Comparison of signalized junction control strategies using individual vehicle position data

This paper is concerned with the development of control strategies for urban signalized junction that can make use of individual vehicle position data from localization probes on board the vehicles. Strategy development involves simulating the behaviour of vehicles as they negotiate junctions controlled by prototype strategies and evaluating performance. Two strategies are discussed in this paper, a simple auctioning agent strategy and an extended auctioning agent strategy where a machine learning approach is used to enable agents to be trained by a human expert to improve performance. The performance of these two strategies are compared with each other and with the MOVA algorithm in simulated tests. The results show that auctioning agents using individual vehicle position data can out perform MOVA, but that this performance can be improved further still by using learning auctioning agents trained by a human expert

The use of simulation in the design of a road transport incident detection algorithm

Automatic incident detection is becoming one of the core tools of urban traffic management, enabling more rapid identification and response to traffic incidents and congestion. Existing traffic detection infrastructure within urban areas (often installed for traffic signal optimization) provides urban traffic control systems with a near continuous stream of data on the state of traffic within the network. The creation of a simulation to replicate such a data stream therefore provides a facility for the development of accurate congestion detection and warning algorithms. This paper describes firstly the augmentation of a commercial traffic model to provide an urban traffic control simulation platform and secondly the development of a new incident detection system (RAID-Remote Automatic Incident Detection), with the facility to use the simulation platform as an integral part of the design and calibration process. A brief description of a practical implementation of RAID is included along with summary evaluation results

SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

BACKGROUND: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript

Signal control using vehicle localization probe data

This paper presents a simulation test bed and methodology for evaluating urban signalized junction control algorithms that use localization probe data from all vehicles in the local area. The simulator is based on SIAS Paramics micro-simulation software with bespoke software modules built on top for automatic network generation, localization data processing and signal control. Localization algorithms tested use a hierarchical structure of auctioning agents. Early tests of control algorithms on an isolated signalized junction indicate performance that compares favourably with the MOVA algorithm using inductive loop data.<br/

Towards safer roadside behavior on the school journey through interactive video training

Active travel in the form of walking can contribute to recommended levels of daily exercise and is linked to increased health and wellbeing. Promoting active modes for school travel, such as walking, has become commonplace in recent years. In the United Kingdom, Safe Routes to Schools programs demonstrate one method of promoting walking, whilst attempting to ensure the safety of children during their school journey through interventions which include child pedestrian training. The quality of child pedestrian training programs in the United Kingdom has suffered in recent years due to austerity measures and time pressures forcing local authorities to reduce the amount of practical training and increase the amount of less effective, but cheaper, paper-based classroom activities. This paper considers the effectiveness of an interactive video which has been developed as an alternative to these paper-based activities designed to target and improve the crossing behavior of children between parked cars. In an exploratory study targeted at elementary school aged children, significant improvements in certain crossing behaviors were demonstrated as a result of training with the interactive video, indicating its potential to significantly improve the range of resources currently available for use by road safety training 15 professional

Simulating the impacts of strong bus priority measures

Policies to reduce levels of traffic congestion and pollution in major urban areas often focus strongly on the concept of a sustainable transport system, but to achieve this vision a significant modal shift from private car to public transport will be required. This paper reports on a recent research study which provides a framework within which to model the behavioral responses of travelers following the implementation of strong bus priority measures (where road capacity is deliberately removed from general traffic and given to buses). A summary of the different behavioral responses which can be expected is given and results from a practical implementation of the framework which has been based on two commercial transport modeling packages (CONTRAM and TRIPS) are discussed. These results suggest firstly that the effect of implementing such strong bus priority measures is as dependent on the characteristics of the local travelers as on the scheme itself and secondly that implementing too strong a scheme may not benefit public transport overall

A data fusion framework for travel time estimation in urban traffic networks

Underlying all attempts to manage urban traffic congestion is the need for a comprehensive knowledge and understanding of the state of all parts of the network at all times. This need has given rise to a diverse range of real time traffic detection methodologies and while much research on network state estimation has been carried out based on these data sources, the different characteristics of the detection datasets often potentially produce differing (if not conflicting) estimates of either the absolute value or the short term trend in urban travel times. This paper presents a theoretical data fusion framework which enables two of the commonest forms of real time traffic data to be combined to create a single best estimate of travel time along an urban road segment. It is proposed that rather than the unidirectional evolutionary approach of many existing travel time estimation systems, improvements in the absolute accuracy of the travel time estimates and reductions in time lag effects can be achieved by combining the currency (but limited spatial relevance) of inductive loop data with the accuracy (but post-event nature) of number plate recognition and matching data. This is achieved by applying a principle of memory to the estimated travel time series where, rather than being discarded as the estimated series evolves, previous estimates are reassessed when their accuracy is revealed at a future moment in time, with the revealed error being translated back to the current time point to improve the accuracy of future estimates. This paper proposes a generic data fusion framework based on this principle, building on both existing and emerging real time traffic detection systems and existing research into urban travel time estimation

Comparison of signalized junction control strategies that use localization probe data

This paper presents a simulation test bed and a methodology for evaluating urban signalized junction control algorithms that use localization probe data from all vehicles in the local area. The simulator is based on SIAS Paramics microsimulation software with bespoke software modules built on top for automatic network generation, localization data processing and signal control. Also presented are results from tests carried out using the simulation test bed to evaluate localization strategies. The tested strategies use a hierarchical structure of auctioning agents. Results from tests on an isolated signalized junction indicate that the performance of the auctioning agent algorithms compare favourably with the MOVA algorithm using inductive loop data. Results are also presented for tests on a twin junction where strategies are synchronized. These show a significant improvement in performance through synchronization

Determining rail network accessibility

The usual representation of optimal path finding problems within transport networks is focused on well established algorithms for identifying the optimal path (or set of paths) between two specific network nodes. When the required solution is the identification of the optimal route between every possible pair of nodes in the network however, these algorithms are inefficient.The Floyd-Warshall algorithm provides an efficient way to compare all possible paths through each pair of nodes more efficiently, requiring only N3 comparisons for a network of N nodes. To illustrate the potential of this approach to network analysis within transport research, this paper considers the issue of determining accessibility between railway stations (on the route between Weymouth and London Waterloo) served by a mixture of high-speed and stopping services.A rail network is physically defined by the locations of tracks, but travel times are also dependent on whether stations are visited by high-speed services as well as stopping services. A single rail route therefore has to be represented not as a (topologically) straight line, but as a more traditional graph with high connectivity between nodes. Reformulating this into a matrix-based definition allows the Floyd-Warshall algorithm to efficiently determine the optimal routing (and hence travel times) between<br/

Are we looking where we are going? An exploratory examination of eye movement in high speed driving

This paper reports on results of an exploratory study aimed at examining driver glance behaviour, near the onset of and during congestion on motorways and how it is affected by factors external to the vehicle. Data has been collected on eye movements from six test subjects, each undertaking three test drives. Analysis has examined average glance times and fraction of time spent looking into a number of broadly defined areas. The study has revealed that on the whole drivers spend 80% of their time looking into a ‘forward’ area and, on average, look away from the forward scene for around 0.65 sec. at a time. In addition to variations between subjects, factors such as road section were found to contribute to variation, however no firm dependence on the level of traffic flow was found. It is hoped that this exploratory study has helped to reveal a number of ‘baseline’ dependencies regarding glance behaviour, and further, that this information will be of use to a range of fields, from the design of invehicle telematics systems, though to simulation science