Modelling life trajectories and mode choice using Bayesian belief networks

Traditionally, transport mode choice was primarily examined as a stand alone problem. Given a purpose and destination, the choice of transport mode was modelled as a function of the various attributes of the transport mode alternatives. Later, transport mode choice decisions were modelled as part of more comprehensive models (activity-based approach). There is a need in the transport research community to explore and model dynamics in activity-travel patterns along various time horizons. This will lead to dynamic models of behavioural change. In this thesis, it is argued that a life course perspective offers some potential advantages in understanding and modelling activity-travel decisions, including transport mode choice. Central concepts in the life course approach are life trajectories, transitions and events. An individual life course is composed of multiple, interdependent careers (i.e. housing, household, education, occupational career) which develop over time in parallel. Earlier life transitions may have a cumulative effect on later life. The concepts of timing, sequencing, duration and spacing are used to describe life events, transitions and trajectories. The assumed effect of events on activity-travel decisions is captured in terms of a theory of learning and adaptation. Individuals develop and continuously adapt choice rules while interacting with their environment. The context is nonstationary, uncertain and highly dynamic and therefore it is assumed that individuals adapt their behaviour. Under stationary conditions, individuals will show habitual behaviour after some period of time. A life course event is seen as a trigger that may induce individuals and households to reorganise their activities in time and space. A particular event may also lead to other life course events. Thus, life course events may have direct and indirect effects on activity-travel patterns. An event does not necessarily lead to immediate changes in particular facets of activity-travel patterns. Behavioural change may also occur in anticipation of life course events. Bayesian Belief Networks is the approach adopted in this thesis to model the direct and indirect effects of life course effects on transport mode choice. More complex causation patterns can be included and results can be directly interpreted in terms of the classified events. Such networks need as input empirical data to learn the structure of the network and the conditional probability tables of the variables that are identified to be relevant. Data was collected using a retrospective Internet-based survey. Retrospective surveys, especially when administered through the Internet, are a good alternative for (quasi-)longitudinal data collection methods, like panel surveys, repeated cross sectional surveys, and cohort pseudo-panel surveys. One would expect that the quality of data coming from a retrospective survey depends on the nature of the event about which information is collected and on the time elapsed between the occurring of the events and the time of the retrospective survey. The quality of the data was tested and the results were positive. In case of life course events memory lapses are less of a problem. Life course events can be better recollected than other events. In this study, the results of the reliability and validity tests of the collected data showed that item nonresponse in general was relatively low, especially for those life course events that serve as markers unfolding one’s life. A statistical analysis suggested that memory / cohort effects were not found for the more salient life course events, such as housing, work and study related events. Memory may have an effect in reporting of events in case of income and transport mode related events (car availability and PT pass). The study illustrated that certain details of events, such as housing type and housing state are more difficult to recall. The time effect of an influence of life course events on mode choice was tested with a simple multinomial logit model. The results support the conclusion that a certain time influence exists in the response to events. The data of the retrospective Internet-based survey was used as input for two Bayesian Belief Networks, a life trajectory and a mode choice network. A year is chosen as the unit of analysis for these networks. Both networks were successfully learned from the data. The first network can be used to simulate a person’s life trajectory and the second network can be used to predict mode choice for an individual at a certain time given the individual life trajectory. The goodness-of-fit of the learned Bayesian Belief Networks was assessed on the basis of the log likelihood statistic. The values indicated that both networks perform relatively well. It was also investigated whether the life trajectory network was capable of reproducing observed characteristics of complete life trajectories. The observed and predicted life trajectories were compared in terms of the following criteria: the number of occurrences, interval times between occurrences of events, simultaneous occurrences of events and sequence of occurrences of events. The life trajectory network reproduced the number of occurrences in the life trajectories quite well. In general, the network predicted more or less the same means of interval times for the events, except for the PT pass event. The network was less successful in predicting correctly the observed incidence of synchronic events. The results of the sequence alignment analysis indicate that the network predicts the sequence of the occurrences in the life trajectories relatively good. The modal split (car, public transport and slow transport) of the predicted mode choice was compared with the observed mode choice. Results indicated a relatively small over prediction of public transport and under prediction of car and slow transport. This suggests that the mode choice network is able to simulate more or less the same mode choice as registered in the data. The learned networks were used to study direct and indirect effects of one variable on other variables in the network. The described effects seem logical. A simulation illustrated the dynamics of the lives of ten inhabitants of a newly build neighbourhood. It showed that, insight in dynamics of life trajectory events and mode choice can lead to a better understanding which can support the development of better or different policy measures

On agent-based modeling: Multidimensional travel behavioral theory, procedural models and simulation-based applications

This dissertation proposes a theoretical framework to modeling multidimensional travel behavior based on artificially intelligent agents, search theory, procedural (dynamic) models, and bounded rationality. For decades, despite the number of heuristic explanations for different results, the fact that "almost no mathematical theory exists which explains the results of the simulations" remains as one of the large drawbacks of agent-based computational process approach. This is partly the side effect of its special feature that "no analytical functions are required". Among the rapidly growing literature devoted to the departure from rational behavior assumptions, this dissertation makes effort to embed a sound theoretical foundation for computational process approach and agent-based microsimulations for transportation system modeling and analyses. The theoretical contribution is three-fold: (1) It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modeled and integrated in a unified and coherent approach. (2) Procedural and dynamic agent-based decision-making is modeled. Within the model, agents make decisions. They also make decisions on how and when to make those decisions. (3) Replace conventional user equilibrium with a dynamic behavioral user equilibrium (BUE). Search start/stop criteria is defined in the way that the modeling process should eventually lead to a steady state that is structurally different to user equilibrium (UE) or dynamic user equilibrium (DUE). The theory is supported by empirical observations and the derived quantitative models are tested by agent-based simulation on a demonstration network. The model in its current form incorporates short-term behavioral dimensions: travel mode, departure time, pre-trip routing, and en-route diversion. Based on research needs and data availability, other dimensions can be added to the framework. The proposed model is successfully integrated with a dynamic traffic simulator (i.e. DTALite, a light-weight dynamic traffic assignment and simulation engine) and then applied to a mid-size study area in White Flint, Maryland. Results obtained from the integration corroborate the behavioral richness, computational efficiency, and convergence property of the proposed theoretical framework. The model is then applied to a number of applications in transportation planning, operations, and optimization, which highlights the capabilities of the proposed theory in estimating rich behavioral dynamics and the potential of large-scale implementation. Future research should experiment the integration with activity-based models, land-use development, energy consumption estimators, etc. to fully develop the potential of the agent-based model

Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data

Origin-destination flow of passengers in bus networks is a crucial input to the public transport planning and operational decisions. Smart card systems in many cities, however, record only the bus boarding information (namely an open system), which makes it challenging to use smart card data for origin-destination estimations and subsequent analyses. This study addresses this research gap by proposing a machine learning approach and applying the gradient boosting decision tree (GBDT) algorithm to estimate the alighting stops of bus trips from open smart card data. It advances the state-of-the-art by including, for the first time, weather variables and travel history of individuals in the GBDT algorithm alongside the network characteristics. The method is applied to six-month smart card data from the City of Changsha, China, with more than 17 million trip-records from 700 thousand card users. The model prediction results show that, compared to classic machine learning methods, GBDT not only yields higher prediction accuracy but more importantly is also able to rank the influencing factors on bus ridership. The results demonstrate that incorporation of weather variables and travel history further improves the prediction capability of the models. The proposed GBDT-based framework is flexible and scalable: it can be readily trained with smart card data from other cities to be used for predicting bus origin-destination flow. The results can contribute to improved transport sustainability of a city by enabling smart bus planning and operational decisions

Hierarchical generative modelling for autonomous robots

Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control in the setting of autonomous robotic operations. We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control. Here, temporal depth refers to the nested time scales at which successive levels of a forward or generative model unfold, for example, delivering an object requires a global plan to contextualise the fast coordination of multiple local movements of limbs. This separation of temporal scales also motivates robotics and control. Specifically, to achieve versatile sensorimotor control, it is advantageous to hierarchically structure the planning and low-level motor control of individual limbs. We use numerical and physical simulation to conduct experiments and to establish the efficacy of this formulation. Using a hierarchical generative model, we show how a humanoid robot can autonomously complete a complex task that necessitates a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities. Our findings demonstrated the effectiveness of using human-inspired motor control algorithms, and our method provides a viable hierarchical architecture for the autonomous completion of challenging goal-directed tasks

Understanding Mobility and Transport Modal Disparities Using Emerging Data Sources: Modelling Potentials and Limitations

Transportation presents a major challenge to curb climate change due in part to its ever-increasing travel demand. Better informed policy-making requires up-to-date empirical mobility data to model viable mitigation options for reducing emissions from the transport sector. On the one hand, the prevalence of digital technologies enables a large-scale collection of human mobility traces, providing big potentials for improving the understanding of mobility patterns and transport modal disparities. On the other hand, the advancement in data science has allowed us to continue pushing the boundary of the potentials and limitations, for new uses of big data in transport.This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from new transport modes, to enhance the understanding of mobility and transport modal disparities, e.g., how car and public transit support mobility differently. Specifically, this thesis aims to answer two research questions: (1) What are the potentials and limitations of using these emerging data sources for modelling mobility? (2) How can these new data sources be properly modelled for characterising transport modal disparities? Papers I-III model mobility mainly using geotagged social media data, and reveal the potentials and limitations of this data source by validating against established sources (Q1). Papers IV-V combine multiple data sources to characterise transport modal disparities (Q2) which further demonstrate the modelling potentials of the emerging data sources (Q1).Despite a biased population representation and low and irregular sampling of the actual mobility, the geolocations of Twitter data can be used in models to produce good agreements with the other data sources on the fundamental characteristics of individual and population mobility. However, its feasibility for estimating travel demand depends on spatial scale, sparsity, sampling method, and sample size. To extend the use of social media data, this thesis develops two novel approaches to address the sparsity issue: (1) An individual-based mobility model that fills the gaps in the sparse mobility traces for synthetic travel demand; (2) A population-based model that uses Twitter geolocations as attractions instead of trips for estimating the flows of people between regions. This thesis also presents two reproducible data fusion frameworks for characterising transport modal disparities. They demonstrate the power of combining different data sources to gain new insights into the spatiotemporal patterns of travel time disparities between car and public transit, and the competition between ride-sourcing and public transport

A Microscopic Simulation Laboratory for Evaluation of Off-street Parking Systems

The parking industry produces an enormous amount of data every day that, properly analyzed, will change the way the industry operates. The collected data form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as how to set specific parking policies (e.g. electrical charging only parking space) to achieve the sustainable and eco-friendly parking. However, there lacks an intelligent tool to assess the layout design and operational performance of parking lots to reduce the externalities and increase the revenue. To address this issue, this research presents a comprehensive agent-based framework for microscopic off-street parking system simulation. A rule-based parking simulation logic programming model is formulated. The proposed simulation model can effectively capture the behaviors of drivers and pedestrians as well as spatial and temporal interactions of traffic dynamics in the parking system. A methodology for data collection, processing, and extraction of user behaviors in the parking system is also developed. A Long-Short Term Memory (LSTM) neural network is used to predict the arrival and departure of the vehicles. The proposed simulator is implemented in Java and a Software as a Service (SaaS) graphic user interface is designed to analyze and visualize the simulation results. This study finds the active capacity of the parking system, which is defined as the largest number of actively moving vehicles in the parking system under the facility layout. In the system application of the real world testbed, the numerical tests show (a) the smart check-in device has marginal benefits in vehicle waiting time; (b) the flexible pricing policy may increase the average daily revenue if the elasticity of the price is not involved; (c) the number of electrical charging only spots has a negative impact on the performance of the parking facility; and (d) the rear-in only policy may increase the duration of parking maneuvers and reduce the efficiency during the arrival rush hour. Application of the developed simulation system using a real-world case demonstrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating smart parking facilities

Proposal of an adaptive infotainment system depending on driving scenario complexity

Tesi en modalitat Doctorat industrialPla de Doctorat industrial de la Generalitat de CatalunyaThe PhD research project is framed within the plan of industrial doctorates of the “Generalitat de Catalunya”. During the investigation, most of the work was carried out at the facilities of the vehicle manufacturer SEAT, specifically at the information and entertainment (infotainment) department. In the same way, there was a continuous cooperation with the telematics department of the UPC. The main objective of the project consisted in the design and validation of an adaptive infotainment system dependent on the driving complexity. The system was created with the purpose of increasing driver’ experience while guaranteeing a proper level of road safety. Given the increasing number of application and services available in current infotainment systems, it becomes necessary to devise a system capable of balancing these two counterparts. The most relevant parameters that can be used for balancing these metrics while driving are: type of services offered, interfaces available for interacting with the services, the complexity of driving and the profile of the driver. The present study can be divided into two main development phases, each phase had as outcome a real physical block that came to be part of the final system. The final system was integrated in a vehicle and validated in real driving conditions. The first phase consisted in the creation of a model capable of estimating the driving complexity based on a set of variables related to driving. The model was built by employing machine learning methods and the dataset necessary to create it was collected from several driving routes carried out by different participants. This phase allowed to create a model capable of estimating, with a satisfactory accuracy, the complexity of the road using easily extractable variables in any modern vehicle. This approach simplify the implementation of this algorithm in current vehicles. The second phase consisted in the classification of a set of principles that allow the design of the adaptive infotainment system based on the complexity of the road. These principles are defined based on previous researches undertaken in the field of usability and user experience of graphical interfaces. According to these of principles, a real adaptive infotainment system with the most commonly used functionalities; navigation, radio and media was designed and integrated in a real vehicle. The developed system was able to adapt the presentation of the content according to the estimation of the driving complexity given by the block developed in phase one. The adaptive system was validated in real driving scenarios by several participants and results showed a high level of acceptance and satisfaction towards this adaptive infotainment. As a starting point for future research, a proof of concept was carried out to integrate new interfaces into a vehicle. The interface used as reference was a Head Mounted screen that offered redundant information in relation to the instrument cluster. Tests with participants served to understand how users perceive the introduction of new technologies and how objective benefits could be blurred by initial biases.El proyecto de investigación de doctorado se enmarca dentro del plan de doctorados industriales de la Generalitat de Catalunya. Durante la investigación, la mayor parte del trabajo se llevó a cabo en las instalaciones del fabricante de vehículos SEAT, específicamente en el departamento de información y entretenimiento (infotainment). Del mismo modo, hubo una cooperación continua con el departamento de telemática de la UPC. El objetivo principal del proyecto consistió en el diseño y la validación de un sistema de información y entretenimiento adaptativo que se ajustaba de acuerdo a la complejidad de la conducción. El sistema fue creado con el propósito de aumentar la experiencia del conductor y garantizar un nivel adecuado en la seguridad vial. El proyecto surge dado el número creciente de aplicaciones y servicios disponibles en los sistemas actuales de información y entretenimiento; es por ello que se hace necesario contar con un sistema capaz de equilibrar estas dos contrapartes. Los parámetros más relevantes que se pueden usar para equilibrar estas métricas durante la conducción son: el tipo de servicios ofrecidos, las interfaces disponibles para interactuar con los servicios, la complejidad de la conducción y el perfil del conductor. El presente estudio se puede dividir en dos fases principales de desarrollo, cada fase tuvo como resultado un componente que se convirtió en parte del sistema final. El sistema final fue integrado en un vehículo y validado en condiciones reales de conducción. La primera fase consistió en la creación de un modelo capaz de estimar la complejidad de la conducción en base a un conjunto de variables relacionadas con la conducción. El modelo se construyó empleando "Machine Learning Methods" y el conjunto de datos necesario para crearlo se recopiló a partir de varias rutas de conducción realizadas por diferentes participantes. Esta fase permitió crear un modelo capaz de estimar, con una precisión satisfactoria, la complejidad de la carretera utilizando variables fácilmente extraíbles en cualquier vehículo moderno. Este enfoque simplifica la implementación de este algoritmo en los vehículos actuales. La segunda fase consistió en la clasificación de un conjunto de principios que permiten el diseño del sistema de información y entretenimiento adaptativo basado en la complejidad de la carretera. Estos principios se definen en base a investigaciones anteriores realizadas en el campo de usabilidad y experiencia del usuario con interfaces gráficas. De acuerdo con estos principios, un sistema de entretenimiento y entretenimiento real integrando las funcionalidades más utilizadas; navegación, radio y audio fue diseñado e integrado en un vehículo real. El sistema desarrollado pudo adaptar la presentación del contenido según la estimación de la complejidad de conducción dada por el bloque desarrollado en la primera fase. El sistema adaptativo fue validado en escenarios de conducción reales por varios participantes y los resultados mostraron un alto nivel de aceptación y satisfacción hacia este entretenimiento informativo adaptativo. Como punto de partida para futuras investigaciones, se llevó a cabo una prueba de concepto para integrar nuevas interfaces en un vehículo. La interfaz utilizada como referencia era una pantalla a la altura de los ojos (Head Mounted Display) que ofrecía información redundante en relación con el grupo de instrumentos. Las pruebas con los participantes sirvieron para comprender cómo perciben los usuarios la introducción de nuevas tecnologías y cómo los sesgos iniciales podrían difuminar los beneficios.Postprint (published version

Algortitma Genetik Pada Penjadwalan Transportasi Kapal Laut (Studi Kasus PT. Pelni)

Penjadwalan dalam transportasi merupakan bagian terpenting bagi sebuah perusahaan yang mengalami keterbatasan jumlah moda transportasi. Pada transportasi laut selain keterbatasan moda transportasi, biasanya setiap pelabuhan tidak bisa menerima semua jenis kapal laut, sehingga semakin terbatas moda transportasi yang bisa berlabuh pada pelabuhan tertentu. Menjadi penting bagi perusahaan moda transportasi laut untuk menentukan rute yang tepat dan penugasan kapal yang cocok serta sesuai dengan pelabuhan. Algoritma genetik merupakan salah satu metode heuristic yang biasa digunakan dalam pemecahan masalah penjadwalan. Pada kasus transportasi kapal laut, metode ini menjadi salah satu opsi heuristic terbaik untuk mendapatkan penjadwalan terbaik dalam penentuan rute dan penugasan kapal. Dalam pemakaiannya, algoritma genetik dimulai dengan pembangkitan rute secara heuristic agar di dapatkan inisialisasi rute dari penjadwalan awal. Setelah mendapatkan inisialisasi dilakukan pendekatan operator genetic (elitis, crossover dan mutasi) untuk menghasilkan penjadwalan baru dengan fitness function yang lebih baik. Sebagai model penelitian digunakan Sistem PT. Pelni sebagai studi kasusnya dengan fitness function berupa biaya operasional terkecil. Hasil dari pemakaian metode ini memberikan penjadwalan secara fitness function lebih murah dengan selisih hingga Rp 2.350.980.244,20 dan waktu tempuh yang lebih panjang dengan selisih 65,36 hari dibandingkan dengan penjadwalan yang ada saat ini. Semakin banyak iterasi yang digunakan pada algoritma genetik, hasil yang diperoleh cenderung akan semakin baik, jika hasilnya belum mencapai tahap jenuh