TRAFFIC CONGESTION MODELING WITH DEEP ATTENTION HAWKES PROCESS

In this thesis, we focus on modeling the traffic congestion in the city of Atlanta. We are trying to predict future congestion events on the main highways in Atlanta. We present a novel framework for modeling traffic congestion events over road networks based on mutually exciting Spatio-temporal point process models. We use multi-modal data by combining traffic sensor networks data with police reports, which contain two types of triggering mechanisms for congestion events. To capture the non-homogeneous temporal dependence of the event on the past, we introduce a novel attention-based approach for the point process model. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the spatial statistics context. We demonstrate the superior performance of our approach compared to the state-of-the-art for both synthetic and real data.M.S

Factors influencing learner driver experiences [Road Safety Grant Report 2009-003]

When compared with more experienced drivers, new drivers have a higher crash risk. This study examined the experiences of learner drivers in Queensland and New South Wales in order to develop an understanding of the factors that influenced them while learning to drive. This will enable the development of more effective licensing systems. The research was informed by a number of heoretical perspectives, particularly social learning theory. Participants were recruited from driver licensing centres as soon as they passed their practical driving test to attain a provisional licence. Of those approached, 392 new drivers from capital cities and regional locations in Queensland and New South Wales completed a 35 minute telephone interview that collected information on a range of personal, social, environmental and socio-demographic factors. Participants were obtaining their licence before several changes to the licensing systems in both Queensland and New South Wales were made in 2007. Several implications for countermeasure development resulted from this research. These included ensuring licensing authorities carefully consider mandating a minimum number of hour of practice as it may inadvertently suppress the amount of practice that some learners obtain. Licensing authorities should consider the use of logbooks for learner drivers, even if there is no minimum amount of supervised practice required as it may assist learners and their supervisors structure their practice more effectively. This research also found that the confidence of learner drivers increases between when they first obtain their learner licence and when they obtain their provisional licence. This is an important issue requiring further attention by licensing authorities

A non-parametric Hawkes process model of primary and secondary accidents on a UK smart motorway

A self-exciting spatio-temporal point process is fitted to incident data from the UK National Traffic Information Service to model the rates of primary and secondary ac- cidents on the M25 motorway in a 12-month period during 2017-18. This process uses a background component to represent primary accidents, and a self-exciting component to represent secondary accidents. The background consists of periodic daily and weekly components, a spatial component and a long-term trend. The self-exciting components are decaying, unidirectional functions of space and time. These components are de- termined via kernel smoothing and likelihood estimation. Temporally, the background is stable across seasons with a daily double peak structure reflecting commuting patterns. Spatially, there are two peaks in intensity, one of which becomes more pronounced dur- ing the study period. Self-excitation accounts for 6-7% of the data with associated time and length scales around 100 minutes and 1 kilometre respectively. In-sample and out- of-sample validation are performed to assess the model fit. When we restrict the data to incidents that resulted in large speed drops on the network, the results remain coherent

Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events

The predictive hotspot mapping of sparse spatio-temporal events (e.g., crime and traffic accidents) aims to forecast areas or locations with higher average risk of event occurrence, which is important to offer insight for preventative strategies. Although a network-based structure can better capture the micro-level variation of spatio-temporal events, existing deep learning methods of sparse events forecasting are either based on area or grid units due to the data sparsity in both space and time, and the complex network topology. To overcome these challenges, this paper develops the first deep learning (DL) model for network-based predictive mapping of sparse spatio-temporal events. Leveraging a graph-based representation of the network-structured data, a gated localised diffusion network (GLDNet) is introduced, which integrating a gated network to model the temporal propagation and a novel localised diffusion network to model the spatial propagation confined by the network topology. To deal with the sparsity issue, we reformulate the research problem as an imbalance regression task and employ a weighted loss function to train the DL model. The framework is validated on a crime forecasting case of South Chicago, USA, which outperforms the state-of-the-art benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively

Situation awareness measurement: A review of applicability for C4i environments

The construct of situation awareness (SA) has become a core theme within the human factors (HF) research community. Consequently, there have been numerous attempts to develop reliable and valid measures of SA but there is a lack of techniques developed specifically for the assessment of SA in command, control, communication, computers and intelligence (C4i) environments. During the design, development and evaluation of novel systems, technology and procedures, valid and reliable situation awareness measurement techniques are required for the assessment of individual and team SA, in order to determine the improvements (or in some cases decrements) resulting from proposed design and technological interventions. The paper presents a review of existing situation awareness measurement techniques for their suitability for use in the assessment of SA in C4i environments. Seventeen SA measures were evaluated against a set of HF methods criteria. It was concluded that current SA measurement techniques are inadequate by themselves for use in the assessment of SA in C4i environments, and a multiple-measure approach utilising different approaches is recommended

The prevalence of cocaine-associated chest pain in a London hospital

Objectives: To determine the prevalence of cocaine misuse in patients presenting to an Accident and Emergency department with chest pain. 4 of 12 FAEM abstracts www.emjonline.co