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

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Transportation Systems Analysis and Assessment

The transportation system is the backbone of any social and economic system, and is also a very complex system in which users, transport means, technologies, services, and infrastructures have to cooperate with each other to achieve common and unique goals.The aim of this book is to present a general overview on some of the main challenges that transportation planners and decision makers are faced with. The book addresses different topics that range from user's behavior to travel demand simulation, from supply chain to the railway infrastructure capacity, from traffic safety issues to Life Cycle Assessment, and to strategies to make the transportation system more sustainable

Climate change impacts and mitigation : reducing CO2 emissions from the freight transport sector : lessons for Mexico from the UK experience and future policy

The United Kingdom and Mexico have established goals to reduce CO2 emissions. With the publication of the Climate Change act in 2008 Britain acknowledges that is technologically ready to implement changes to bring important reductions of CO2 emissions. Mexico included Climate Change abatement in its 2007 development program. UK aims to achieve a reduction of 80% and Mexico a reduction of 50% in their CO2 emissions by the year 2050. To achieve these reductions both countries face the challenge of improving activities such as better use of fuels, for example natural gas for energy production or diesel used in road freight transport vehicles. Freight transport currently accounts 25% of global carbon emissions; with road freight as the fastest growing sector for both countries. The use of biofuels or clean energy powered vehicles is far from a 100% implementation in the fleet. Because of this improving the fuel efficiency in the current operation signifies an opportunity to reduce emissions. The United Kingdom is ahead in legislation through taxation, market incentives and research to encourage reductions from freight transport. Mexico is in its way to the creation of a Climate Change Law. This dissertation aims to determine which lessons Mexico can learn from the United Kingdom in its improvement of freight transport sector in two levels. The Macro level looks at legislation and private sector initiatives, and the Micro level simulating 11 scenarios using real data from operation of a food manufacturer provided by the StarFish Project. The scenarios simulate the implementation of a series of best practice recommendations to reduce emissions and improve operation. The results evidence that at a Macro level Mexico can implement legislation mechanisms to stimulate the reduction of CO2 emissions in the transport sector. At a Micro level the simulations show that even for developed countries like the United Kingdom there is a big potential to reduce carbon emissions from the freight transport sector. The outcome of the dissertation is that learn from experiences from other countries applies not only for Mexico and other developing countries but for every country aiming to improve the reduction of CO2 emissions

Shared autonomous vehicle services: A comprehensive review

© 2019 Elsevier Ltd The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed

A Framework for Big Data in Urban Mobility and Movement Patterns Analysis

Novel large consumer datasets (called ‘Big Data’) are increasingly readily available. These datasets are typically created for a particular purpose, and as such are skewed, and further do not have the broad spectrum of attributes required for their wider application. Railway ticket data are an example of consumer data, which often have little or no supplementary information about the passengers who purchase them, or the context in which the ticket was used (like crowding-level in the train). These gaps in consumer data present challenges in using these data for planning, and inference on the drivers of mobility choice. Heckman’s in-depth discussion of ‘sample selection’ bias and ‘omitted variables’ bias (Heckman, 1977), and Rubin’s seminal paper on ‘missing values’ (Rubin, 1976) laid the framework for addressing omitted variables and missing data problems today. On the strength of these, a powerful set of complementary concerted methodologies are developed to harness railways consumer (ticketing) data. A novel spatial microsimulation methodology suitable for skewed interaction data was developed to combine LENNON ticketing, National Rail Travel Survey, and Census interaction data, to yield an attribute-rich micro-population. The micro-population was used as input to a GIS network, logistically constrained by the transit feed specification (GTFS). This identifies the context of passenger mobility. Bayesian models then enable the identification of passenger behaviour, like missing daily trip rates with season tickets, and flows to group stations. Case studies using the micro-level synthetic data reveal a mechanism of rail-heading phenomena in West Yorkshire, and the impact of a new station at Kirkstall Forge. The spatial microsimulation and GIS-GTFS methods are potentially useful to network operators for the management and maintenance on the railways. The representativeness of the micro-level population created has the potential to alter multi-agent transport simulation genres, by precluding the need for the complexities of utility-maximizing traffic assignment

Airport access and travel time uncertainty

The implications of travel time uncertainty on the operational efficiency of airport terminals have until now not been examined. With the forecast growth in congestion levels predicted for all modes of transport, not only will travel time uncertainty increase but its impact may increase also. The first part of this thesis covers the analysis of two passenger surveys conducted at Manchester Airport and Birmingham Airport. These surveys had the objective of providing evidence to support or dispute the belief that air travellers react to travel time uncertainty. The research identifies that passengers do react by allowing margins of safety for their access journeys, and that this change in behaviour will modify the arrival distribution patterns at airports. The second part of this thesis examines how airport passenger flows could be altered by a change in the arrival distribution of originating passengers at airport terminals. Three airports - Manchester, Birmingham and East Midlands International - are modelled using a simulation tool and tested to assess how a shift in arrival distribution affects queuing and peak passenger volumes within the airport terminal. The findings of this thesis show that airport passenger terminal operational efficiency is affected by access journey time uncertainty. It also identifies that passenger decision making can only be explained by various combinations of factors. Possible methods of minimising the effects of travel time uncertainty are considered. The advantages and disadvantages of access journey time uncertainty for airports and airlines are discussed. It concludes that, to be successful in overcoming negative aspects, both parties must provide a service that results in customer satisfaction. This is the only sure way to maintain their respective revenue levels and secure their future in what is becoming an increasingly competitive industry

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17

A systems design study introducing a collection point for baggage transfer services at a railway station in the UK

Railway transport has shown a steady growth in passenger numbers over the past 20 years across the UK. Passengers travel with luggage. It has been forecasted that there will be a reduction of “luggage racks-to-seats” ratio in the future passenger train fleet. There are currently no baggage transfer systems at any of the train stations as part of the urban rail transit system in the UK. Hence, the purpose of this study is to investigate the possibility of having a baggage transfer service at Newcastle Central Station, which can serve different travel destinations. A simulation modelling study of different parts of the baggage check-in shop within the railway station is offered. Check-in point, movement of luggage around Newcastle Central, baggage reclaims and storage areas have been contemplated and evaluated using SIMUL8 event-based simulation package. The results for the simulation model developed show that a baggage transfer service at Newcastle Central Station is possible with a mixture of walk-in and online check-in options