Our results suggest that two commonly used NEIO methods-conjectural variation and non-nested model comparison-exhibit quite good convergence with each other and are consistent with a traditional time series method. This suggests that simpler methods such as conjectural variations deserve more credit. We also find that using these methods in tandem provides valuable additional information that may not be available when using any one method alone. While the emphasis in this study is on comparing different methods of analyzing competitive interaction, the findings also reveal some substantive insights about each market studied.     

An evaluation of the red-light camera programme in the city of Dammam,the Kingdom of Saudi Arabia

This study evaluated the effectiveness of red-light cameras in Dammam, the Kingdom of Saudi Arabia. Two methods were used to assess changes in crash risk at the intersections at which the red-light camera programme had been operating. Geospatial analysis was used to visualize trends in road crash density over the geographical region of Dammam and a pre--post-camera crash analysis was undertaken. The distribution of injury crashes was greater than that of crashes which included property damage, the latter of which was concentrated around central Dammam. The five red-light cameras installed in 2012 were located outside areas of high-crash and injury density, and the total number of crashes reported in the three-month periods after installation was double that before the cameras were installed. This increase in the number of crashes also occurred at the five comparison sites (without red-light cameras), indicating a null effect. The findings from this study are contrary to previous evaluations of speed management programmes associated with red-light cameras. The study highlights the challenges in obtaining data for such research and illustrates that a reliance on overt, fixed camera's to manage speed is unlikely to deliver significant reductions in road trauma.     

Gender and age distribution of motorcycle crashes in Spain

This study analysed motorcycle crashes in Spain. Ninety-nine thousand three hundred and four motorcycle crash reports filed in the years 2006–2011 were extracted from the Directorate General of Traffic database of crashes with victims. These data were analysed in terms of gender, age groups, trip purpose, type of crash, speed violation, day of the week, harm caused, use of helmet and psychophysical conditions of the driver to study the characteristics of motorcycle crashes in Spain and to assess the differences between male and female motorcycle drivers in these crashes. Significant differences were found in all the variables considered in the study, which implies gender differences in the profile of the injured motorcycle driver. The severity of motorcycle crashes suffered by male drivers is higher than that of women. These results corroborate the need to develop measures differentiated by gender, based on their profile.     

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.     

Factors affecting global integration of Chinese multinationals in Australia: A qualitative analysis

This study utilizes cross-level and in-depth interviews to extend empirical and conceptual studies that have identified the factors underlying the choices made by OECD based multinational enterprises (MNEs) when determining their approach to global integration. The study examines the applicability of these factors in the context of Chinese MNEs with operations in Australia. In so doing, it pioneers the use of qualitative methods to determine which factors affect emerging market MNEs’ global integration and how these elements function and interrelate. Furthermore, the study contributes to theory-building by classifying the factors affecting global integration into organizational, industrial, and environmental groupings. The implications of this study are drawn from the data analysis and directions for future studies are advanced.