Modelling the effects of social networks on activity and travel behaviour

Activity-based models of transport demand are increasingly used by governments, engineering firms and consultants to predict the impact of various design and planning decisions on travel and consequently on noise emissions, energy consumption, accessibility and other performance indicators. In this context, non-discretionary activities, such as work and school, can be relatively easily explained by the traveller’s sociodemographic characteristics and generalised travel costs. However, participation in, and scheduling of, discretionary and joint activities are not so easily redicted. Understanding the social network that lies on top of the spatial network could lead to better prediction of social activity schedules and better forecasts of travel patterns for joint activities. Existing models of activity-travel behaviour do not consider joint activities in detail, except within households to a limited extent. A recent attempt developed at ETH Zurich to incorporate social networks in a single-day optimisation scheduling model did not model joint activities as such, rather rewarding individuals for scheduling activities at the same location and at the same time as their friends. Realistic social networks were also not incorporated. The aim of this thesis is to contribute to this rapidly expanding field by developing a simulation of activity and travel behaviour incorporating social processes and joint activities to investigate the effects on activity and travel behaviour over a simulated period of weeks. The model developed is intended as a proof-of-concept. In order to achieve this aim, an agent-based simulation was designed, implemented in Java, and calibrated and partly verified with real-world data. The model generates activities on a daily basis, including the time of day and duration of the activity. An interaction protocol has been developed to model the activity decision process. Data collected in Eindhoven on social and joint activities and social networks has been used for calibration and verification. Alongside the model development, several issues are addressed, such as exploring which parameters are useful and their effects, the data required for the validation of agent-based travel behaviour models, and whether the addition of social networks to models of this type makes adifference. Sensitivity testing was undertaken to explore the effects of parameters, which was applied to increasingly more complex versions of the model (starting from one day of outputs with no interactions between individuals and finishing with full interactions over many days). This showed that the model performed as expected when certain parameters were altered. Due to the components included in the model, scenarios of interest to policy makers (such as changes in population, land-use changes, and changes in institutional contexts) can be explored. Altering the structure of the in- put social networks and the interaction protocols showed that these inputs do have a difference on the outputs of the model. As a result, these elements of the model require data collection on the social network structure and the decision processes for each local instantiation. Two more "traditional" transport planning policy scenarios, an increase in free time and an increase in travel cost, showed that the model performs as expected for these scenarios. It is shown that the use of agent-based modelling is useful in permitting the incorporation of social networks. The social network can have a significant impact on model results and therefore the decisions made by planners and stakeholders. The model can be extended further in several different directions as new theories are developed and data sets are collected

Transport systems analysis : models and data

Funding: This research project has been funded by Spanish R+D Programs, specifcally under Grant PID2020-112967GB-C31.Rapid advancements in new technologies, especially information and communication technologies (ICT), have significantly increased the number of sensors that capture data, namely those embedded in mobile devices. This wealth of data has garnered particular interest in analyzing transport systems, with some researchers arguing that the data alone are sufficient enough to render transport models unnecessary. However, this paper takes a contrary position and holds that models and data are not mutually exclusive but rather depend upon each other. Transport models are built upon established families of optimization and simulation approaches, and their development aligns with the scientific principles of operations research, which involves acquiring knowledge to derive modeling hypotheses. We provide an overview of these modeling principles and their application to transport systems, presenting numerous models that vary according to study objectives and corresponding modeling hypotheses. The data required for building, calibrating, and validating selected models are discussed, along with examples of using data analytics techniques to collect and handle the data supplied by ICT applications. The paper concludes with some comments on current and future trends

Data driven agent-based micro-simulation in social complex systems

We are recently witnessing an increase in large-scale micro/individual/- granular level behavioural data. Such data has been proven to have the capacity to aid the development of more accurate simulations that will ef- fectively predict the behaviours of complex systems. Despite this increase, the literature has failed to produce a structured modelling approach that will effectively take advantage of such granular data, in modelling com- plex systems that involve social phenomenons (i.e. social complex sys- tems). In this thesis, we intend to bridge this gap by answering the question of how novel structural frameworks, that systematically guides the use of micro-level behaviour and attribute data, directly extracted from the ba- sic entities within a social complex system can be created. These frame- works should involve the systematic processes of using such data to di- rectly model agent attributes, and to create agent behaviour rules, that will directly represent the unique micro entities from which the data was ex- tracted. The objective of the thesis is to define generic frameworks, that would create agent based micro simulations that would directly reflect the target complex system, so that alternative scenarios, that cannot be inves- tigated in the real system, and social policies that need to be investigated before being applied on the social system can be explored. In answering this question, we take advantage of the pros of other model- ing techniques such as micro simulation and agent based techniques in cre- ating models that have a micro-macro link, such that the micro behaviour that causes the macro emergence at the simulation’s global level can be easily investigated. which is a huge advantage in policy testing. We also utilized machine learning in the creation of behavioural rules.This created agent behaviours that were empirically defined. Therefore, this thesis also answers the question of how such structural framework will empirically create agent behaviour rules through machine learning algorithms. In this thesis we proposed two novel frameworks for the creation of more accurate simulations. The concepts within these frameworks were proved using case studies, in which these case studies where from different so- cial complex systems, so as to prove the generic nature of the proposed frameworks. In concluding of this thesis, it was obvious that the questions posed in the first chapter had been answered. The generic frameworks had been created, which bridged the existing gap in the creation of accurate mod- els from the presently available granular attribute and behavioral data, al- lowing the simulations created from these models accurately reflect their target social complex systems from which the data was extracted from

Passengers, Crowding and Complexity : Models for passenger oriented public transport

Passengers, Crowding and Complexity was written as part of the Complexity in Public Transport (ComPuTr) project funded by the Netherlands Organisation for Scientific Research (NWO). This thesis studies in three parts how microscopic data can be used in models that have the potential to improve utilization, while preventing excess crowding. _In the first part_, the emergence of crowding caused by interactions between the behavior of passengers and the public transport operators who plan the vehicle capacities is modeled. Using simulations the impact of the information disclosed to the passengers by public transport operators on the utilization and passenger satisfaction is analyzed. A quasi-experiment with a large group of students in a similar setting finds that four types of behavior can be observed. _In the second part_, algorithms that can extract temporal and spatial patterns from smart card data are developed and a first step to use such patterns in an agent based simulation is made. Furthermore, a way to generate synthetic smart card data is proposed. This is useful for the empirical validation of algorithms that analyze such data. _In the third and final part_ it is considered how individual decision strategies can be developed in situations where there exists uncertainty ab

The physics of traffic and regional development

This contribution summarizes and explains various principles from physics which are used for the simulation of traffic flows in large street networks, the modelling of destination, transport mode, and route choice, or the simulation of urban growth and regional development. The methods stem from many-particle physics, from kinetic gas theory, or fluid dynamics. They involve energy and entropy considerations, transfer the law of gravity, apply cellular automata and require methods from evolutionary game theory. In this way, one can determine interaction forces among driver-vehicle units, reproduce breakdowns of traffic including features of synchronized congested flow, or understand changing usage patterns of alternative roads. One can also describe daily activity patterns based on decision models, simulate migration streams and model urban growth as a particular kind of aggregation process

Understanding the impact of built environment on travel behaviour with activity-based modelling: Evidence from Beijing

The built environment has long been considered as a potentially influential factor in shaping and changing people’s travel behaviour. However, many gaps still exist in the understanding of the direction, size and mechanism of this influence. This thesis explores the complexities in the influence of the built environment on daily travel using a behaviour-oriented, activity-based modelling approach based on the notion of utility maximisation. The model simulates the full process of decision making in daily activity participation and travel, which involves the decisions on the type and frequency of activity participation, the sequence of activities, the choice of destinations and the time and mode of travel. Moreover, the thesis also addresses the lack of understanding on the influence of the ‘third dimension’ of the built environment — the street facades. A machine learning-based method is proposed to automatically evaluate the qualities of street facades from street view images. Scenario analyses using the proposed model show that, both commute and non-commute travel are more sensitive to the built environment in proximity to home (in my experiment, 500 metre buffer zone). In the context of Beijing, the total car use and commute car use of a person is significantly affected by the level of land use mix and the continuity of street facades around home, among all built environment features. Non-commute car use is significantly affected by employment density, retail density, accessibility to commercial clusters, bus coverage, road density and the quality and continuity of street facades. Similar effects on the final outcomes of travel behaviour (such as total car use) by different built environment features can happen through diverse processes and have different implications for people’s actual experience and the urban system. Some of the results are consistent with theoretical assumptions and some are not, which provides alternative insights into the relationship between the built environment and travel behaviour.China Scholarship Council Cambridge Overseas Trus

A framework for evaluating the impact of communication on performance in large-scale distributed urban simulations

A primary motivation for employing distributed simulation is to enable the execution of large-scale simulation workloads that cannot be handled by the resources of a single stand-alone computing node. To make execution possible, the workload is distributed among multiple computing nodes connected to one another via a communication network. The execution of a distributed simulation involves alternating phases of computation and communication to coordinate the co-operating nodes and ensure correctness of the resulting simulation outputs. Reliably estimating the execution performance of a distributed simulation can be difficult due to non-deterministic execution paths involved in alternating computation and communication operations. However, performance estimates are useful as a guide for the simulation time that can be expected when using a given set of computing resources. Performance estimates can support decisions to commit time and resources to running distributed simulations, especially where significant amounts of funds or computing resources are necessary. Various performance estimation approaches are employed in the distributed computing literature, including the influential Bulk Synchronous Parallel (BSP) and LogP models. Different approaches make various assumptions that render them more suitable for some applications than for others. Actual performance depends on characteristics inherent to each distributed simulation application. An important aspect of these individual characteristics is the dynamic relationship between the communication and computation phases of the distributed simulation application. This work develops a framework for estimating the performance of distributed simulation applications, focusing mainly on aspects relevant to the dynamic relationship between communication and computation during distributed simulation execution. The framework proposes a meta-simulation approach based on the Multi-Agent Simulation (MAS) paradigm. Using the approach proposed by the framework, meta-simulations can be developed to investigate the performance of specific distributed simulation applications. The proposed approach enables the ability to compare various what-if scenarios. This ability is useful for comparing the effects of various parameters and strategies such as the number of computing nodes, the communication strategy, and the workload-distribution strategy. The proposed meta-simulation approach can also aid a search for optimal parameters and strategies for specific distributed simulation applications. The framework is demonstrated by implementing a meta-simulation which is based on case studies from the Urban Simulation domain