Public Safety Departments Research Articles

Unraveling the propagation dynamics of passenger congestion in public transportation systems using big smart card data

We propose a comprehensive framework to explore propagation of passenger crowdedness in public transportation systems using 74.16 million trip chains. A deep learning method HSTGCNT is adopted which firstly models normal spatio-temporal dependencies in time series data, and assigns an anomaly score to new data based on its prediction error. The derived discrete anomaly events are further aggregated into groups based on their spatio-temporal proximity by using a clustering algorithm that binds shared nearest neighbors together based on pairwise correlations. The clustering results uncover several key stages located in a propagation chain, such as a core event, adjacent events with spatio-temporal proximity, or a broadcasting group. Weibo data serves as a third-party dataset to verify the above anomaly clusters. Results show that the framework is effective to detect outlier clusters, with recall value being 0.86. Findings may assist public safety departments in taking pro-active actions to interrupt chaos from broadcasting.

Fix‐it voucher program aims to improve road safety, ease student stress

Improving campus safety can have wide‐ranging benefits for the community at large. That was the ethos behind the new Fix‐a‐Falcon Bulb Replacement Program at the Solano Community College Department of Public Safety, which allows students to use vouchers to deal with vehicle bulb replacement‐related safety violations.

Colorado drugged driving prevalence and impaired driving conviction rates: Effects of impaired driving definitions and a 5-nanogram limit for delta-9 tetrahydrocannabinol

Objectives: How prevalent is drugged driving among Colorado drivers convicted of Driving Under the Influence (DUI)? What are the conviction rates of Colorado drivers charged with DUI, including impairment by marijuana’s delta-9 tetrahydrocannabinol (THC)? Is Colorado’s THC permissible inference law effective? To answer these questions, this report analyzes data published primarily in appendices of Colorado drugged driving reports. Methods: In 2017 Colorado began requiring annual analyses of Driving Under the Influence (DUI) offenses, including causes and judicial consequences of DUI offenses. These analyses are performed by the Division of Criminal Justice’s Office of Research and Statistics (ORS) within the Department of Public Safety. Each analysis requires ORS to link toxicology and court data bases. Data linking enables reporting of charges and convictions by categories including alcohol only, THC only, and polydrug use (two or more drugs simultaneously). Reports have been published annually for 5 years, the latest published in 2023 which covers case filings for 2020. Results: A rough estimate of one-half of the state’s DUI filings were attributed to drug use and half were attributed to alcohol only. The largest component of drugged driving was polydrug impairment, rather than impairment by a single drug like THC. Conviction rates in 2020 were 91% for alcohol only, 90% for polydrug cases, and 72% for THC only. Blood drug levels and law structure (per se, permissible inference, DUI definition) affected conviction rates significantly by defendant subsets. THC conviction rates in 2020 ranged from 11% to 100%, depending on blood drug levels and the legal charges. Conclusions: Efforts to educate the public about the dangers of drugged driving should emphasize polydrug impairment, not simply THC impairment. States should analyze data on causes and consequences of DUI arrests to understand what their drugged driving problems are and what they are not. Non-zero drug per se levels and defining DUI as “incapable of safe driving” can severely reduce the effectiveness of DUI laws.

Sherman v. Department of Public Safety: Institutional Responsibility for Sexual Assault

Abstract This article addresses the intersection of three important topics: sexual assault, police misconduct, and employer liability for employee torts. As to the last of these, while there have long been debates among jurists in the U.S. concerning the proper scope of respondeat superior liability, courts have mostly adhered to an approach that focuses on whether the employee acted for the purpose of serving the employer’s interests. The narrowness of this purpose-based test, as compared to available alternatives, makes it imperative for lawyers, judges, and scholars to be attentive to other, less well-known, bases for employer liability. In Sherman v Department of Public Safety, the Delaware Supreme Court applied a particular version of one such doctrine – the “aided-by-agency” doctrine – to hold a police department accountable for its officer’s sexual assault of an arrestee. By articulating this doctrine in a thoughtful and circumscribed manner, the Court affirmed its reputation as a leader in the development of agency law, while also providing a helpful framework that can be applied to hold certain employers liable when employees take advantage of their employment-based authority over their victims to perpetrate assaults.

Learning the value of additional learning

After what I’ve affectionately referred to as a bit of a “gap year,” I’m back working in higher education campus safety fulltime. While I’ve been able to enjoy the opportunity to venture into some select security consulting and assessment work during this time, I’ve remained confident that I would like to re‐engage within a leadership role once more in higher education. In particular, I knew I’d like to re‐engage with a leadership role in higher education within a public safety department, perhaps to include the familiar concurrent oversight of parking, emergency services management, and special events security.

Financial status and travel time to driving schools as barriers to obtaining a young driver license in a state with comprehensive young driver licensing policy

The highest lifetime risk for a motor vehicle crash is immediately after the point of licensure, with teen drivers most at risk. Comprehensive teen driver licensing policies that require completion of driver education and behind-the-wheel training along with Graduated Driver Licensing (GDL) are associated with lower young driver crash rates early in licensure. We hypothesize that lack of financial resources and travel time to driving schools reduce the likelihood for teens to complete driver training and gain a young driver’s license before age 18. We utilize licensing data from the Ohio Bureau of Motor Vehicles on over 35,000 applicants between 15.5 and 25 years old collected between 2017 and 2019. This dataset of driving schools is maintained by the Ohio Department of Public Safety and is linked with Census tract-level socioeconomic data from the U.S. Census. Using logit models, we estimate the completion of driver training and license obtainment among young drivers in the Columbus, Ohio metro area. We find that young drivers in lower-income Census tracts have a lower likelihood to complete driver training and get licensed before age 18. As travel time to driving schools increases, teens in wealthier Census tracts are more likely to forgo driver training and licensure than teens in lower-income Census tracts. For jurisdictions aspiring to improve safe driving for young drivers, our findings help shape recommendations on policies to enhance access to driver training and licensure especially among teens living in lower-income Census tracts.

Implementation of SafeVoice Nevada: A Statewide Anonymous Tip Line for School-Age Youth

ABSTRACT The State of Nevada launched the SafeVoice anonymous tip line to prevent harmful events in schools and to promote student safety, health, and wellbeing. SafeVoice is a joint effort of the Nevada Department of Education and Nevada Department of Public Safety. Since its inception in 2017, more than 27,000 tips have been submitted across the state. This article, which presents one of the first detailed studies of a statewide tip line for schools, focuses on the implementation of SafeVoice during its first five years of operation. We present quantitative data about the program (e.g. tip volume and characteristics) from the program database and school staff experiences from an annual survey, as well as qualitative data from documents and interviews with school staff, students, and parents.

Evaluation of deep learning-based algorithms on person re-identification

At present, CCTVs and multifunctional cameras have been well equipped everywhere in our daily lives. However, most of the public sectors especially the department of public safety are still using the traditional approach, analysing these data by humans. This research presents a summary of the current technologies and algorithms based on deep learning in person Re-identification (Re-id), which aims to recognize a particular individual in several camera views. This paper firstly described the current challenges in using deep learning for person Re-id. Then, a brief introduction of standard person Re-id including feature learning and metric learning is given. Furthermore, the open world Re-id problem is examined from three aspects: person re-id with occlusions, unsupervised learning-based method, and cross-modal person re-Id. The author then evaluated the regularly used datasets and analysed the performance of the algorithms from recent times. Finally, some potential future directions of research for Re-id are proposed.

Did court dismiss officer's First Amendment claim?

A student at the University of Michigan-Dearborn reported to the Department of Public Safety that she was sexually assaulted by one of her professors. A security officer with the department allegedly observed a lack of progress with the investigation and began to worry about a cover-up.

Did court dismiss officer's First Amendment claim?

A student at the University of Michigan‐Dearborn reported to the Department of Public Safety that she was sexually assaulted by one of her professors. A security officer with the department allegedly observed a lack of progress with the investigation and began to worry about a cover‐up.

Sherman V. Department of Public Safety: Institutional Responsibility for Sexual Assault

Sherman V. Department of Public Safety: Institutional Responsibility for Sexual Assault

After-Action Reviews and the Big Black Hole of Improvement Processes

Abstract University and campus public safety departments deal with a wide range of planned events and incidents from felonious crimes, chemical spills, and other emergent incidents to routine activities that require consistent reviews like commencement ceremonies. After-Action reviews (AARs) are a standard process in several industries including public safety, yet there is little empirical research when it comes to how AARs are applied to campus public safety entities and their outcomes. The Indiana University Public Safety Department (IUPS) reviewed 153 AAR reports, from 2017 through 2020, across seven Indiana University campuses and two academic centres. We highlight the key empirical findings from that review and how they influenced policy and practice at IUPS. We also highlight the role of the researcher–practitioner partnership in the process. We present lessons learned from the project along with suggestions for agencies interested in implementing a robust AAR process.

Linking Public Health and Public Safety Data on Co-occurring Disorders Among Adults in Hawaii's Criminal Justice System.

To better understand the behavioral health treatment needs of adults involved in the criminal justice system and to improve the continuum of services provided to this vulnerable population, Hawaii initiated a data linkage project that connects substance use and mental health data from the state Department of Public Safety with behavioral health treatment data from the state Department of Health for the State of Hawaii. Specifically, this linkage project begins to examine behavioral health treatment levels recommended by the criminal justice system and Hawaii State Hospital inpatient psychiatric admissions. We provide a preliminary summary on individuals who were both involved in the criminal justice system and received court-ordered inpatient psychiatric treatment and outline data governance procedures, future directions, and practice recommendations.

Real-time traffic congestion prediction using big data and machine learning techniques

PurposeThe study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.Design/methodology/approachThis study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.FindingsThe study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.Research limitations/implicationsThe model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.Practical implicationsTo help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.Social implicationsTo estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.Originality/valueThe proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

Maine's Overdose Prevention Through Intensive Outreach, Naloxone and Safety (OPTIONS) Initiative.

This article outlines the successes, barriers, and lessons learned in implementing the Overdose Prevention Through Intensive Outreach, Naloxone and Safety (OPTIONS) initiative in Maine during its first year. With leadership from the Governor's Office, the Department of Health and Human Services, and the Department of Public Safety, the Office of Behavioral Health contracted with behavioral health organizations to hire and provide supervision for 16 clinicians. These behavioral health clinicians, known as OPTIONS liaisons, were each assigned to provide services for one county in the state and were embedded within a public safety agency. A technical assistance team was also assembled to assist in guiding implementation and improving this public health and public safety partnership.

Trauma-Specific Performance Improvement Activities for Emergency Medical Services Providers: A Statewide Perspective

IntroductionTrauma-specific performance improvement (PI) activities are highly variable among Emergency Medical Services (EMS) providers. This study assesses the perception of the trauma PI activities of EMS providers in the state of Ohio and identifies potential barriers to conducting a successful program. MethodsAn institutional review board–approved, voluntary, and anonymous Qualtrics survey was disseminated to all EMS agencies registered under the Ohio Department of Public Safety throughout the 88 counties of Ohio. It included questions regarding what agencies considered trauma-specific PI activities, how frequently they completed those activities, and barriers related to conducting such PI activities. There were both open-ended and closed-ended questions in the survey, along with a follow-up interview. The data were descriptively and thematically analyzed. ResultsFrom the recorded responses (341), most the respondents (98.5%) either agreed or strongly agreed that trauma-specific PI activities improve performance of EMS providers, while only 63.8% (218) of the agencies performed them. Some activities considered as trauma PI and conducted at least once a month included (1) record keeping (74.6%), (2) confirmation on the use of correct triage protocols (66.9%), (3) measuring response time on trauma calls (60.0%), (4) PI reviews of trauma cases (56.9%), and (5) obtaining feedback from the receiving facility and or authorizing physicians (48.5%). Primary barriers to performing trauma PI activities included a lack of interest and financial resources, followed by system-level reasons such as unavailability of training centers and a lack of regional/state support. Thematic analysis of the data suggested that improved communication and awareness of trauma PI, sharing statewide data on trauma PI, better synchronization among EMS agencies and trauma centers, and enhanced EMS funding could potentially improve trauma-specific PI programs at the EMS level. ConclusionsOur results showed variability in the perception, execution, and availability of trauma-specific PI activities among EMS agencies in the state. Common barriers could potentially be mitigated by collaboration between agencies, trauma centers, and state-led initiatives. With the increased frequency of mass shootings and other large-scale trauma disasters, it is imperative from a state and regional level to address these inconsistencies and further elucidate effective measures of trauma PI for the EMS community.

Gain confidence in your department by putting best interests of your officers, students first

Before coming to work at the University of Hawai'i, Manoa, as the Chief of the Department of Public Safety, Andrew Black also served as an assistant state's attorney, and in multiple positions for the Federal Bureau of Investigations. One of those roles was as an instructor, teaching as an adjunct at a university for several years before leaving the FBI. Doing so made Black realize how much he loved the university environment.

The Ranger Ideal Volume 3: Texas Rangers in the Hall of Fame, 1898–1987 by Darren L. Ivey

Reviewed by: The Ranger Ideal Volume 3: Texas Rangers in the Hall of Fame, 1898–1987 by Darren L. Ivey Bob Cavendish The Ranger Ideal Volume 3: Texas Rangers in the Hall of Fame, 1898–1987. By Darren L. Ivey. (Denton: University of North Texas Press, 2021. Pp. 843. Illustrations, notes, bibliography, index.) Texas Rangers in the Hall of Fame, 1898–1987 concludes Darren Ivey's trilogy of Texas Ranger Hall of Fame members. Beginning with William Wright, who entered the Rangers in 1899, and closing with Stanley Guffey, whose Ranger service came to a tragic end in 1987, this volume presents the last twelve inductees, whose tenures occurred during Texas's transition from its "old west" past. Rangers' attention to frontier defense was eventually replaced with labor union unrest, counterfeiting, border incursions, prohibition, and gangster violence. In response, Ranger responses became more sophisticated as the Colt dragoon revolver, lever-action Winchester, and horseback transportation evolved into crime laboratory methodology and automobiles. Yet, law enforcement could also mean roundups and expulsions of such undesirables as gamblers, prostitutes, and vagrants, all handled with full endorsement of local and state officials. Rangers experienced political interference, notably from Governor Miriam Ferguson, who dismissed several Rangers deemed hostile to her administration and replaced them with inexpert partisan devotees. The Texas Legislature restricted Ranger investigations unless they were invited by local authorities. Ranger budgets constantly suffered reductions. Finally, in 1935, Governor James Allred modernized Texas law enforcement by creating the new Texas Department of Public Safety and incorporating the established Texas Rangers. Ranger Ideal is an oblique chronicle of Texas's social evolution seen through the prisms of twelve individuals facing contemporary issues. Texas was maturing and experiencing growing pains. Along the way, Ivey reveals, Ranger tactics changed with the era, albeit not without rough edges. These twelve concise biographies appear in chronological sequence but can be read in random order. Few in number, these men often knew each other, occasionally crossing career paths. Rangers Frank Hamer, Thomas Hickman, and Charles Miller, for instance, each receive an entire chapter and also reappear in subsequent chapters as participants in separate Ranger assignments or incidents, thereby creating an informal network of men and talents. Each of these men stands with his own strengths and vulnerabilities. Ranger Captain Hamer (1884–1955) might have followed "a different path, that of the outlaw" (83), but he instead became part of the team [End Page 532] that stopped the notorious gangster duo Bonnie Parker and Clyde Barrow (dismissed by Ivey as second-rate hoodlums romanticized in a 1967 movie). Hamer, once arrested, found guilty, and fined for assaulting a reporter, nevertheless humbly credited his career survival to God and his own "doing what is right" (107). Manuel Gonzaullas (1891–1977), another captain, had a mysterious past with no clear personal history prior to his Ranger career. His biographers "had to rely on the contradictory information he himself provided about his early life" (285). He became superintendent of the Bureau of Intelligence and later director of the State Crime Detection Laboratory while continuing his fieldwork. Each Ranger had his personality. Captain Thomas Hickman always dressed the part, wearing a wide-brimmed Stetson hat with tailored western clothes. Marvin Burton's gentle demeanor, once confronted by lawbreakers, erupted into "a one man riot squad" (373). Clinton Peoples's reputation for self-promotion made him unpopular with some, but he was an aggressive proponent for the Ranger Museum in Waco even after leaving Ranger service. Ranger Ideal Volume 3 works as a valuable secondary source and as a highly readable survey of Texas in transition told through lives of twelve men charged with keeping order. While sympathetic toward his subjects, Ivey has not written a hagiography. He defines these Rangers as human beings who dedicated their lives to protection and law enforcement but occasionally stumbled along the way. Without silver bullets or white steeds these imperfect men "moved toward the danger in order to protect that which they held most dear" (613). Bob Cavendish Buda, Texas Copyright © 2022 The Texas State Historical Association

Menopause-related medication use among women age 45-75 experiencing incarceration in North Carolina 2015-2016

PurposeThis study aims to estimate the prevalence of individuals receiving hormone therapy for menopause management and the prevalence of underlying conditions that may constrain options for pharmacologic menopause management in the prison context.Design/methodology/approachThis study reviewed all prescriptions dispensed by the North Carolina Department of Public Safety between July 1, 2015, and June 30, 2016, for relevance to menopause management. Relevant medications were those either recommended for menopause management or those indicated for management of conditions that may complicate menopause management, as per the 2015 clinical decision-support algorithm tool developed by the North American Menopause Society. Analysis was restricted to women between the ages of 45 and 75.FindingsOf 1,120 women, a majority (77.8%) were between the ages of 45 and 54. Less than 5% of individuals in this study were prescribed estrogen-containing therapy. The most commonly prescribed medications that may constrain options for menopause treatment were related to hypertension and other cardiovascular disease or mental health conditions.Research limitations/implicationsThe retrospective nature of this data set limits the findings, given that researchers did not have access to diagnoses or data on polypharmacy. Still, this study indicates that many women over 45 experiencing incarceration are living with health conditions that may complicate menopause symptom management with hormone therapy. Future research in carceral settings must examine the prevalence of menopause-related symptoms as well as access to and quality of comprehensive menopause management.Originality/valueThere is a paucity of literature around the menopause-related needs of individuals experiencing incarceration. To the best of the authors’ knowledge, no other research has examined prevalence of pharmacologic menopause management among women who are incarcerated.

FIGAT: Accurately Classify Individual Crime Risks with Multi-information Fusion

Crime prediction plays a vital role in public security. Existing studies infer crime locations or crime groups without considering individual GPS trajectory data. They ignore joint influence on crime patterns coming from the internal relationship between criminals, locations, and time. In this study, we propose Fusion Information Graph Attention Networks (FIGAT), which classifies individuals into high and low risks with personal movement time series and location trajectories. To solve the independence of individual crime behavior and the fusion information loss problem, FIGAT proposes Multi-dimension Fusion Information Graph to combine semantic correlation features with conventional person basic features, time features, and location features. FIGAT constructs a multi-relation graph attention layer, which utilizes the semantic relationship and node information to accurately classify individuals into high and low risks. We evaluate FIGAT with 14,625,884 GPS trajectories from 1038 individuals collected by a real-world public safety department. The results demonstrate that FIGAT improves F1 score by 41%, 32%, and 23% compared with legacy machine learning, RNN-based deep learning, and graph neural network SOTA methods, respectively. T-SNE results and ablation experiments further prove the effectiveness of FIGAT.