Sensing and perception technology to enable real time monitoring of passenger movement behaviours through congested rail stations

© 2015 ATRF, Commonwealth of Australia. All rights reserved. Passenger behaviour can have a range of effects on rail operations from negative to positive. While rail service providers strive to design and operate systems in a manner that promotes positive passenger behaviour, congestion is a confounding factor, which can create responses that may undermine these efforts. The real time monitoring of passenger movement and behaviour through public transport environments including precincts, concourses, platforms and train vestibules would enable operators to more effectively manage congestion at a whole-of-station level. While existing crowd monitoring technologies allow operators to monitor crowd densities at critical locations and react to overcrowding incidents, they do not necessarily provide an understanding of the cause of such issues. Congestion is a complex phenomenon involving the movements of many people though a set of spaces and monitoring these spaces requires tracking large numbers of individuals. To do this, traditional surveillance technologies might be used but at the expense of introducing privacy concerns. Scalability is also a problem, as complete sensor coverage of entire rail station precinct, concourse and platform areas potentially requires a high number of sensors, increasing costs. In light of this, there is a need for sensing technology that collects data from a set of ‘sparse sensors’, each with a limited field of view, but which is capable of forming a network that can track the movement and behaviour of high numbers of associated individuals in a privacy sensitive manner. This paper presents work towards the core crowd sensing and perception technology needed to enable such a capability. Building on previous research using three-dimensional (3D) depth camera data for person detection, a privacy friendly approach to tracking and recognising individuals is discussed. The use of a head-to-shoulder signature is proposed to enable association between sensors. Our efforts to improve the reliability of this measure for this task are outlined and validated using data captured at Brisbane Central rail station

Potential applications of randomised graph sampling to invasive species surveillance and monitoring.

Abstract Many invasive plants and animals disperse preferentially through linear networks in the landscape, including road networks, riparian corridors, and power transmission lines. Unless the network of interest is small, or the budget for surveillance is large, it may be necessary to draw inferences from a sample rather than a complete census on the network. Desired features of a surveillance system to detect and quantify invasion include: (1) the ability to make unbiased statements about the spatial extent of invasion, the abundance of the invading organism, and the degree of impact; (2) the ability to quantify the uncertainty associated with those statements; (3) the ability to sample by moving within the network in a reasonable fashion, and with little wasted non-measurement time; and (4) the ability to incorporate auxiliary information (such as remotely sensed data, ecological models, or expert opinion) to direct sampling where it will be most fruitful. Randomised graph sampling (RGS) has all of these attributes. The network of interest (such as a road network) is recomposed into a graph, consisting of vertices (such as road intersections) and edges (such as road segments connecting nodes). The vertices and edges are used to construct paths representing reasonable sampling routes through the network; these paths are then sampled, potentially with unequal probability. Randomised graph sampling is unbiased, and the incorporation of auxiliary information can dramatically reduce sample variances. We illustrate RGS using simplified examples, and a survey of Polygonum cuspidatum (Siebold & Zucc.) within a high-priority conservation region in southern Maine, USA

Understanding Extended Projected Profile (EPP) Trajectory Error Using a Medium-Fidelity Aircraft Simulation

A critical component of Trajectory-Based Operations is the ability for a consistent and accurate 4-dimensional trajectory to be shared and synchronized between airborne and ground systems as well as amongst various ground automation systems. The Aeronautical Telecommunication NetworkBaseline 2 standard defines the Extended Projected Profile (EPP) trajectory that can be sent via Automatic Dependent Surveillance-Contract from an aircraft to ground automation. The EPP trajectory message contains a representation of the reference trajectory from an aircrafts Flight Management System (FMS). In this work, a set of scenarios were run in a medium-fidelity aircraft and FMS simulation to perform an initial characterization of EPP trajectory errors under a given set of conditions. The parameters investigated were the route length, route type, wind magnitude error, wind direction error, and with and without a required time-of-arrival constraint

Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies

Study of real-time traffic state estimation and short-term prediction of signalized arterial network considering heterogeneous information sources

Compared with a freeway network, real-time traffic state estimation and prediction of a signalized arterial network is a challenging yet under-studied field. Starting from discussing the arterial traffic flow dynamics, this study proposes a novel framework for real-time traffic state estimation and short-term prediction for signalized corridors. Particle filter techniques are used to integrate field measurements from different sources to improve the accuracy and robustness of the model. Several comprehensive numerical studies based on both real world and simulated datasets showed that the proposed model can generate reliable estimation and short-term prediction of different traffic states including queue length, flow density, speed and travel time with a high degree of accuracy. The proposed model can serve as the key component in both ATIS (Advanced Traveler's Information System) and proactive traffic control system

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: i, anatomizing the whole spread process among all subpopulations into disjoint componential patches; ii, inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; iii, recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.Comment: 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetic