Strategic review of travel information research

Report to The Department for Transport, London, U

Assessing the role of human behaviors in the management of extreme hydrological events: an agent-based modeling approach

This thesis aims to assess the role of human behaviors in the management of extreme hydrological events. Using an agent-based modeling (ABM) approach, three specific issues associated with modeling human behaviors are addressed: (1) behavioral heterogeneity, (2) social interaction, and (3) the interplay of multiple behaviors. The modeling approach is applied to two types of extreme hydrological events: floods and droughts. In the case of flood events, an ABM is developed to simulate heterogeneous responses to flood warnings and evacuation decisions. The ABM is coupled with a traffic model to simulate evacuation processes on a transportation network in an impending flood event. Based on this coupled framework, the model further takes account of social interactions, in the form of communication through social media, and evaluates how social interactions affect flood risk awareness and evacuation processes. The case of drought events considers a hypothetical agricultural water market based on double auction. Farmers’ multiple behaviors (irrigation and bidding behaviors) are modeled in an ABM framework. The impacts of the interplay of these behaviors on water market performance are evaluated under various hydrological conditions. The results from the ABMs show that the three aforementioned aspects of human behaviors can significantly affect the effectiveness of the management policies in extreme hydrological events. The thesis highlights the importance of including human behaviors for policy design in flood and drought management. Further, the thesis emphasizes the efforts in collecting empirical data to better represent and simulate human behaviors in coupled human and hydrological systems

Analysis of the risks related to the logistics of the Hazardous Materials

Today, the number of industrial enterprises producing, using, storing and transporting hazardous materials is constantly increasing worldwide. This growth is linked to the progressive demand in various sectors, which makes our world riskier because of the nature and diversity of the dangerous events that may occur. The risks incurred by the hazardous materials transport activity, in case of the occurrence of an incident that may occur and have serious consequences for persons, the environment, property, a fire as an example accompanied by a release of toxic smoke, pollution of the soil and / or water, it can lead in case of non-control of the fire or the reactivity of the goods transported to an explosion. To this purpose, it is essential to protect the health and safety of personnel and to preserve the environment from any deterioration related to the risks incurred by the Transport of Dangerous Goods (TDG) business, which presents important issues for population, state and highly urbanized areas The aim of this thesis is to propose a systemic approach to risk assessment, taking into account in a global way the risks related to hazardous materials throughout the logistics chain (transport & storage). The approach consists of using the modeling and simulation techniques of an accident, to understand the consequences generated in the various scenarios in the event of the occurrence of a hazardous materials accident. This approach will allow the presentation of an industrial safety reasoning method based on actual case studies, rather than a detailed analysis of how to prevent and protect a given hazard. In the process of assessing the technological risks associated with the Transport of Dangerous Goods (TDG), the essential step is the evaluation of the risk intensity when an accidental event occurs, which is to quantify the risks involved. effects or impacts, in order to respond quickly and prioritize relief actions for the protection of the population and the environment. The assessment of the intensity of a technological risk can be carried out using an effects model, capable of estimating the effects induced by the hazardous phenomenon from a quantitative point of view, in order to determine the geographical area of the hazard where the intensity of the risk is deemed too high. In this context, the first issue addressed in this thesis is to assess the level of risk of hazardous goods transport areas for both road and marine modes of transportation, while the second issue of assessing risks in an industrial facility fixed

Risk, Resilience, and Sustainability-Informed Assessment and Management of Aging Structural Systems

During their service life, structural systems (e.g., civil and marine structures) may be subjected to aggressive deteriorations such as corrosion and fatigue and/or extreme events such as floods, collisions, earthquakes, and fires. These deteriorations may start from the day the structures enter in service and, if not effectively managed, can cause a significant reduction in structural functionality and safety. Maintaining performance and functionality of structural systems under these adverse effects is gaining increased attention. This highlights the necessity of effective assessment and management of civil and marine structures in a life-cycle context.The main objective of this study is to develop a risk, sustainability and resilience-informed approach for the life-cycle management of structural systems with emphasis on highway bridges, bridge networks, buildings, interdependent structural systems, and ship structures. Risk - based performance indicators combining the probability of structural failure with the consequences associated with a particular failure event are investigated in this study. Furthermore, a wide range of performance measures is covered under “sustainability” to reflect three aspects: economic, social, and environmental. Sustainability is described as “meeting the needs of present without altering the needs of future generations” (Adams 2006). Sustainability can serve as a useful tool in decision making and risk mitigation associated with civil and marine structures. In addition to risk and sustainability, resilience is another indicator that accounts for structural functionality and recovery patterns after extreme events. Presidential Policy Directive (PPD 2013) defines resilience as “a structure’s ability to prepare for and adapt to changing conditions while simultaneously being able to withstand and recover rapidly from functionality disruptions”. Overall, risk, sustainability, and resilience assessment considering aging and multi-hazard effects are of vital importance to ensure structural safety and functionality of structural systems during their service life.Risk is assessed for highway bridges under the effects of climate change and multiple hazards, including aging effects, flood-induced scour, and earthquake, whereas the adverse effects associated with aging and earthquake are investigated for bridge networks. The sustainability of highway bridges and bridge networks is assessed considering social, economic, and environmental metrics. The seismic resilience of highway bridges under mainshock (MS) only and mainshock-aftershock (MSAS) sequences is investigated to account for structural performance and recovery patterns under extreme events. Additionally, the seismic performance of buildings and interdependent healthcare - bridge network systems is investigated considering correlation effects and uncertainties. Furthermore, a probabilistic methodology to establish optimum pre-earthquake retrofit plans of bridge networks based on risk and sustainability is developed. For ship structures, a decision support system considering structural deteriorations (i.e., corrosion and fatigue) and extreme events (e.g., collision) is established. Specifically, the probabilistic ship collision risk and sustainability are investigated incorporating the attitude of a decision maker. A novel approach is developed to evaluate the time-variant risk of ship structures under corrosion and fatigue during the investigated time interval. Furthermore, a multi-objective optimization problem, which accounts for structural deteriorations and various uncertainties, is formulated to determine optimum inspection planning that reduces the extent of adverse consequence associated with ship failure while simultaneously minimizing the expected total maintenance cost. Additionally, a probabilistic approach for reliability and risk updating of both inspected and uninspected fatigue-sensitive details at both component and system levels is developed considering uncertainties and correlation effects. Overall, this study provides methodologies for the risk, sustainability, and resilience-informed assessment and management of structural systems under structural deteriorations and extreme events in a life-cycle context. Based on the inspection information, the reliability and risk could be updated for the near real-time decision making of deteriorating structures. The proposed probabilistic frameworks are illustrated on highway bridges, bridge networks, buildings, interdependent structural systems, and ship structures. The proposed methodology can be used to assist decision making regarding risk mitigation activities and, ultimately, improve the sustainability of structural systems in a life-cycle context

Modeling the Dynamics of Opinion Formation and Propagation: An Application to Market Adoption of Transportation Services

The objective of this research is to present a model that utilizes social and learning mechanisms to first explore the underlying dynamics of opinion formation and propagation, and then applies those mechanisms to an application of freight mode choice to investigate the effect that opinions have on choice set considerations, attribute perceptions, and the market adoption of a new rail freight service. Primary contributions of this research include the explicit modeling of social and learning mechanisms and their effects on opinion formation and propagation, the evolution of these opinions over time, and an exploration of the role that opinion dynamics have in choice processes. Research findings will offer insight to the process of evolving attitudes, perceptions, and opinions and the effects on individuals' judgment and decision making. It will also offer insight to the effects of attribute distortion on decision making

Multi-scale Pedestrian Navigation and Movement in Urban Areas

Sustainable transport planning highlights the importance of walking to low-carbon and healthy urban transport systems. Studies have identified multiple ways in which vehicle traffic can negatively impact pedestrians and inhibit walking intentions. However, pedestrian-vehicle interactions are underrepresented in models of pedestrian mobility. This omission limits the ability of transport simulations to support pedestrian-centric street design. Pedestrian navigation decisions take place simultaneously at multiple spatial scales. Yet most models of pedestrian behaviour focus either on local physical interactions or optimisation of routes across a road network. This thesis presents a novel hierarchical pedestrian route choice framework that integrates dynamic, perceptual decisions at the street level with abstract, network based decisions at the neighbourhood level. The framework is based on Construal Level Theory which states that decision makers construe decisions based on their psychological distance from the object of the decision. The route choice framework is implemented in a spatial agent-based simulation in which pedestrian and vehicle agents complete trips in an urban environment. Global sensitivity analysis is used to explore the behaviour produced by the multi-scale pedestrian route choice model. Finally, simulation experiments are used to explore the impacts of restrictions to pedestrian movement. The results demonstrate the potential insights that can be gained by linking street scale movement and interactions with neighbourhood level mobility patterns

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

Applications of stochastic modeling in air traffic management:Methods, challenges and opportunities for solving air traffic problems under uncertainty

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management

Modeling sustainable traffic assignment policies with emission functions and travel time reliability

Urban transport systems play a crucial role in maintaining sustainability. In this study, we focus on two types of sustainability measures; the gas emission and travel time reliability. We propose several bilevel optimization models that incorporate these sustainability measures. The upper level of the problem represents the decisions of transportation managers that aim at making the transport systems sustainable, whereas the lower level problem represents the decisions of network users that are assumed to choose their routes to minimize their total travel cost. We determine the emission functions in terms of the traffic flow to estimate the accumulated emission amounts in case of congestion. The proposed emission functions are incorporated into the bilevel programming models that consider several policies, namely, the toll pricing and capacity enhancement. In addition to the gas emission, the travel time reliability is considered as the second sustainability criterion. In transportation networks, reliability reflects the ability of the system to respond to the random variations in system variables. We focus on the travel time reliability and quantify it using the conditional value at risk (CVaR) as a risk measure on the alternate functions of the random travel times. Basically, CVaR is used to control the possible large realizations of random travel times. We model the random network parameters by using a set of scenarios and we propose alternate risk-averse stochastic bilevel optimization models under the toll pricing policy. We conduct an extensive computational study with the proposed models on testing networks by using GAMS modeling language

Measuring Risk In Networks

Participation in networks inevitably involves risk. However, the study of networks has, perhaps surprisingly, not had much to say about network risk in the sense that most economists would use the term ‘risk.’ No consensus has even emerged on what such a model would constitute. Network risk appears to be present in the world, whether in the financial sector, in transportation, or with regards to interpersonal connections, and yet we have few tools for modeling it. The primary contribution of this thesis is a formal notion of network risk, and a set of tools for measuring it