Understanding Travel Behaviour: Some Appealing Research Directions

This paper presents one researchers perception of selective emphases in the body of travel behaviour research which have had and/or may in the future have a non-marginal impact on the way that research activity is undertaken. Some of the contributions are well established and have moved from state of the art to state of practice; other efforts are relatively new and maturing in their role as paradigms of thought. The contributions can broadly be grouped into four classes of research: decision paradigms, in particular the interpretation of the choice process within a broad activity framework, and the recognition that agents making decisions do not always operate in a perfectly competitive market; releasing the analytical formalism of the choice/decision process from the restrictive IIA paradigm of the great majority of applied travel choice modelling - moving to nested structures, free variance and correlation among alternatives, random taste weights, accommodating unobserved heterogeneity and mixed 'logits'; combining sources of preference and choice data, including joint analysis of market and experimental choice data, interfaces between attitudinal and behavioural data, and generalising valuation to valuation functions; and advances in the study of the dynamics of traveller behaviour, especially the timing of change and its importance in establishing hurdle dates for forecasting traffic and revenue for infrastructure projects

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Multi-Criteria Decision Making in Complex Decision Environments

In the future, many decisions will either be fully automated or supported by autonomous system. Consequently, it is of high importance that we understand how to integrate human preferences correctly. This dissertation dives into the research field of multi-criteria decision making and investigates the satellite image acquisition scheduling problem and the unmanned aerial vehicle routing problem to further the research on a priori preference integration frameworks. The work will aid in the transition towards autonomous decision making in complex decision environments. A discussion on the future of pairwise and setwise preference articulation methods is also undertaken. "Simply put, a direct consequence of the improved decision-making methods is,that bad decisions more clearly will stand out as what they are - bad decisions.

Investigating the potential of the combination of random utility models (CoRUM) for discrete choice modelling and travel demand analysis

People make choices every day. Many choices have a strong impact on the quality or their life.Each day a person wakes up and chooses which action he wants to do before, what to have for breakfast, what to wear, what time to go outside, how to manage his/her day by virtue of the budget and time constraints, which place to move to and how, which activity to do, which one to do before or after and so forth. There are choices that are not made every day, but they have a strong impact on the decision maker’s well-being. In fact, sooner or later, a person will decide his household location, whether to own a car, the typology and the vehicle model, whether to own a pet, which breed or size, how many children to have, in which school register them and much more. Some of the above-mentioned choice examples involve mobility. Thus, it is easy to recognize why these kind of choices form the basis for the planning and policy actions in the transport field. What is called, at aggregate level, congestion or traffic, represents the sum of individual choices that everyone makes at different levels: do I move? What time do I move? Where I want to go? Which transport mode do I want to use? What itinerary do I travel? This kind of choices, that can be termed transport choices, relating the so-called travel behaviour, are characterized by a significant modelling complexity. The random utility theory represents the most widely used paradigm in modelling the behaviour of people who make choices. This thesis investigates the potential of the combination of random utility models (CoRUM; Papola, 2016) for travel demand analysis and discrete choice modelling in general. In the current work, several theoretical advances and some specific transport-field applications are carried out. The CoRUM framework, in fact, is very general and allows for handling several discrete choice modelling crucial issues. The thesis is structured as follows: Chapter 2 reviews the state of the art on random utility theory and its application to route choice. In particular, the Section 2.1 provides the basic setup for the description of RUMs; Section 2.2 reviews the random utility models available in the literature, with reference to the two main problems of the error structure (inter-correlations and heteroskedasticity problems) and the inter/intra-respondent taste variation; Section 2.3 briefly summarizes the main applications of the random utility theory to the route choice problem; Section 2.4 describes the main assumptions of the Combination of random utility models (CoRUM) as a general framework for modelling discrete choices, with particular reference to travel choices. Chapter 3 investigates more general specifications of the CoRUM than those previously analysed, allowing accommodating also the taste heterogeneity and the heteroscedasticity, in particular by combining mixtures of RUMs. To this end, the chapter proposes a theoretical generalization of the CoRUM framework and a real-world application on data collected on a stated survey of 1688 observations of 211 respondents. Chapter 4 represents an estimation exercise with applications on future scenarios on the main closed form random utility models, on synthetic datasets with variable sample sizes and complex underlying correlation scenarios. Such correlation scenarios, on the other hand, can be representative of typical mode choice or route choice contexts. The aim of this chapter is investigating the potential of the CoNL (and the other models) in terms of forecasting, and comparing it with the models goodness of fit performances. Chapter 5 proposes a new route choice model obtained under the CoRUM framework. It describes an algorithm to generate a CoNL specification, allowing detecting a set and a composition for the components of the model, and a way to compute all the structural model parameters, whatever the network. Chapter 6 is currently an original contribution of this thesis and describes several advance compared to the published work in Chapter 5. In particular, an implicit enumeration algorithm theoretically consistent with the CoRUM route choice model,is proposed and tested on toy networks; an in-depth analysis of the complex route choice models is carried out on their ability to reproduce complex correlation scenarios, drawing important conclusions, both theoretical and applicative, on the novel CoNL route choice model, proposed in Chapter 5, and on the existent Link Nested Logit model; some practical advance on the original route choice model is proposed and tested both on toy networks and on a real network (Region Campania network). The goodness of fit of the CoNL route choice has been analysed and compared with the one of the other route choice models, using real observations collected by means of GPS detection of about 200 trajectories. Chapter 7 reports a summary of the conclusions reached in the whole thesis and proposes several ideas for future research steps

Understanding mode choice behaviour when new modes come into play

Smart mobility has become increasingly prevalent nowadays, and new travel modes have been emerging in this process. The entry of these new modes not only fosters diversity of transport systems, but also would lead to changes of the characteristics of the transport system itself. This may induce changes in individual travel behaviour. For example, some people would shift to a new mode from other existing modes, while other individuals might be induced to make additional travel which would not be made if the new mode is not available. Some unique underlying characteristics may also drive these changes in travel behaviour. For instance, while some individuals are resistant to change, others may be prone to adopt novel options. This necessitates the investigation of the impact of variety-seeking on how people make choices when new modes are involved. Secondly, while choices are relatively stable for some individuals, others may have stronger tendencies to vary their choices more frequently over choice occasions. Exploration into this characteristic is needed to facilitate better understanding of people's consecutive choices over time. Thirdly, a new mode is usually associated with some new attributes with which individuals may be less familiar. This entails obtaining more knowledge of the role that attributes play in choice making for travel behaviour researchers. This thesis aims at examining mode choice behaviour at an individual level and uncovering travel demand through empirical analyses. Contributions are made to accounting for the three unique underlying characteristics in behaviour as mentioned above, which enhance understanding of the determinants behind mode choices and heterogeneity in preferences in the context of the introduction of new modes. This thesis exclusively uses stated preference (SP) data, as SP data can be used for preference elicitation in hypothetical scenarios, whereas it is much more difficult to collect revealed preference data when new modes have not yet been launched or have only existed in the market for a short period. This research relies on discrete choice modelling (DCM), which is a well-established econometric method for analysing individual choice behaviour and aggregate demand. DCM enables the accommodation of complex heterogeneity in preferences both across individuals and within individuals, and to achieve greater behavioural realism in delineating decision-making. The integrated choice and latent variable (ICLV) model is adopted in different manners, illustrating that the incorporation of latent variables is not confined to investigating the impact of unobserved psychological factors (e.g. variety-seeking) in choices or in class allocation, but could be extended for the purpose of combining stated choice (SC) data with other alternative SP data, e.g. best-worst scaling (BWS) data. The research findings are as expected. The study in the context of HSR (high-speed rail)-air intermodality suggests that people with stronger variety-seeking tendencies are more likely to adopt the new mode introduced. The same finding has been discovered in the second study that applies to the context where a hypothetical air taxi service is involved, which further shows that stronger variety-seeking tendencies can also lead to more unstable preferences across choices. The third study that synthesises traditional SC data and additional BWS data demonstrates the correlation between these different types of collection methods, illustrates that attributes play a relatively consistent - though not one-to-one - role across different methods, and enables the exploration of behavioural information per individual to a greater extent. In general, this thesis contributes to deeper understanding of mode choice behaviour in the context of the introduction of new modes. That is, the investigation into the impact of various level-of-service attributes provides empirical evidence for transport practitioners in willingness-to-pay evaluation. Moreover, the research indicates that while variety-seekers are more likely to be attracted to adopt a new mode at an early stage, they might in the meantime have less consistency in using the new mode. Thus, policy makers could expect an initial uptake of the new mode in the population, but it does not necessarily mean that people would keep on using the new mode over time. Furthermore, this research shows that when confronting the introduction of a new mode characterised with new attributes, an applicable approach for policy makers to improve the understanding of trade-offs and forecast of travel demand would be jointly using alternative preference elicitation methods together with the traditional SC survey

Assessing the Impact of Multi-variate Steering-rate Vehicle Control on Driver Performance in a Simulation Framework

When a driver turns a steering-wheel, he or she normally expects the vehicle\u27s steering system to communicate an equivalent amount of signal to the road-wheels. This relationship is linear and occurs regardless of the steering-wheel\u27s position within its rotational travel. The linear steering paradigm in passenger vehicles has gone largely unchanged since mass production of passenger vehicles began in 1901. However, as more electronically-controlled steering systems appear in conjunction with development of autonomous steering functions in vehicles, an opportunity to advance the existing steering paradigms arises. The following framework takes a human-factors approach toward examining and evaluating alternative steering systems by using Modeling and Simulation methods to track and score human performance. Present conventional steering systems apply a linear relationship between the steering-wheel and the road wheels of a vehicle. The rotational travel of the steering-wheel is 900° and requires two-and-a-half revolutions to travel from end-stop to opposite end-stop. The experimental steering system modeled and employed in this study applies a dynamic curve response to the steering input within a shorter, 225° rotational travel. Accommodation variances, based on vehicle speed and steering-wheel rotational position and acceleration, moderate the apparent steering input to augment a more-practical, effective steering rate. This novel model follows a paradigm supporting the full range of steering-wheel actuation without necessitating hand repositioning or the removal of the driver\u27s hands from the steering-wheel during steering maneuvers. In order to study human performance disparities between novel and conventional steering models, a custom simulator was constructed and programmed to render representative models in a test scenario. Twenty-seven males and twenty-seven females, ranging from the ages of eighteen to sixty-five were tested and scored using the driving simulator that presented two successive driving test vignettes: One vignette using conventional 900° steering with linear response and the other employing the augmented 225° multivariate, non-linear steering. The results from simulator testing suggest that both males and females perform better with the novel system, supporting the hypothesis that drivers of either gender perform better with a system augmented with 225° multivariate, non-linear steering than with a conventional steering system. Further analysis of the simulated-driving scores indicates performance parity between male and female participants, supporting the hypothesis positing no significant difference in driver performance between male and female drivers using the augmented steering system. Finally, composite data from written questionnaires support the hypothesis that drivers will prefer driving the augmented system over conventional steering. These collective findings support justification for testing and refining novel steering systems using Modeling and Simulation methods. As a product of this particular study, a tested and open-sourced simulation framework now exists such that researchers and automotive designers can develop, as well as evaluate their own steering-oriented products within a valid human-factors construct. The open-source nature of this framework implies a commonality by which otherwisedisparate research and development work can be associated. Extending this framework beyond basic investigation to reach applications requiring morespecialized parameters may even impact drivers having special needs. For example, steeringsystem functional characteristics could be comparatively optimized to accommodate individuals afflicted with upper-body deficits or limited use of either or both arms. Moreover, the combined human-factors and open-source approaches distinguish the products of this research as a common and extensible platform by which purposeful automotive-industry improvements can be realized—contrasted with arbitrary improvements that might be brought about predominantly to showcase technological advancements

Documenting & Using Cognitive Complexity Mitigation Strategies (CCMS) to Improve the Efficiency of Cross-Context User Transfers

Cognitive complexity mitigation strategies are methods and approaches utilized by users to reduce the apparent complexity of problems thus making them easier to solve. These strategies often effective because they mitigate the limitations of human working memory and attention resources. Such cognitive complexity mitigation strategies are used throughout the design, development and operational processes of complex systems. Thus, a better understanding of these strategies, and methods that leverage them, can help improve the efficiency of such processes. Additionally, changes in the use of these strategies across various environments can identify cognitive differences in operating and developing across these contexts. This knowledge can help improve the effectiveness of cross-context user transfers by suggesting change management processes that incorporate the degree of cognitive difference across contexts. In order to document cognitive complexity mitigation strategies and the change in their usage, two application domains are studied. Firstly, cognitive complexity mitigation strategies used by designers during the engineering design process are found through an ethnographic immersion with a participating engineering firm, followed by an analysis of the designer's logbooks and validation interviews with the designers. Results include identification of five strategies used by the designers to mitigate design complexity. These strategies include Blackbox Modeling, Whitebox Modeling, Decomposition, Visualization and Prioritized Lists. The five complexity mitigation strategies are probed further across a larger sample of engineering designers and the usage frequency of these strategies is assessed across commonly performed engineering design activities which include the Selection, Configuration and Parametric activities. The results indicate the preferred use of certain strategies based on the engineering activity being performed. Such preferential usage of complexity mitigation strategies is also assessed with regards to Original and Redesign projects types. However, there is no indication of biased strategy usage across these two project characterizations. These results are an example of a usage-frequency based difference analysis; such analyses help identify the strategies that experience increased or reduced usage when transferring across activities. In contrast to the first application domain, which captures changes in how often strategies are used across contexts, the second application domain is a method of assessing differences based on how a specific strategy is used differently across contexts. This alternative method is developed through a project that aims to optimize the transfer of air traffic controllers across different airspace sectors. The method uses a previously researched complexity mitigation strategy, knows as a structure based abstraction, to develop a difference analysis tool called the Sector Abstraction Binder. This tool is used to perform cognitive difference analyses between air traffic control sectors by leveraging characteristic variations in how structure based abstractions are applied across different sectors. This Sector Abstraction Binder is applied to two high-level airspace sectors to demonstrate the utility of such a method

Space Network Control Conference on Resource Allocation Concepts and Approaches

The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process

Using dates as contextual information for personalised cultural heritage experiences

We present semantics-based mechanisms that aim to promote reflection on cultural heritage by means of dates (historical events or annual commemorations), owing to their connections to a collection of items and to the visitors’ interests. We argue that links to specific dates can trigger curiosity, increase retention and guide visitors around the venue following new appealing narratives in subsequent visits. The proposal has been evaluated in a pilot study on the collection of the Archaeological Museum of Tripoli (Greece), for which a team of humanities experts wrote a set of diverse narratives about the exhibits. A year-round calendar was crafted so that certain narratives would be more or less relevant on any given day. Expanding on this calendar, personalised recommendations can be made by sorting out those relevant narratives according to personal events and interests recorded in the profiles of the target users. Evaluation of the associations by experts and potential museum visitors shows that the proposed approach can discover meaningful connections, while many others that are more incidental can still contribute to the intended cognitive phenomena