The development of pervasive personal digital devices such as phones, watches, and headphones, interconnected by technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor both vehicular and pedestrian traffic flows within their cities. In modern vehicles, navigation systems interconnect the personal devices of drivers and passengers typically via Bluetooth. By continuously monitoring such devices when they are in discover mode, traffic flows can be estimated almost in real-time. This paper examines traffic data collected from a Bluetooth Traffic Monitoring Systems installed by the Wellington City Council. Potentially such an installation could assist a local authority in real-time monitoring of normal traffic, as well as events including concerts and sport events, or in reaction to unanticipated events such as disasters. The limitation in this technology is that the data collected is of low fidelity, that is: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This paper enumerates and investigates these challenges through statistical modelling, cleaning and data analysis. We present two novel algorithms for the processing of Bluetooth traffic data and validate our algorithms against a physical road counter. A case study is of a major earthquake is then presented as a proof of concept. The earthquake led to road closures, building collapse and other infrastructure damage and we examine three weeks of BTMS data and visualise how this earthquake impacted daily traffic flows.

In this paper we present a novel and comprehensive simulation framework that we have named AMEM (A Multidisciplinary Evacuation Model) for vehicle traffic modelling in urban areas – with a specific focus on large-scale evacuation scenarios. In general, the value of a comprehensive urban traffic modelling system is that it can assist authorities in identifying parts of a road transportation network that exhibit poor performance, or unanticipated and negative emergent properties under a variety of conditions. Such conditions can arise from planned or projected changes to the road infrastructure, or more interestingly, in reaction to uncommon or rare 100 year events. These are not the typical day to day traffic events that can be monitored and measured directly. In AMEM, we combine a number of different elements in our modelling, including routing, car-following, behaviour, driving culture, traffic light signalling, and psychological patterns. We validated the AMEM framework using real traffic data harvested from a network of Bluetooth and road sensors deployed in Wellington, New Zealand, and used this data as the basis for 13 scenarios. We also included in our study a unique socio-technical factor – the use of navigation systems, in part to address the question as to if such systems help or hinder traffic movement during an evacuation. Our results suggest that the use of navigation systems, as currently implemented, have a potentially negative impact on the evacuation process in dense urban areas.

In this article, we present a model for delayed reporting of faults: multiple nonfatal faults are accumulated and then simultaneously reported and repaired. The reporting process is modeled as a stochastic process dependent on the underlying stochastic process generating the faults. We derive the joint distribution of the reporting times and numbers of reported faults, giving general results and results specific to faults generated by a Poisson process. We investigate a number of extensions to the basic model, including multiple fault types (including invisible and fatal faults), preventative maintenance, and customer rush. We show how to simulate from the model and implement maximum likelihood parameter estimation in a simulated dataset and a real dataset of warranty claims from a car manufacturer.

Multicomponent systems may experience failures with correlations amongst failure times of groups of components, and some subsets of components may experience common cause, simultaneous failures. We present a novel, general approach to model construction and inference in multicomponent systems incorporating these correlations in an approach that is tractable even in very large systems. In our formulation, the system is viewed as being made up of independent overlapping subsystems (IOS). In these systems, components are grouped together into overlapping subsystems, and further into nonoverlapping subunits. Each subsystem has an independent failure process, and each component's failure time is the time of the earliest failure in all of the subunits of which it is a part. We apply this method to observations of an IOS model based on a multicomponent system accumulating damage due to a series of shocks, and with no repair/rectification actions. The model associates individual shock processes with each subsystem, and includes the Marshall-Olkin multivariate exponential model as a special case. We present approaches to simulation and to the estimation of the parameters of the model, given component failure data for various system configurations (series, parallel, and other arrangements).

We extend our earlier studies of a multicomponent system accumulating damage due to a series of fatal and nonfatal shocks. The model introduces statistical dependence among the system components by associating individual shock processes with potentially overlapping subsystems made up of groupings of components. We construct an aging and statistical dependence model where damage accumulates multiplicatively with each shock. We derive a representation of the system's joint survival function, and show that the Marshall-Olkin multivariate exponential model can be obtained as a special case of this model. We propose an approach to the simulation of the performance of the system, and provide several illustrative examples. We conclude by identifying possible further extensions of this model.