Browsing by Author "Quddus, Mohammed"

Now showing 1 - 7 of 7
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    Analysing parking search (‘cruising’) time using generalised multilevel structural equation modelling
    (Journal of Transport Economics and Policy, 2018-07-01) Brooke, Sarah; Ison, Stephen; Quddus, Mohammed
    The aim of this paper is to identify factors influencing parking search (cruising) time. A revealed-preference on-street parking survey was undertaken with individual drivers in four UK cities to investigate the influence of personal, trip, socio-economic, physical, time49 related, and price-related variables on parking search. In order to address the potential endogeneity problems between the factors (e.g. parking fee and parking search time) and hierarchical issues in the survey data, a generalised multilevel structural equation model was applied. It was revealed that cruising time could be reduced by seeking drivers to pay for parking as a way of improving social welfare.
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    Commercial airline pilots' job satisfaction before and during the COVID-19 pandemic: A comparative study
    (Elsevier, 2024-02-20) Vulturius, Saskia; Budd, Lucy; Ison, Stephen; Quddus, Mohammed
    The impact of the COVID-19 pandemic on the world's commercial aviation industry was unprecedented. National lockdowns and border closures effectively prohibited passenger air travel. Airlines responded by reducing operations, parking aircraft and making staff, including pilots, redundant. This research aims to examine the impact of COVID-19 on commercial airline pilots' job satisfaction before and during the pandemic and identify the workplace factors that affect it. Empirical data was gathered via an online survey which was distributed to members of three commercial airline pilot unions in Europe and Australasia in November 2021. 346 complete responses were received. Using Herzberg's 16 workplace factors as a theoretical frame for the survey and subsequent analysis, the findings showed that, overall, job satisfaction decreased during the pandemic. The largest effect sizes were observed for Salary, Job Security and Working Conditions while the smallest effect sizes were observed for Impacts on Personal Life, Responsibility and Recognition. The importance of effective communication between airline management and pilots was highlighted. The findings and recommendations regarding employee compensation, benefits and support packages are of relevance not only to airlines but also to other transport and economic sectors facing future disruptive events.
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    Evaluating the impact of a workplace parking levy on local traffic congestion: The case of Nottingham UK
    (Elsevier, 2017-07-28) Dale, Simon; Frost, Matthew; Ison, Stephen; Quddus, Mohammed; Warren, Peter
    A Workplace Parking Levy (WPL) scheme raises a levy on private non-domestic off street parking provided by employers. In April 2012 Nottingham became the first UK City to implement such a scheme with the revenue generated hypothecated for funding transport improvements. The lag between the introduction of the WPL and the opening of related public transport improvements represents an opportunity to study the impact of a WPL on congestion as a standalone measure. In order to achieve this it is necessary to consider changes to variables external to the WPL, which also impact on congestion, which may obscure any beneficial impact of the scheme. An autoregressive time series model which accounts for the impact of these exogenous variables is used to evaluate the impact of the introduction of the WPL on congestion. Delay per Vehicle Mile is used as the dependent variable to represent congestion while the number of Liable Workplace Parking Places (LWPP) is used as a continuous intervention variable representing the introduction of the WPL. The model also contains a number of economic, transportation and climatic control variables. The results indicate that the introduction of the WPL as measured by the number of LWPP has a statistically significant impact on traffic congestion in Nottingham. Additionally, external explanatory variables are also shown to impact on congestion, suggesting that these may be masking the true impact of the scheme. This research represents the first statistical analysis of the link between the introduction of a WPL and a reduction in congestion.
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    A New Modelling Approach for Predicting Vehicle-based Safety Threats
    (IEEE, 2022-03-14) Formosa, Nicolette; Quddus, Mohammed; Ison, Stephen; Timmis, Andrew
    Existing autonomous driving systems of intelligent vehicles such as advanced driver assistant systems (ADAS) assess and quantify the level of potential safety threats. However, they may not be able to plan the best response to unexpected dangerous situations and do not have the ability to cope with uncertainties since not all vehicles can always keep a safe gap from preceding vehicles and drive at a desired velocity. Previous research has not taken such uncertainties into account, it is, therefore, necessary to develop models which are not restricted by the predefined movement patterns of a vehicle. Existing systems are based on a model that estimates the threat level based only on one factor Time-To-Collision (TTC). This approach is limited since it cannot handle all scenarios and ignores all uncertainties. To overcome these limitations, this paper utilised deep learning to develop a range of models that rely on a group of factors to reliably estimate the threat level and predict conflicts under uncertainty using the concept of looming ’. Comparative analyses were undertaken by incorporating new varying input factors to each model (e.g., surrogate safety measures, vehicle kinematics, macroscopic traffic data). Real-world experiments demonstrated that adding new factors increases the reliability and sensitivity of the models. Results also indicated that the models that consider looming provide low false alarm rate extending their applications for a wider spectrum of traffic scenarios. This is paramount for ADAS as uncertainties are inherent in the deployment of connected and autonomous vehicles in a mixed traffic stream.
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    Predicting real-time traffic conflicts using deep learning
    (Elsevier, 2020-01-10) Formosa, Nicolette; Quddus, Mohammed; Ison, Stephen; Abdel-Aty, Mohamed; Jinghui, Yuan
    Recently, technologies for predicting traffic conflicts in real-time have been gaining momentum due to their proactive nature of application and the growing implementation of ADAS technology in intelligent vehicles. In ADAS, machine learning classifiers are utilised to predict potential traffic conflicts by analysing data from in-vehicle sensors. In most cases, a condition is classified as a traffic conflict when a safety surrogate (e.g. time-to-collision, TTC) crosses a pre-defined threshold. This approach, however, largely ignores other factors that influence traffic conflicts such as speed variance, traffic density, speed and weather conditions. Considering all these factors in detecting traffic conflicts is rather complex as it requires an integration and mining of heterodox data, the unavailability of traffic conflicts and conflict prediction models capable of extracting meaningful and accurate information in a timely manner. In addition, the model has to effectively handle large imbalanced data. To overcome these limitations, this paper presents a centralised digital architecture and employs a Deep Learning methodology to predict traffic conflicts. Highly disaggregated traffic data and in-vehicle sensors data from an instrumented vehicle are collected from a section of the UK M1 motorway to build the model. Traffic conflicts are identified by a Regional–Convolution Neural Network (R-CNN) model which detects lane markings and tracks vehicles from images captured by a single front facing camera. This data is then integrated with traffic variables and calculated safety surrogate measures (SSMs) via a centralised digital architecture to develop a series of Deep Neural Network (DNN) models to predict these traffic conflicts. The results indicate that TTC, as expected, varies by speed, weather and traffic density and the best DNN model provides an accuracy of 94% making it reliable to employ in ADAS technology as proactive safety management strategies. Furthermore, by exchanging this traffic conflict awareness data, connected vehicles (CVs) can mitigate the risk of traffic collisions.
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    The impact of COVID-19 related flight reductions on bird prevalence and behaviour at Manchester Airport, UK: the implications for airport management.
    (Elsevier, 2024-01-05) Budd, Lucy; Bloor, George; Ison, Stephen; Quddus, Mohammed
    Airport management is a complex and multifarious activity, involving many operators including airlines, retailers and ground handlers, and processes. The presence of wildlife at airports poses a safety risk to aircraft operations and as such managing wildlife hazards is a mandatory legal responsibility. This is important not only from a safety perspective but also from the fact that safety incidents can impact the operational efficiency and the reputation of an airport. Airport operators are required to devise and enact site-specific Wildlife Hazard Management Plans (WHMP) to reduce the risk of aircraft-wildlife interaction under normal airport operating conditions. The COVID-19 pandemic, however, led to an unprecedented reduction in commercial air traffic and the partial or total suspension of flights at some airports. The aim of this paper is to examine the impact of COVID-19 related flight reductions on bird prevalence and behaviour and the potential implications for airport management. Drawing on an empirical dataset of wildlife observations at Manchester Airport, UK, in 2019 and 2020, this paper details the airfield ornithology before and during the pandemic and examines the impact of COVID-19 related flight reductions on bird prevalence and behaviour. The findings reveal variations in the frequency and apparency of individual species as well as changes in the spatial location of bird sightings on the airfield. The paper concludes by discussing the implications of these findings for post-pandemic operations and for the formulation of future airport wildlife hazard management policies.