Correspondence Cluster Analysis Research Articles

Generation of nighttime pedestrian fatal precrash scenarios at junctions in Tamil Nadu, India, using cluster correspondence analysis

Objective Modern transportation amenities and lifestyles have changed people’s behavioral patterns while using the road, specifically at nighttime. Pedestrian and driver maneuver behaviors change based on their exposure to the environment. Pedestrians are more vulnerable to fatal injuries at junctions due to increased conflict points with vehicles. Generation of precrash scenarios allows drivers and pedestrians to understand errors on the road during driver maneuvering and pedestrian walking/crossing. This study aims to generate precrash scenarios using comprehensive nighttime fatal pedestrian crashes at junctions in Tamil Nadu, India. Methods Though numerous studies were available on identifying pedestrian crash patterns, only some focused on identifying crash patterns at junctions at night. We used cluster correspondence analysis (CCA) to address this research gap to identify the patterns in nighttime pedestrian fatal crashes at junctions. Further, high-risk precrash scenarios were generated based on the positive residual means available in each cluster. This study used crash data from the Road Accident Database Management System of Tamil Nadu State in India from 2009 to 2018. Characteristics of pedestrians, drivers, vehicles, crashes, light, and roads were input to the CCA to find optimal clusters using the average silhouette width, Calinski-Harabasz measure, and objective values. Results CCA found 4 clusters with 2 dimensions as optimal clusters, with an objective value of 3.3618 and a valence criteria ratio of 80.03%. Results from the analysis distinctly clustered the pedestrian precrash behaviors: Clusters 1 and 2 on pedestrian walking behaviors and clusters 3 and 4 on crossing behaviors. Moreover, a hidden pattern was observed in cluster 4, such as transgender drivers involved in fatal pedestrian crashes at junctions at night. Conclusion The generated precrash scenarios may be used to train drivers (novice and inexperienced for nighttime driving), test scenario creation for developing advanced driver/rider assistance systems, hypothesis creation for researchers, and planning of effective strategic interventions for engineers and policymakers to change pedestrian and driver behaviors toward sustainable safety on Indian roads.

Exploring Attribute Associations in Pedestrian-Involved Hit-and-Run Crashes through Cluster Correspondence Analysis

Pedestrian-involved hit-and-run (PIHR) crashes represent a significant public health concern, and identifying patterns in these crashes can aid in developing effective countermeasures. Cluster correspondence analysis (CCA) is a multidimensional statistical technique that combines dimension reduction and clustering to identify patterns in categorical data. This method provides insights into underlying patterns and relationships among categories. The current study analyzed a Louisiana crash dataset of 2,201 PIHR crashes from 2015 to 2019 using CCA to identify underlying patterns. CCA identified six clusters, examined the top associative attributes, and assessed their cluster-to-dataset percentage ratio. The first two clusters, representing 66% of PIHR crashes, mainly involved crashes on city streets, occurring primarily during early night (7 to 11 p.m.) in Cluster 1 and the afternoon (12 noon to 4 p.m.) in Cluster 2. Clusters 3 and 4, accounting for 30% of PIHR crashes, predominantly exhibited crashes on U.S. and state highways. Cluster 4, which featured fatalities, primarily concentrated on state highways during the early morning hours (4 to 6 a.m.). Meanwhile, Clusters 5 and 6 focused on high-speed highways, specifically interstates involving pedestrian fatalities. A discussion on implementing strategic countermeasures tailored to the distinct characteristics of each cluster is presented. Alongside improvements in context-based countermeasures to ease pedestrian movement and enhance their visibility, strategies such as advocating for stringent hit-and-run laws, incentivizing the use of dashcams, and broadly publicizing resources for crash reporting are projected to be highly effective in curbing PIHR crashes.

High-rise campus building cooling energy share using floor area of spaces

High-rise campus had varying energy characteristics on each floor, impacting cooling energy consumption. Understanding the cooling energy needs based on space characteristics plays a role in designing strategies to reduce energy consumption. It is essential to identify the cooling energy consumption for each floor function within high-rise campus buildings. The study conducted an analysis of high-rise campus characteristics by identifying and categorizing spaces based on their energy consumption zones. Spatial data was processed using mapping, and cluster correspondence analysis was employed to understand spatial relationships within high-rise campus buildings and their energy consumption. This research was carried out using Energy plus as an energy simulation based on a high-rise campus building model in the Jakarta sub urban region on a hot humid tropic. Campus building could be divided into four types of floors, as general, office, classroom and laboratory. The highest cooling energy was the laboratory.

Examining urban delivery service user profiles and determinants of drone delivery adoption in Ghana considering usage before and after the COVID-19 pandemic

The demand for urban delivery services (UDS) in low- and middle-income countries (LMICs) has been growing due to the spike in e-commerce activities. This phenomenon has led to the search for more innovative UDS modes, such as drones, as they provide fast, safer, and eco-friendly services. Although researchers have examined the perceptions regarding the adoption of drone delivery, there are many unknowns about the intentions to use this futuristic delivery mode. By employing survey data obtained through an online survey conducted in Ghana, this study aims to gain a deeper understanding of the current UDS user profiles and investigate their intentions to adopt drone deliveries in the future, considering the lived experiences of traditional UDS usage pre/post the COVID-19 pandemic. Regarding UDS user profiling, the study applied an advanced unsupervised machine learning method known as cluster correspondence analysis to simultaneously identify critical clusters in the data as well as key factor associations for each group. The findings demonstrated that most people in Ghana who seldom used the traditional UDS usually experienced poor service levels. Females living in the capital of Ghana who are frequent users of UDS mainly experienced acceptable service levels. Using an ordered probit regression model, the study identified that variables such as males, people who desired contactless UDS, and frequent UDS users at work or school during the COVID-19 pandemic were more inclined to embrace drone deliveries when introduced. Conversely, individuals who frequently used UDS services at home, those living in large cities, and those with lower income levels expressed reluctance toward drone delivery services. This study augments the urban logistics literature in LMICs by offering insightful suggestions in the areas of education, enhanced marketing strategies, and delivery optimization, which are instrumental for informing policy decisions. Government-related transportation bodies, private logistics firms, and trade unions can collaborate and adopt the recommendations provided to improve urban delivery services and to pave the way for integrating drone delivery services in the near future.

Single-vehicle roadway departure crashes at rural two-lane highway curved segments: A diagnosis using pattern recognition

Curved segments account for a disproportionately high proportion of fatal and serious injury crashes, with most of these crashes occurring on rural two-lane (R2L) highways. During the 10-year period from 2008 to 2017, a total of 1 234 fatal single-vehicle roadway departure (SV-RwD) crashes occurred on R2L roads in Louisiana, out of which 635 (51.5 %) crashes occurred on curved segments. Therefore, it is critical to investigate the causes of SV-RwD crashes, specifically those that occur on curved segments. This study aimed to investigate the ‘association knowledge’ of the factors contributing to SV-RwD crashes on R2L curved segments in Louisiana using fatal and injury crash data collected from 2008 to 2017. The study utilized Cluster Correspondence Analysis (CCA), a robust joint dimension reduction and clustering method for handling high-dimensionality and multicollinearity of crash data, to achieve this objective. Based on the cluster validation measures, the study identified five clusters with specific traits, including alcohol-impaired male drivers with no seatbelt usage, young (15–24 years old) female drivers’ crash involvement in cloudy weather conditions, animal-involved crashes in rainy weather conditions, crashes occurring on hillcrest locations under cloudy weather conditions, and crashes in the dark with the presence of streetlights and higher traffic volume. Furthermore, young (15–24 years) female drivers were identified in most clusters, implying that this specific age group of female drivers requires special consideration when dealing with SV-RwD collisions on R2L curved segments. To improve safety on R2L curved segments, policymakers can use the findings of this study to develop targeted countermeasures.

Key factors affecting motorcycle-barrier crash severity: an innovative cluster-regression technique

Highway motorcycle-barrier crashes are uncommon but are associated with severe ramifications. Little has been done to understand the factors related to these crashes, making it difficult to establish appropriate mitigation policies. This study identifies homogeneous groups of motorcycle-barrier crashes on highways and investigates cluster-specific key factor associations and the determinants of injury severity. Cluster Correspondence Analysis was employed to discover latent clusters and cluster-specific key factor associations using motorcycle-barrier crashes from Massachusetts. Further, an ordered probit regression technique was employed to investigate the effect of factors on injury severity outcomes at the cluster level. Three highway access control type-related clusters were identified. While seniors, collectors, intersections/roundabouts, daylight, and summer were associated with no/partial access-controlled segment crashes, interstates, ramps, medians, dark-lighted roads, and winter correlated with full access-controlled segment crashes. Factors influencing fatalities differed for each cluster. From the insightful findings, targeted countermeasures geared at improving motorcycle safety are suggested.

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Diagnosis of Encroachment-Related Work-Zone Crashes by Applying Pattern Recognition

Work-zone safety is one of the critical goals of transportation agencies. Vehicles are required to change travel paths and lanes over a short length of a road section at work zones. Distracted drivers, unable to see advanced warning signals and pavement markings delineating the work-zone travel paths, could increase the likelihood of a crash. Recent statistics showed that fatal collisions in work zones had increased by 46% in 2019 compared with 2011. The frequency of roadway departures at work zones, the higher risk of fatalities, and little insight into encroachment types at work zones underscored the need for a thorough study. This study aimed to examine the vehicle encroachment conditions associated with work-zone locations and focused on 4 years (2016 to 2019) of crash data from the Texas Department of Transportation by applying a unique data-mining method known as cluster correspondence analysis. This method identified four clusters in both “no-injury” and “fatal and injury” crash data. Major factors contributing to vehicle encroachment were identified. Three dominating clusters were median-related crashes on two-lane divided high-volume roadways; single-vehicle overturning collisions on two-way divided roadways with unprotected median; and overturning crashes on two-lane undivided roadways in controlled traffic. The findings of this study will be useful for safety engineers to contribute to reducing encroachment-related work-zone crashes.

Pattern recognition from injury severity types of frontage roadway crashes

Frontage roads are the supporting roadways that are along freeways and fully controlled principal arterial roadway networks in the U.S. These roads are designed in a way to provide access between the freeways, principal arterials, and surrounding business entities. For Texas, these roadways are the leading design resolution for providing access along rural freeways and principal arterial roadways. These roadways are generally two-ways for rural and less developed urban areas and are mostly one-way for urban and city-centered roadways. Although frontage roadways possess major safety concerns, the safety performance of these roadways has not been well studied. This study collected six years of frontage road crash data from Texas to determine the patterns of associated factors by applying a dimension reduction method known as cluster correspondence analysis (CCA). The results revealed four clusters for each of the two datasets based on crash injury types. For fatal and injury crashes, the major clusters are distraction-related crashes at signalized intersections, segment-related crashes at dark unlighted conditions, yield signed intersection locations and segments with no TCDs, and intersection crashes on undivided roadways. For the no injury crash dataset, the key clusters are segment crashes in dark conditions and rain, crashes at signalized intersections with both drivers going straight, segment crashes with both drivers going straight with marked lanes or no TCDs, and intersection-related collisions on undivided roadways. Based on the evaluation results, suitable safety countermeasures and policy initiatives to reduce frontage road crash frequencies can be singled out.

In-Depth Understanding of Near-Crash Events Through Pattern Recognition

Studying near-crashes can help safety researchers understand the nature of crashes from different perspectives. Conventional crash data sets lack information about what occurred directly before the crash event. This study used a near-crash data set extracted from a naturalistic driving study that includes features describing the vehicles, drivers, and information on other vehicles involved before and during the near-crash incidents. This data set provides us with a unique perspective to understand the patterns of near-crashes. This study applied the cluster correspondence analysis (cluster CA) algorithm to explore the patterns and the magnitude of each feature’s dominance within and between the clusters through dimension reduction. The analysis identifies six clusters with four types of near-crashes: near-crash with adjacent vehicles; near-crash with the following or leading vehicles; near-crash with turning vehicles; and near-crash with objects on the roadway. The results also show that the first two types of near-crash are the most common. The patterns for these two most common types of near-crash are different with or without the engagement of secondary tasks. The findings of this study provide a fresh perspective to understand different types of crash and associated patterns. Furthermore, these findings could help transportation agencies or vehicle designers develop a more effective countermeasure to mitigate the risk of collision.

Work from home in the post-COVID world

The working standard of shared office spaces has evolved in recent years. Due to the ongoing COVID-19 pandemic, many companies have instituted work from home (WFH) policies in accordance with public health guidelines in order to increase social distancing and decrease the spread of COVID-19. As the pandemic and WFH-related policies have continued for more than a year, there has been a rise in people becoming accustomed to the remote environments; however, others are more enthusiastic about returning to in-person work environments, reflecting the desire to restore pre-pandemic environments. As working from home is related to transportation issues such as changing commuting patterns and decreased congestion, motorized trips, and emission, there is a need to explore the extent of public attitudes on this important issue. This study used unique open-source survey data that provides substantial information on this topic. Using an advanced categorical data analysis method known as cluster correspondence analysis, this study identified several key findings. Not having prior WFH experiences, being eager to interact with colleagues, difficulties with adapting to virtual meeting technologies, and challenges with self-discipline while WFH were strongly associated with individuals who refused to continuously WFH at all after the pandemic. Individuals holding a strong view against the seriousness of the COVID-19 pandemic were also largely associated with never choosing WFH during and after the pandemic. For individuals with some prior WFH experiences, the transition to WFH every day in response to the outbreak was much easier, compared to those without prior experiences. Moreover, being forced to WFH during the COVID-19 pandemic positively influences the choice of WFH after the pandemic. The findings of this study will be beneficial to help policymakers and sustainable city planners understand public opinions about WFH.

Visualizing Class Specific Heterogeneous Tendencies in Categorical Data

In multiple correspondence analysis, both individuals (observations) and categories can be represented in a biplot that jointly depicts the relationships across categories and individuals, as well as the associations between them. Additional information about the individuals can enhance interpretation capacities, such as by including class information for which the interdependencies are not of immediate concern, but that facilitate the interpretation of the plot with respect to relationships between individuals and categories. This article proposes a new method which we call multiple-class cluster correspondence analysis that identifies clusters specific to classes. The proposed method can construct a biplot that depicts heterogeneous tendencies of individual members, as well as their relationships with the original categorical variables. A simulation study to investigate the performance of the proposed method and an application to data regarding road accidents in the United Kingdom confirms the viability of this approach. Supplementary materials for this article are available online.

Using Cluster Correspondence Analysis to Explore Rainy Weather Crashes in Louisiana

Rainy weather significantly affects traffic safety, especially in the State of Louisiana. With the aim of identifying the patterns of collective association of attributes in rainfall-involved crashes statewide, crashes that occurred during rainy weather and resulted in two injury groups, fatal and severe injury (FSI) and moderate injury (MI), were extracted from databases acquired from the Louisiana Department of Transportation and Development. A total of 3,381 crashes were extracted, comprising 502 FSI crashes (14.85%) and 2,879 MI crashes (85.15%). This study applied the cluster correspondence analysis (CCA) method, a unique method in combination with cluster analysis and correspondence analysis, to generate clusters by partitioning of individual attributes based on the profiles over the categorical variables identified through dimensional reduction of the dataset. In addition to the biplots illustrating the association of all attributes in the clusters, the top 20 standardized residuals indicating the stronger association are presented in bar plots. Four optimum clusters from FSI and MI crashes reveal that the association of roadway, crash environment, and driver condition characteristics identified in the clusters are highly distinguishable across roadway functional classes. Specifically, varieties of attributes linked to speed limit, lighting condition, alignment, area type, manner of collision, restraint usage, and alcohol/drug can have associative impacts on these two injury severities. The identified associations of crash attributes across various functional class roadways could provide valuable understanding for the development of countermeasures which prioritize the prevention of fatalities and injuries.

Understanding patterns of moped and seated motor scooter (50 cc or less) involved fatal crashes using cluster correspondence analysis

ABSTRACT Moped and seated motor scooter (50 ccs or less) riders have a relatively high risk of becoming crash casualties. Comparison between 2015 and 2019 fatal crash data indicates that fatal moped crashes have increased by 76%, whereas fatal motorcycle crashes have decreased by 2%. This study collected moped and seated motor scooter-related fatal crash data for five years (2015–2019) from the Fatality Analysis Reporting System (FARS) to perform the analysis. Using an innovative categorical data analysis method known as cluster correspondence analysis (CCA), this study identified some critical clusters with a group of co-occurring variable categories. The contextual understanding of fatal crash patterns could guide authorities in developing data-driven interventions and countermeasures aiming to minimize moped collisions and related fatalities. The findings of this study can provide a better understanding of the patterns of contributing factors in moped and seated motor scooter fatal crashes.

Pattern recognition from light delivery vehicle crash characteristics

In the era of food delivery and grocery delivery startups, traffic crashes associated with light delivery vehicles have increased significantly. Since the number of these crashes is increasing, it is important to investigate light vehicle crashes to gain insights into potential contributing factors. This study collected seven years (2010-2016) of data from traffic crash narrative reports and structured traffic crash data from Louisiana. Using text search options and manual exploration, a database of 1,623 light delivery-related crashes was examined with a comparatively robust clustering method known as cluster correspondence analysis. The findings identified six clusters with specific traits. The key clusters are fatigue, alcohol impairment, young drivers on low to moderate speed roadways, open country and moderate speed state/U.S. highways, and interstate-related crashes due to inattention. Policymakers can use the findings of the current study to perform data-driven policy development and promote safety for delivery-related travels.

Identifying Patterns of Key Factors in Sun Glare-Related Traffic Crashes

Sun glare is one of the major environmental issues contributing to traffic crashes. Every year, many traffic crashes in the United States are attributed to sun glare. However, quantitative analysis of the influence of sun glare on traffic crashes has not been widely undertaken. This study used traffic crash narrative data for 7 years (2010–2016) from Louisiana to identify crash reports that provided evidence of drivers indicating sun glare as the primary contributing factor of the crashes. Additional geometry and traffic information was collected to identify the list of key crash-contributing factors. This study used cluster correspondence analysis to perform the data analysis. After performing several iterations, six clusters were identified that provided additional insight in relation to sun glare-related crashes. The six clusters are associated with mixed (business and residential) localities, intersection-related crashes on U.S. roadways, single-vehicle crashes on residential two-lane undivided roadways, curve-related crashes on parish roadways in residential localities, interstate-related crashes in open country localities, and curve-related crashes in open country localities. The findings of the current study can add insights to the ongoing safety analysis on sun glare-related crashes.

Indus river estuary: an assessment of potential risk of contaminants and ecosystem susceptibility

The Indus River is proclaimed as second most plastic-polluted rivers of the world. This river is the principal river of Pakistan and supplies freshwater for agriculture and human consumption. Its terminus into the northern Arabian Sea creates a unique ecosystem that supports a variety of aquatic organisms. In this study we have evaluate the heavy metal concentration in fishes sampled from the IRE. Muscle tissues from five fish species of ecologically and economically important were sampled and the concentrations of cadmium (0.125‒1.025 µg g−1, 0.93 ± 0.33), lead (0.250‒2.560 µg g−1, 0.92 ± 0.86), arsenic (4.178‒6.337 µg g−1, 4.24 ± 2.13) and mercury (BDL‒0.116 µg g−1, 0.05 ± 0.04) were found to be beyond optimum level. We determined the pollution load index which indicated that the IRE pollution exhibits significant seasonal oscillations. In addition to the heavy metal assay we note the frequent appearance of abnormal fishes caught in the IRE, which validates the pollution load. Multivariate approaches, canonical correspondence analysis and cluster analysis, were used to evaluate the relationships among environmental variables that influence metal concentration. This study is the first to document heavy metals detected from fishes inhabiting in IRE and highlights concerns regarding the need for management measures.HighlightsWe examined heavy metals in commercial and ecological valuable fishes in the Indus River Estuary (IRE) for the first time.Multivariate approaches were used to determine the efficacy of environmental parameters to predict heavy metal concentration.We described the presence of fish abnormalities as a result of contamination in the IRE.We observe that the mechanism of action of organism health and food contamination in the IRE is poorly understood.

Integrating Ecosystem Services in Nature Conservation for Colombia

The ecosystem services (ES) approach has been introduced in environmental policies and management to serve as a link between nature and society. Communication, education, and participation actions (CEPA) have the potential to facilitate this link. In this research, we evaluated how CEPA have been implemented in biodiversity conservation projects that consider ES. We used content analysis to review 182 biodiversity conservation projects executed by 33 environmental authorities in Colombia. We also used multiple correspondence analysis and cluster analysis to classify projects on the basis of the purpose of CEPA, type of CEPA, integration of CEPA, ES addressed, main stakeholders, and aim of conservation. We found that five aspects are key to fostering social engagement in environmental management projects: promoting explicit consideration of the ES approaches, increasing conservation efforts focused on the non-material benefits of the ES, integrating different types of CEPA, including overlooked key actors (e.g., indigenous communities and women), and developing and implementing social indicators. These considerations might lead environmental managers to revise their daily practices and, eventually, inform policies that foster an explicit link between CEPA and ES approaches.

An empirical reappraisal of the four types of cyclists

• The Four Types of Cyclists typology generates heterogeneous comfort patterns. • Our data-driven segmentation yields more homogeneous comfort patterns. • Cyclists form three categories, not four, based on comfort, intent, frequency. • Types: Uncomfortable or Uninterested, Cautious Majority, Very Comfortable Cyclists. The Four Types of Cyclists is a widely adopted typology developed by Portland’s Bicycle Coordinator, Roger Geller. No Way No How, Interested but Concerned, Enthused and Confident, and Strong and Fearless have become ubiquitous in academic literature and practice. However, the classification was subjectively developed and contains several known contradictions. This research aims to develop a data-driven typology using near-identical explanatory variables to those of the Four Types of Cyclists. The objective is to develop a typology with a similar functional purpose, but derived using statistical methods. An online survey was distributed to a panel of Edmonton, Canada, residents to this effect, and the use of video clips rather than descriptions is tested as a means to assess comfort on different types of cycling infrastructure. Cluster Correspondence Analysis is used to carry out the segmentations, including variables of comfort, cycling intent, and cycling in the previous summer. The survey sample tends to segment into three categories, as opposed to the four suggested by Geller: Uncomfortable or Uninterested, Cautious Majority, and Very Comfortable Cyclists. The Four Types of Cyclists typology is also shown to generate heterogeneous comfort patterns within each cyclist type, a limitation our empirically-derived segmentations overcome.

New social media for health promotion management: a statistical analysis

The literature from several areas of study reports social media Web sites plays an important role for society in the third millennium. Using the Internet as a tool for health is increasingly common: In particular, Facebook has played increasing role in health promotion management. The objective of the paper was to demonstrate, by a sample survey, how and how much Facebook promotes health by encouraging healthy lifestyles. The online questionnaire has been loaded on Facebook pages and groups that promote healthy lifestyle and well-being. Statistical data analysis, used in an integrated and complementary approach, has been multiple correspondence analysis, cluster analysis and cross-tabulations. The final sample consisted of 3.640 Facebook users. Social media Web sites are widely used by younger generation, but also by adult and senior population to find health information. Starting from the research results, it can be argued that Facebook users would like to have more “certified information” on health through social media Web sites. Starting from this information, governments could promote healthy lifestyles through Facebook, paying attention to certify the contents.