Search results

The purpose of this paper is to describe how the Philadelphia Police Department instituted a large‐scale randomized controlled trial of foot patrol as a policing strategy and experienced 23 percent fewer violent crimes during the treatment period. The authors examine whether activities patrol officers were conducting might have produced the crime reduction. The activities of foot and car patrol officers research takes a closer look at what types are examined separately and differences between car patrol activities pre‐intervention and during the intervention are explored. Activities of foot versus car patrol officers during the study period are compared across treatment and control areas.

Official data on police officer activity are used to compare activities conducted by foot patrol officers with those by car patrol officers in 60 treatment (foot beat) and 60 control areas consisting of violent crime hot spots. Activities of car patrol officers are described pre‐intervention and during the intervention. Foot patrol officers’ activities are described within treatment and control areas during the treatment phase of the experiment. Car patrol officers’ activities are reported separately. The statistical significance of changes in car patrol activity pre and during intervention is evaluated using a series of mixed model ANOVAs.

There were noticeable differences in the activities conducted by foot and car patrol. Foot patrol officers spent most of their time initiating pedestrian stops and addressing disorder incidents, while car patrol officers handled the vast majority of reported crime incidents. Car patrol activity declined in both treatment and control areas during the intervention but there was no statistically significant difference between the treatment and the control areas.

The major limitation of this study is the restricted set of data describing officer activity that is captured by official records. Future studies should include a more robust ethnographic component to better understand the broad spectrum of police activity in order to more effectively gauge the ways in which foot patrol and car‐based officers’ activities interact to address community safety. This understanding can help extend the literature on “co‐production” by highlighting the safety partnerships that may develop organically across individual units within a police organization.

The study provides evidence that individual policing strategies undertaken by agencies impact one another. When implementing and evaluating new programs, it would be beneficial for police managers and researchers to consider the impact on activities of the dominant patrol style, as necessary, to understand how a specific intervention might have achieved its goal or why it might have failed to show an effect.

The research contributes to the understanding of the separate and joint effects of foot and car patrol on crime. In addition, it provides police managers with a clearer picture of the ways in which foot patrol police and car‐based officers work to co‐produce community safety in violent inner‐city areas.

The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue.

This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.

This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car.

The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.

In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.

Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

This study investigates the mediating role of consumer engagement (CE) in the relationship between perceived behavioral control (PBC) and purchase intent and the moderating role of perceived safety in the relationship between PBC and CE in the self-driving car (SDC) context.

To test the model, a sample of 368 consumers was deployed using partial least-squares structural equation modeling (PLS-SEM).

The findings reveal that consumers' SDC engagement mediates the relationship between PBC and their intent to purchase an SDC. Consumer-perceived SDC safety also moderates the association of PBC/engagement.

While prior research has examined consumer-based drivers of SDC adoption, understanding of consumers' SDC engagement-related dynamics and outcomes lags behind. Addressing this gap, we propose and test a model that explores consumers' SDC engagement vis-à-vis its drivers (perceived SDC safety/behavioral control) and outcomes (SDC purchase intent).