LIFE Project: Implementing a modelling framework for emergency vehicles advanced priority strategies

Given the aging demographics and rapid urbanisation, cities need to be equipped to respond to emergency (eg. 999 calls) more quickly. By 2050, over 25% of the UK’s population will be over 65. This has implications on the overall health services as well as the NHS Trust to cope with anticipated rise in ambulance call outs amidst worsening urban congestion. Ambulance services are required to reach 75% of emergency calls within 8 minutes. For this reason, there is a growing need to develop new and innovative applications for an even more intelligent use of the existing transport system that will support in real-time emergency vehicles to reach life threatening emergency cases quicker. this paper will discuss the methodology and the preliminary results of the modelling framework implementation of a “Life First Emergency Traffic Control” or “LiFE” system, a ITS implementation seeking to identify the best solution to reduce the time to respond to emergency calls, whilst operating a resilient service with a cost and fuelefficient fleet. Results of the application of a microsimulation model to replicate the behaviour of ambulances in urban area and how different reactions of general traffic can impact on the travel time of an ambulance are presented. The proposed microsimulation modelling framework has been developed with the final aim to understand and evaluate the impacts and the best scenarios to improve ambulance (or any Emergency vehicle) response time and gains in cost-saving, whilst assessing mitigation strategies to reduce other impacts such as residual congestion. The work is part of an Innovate UK collaborative funded project, namely Life First Emergency Traffic Control (LiFE) with the aim to develop an innovative application for an intelligent transport system that operates in realtime to enable ambulances to reach life threatening emergency cases quicker by integrating ambulance route finder applications with traffic management systems

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Demand responsive transport: Generation of activity patterns from mobile phone network data to support the operation of new mobility services

Demand Responsive Transport (DRT), covering the first/last mile of a journey, plays a pivotal role in the delivery of a seamless integrated door-to-door service, which is a fundamental requirement for the implementation of Mobility as a Service (MaaS). Business models currently in use do not deliver sustainable and durable DRT in urban areas. This can be minimised using transport modelling tools ahead of the operation phase. However, transport models are not fit for purpose when it comes to model on-demand shared mobility services and the integration of these services in a complex public transport ecosystem. This paper focuses on how to model demand for ride-shared mobility services and how to plan for these services when running in integration with mass transit. An Agent Based Model (ABM), built in the open-source Multi-Agent Transport Simulation (MatSim) platform for Bristol (UK), has used an activity-based approach to model demand for two New Mobility Services (NMS). This was then generated using anonymised and aggregated Mobile phone Network Dataset (MND), both as a trip-based and trip chains dataset to assess the capabilities of MND. Results show that the simulations built using the trip chains MND datasets (722,752 agents generated) lead to better insights in users’ travel patterns. An advanced method using additional data sources covering land-use (location of business, services and transport facilities) was used to infer purpose and mode of transport during the multimodal journeys. The output of the ABM predicts demand for two flexible on-demand services, identifying best routes to maximise the number of users served and quantifying the benefits in the integration with public transport services and in modal shift from private cars. This is expected to be useful either for Local Authorities for transport planning purposes, and for operators looking at financially sustainable DRT