Routine pattern discovery and anomaly detection in individual travel behavior

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling

Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities

As a public infrastructure service, remote sensing data provided by smart cities will go deep into the safety field and realize the comprehensive improvement of urban management and services. However, it is challenging to detect criminal individuals with abnormal features from massive sensing data and identify groups composed of criminal individuals with similar behavioral characteristics. To address this issue, we study two research aspects: pickpocketing individual detection and pickpocketing group identification. First, we propose an IForest-FD pickpocketing individual detection algorithm. The IForest algorithm filters the abnormal individuals of each feature extracted from ticketing and geographic information data. Through the filtered results, the factorization machines (FM) and deep neural network (DNN) (FD) algorithm learns the combination relationship between low-order and high-order features to improve the accuracy of identifying pickpockets composed of factorization machines and deep neural networks. Second, we propose a community relationship strength (CRS)-Louvain pickpocketing group identification algorithm. Based on crowdsensing, we measure the similarity of temporal, spatial, social and identity features among pickpocketing individuals. We then use the weighted combination similarity as an edge weight to construct the pickpocketing association graph. Furthermore, the CRS-Louvain algorithm improves the modularity of the Louvain algorithm to overcome the limitation that small-scale communities cannot be identified. The experimental results indicate that the IForest-FD algorithm has better detection results in Precision, Recall and F1score than similar algorithms. In addition, the normalized mutual information results of the group division effect obtained by the CRS-Louvain pickpocketing group identification algorithm are better than those of other representative methods

Identifying elderlies at risk of becoming more depressed with Internet-of-Things

Singapore National Research Foundation; Singapore Ministry of National Developmen

Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

© 2018 IEEE. The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.e., analyzing the characteristic of the neighborhood space from a single perspective, and could not obtain rich data characteristics. In this paper, we analyze the high-dimensional neighborhood space from multiple perspectives. By simulating each dimension of a data point's k nearest neighbors space (k NNs) as a lever, we apply the lever principle to compute the balance fulcrum of each dimension after proving its inevitability and uniqueness. Then, we model the distance between the projected coordinate of the data point and the balance fulcrum on each dimension and construct the DHBlan coefficient to measure the balance of the neighborhood space. Based on this theoretical model, we propose a simple yet effective cluster boundary detection algorithm called Lever. Experiments on both low- and high-dimensional data sets validate the effectiveness and efficiency of our proposed algorithm

Detective Policing and the State in Nineteenth-century England: The Detective Department of the London Metropolitan Police, 1842-1878

This thesis evaluates the development of surveillance-based undercover policing in Victorian England through an examination of the first centralized police detective force in the country, the Detective Department of the London Metropolitan Police (1842-1878). It argues that the Detective Department overcame British fears that detective police were incompatible with individual liberty and parliamentary democracy, making the English detective a familiar and reliable public servant. The Detective Department, which worked from Scotland Yard, was formed in 1842 in response to criticism that the Metropolitan Police was unable to successfully investigate homicide. This was a surprising development in a country where property crime had always spurred developments in criminal justice. London’s newspapers played a key role in the creation of this detective force by creating a murder scare and demanding that the Metropolitan Police devote more specialized attention to complicated investigations, including homicide. The new detective force remained small to protect the police from accusations of spying. Since murders were infrequent, the new detectives devoted most of their attention to property crime, especially theft. During the 1860s and the economically depressed 1870s, detective priorities reflected a government crackdown on forgery and fraud, crimes that threatened the paper economy upon which Britain’s industrial and mercantile power rested. Detectives also regularly worked for the Home Office to help supplement limited investigative machinery in the counties. Scotland Yard detectives routinely travelled throughout England helping local magistrates investigate felonies ranging from homicide to arson. Scotland Yard’s close relationship with the Home Office was unique in England and resulted from London’s lack of municipal authority. For this reason, Metropolitan Police detectives often acted as agents of the British government, especially when they monitored foreign nationals and refugees that arrived in England following European revolutions in 1830 and 1848. Detectives’ non-felony work for the Home Office, which also included evaluating naturalization applications and performing extraditions, offers a new perspective on Victorian detectives and their cases that is neglected in current historiography

The crime-commission process of sexual offences on London trains (SOLT): offending in plain sight, not just at night

This thesis explores sexual offences that are committed on London trains, which has seen an increase over the past 3 years (BTP, 2018). This research aims to produce a detailed and comprehensive descriptive account of sexual offences on London trains (SOLT), utilising psychological and criminological theoretical constructs, for example, crime script theory and narrative criminology, to understand the commission of such offences. The initial study conducted with proactive officers from the British Transport Police (BTP) (N = 14), provided preliminary findings in relation to the offence specific characteristics and behaviours of SOLT that related to situational and environmental factors. A further study was conducted with a sample of convicted offenders to understand how they make sense of themselves and their actions as perpetrators. Key factors were their post hoc rationalisations for their behaviours and insights regarding how these factors influence their decision-making. The final study of archival police records explored the importance of spatial and temporal factors relating to the behaviours of individuals committing sexual offences in the train environment. Offence characteristics were interrogated to explore relationships between variables, to distinguish between the different sexual offending behaviours for the different offence types. This thesis adds to existing knowledge of how psychological theories can be employed to inform the policing approach and practice to SOLT, as well as adding to the wider literature on sex offending. This research identifies how the complexities of the political, organisational and individual factors impact on the outcome of policing strategies to address SOLT. To complement this new theoretical model, the findings presented in this thesis also provide useful direction for strategic thinking and operational practice within BTP

Protecting Surface Transportation Systems and Patrons from Terrorist Activities, Research Report 94-04

Contemporary terrorists have made public transportation a new theater of operations. Algerian extremists set off bombs on the subways of Paris in 1995 and 1996; the Irish Republican Army has waged a long running terrorist campaign against Britain’s passenger trains and London’s subways; Palestinian terrorists have carried out suicide bombings on Israel’s buses; and an individualor a group calling itself “Sons of the Gestapo” derailed a passenger train in Arizona in 1995. Islamic extremists planned to set off car bombs in New York’s tunnels and bridges in 1993 and in 1997 they plotted suicide bombings in New York subways. The nerve gas attack on Tokyo’s subways by members of the Aum Shinrikyo sect in 1995 raised the specter that terrorists in the future might resort to weapons of mass destruction to which public transportation is uniquely vulnerable. In order to effectively meet the threat posed by terrorism and other forms of violent crime, it is essential that transportation system operators have a thorough understanding of the security measures employed elsewhere, especially by those transportation entities that have suffered terrorist attacks or that confront high threat levels. This volume reports on the first phase of a continuing research effort carried out by the Norman Y. Mineta International Institute for Surface Transportation Policy Studies (IISTPS) on behalf of the U.S. Department of Transportation. It comprises a chronology of attacks on surface transportation systems; four case studies of transportation security measures (in Paris, Atlanta, and New York, and at Amtrak); security surveys of nine additional cities in the United States; and an annotated bibliography of current literature on the topic