The global water sector faces unprecedented challenges from climate change, rapid urbanisation, and ageing infrastructure, necessitating a shift towards proactive, digital strategies. Historically characterised as "data rich but information poor," the sector struggles with underutilised and siloed operational data. Traditional machine learning (ML) models have provided a foundation for smart water management, and subsequently deep learning (DL) approaches utilising algorithmic breakthroughs and big data have proved to be even more powerful under the right conditions. This paper explores and reviews the transformative potential of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs), enabling a paradigm shift towards data-centric thinking. GenAI, particularly when augmented with Retrieval-Augmented Generation (RAG) and agentic AI, can create new content, facilitate natural language interaction, synthesise insights from vast unstructured data (of all types including text, images and video) and automate complex, multi-step workflows. Focusing on the critical area of drinking water quality, we demonstrate how these intelligent tools can move beyond reactive systems. A case study is presented which utilises regulatory reports to mine knowledge, providing GenAI-powered chatbots for accessible insights and improved water quality event management. This approach empowers water professionals with dynamic, trustworthy decision support, enhancing the safety and resilience of drinking water supplies by recalling past actions, generating novel insights and simulating response scenarios.

Many responsive traffic signal control strategies are “pressure-driven”. These strategies move green-time from stages with a lower pressure to stages with a higher pressure, at each junction; often aiming to approximately equalise stage pressures at each junction. In some of these pressure-driven strategies each stage pressure at a junction includes a “backpressure” term, which depends on downstream links. Such backpressure terms reduce the pressure on stages and reduce pressure most for those stages leading toward the most highly congested downstream road links. The expectation is that there will then be a reduction in traffic flow exiting the junction toward already highly congested road links, and that this will reduce queue lengths and network delays. This paper shows that this is not always the case by giving two examples where having backpressure within the control policy on a network with two signal controlled junctions(1) increases both equilibrium delays at one of the junctions for certain demands, and(2) never reduces any of the equilibrium delays at either junction.A simple dynamic evolution with a steady reduction in demand is also shown in which two of the considered policies, one with backpressure, give rise to equilibrium delays and queues which tend to infinity, and reveal a gap; in this gap feasible demands have no equilibrium consistent with the two control policies and in a day-to-day dynamical context delays and queues are unbounded. This paper considers only the simple network shown in figure 1 under certain specified conditions. One main avenue for further research might be to consider whether the results shown here also hold in different networks under different conditions.

The local responsive traffic signal control policy P0 was designed to maximise network capacity under certain conditions and it has been shown, in Smith (1979a, b, 1980) and Smith et al. (2019a, 2022), that the P0 policy and related policies do indeed maximise the capacity of many steady state networks or quasi-dynamic networks with vertical and spatial queues under various conditions. This current paper shows, by giving an example, that if queueing is spatial then the original policy P0 itself may not maximise network capacity, even if the queue storage capacity of each link is very large.

This paper considers a simple network with a merge and a downstream bottleneck, which corresponds to a very congested part of the City of York road network -Gillygate. We focus on "upstream-gating" control strategies which hold traffic back at traffic signals just ahead of the merge to prevent the formation of a queue at a downstream bottleneck; and we also consider different ways of further, additionally, controlling the two inflows to the merge. We show, by considering the simple network, that if the added upstream merge-control uses only flows to control the two approaches to the merge then equilibrium may not exist. For example, with the "zipper" rule (which equalises the two inflows at the merge) an equilibrium cannot exist for certain feasible demands on this network. On the other hand, we show that adding upstream merge-control which equalises the two delays felt at the merge allows an equilibrium for all feasible demands on this network, and so maximises the network capacity, notwithstanding the upstream gating. This suggests that, in general, delays should probably be used to control merges and downstream queues, rather than only flows, if network capacity is to be maximised. This observation may help the design of good control strategies, using both flows and delays, for upstream-gating designed to remove or reduce queues at specific downstream locations. The equilibrium analysis in our example is supported by (i) a dynamic analysis allowing for the dynamic growth of queues in the example network and (ii) real-life results of upstream-gating applied to Gillygate in York (UK) which provided motivation for this paper. The analysis here makes reasonable allowance for the spatial extent of queues but does not consider within-cycle or cycle-to-cycle queueing dynamics.

Reducing stationary or very slowly moving queues is one way of reducing congestion, pollution, inefficient stop–start travel and carbon emissions in cities. This paper considers traffic signal control and road pricing together; aiming to eliminate queueing in at least a subnetwork. Link-exit green-times and link-exit bottleneck delays are considered first in some detail; largely using a simple network. The paper then shows that policy P0, specified in Smith (1980, 2015), is capacity-maximising for a general network with vertical queueing delays. Then link exit prices, co-ordinated with green-times and also red times, are considered. It is shown that using prices (instead of delays) in the P0 control policy maximises the capacity of a general steady state network, with zero queues. This steady state capacity-maximisation ＋ zero-queue result is then extended to dynamic networks in two ways; an equilibrium extension and a day-to-day stability extension. The equilibrium extension shows that P0-with-prices maximises network capacity with zero queues in a dynamic network and the stability extension shows that a smoothed version of the P0-with-prices policy, called P0f, is able to deliver some stability as well as zero queue capacity maximisation. A simple example network has been given to illustrate several of the control-with-prices policies. It is shown that a biased version of P0-with-prices, Ph-with-prices, yields, for this simple network, higher utility than P0-with-prices itself.

Mobility is fundamentally important in enabling people to access services, which can deliver substantial benefits to people's quality of life. This is particularly important in rural areas where the range and extent of public transport services are, in general, significantly lower than in urban areas, and, indeed, below the level required to provide a level of service that is sufficiently high to enable people in rural areas without private cars to access these services. This paper looks at the role of governments in institutional, organisational, regulatory and financial frameworks in supporting rural transport services at a level that enables this access. A cluster analysis is performed to identify distinct classes of framework types across Europe. In addition, the paper gives examples of good practice and innovation in rural transport from across Europe incorporating a variety of themes: the use of Information and Communications Technology, intermodal service coordination, demand-responsive transport, shared mobility and good governance. Key success factors for introducing, sustaining and transferring these forms of good practice are then discussed.

This paper seeks traffic signal control policies which maximise long-run network throughput when demand is within network capacity and also when demand is beyond network capacity. The paper considers smooth versions of two well-known responsive traffic signal control policies: the Po policy (see Smith, 1979b, 1980 or Smith et al., 2015) and the maxpressure or MP policy (see Varaiya, 2013a). The paper gives two simple example networks where both of these smooth responsive traffic control policies give rise to decreasing throughput for increasing demands; when these demands are beyond the network capacity. The paper then suggests a simple modification of the two responsive traffic control policies and shows that the two modified policies do maximise the long run throughput of the two signal-controlled networks; both when a steady input demand D is within network capacity and also when a steady demand exceeds network capacity. The modified version of P-0 is as follows: for a certain positive number K, for D <= K use responsive policy P-0, and for D > K use the green-times generated by P-0 when D = K. Thus in this modification the green-time proportions generated by responsive P-0 are frozen as demand passes through K; and remain fixed for all demands D which exceed K. The paper shows that the above modification when applied to a continuous version of MP also maximises throughput in both networks for all values of the steady demand D. Finally the paper considers a signal controlled network with route choice. It is shown that if routes are chosen by travellers in their own best interests then the continuous version of MP does not always maximise throughput, even if the constant input demand D is within the network capacity, but P-0 does always maximise throughput, even when the constant input demand D is beyond the network capacity.

City transport systems often struggle to cope with high volumes of traffic and become congested, despite the use of various traffic management strategies. The concentration of traffic around city centres results in pollution and poor urban air quality, although the increasing popularity of electric vehicles is helping ameliorate these effects. One reason for the growing momentum behind electric vehicles is the emergence of mobility operators such as car-sharing companies, who target users wishing to rent out vehicles on a short-term basis. There is currently rapid growth in one-way car-sharing, in which the vehicle can be dropped off at a different location to the pickup point. Crucially, one-way car-sharing gives the opportunity for travellers to utilise car-sharing in conjunction with other modes, such as public transport modes, for their journey provided the requisite intermodal connections are present. This paper looks at how one-way electric vehicle car-sharing systems have the potential to become important components of future city transport systems. The future role of shared autonomous vehicles is also considered.

The following images and texts taken from the future presentation to be made in April at CORP 2018 conference and used here to constitute a full paper.

Rural areas generally have lower and more dispersed demands for travel which cannot sustain conventional public transport services and consequently have a greater number of flexible and demand responsive transport services operating. These services usually operate on a stand-alone basis, are often subsidized and are typically only accessible by certain passenger types or for specific trip purposes. This generally results in uncoordinated and inefficient transport provision overall. The Flexible integrated transport services (FITS) system featured in this paper has been designed to address this problem. FITS can be used as a planning tool to assess potential benefits from relaxing operating constraints (e.g., a service's operating boundaries), which can potentially suggest service redesign. It also includes the capacity to assign subsidy payments on a trip by trip basis to increase cost efficiency whilst meeting a greater proportion of transport needs. The case study in the paper focusses on transport to health in the Aberdeenshire and Morayshire areas of Scotland in the UK. Despite flexible transport operators receiving public funds to meet passenger needs, this is currently being supplemented by public bodies paying large amounts in taxi fares in instances where there is a statutory obligation to provide travel but where no other suitable transport service exists. The results demonstrate the potential substantial savings which could be realized by allowing transport operators to redesign their services by relaxing constraints and by the reassignment of subsidies: resulting in more passenger demands being met and a reduction in public spending on taxi fares.

Purpose This chapter presents a novel visualisation tool, known as Flexible Integrated Transport Services (FITS) that transport commissioners, providers and administrators could employ to specify and edit the operating constraints as they redesign transport services.

The traffic assignment problem aims to calculate an equilibrium route flow vector, generally by seeking a zero of an appropriate objective function. If a continuous dynamical system follows a descent direction for this objective function at each nonequilibrium route flow vector, the system converges to equilibrium. It is shown that when this dynamical system is discretized with a fixed step length, the system eventually approaches close to equilibrium provided that the objective function is continuously differentiable and that the rate of descent is bounded below. The method of successive averages is widely used in traffic assignment; it has a decreasing step size at each iteration. With the same conditions as above, it is shown that the resulting dynamical system converges to equilibrium. In the steady-state model, the necessary conditions are shown to be satisfied, provided that the route cost vector is a continuously differentiable monotone function of the route flow vector. However, continuous differentiability of the cost function is shown not to hold in the dynamic queueing model.

This paper presents idealised natural general and special dynamical models of day-to-day re-routeing and of day to day green-time response. Both green-time response models are based on the responsive control policy P0 introduced in Smith (1979a, b, c 1987). Several results are proved. For example, it is shown that, for any steady feasible demand within a flow model, if the general day to day re-routeing model is combined with the general day to day green-time response model then under natural conditions any (flow, green-time) solution trajectory cannot leave the region of supply-feasible (flow, green-time) pairs and costs are bounded. Throughput is maximised in the following sense. Given any constant feasible demand; this demand is met as any routeing / green-time trajectory evolves (following either the general or the special dynamical model). The paper then considers simple “pressure driven” responsive control policies, with explicit signal cycles of fixed positive duration. A possible approach to dynamic traffic control allowing for variable route choices is outlined. It is finally shown that modified Varaiya (2013) and Le at al (2013) pressure-driven responsive controls may not maximise network capacity, by considering a very simple one junction network. It is shown that (with each of these two modified policies) there is a steady demand within the capacity of the network for which there is no Wardrop equilibrium consistent with the policy. In contrast, responsive P0 on this simple network does maximise throughput at a quasi-dynamic user equilibrium consistent with P0; queues and delays remain bounded in natural dynamical evolutions in this case. It is to be expected that this P0 result may be extended to allow for certain time-varying demands on a much wider variety of networks; to show that this is indeed the case is a challenge for the future.

In order to better leverage past experience of water quality incidents, and to tap into the unique incident database currently being maintained and required by regulatory authorities, a data mining approach is herein proposed. The quality of drinking water is paramount to protecting public health. However water quality failures do occur, with some of the hardest to understand and manage occurring within distribution systems. In the UK, a regulatory process is applied in which water service providers must report on significant water quality incidents, their causes, actions and outcomes. These reports form a valuable resource that can be explored for improved understanding, to help with future incident management and evaluate potential solutions. Case-based reasoning is a knowledge-based problem-solving technique that relies on the reuse of past experience. The WaterQualityCBR software system presented here was developed as such a decision support tool to more effectively manage water quality in distribution systems.

The integration of different transport modes and the development of demand-led transport solutions are seen as promising approaches for enhancing flexibility in transport systems. Moreover, authorities and transport agencies are keen to explore flexible transport systems that will enable transport resources to be optimally utilized, thereby reducing unmet demand. There are particular challenges in rural areas which are often characterized by limited transport service provision, low population density and highly uncertain transport demands. Despite encouraging advances in flexible and demand responsive transport (DRT) services there remains a need to clearly understand operators’ service profiles, service designs and integration challenges (e.g. patient transport with DRT services) if an adequate level of flexibility and integration is to be achieved whilst also taking account of passenger preferences. Against this background, the authors have carried out interviews with stakeholders including DRT service providers, and have organised demonstrations and workshops with transport operators, transport agencies, software developers, local authorities and representatives of NHS in rural Scotland. The main objective of these consultations is to understand service profiles, service designs and other issues including fare models, technology, etc. This has guided the authors in developing a user-friendly prototype platform for a flexible integrated transport system (FITS). The FITS platform acts as a virtual transport marketplace where passenger preferences and requirements are considered; different transport services are integrated and service providers coordinate with one another to create optimal transport options. Argumentation mechanism is used to resolve conflict among passengers’ preferences in a fair manner.

Water distribution systems, and other infrastructures, are increasingly being pervaded by sensing technologies, collecting a growing volume of data aimed at supporting operational and investment decisions. These sensors monitor system characteristics, i.e. flows, pressures and water quality, such as in pipes. This paper presents the application of pattern matching techniques and binary associative neural networks for novelty detection in such data. A protocol for applying pattern matching to automatically recognise specific waveforms in time series based on their shapes is described together with a system called Advanced Uncertain Reasoning Architecture (AURA) Alert for autonomous determination of novelty. AURA is a class of binary neural network that has a number of advantages over standard artificial neural network techniques for condition monitoring including a sound theoretical basis to determine the bounds of the system operation. Results from application to several case studies are provided including both hydraulic and water quality data. In the case of pattern matching, the results demonstrated some transferability of burst patterns across District Metered Areas; however limitations in performance and difficulties with assembling pattern libraries were found. Results for the AURA system demonstrate the potential for robust event detection across multiple parameters providing valuable information for diagnosis; one example also demonstrates the potential for detection of precursor information, vital for proactive management.

Access to safe drinking water is universally considered as a fundamental human right and customers regard a reliable supply of safe, clean water as the most important aspect of the water supply service. However, water quality failures do occur, with some of the hardest to understand and manage occurring within distribution systems. In the UK, a regulatory process is applied in which water companies must report on significant water quality incidents, their causes, actions, responses, and outcomes. The Drinking Water Inspectorate (DWI) assesses these reports on an annual basis and their findings are made publically available. It is hypothesised here that these reports form a valuable resource that can be ‘data mined’ for improved understanding and to help with future incident management. Developed in the late 1970s, case-based reasoning (CBR) is a knowledge-based problem-solving technique that relies on the reuse of past experience. It is based on the assumption that similar problems have similar solutions and hence new problems can be solved by reusing (and adapting) solutions. The WaterQualityCBR software system, reported on here, was developed as a decision support tool for water companies to deal more effectively with water quality incidents (e.g. water discolouration, contamination and loss of supply) by using information from previous incidents. The tool manipulates a database (compiled in XML) of past significant events from several years DWI reporting. The system can provide information at a strategic level, for example to help inform policy or water company guidance documents. In addition, a complete closed CBR cycle is possible for operational event management providing information from similar cases from the past and, importantly, ranking past actions in response to similar incidents. Examples are provided to illustrate both aspects of the software, demonstrating how the CBR methodology can support decision-making for water utilities in managing drinking water incidents.