No description available.

The current regulatory and planning environment means that road-based public transport in UK urban areas (with the exception of London) tends to be planned on a piecemeal basis, and there are often conflicts between the needs and priorities of operators, passengers and planners. In consequence, several local authorities are considering adopting an alternative regulatory environment using quality contracts, with a consequent shift towards centralised service planning. There are though no tools readily available to ensure this centralised service planning will lead to a situation which provides a better balance between the interests of the different stakeholders. This thesis describes the development of a methodology to fill this gap, using Southampton as a case study to diagnose issues with its current bus network, and to explore the corresponding improvement methods which could be applied based on the alternative regulatory environment. Gravity-based accessibility levels from population-weighted centroids of postcodes to key services were calculated as an indicator to measure the performance of the current bus network in Southampton. Based on the accessibility analysis, service improvements, including both route planning and frequency setting, can be developed for the Southampton bus system under the alternative regulatory environment. This service improvement problem is then solved by making use of an optimisation technique, the tabu search algorithm, developed under the environment of ArcObjects for Java. The methodology described above has been shown to work well for the Southampton case study, and the outputs from the optimisation model indicate that the model can deliver a bus network which provides a higher level of accessibility under the alternative regulatory environment. While the methodology is developed in the UK context, the general principles used could be applied more widely to improve transit network planning.

Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on an urban road network. This thesis contributes to the literature by developing an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined in two different ways: (i) those LJTs that are greater than a threshold, (ii) those LJTs that belong to a statistically significant Space-Time Region (STR). These two different ways of defining the term ‘substantially high LJT’ lead to different NRC detection methods. To evaluate these methods, two novel criteria are proposed. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be related to incidents. The proposed NRC detection methodology is tested for London’s urban road network, which consists of 424 links. Different levels of travel demand are analysed in order to establish a complete understanding of the developed methodology. Optimum parameter settings of the two proposed NRC detection methods are determined by sensitivity analysis. Related to the first method, LJTs that are at least 40% higher than their expected values are found to maintain the best balance between the proposed evaluation criteria for detecting NRCs. Related to the second method, it is found that constructing STRs by considering temporal adjacencies rather than spatial adjacencies improves the performance of the method. These findings are applied in real life situations to demonstrate the advantages and limitations of the proposed NRC detection methods. Traffic operation centres could readily start using the proposed NRC detection methodology. In this way, traffic operators could be able to quantify the impact of incidents and develop effective NRC reduction strategies.

Urban road traffic patterns are a product of the behaviours, choices, and movements of many individual drivers. Realistically capturing and modeling the behaviour of these individuals is, as such, central to the accurate simulation of traffic dynamics. However, many conventional simulation approaches tend to represent driver behaviour in only a simplistic fashion. Individuals are assumed to consistently select the least travel time path with consideration of a multitude of possibilities. Research into traveller behaviour would suggest that the human route selection process, in choosing a route from an origin to a destination, is not so straightforward. In this thesis, advances are made to improve the representation of individual behaviour within traffic simulation, and in doing so improve the predictive power of traffic simulation more widely. Central to this work is the development of a range of new models that incorporate human spatial cognition and bounded rationality into a driver behavioural framework. In the first instance, a large dataset of observed route selections is processed and analysed, extracting a range of novel observations with respect to route choice behaviour. Following this stage, new approaches towards the modelling of route choice are developed, building both on these data analyses and integrating wider observations drawn from the literature. The influence of heterogeneity in individual perception of space is also considered, with a model describing variations in spatial knowledge introduced. These elements are integrated within a framework reflecting realistic heterogeneity in motorist behaviour, and then implemented within a large-scale agent-based model of road transportation around central London, United Kingdom. Traffic speed and flow datasets are extracted from the agent-based model and compared against observed traffic data. It is shown how this new representation of driver behaviour enables a marked improvement in the prediction of real-world traffic dynamics than offered by conventional approaches.

In the digital age, data are collected in unprecedented volumes on a plethora of networks. These data provide opportunities to develop our understanding of network processes by allowing data to drive method, revealing new and often unexpected insights. To date, there has been extensive research into the structure and function of complex networks, but there is scope for improvement in modelling the spatio-temporal evolution of network processes in order to forecast future conditions. This thesis focusses on forecasting using data collected on road networks. Road traffic congestion is a serious and persistent problem in most major cities around the world, and it is the task of researchers and traffic engineers to make use of voluminous traffic data to help alleviate congestion. Recently, spatio-temporal models have been applied to traffic data, showing improvements over time series methods. Although progress has been made, challenges remain. Firstly, most existing methods perform well under typical conditions, but less well under atypical conditions. Secondly, existing spatio-temporal models have been applied to traffic data with high spatial resolution, and there has been little research into how to incorporate spatial information on spatially sparse sensor networks, where the dependency relationships between locations are uncertain. Thirdly, traffic data is characterised by high missing rates, and existing methods are generally poorly equipped to deal with this in a real time setting. In this thesis, a local online kernel ridge regression model is developed that addresses these three issues, with application to forecasting of travel times collected by automatic number plate recognition on London’s road network. The model parameters can vary spatially and temporally, allowing it to better model the time varying characteristics of traffic data, and to deal with abnormal traffic situations. Methods are defined for linking the spatially sparse sensor network to the physical road network, providing an improved representation of the spatial relationship between sensor locations. The incorporation of the spatio-temporal neighbourhood enables the model to forecast effectively under missing data. The proposed model outperforms a range of benchmark models at forecasting under normal conditions, and under various missing data scenarios.

Air quality issues in urban areas are always a big concern. Air pollution especially NO2 and PM exceedances in cities are common. This is particularly true with congested traffic and the monitoring station is at the roadside. It is well known that road transport in the urban area is a major source of air pollution. Though all the vehicles have to comply the EU emission standards, the emissions were tested using the legislated standard driving cycles, which could not represent real world driving emissions. This is because compared to the legislated driving cycle, the real world driving uses different powers, different average speeds, different traffic congestion, different road gradients, different maximum acceleration rates, different cold start conditions, different numbers of stop/start events and occurs at different ambient temperatures and pressures and will inevitably have different emissions. In recognition of importance of real driving emissions, the EU plans to introduce RDE (Real driving emission) test procedure in 2017. This work investigated real world emissions on a congested road by a roadside air quality monitoring station that exceeds European air quality standards for NOx and PM using a portable emissions measurement system (PEMS) and a Euro 4 SI passenger car. The PEMS used was the Temet FTIR with Horiba OBS pitot tube exhaust mass flow sensor and gas sampler. Twenty nine hot start repeat journeys were conducted at different times of the day (morning and evening rush hours, lunch time, night) so that a range of traffic conditions were included and eight cold start tests for the same journeys were conducted and compared with the hot start results and shown to give significantly higher emissions. The vehicle was equipped with thermocouple, lambda sensor and GPS for travel parameter analysis. The GHG including CO2, CH4 and N2O, nitrogen species in the exhaust gases including NO, NO2, N2O, NH3 and HCN and other emissions were analysed. The results were compared to the NEDC and WLTC. One of the suggestions from this work is that the proposal for the EU RDE test procedure do not include congested traffic driving and cold start which will compromise its expected purpose and effect.

The main research question motivating the research in this thesis is whether or not land zone placement (as defined in accordance with Planning Policy Guidance definitions) is a genuinely important determinant of trip generation behaviour. In order to answer this question three unique data sets have been assembled. The key source is the UK Trip Rate Information Computer System (TRICS), particularly the site information relating to (i) Office Developments, (ii) Food Superstores and (iii) Residential Developments. These site types were chosen for scrutiny as they have for various reasons been the focus of enduring media, environmentalist and Government scrutiny in the light of the increasingly mainstream acceptance of sustainable development principles. For Office Developments and Food Superstores single equation trip attraction models, based on the tenets of a standard derived demand modelling framework, are estimated; whereas for Residential Developments a system is estimated encompassing the generation of trips in residential sites and how they interact with levels of car ownership. Due to recurrent small sample problems and issues of heteroskedasticiy all models are estimated using a semi-parametric regression model. The results support the contention that land zone features, as a group of indicators, should be accounted for in trip generation models for office developments. For Food Superstores hou~ehold economies of scale and scope are identified for individuals visiting these sites and public transport services are also not found to be kinked with reductions in car traffic.

This applied research has introduced and subsequently investigated the concept of Road Traffic Violations (RTVs) as a yardstick for the assessment of road safety and its underlying causative factors in developing countries. The research has focused in establishing the relationship between RTVs and the deficiencies in the road environment, primarily those related to the Traffic Control Devices( TCDs) at signalised intersections. An exemplar methodology has been developed to help agencies in developing countries to reduce RTVs by examining their causal factors and by devising measures for dealing with deficient TCDs, thereby improving the road environment. The researchh as been accomplished through three extensive studies carried out in the most accident prone locations of Delhi, India. The first study revealed that over 146 million daily RTVs by motorised traffic occurred in Delhi, of which 23% were related to faulty road environment. Based upon these findings, subsequent studies investigated five types of deficiencies in TCDs at signalised intersections and their role in causing five types of RTVs. The most significant findings from this part of the research included:( 1) Drivers do not always commit violations voluntarily and therefore investigation into such involuntary violations could reveal various factors that compel drivers to violate. (2) There are a number of hidden violations,caused by the absence of TCDs at signalised intersections, which when taken into consideration could facilitate a more comprehensive assessment of the risks faced by road users. Furthermore the findings have been compared with the views of the stakeholders and 100 drivers of commercial vehicles. The research has also compared the violations data with feedback from on-the-spot field interviews held with 600 violators. Finally, this applied research has proposed a number of recommendations on engineering, education and enforcement to deal with RTVs and deficiencies in TCDs to improve road safety.

Hydrogen is a potential solution to transport's environmental challenge However. current production and delivery methods may make hydrogen no more environmentally frrendly than many other transport fuels, Transporting hydrogen IS difficult and energy Intensive Given the right production and delivery system, a future Hydrogen economy could address enVIronmental Issues and other major areas of concern such as energy secunty and shortage This research focuses on the viable pathways to deliver hydrogen for fleet vehicles DraWing on a range of sources, including the recent Clean Urban Transport for Europe (CUTE) demonstration projects, the research models a set of pathway options companng:- • Economics - the cost of hydrogen for fleet vehicles compared to a base diesel reference case, • Environmental issues - CO2 equivalent emissions for each of the pathways Overall, the results of this research will show that:- • Hydrogen is potentially competitive with diesel in terms of cost of production, but not for cost of distribution. Overall distribution costs make hydrogen pathways more expensive than diesel. • Localised production of hydrogen is not competitive with centralised production at present. so it is likely that a hydrogen distribution system is going to be needed It is possible that future localised production systems may be competitive but would depend on reduced capital equipment costs. • The cheapest hydrogen pathways may not be the pathways with the least emissions • The storage of hydrogen appears to be a major part of distribution costs. • Gaseous hydrogen delivery by road tanker can only meet small niche markets. • Transporting hydrogen in liquid state is not viable for any supply chain lengths and demands in the UK (within the boundaries of this model ie: 200km and 5,000kg I day). • Gaseous hydrogen delivery by pipeline is needed if a reasonable uptake is sought. This would require significant investment.

Autonomous vehicle navigation can be divided into two major areas of research: Collision avoidance and Track-Keeping. This study focuses on Collision avoidance which is one of the major issues that unmanned autonomous vehicles have to face. Collision avoidance may be further grouped into classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms, while soft-computing techniques are based on Artificial Intelligence. In this study, we focus on the Classical techniques and more specifically in the Potential Field Methods. The potential field algorithms rapidly gained popularity due to their simplicity and elegance. In other words, Potential Field Methods are generic, computationally efficient and generate naturally smooth trajectories. On the other hand, PFM algorithms experience local minima. Nevertheless, local minima for PFM are extensively studied in different environments; they have never studied in a Pure Dynamic Environment (PDE). PDE is a new dynamic environment in which all its elements are guaranteed to be dynamic at their initial state. In this way we have managed to identify and define the causes of Potential Field Agent local minima and trajectory inefficiencies in a number of collision scenarios within PDE. To efficiently and accurately identify and define these causes of local minima and traj ectory inefficiencies, we have introduced the novel concept of the Monovular Autonomous Agent Correlation. Based on this concept we have identified and mathematically defined the Trajectory Equilibrium State (TES) for the first time. This state is responsible for local minima and trajectory inefficiencies of Monovular Autonomous Agents in PDE. Because of TES identification and definition we have designed a lUle based mathematical algorithm that efficiently navigates the Autonomous Agents out of local minima and trajectory inefficiencies in PDE in a number of generic collision scenarios. The algorithm's performance is tested in a number of simulated water based collision scenarios.