Search results

WIth limited research on the effects of variable message sign (VMS) message content and verbiage on revealed driver behavior, this study aims to investigate how different verbiage of crash-related messages are related to the diversion rate.

Using ordered logit models, the associations of message verbiage with diversion rates during crash incidents were assessed using five years of VMS message history within a section of I-15 in the state of Utah.

A significant impact of message verbiage on the diversion rate was observed. Based on the analysis results, the crash message verbiage with the highest diversion was found to be miles to crash + “prepare to stop,” followed by crash location + delay information, miles to crash + “use caution” + lane of the crash, etc. In addition, the diversion rate was found to be correlated to some roadway characteristics (e.g. occupancy in mainline, weather condition and light condition) along with the temporal variations.

These findings could be used by transportation agencies (e.g. state department of transportation [DOTs]) to make informed decisions about choosing the message verbiage during future crash incidents. This study also revealed that higher diversion rates are associated with a shorter distance between the crash location and VMS device location, recommending increasing the number of VMS devices, particularly in crash-prone areas.

Motor-vehicle-related events (MVEs) are the leading cause of on-duty death for law enforcement officers, yet little is known about how officers view this significant job hazard. The purpose of this paper is to explore officers’ motor-vehicle risk perception and examine how prior on-duty MVEs and the death or injury of a fellow officer influences this perception.

A state-wide random sample of 136 law enforcement agencies was drawn using publically accessible databases, stratified on type and size of agency. In total, 60 agencies agreed to participate and a cross-sectional questionnaire was distributed to 1,466 officers. Using six-point Likert scales, composite scores for motor-vehicle and intentional violence risk perception were derived. A linear regression multivariable model was used to examine factors affecting motor-vehicle risk perception.

Motor-vehicle risk perception scores were significantly higher than intentional violence scores. A prior on-duty motor-vehicle crash, prior roadside incident, or knowledge of fellow officer’s injury or death from a MVE significantly increased motor-vehicle risk perception scores. After controlling for potential confounders though, only prior on-duty crashes and roadside incidents impacted motor-vehicle risk perception.

The study comprised primarily small, rural agencies and generalizability may be limited. Also, although the data were collected anonymously, reporting and response biases may affect these findings.

This study involved a large and diverse cohort of officers and explored motor-vehicle risk perception. A better understanding of officers’ risk perceptions will assist in the development and implementation of occupational injury prevention programs, training, and policy.

Empirical research suggests that traffic enforcement is the most common type of proactive activity police officers engage in on a daily basis. Further, agencies often use traffic enforcement to achieve both traffic safety and crime control. Given these goals, the purpose of this paper is to investigate whether (and to what extent) officers are accurately targeting their proactive traffic enforcement with crime and vehicle crashes in two agencies.

The study examines traffic enforcement patterns in two agencies to see whether proactive traffic enforcement aligns spatially with crime and vehicle crashes. This study employs negative binomial regression models with clustered standard errors to investigate this alignment at the micro-spatial level. Key variables of interest are measured with police calls for service data, traffic citation data and vehicle crash data from two law enforcement jurisdictions.

High levels of spatial association are observed between traffic accidents and crime in both agencies, lending empirical support to the underlying theories of traffic enforcement programs that also try to reduce crime (i.e. “DDACTS”). In both agencies, traffic accidents also appear to be the most prominent predictor of police proactive traffic enforcement activities, even across different times of day. However, when vehicle crashes are accounted for, the association between crime and traffic stops is weaker, even during times of day when agencies believe they are using proactive traffic enforcement as a crime deterrent.

No prior study to authors knowledge has examined the empirical association between police proactive traffic activities and crime and traffic accidents in practice. The current study seeks to fill that void by investigating the realities of traffic stops as practiced daily by police officers, and their alignment with crime and vehicle crashes. Such empirical inquiry is especially important given the prevalent use of traffic enforcement as a common proactive policing tool by police agencies to control both traffic and crime problems.

This viewpoint article chronicles the change and transformation that has taken place in the UK Fire and Rescue Service over the last few years, from the perspective of one Chief Fire Officer/Chief Executive. The article describes a fundamental shift in thinking and approaches ‐ from ‘cure to prevention’ ‐ with every firefighter now expected to be both an emergency response professional, as well as a safety professional.The article also describes the shift from traditional fire brigades to networked fire and rescue services, providing a collaborative approach to managing a wide range of civil emergencies ‐ including the terrorist threat and the impact and consequences of climate change. It details some of the leadership challenges associated with changing a very hierarchical, rule‐bound organisation, with a strong embedded culture and a high level of unionisation. The article identifies the positive social/public good outcomes now emerging ‐ including impressive figures for the reduction of emergency incidents and, crucially, deaths and injuries from fire. The article also includes a brief case study of where the ‘brand’ of the Fire and Rescue Service ‐ and the facilities of the local station ‐ have been used to engage young people in sport as an alternative to gang culture/membership.

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with real-time crash likelihood. Unlike incident detection, the purpose of this line of work is to proactively assess crash likelihood and potentially reduce the likelihood through proactive traffic management techniques, including variable speed limit and ramp metering among others.

Methodology – The chapter distinguishes between the traditional aggregate crash frequency-based approach to safety evaluation and the approach needed for real-time crash risk estimation. Key references from the literature are summarised in terms of the reported effect of different traffic characteristics that can be derived in near real-time, including average speed, temporal variation in speed, volume and lane-occupancy, on crash occurrence.

Findings – Traffic and weather parameters are among the real-time crash-contributing factors. Among the most significant traffic parameters is speed particularly in the form of coefficient of variation of speed.

Research implications – In the traffic safety field, traditional data sources are infrastructure-based traffic detection systems. In the future, if automatic traffic detection systems could provide reliable data at the vehicle level, new variables such as headway could be introduced. Transferability of real-time crash prediction models is also of interest. Also, the potential effects of different management strategies to reduce real-time crash risk could be evaluated in a simulation environment.

Practical implications – This line of research has been at the forefront of bringing data mining and other machine-learning techniques into the traffic management arena. We expect these analysis techniques to play a more important role in real-time traffic management, not just for safety evaluation but also for congestion pricing and alternate routing.

With the ascendance of information technology, particularly through the internet, external information sources and their impacts can be readily transferred to influence the performance of financial markets within a short period of time. The purpose of this paper is to investigate how incidents affect stock prices and volatility using vector error correction and autoregressive-generalized auto regressive conditional Heteroskedasticity models, respectively.

To characterize the investors’ responses to incidents, the authors introduce indices derived using search volumes from Google Trends and the Baidu Index.

The empirical results indicate that an outbreak of disasters can increase volatility temporarily, and exert significant negative effects on stock prices in a relatively long time. In addition, indices derived from different search engines show differentiation, with the Google Trends search index mainly representing international investors and appearing more significant and persistent.

This study contributes to the existing literature by incorporating open-source data to analyze how catastrophic events affect financial markets and effect persistence.