Urban  (U) Session 2

Abstract: The scientific understanding of human mobility has advanced in recent years due to the availability of digital traces including mobile phone call records, online social networks data and GPS trajectories from vehicles. There is consensus that distances and waiting times between consecutive locations in an individual’s trajectory are heavy tailed distributed. Mechanisms explaining the emergence of these statistical properties include individuals adopting Lévy-flight strategies and using different transportation modalities. The debate is, however, still open also because limited data resolution has hindered the understanding of human motion at all spatial and temporal scales. Here, we characterise mobility behaviour across an unprecedented range of scales, analysing 850 individuals’ digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best approximated by log-normal distributions across several orders of magnitude and that natural time-scales result from the deep-routed regularity of human mobility. We find that log-normal distributions characterise also waiting times and distances between consecutive discoveries, implying that this property of human motion is not a simple consequence of its regularity and stability across time. Revealing the characteristic features of human trajectories across a wide range of spatial and temporal scales, our findings provide key elements to explain and model the fundamental mechanisms governing human mobility behaviour. Close

Abstract: Decision making processes on complex socio-technical systems can be seen as a game. Multiple actors with different incentives are involved. They are related to one another via a network of interdependencies. Various strategies are performed and information about the technical uncertainties of the system play a big role. Moreover, the decision making process is dynamic since these different elements change over time. This makes the decision making process unstructured and hard to predict. Game concepts used in Public Administration have the ability to describe the decision making process. Examples of these games concepts are the Multi-Issue and Hub-Spoke game. Game theory provides various examples of mathematical constructed cases which help in gaining insight in the situation. However, both Public Administration and Game theory received criticism. Public Administration lacks formalization in the sense that it cannot predict the actions actors should perform in order to reach an optimal solution. Game theory has analytic and predictive features, but highly simplifies the situation by leaving out too many details which makes it hard for the decision maker to apply the results. The paper is a step towards bridging those two disciplines by formalizing Public Administration game concepts using game theoretical tools. For a decision maker the result could provide useful knowledge about the existence of different (optimal) solutions/scenarios/strategies to steer the process. The contribution of this paper is to give a better understanding of the decision making process by providing an overview of game concepts that can be extracted from empirical railway cases. The mapping of Public Administration game concepts to game theory concepts is presented in a common framework such that analysis becomes feasible. Moreover, for each game concept an example from a decision making process in the railway sector is provided in order to illustrate the applicability. Close

Abstract: Understanding human mobility patterns and be able to reproduce them accurately is crucial in a wide range of applications in public health, transport and urban planning and anthropology. However, most of the studies and models proposed in the recent literature focus on long-term mobility, and, most importantly, the travel purpose and the importance given to it are rarely taken into account. Indeed, an individual is not willing to invest the same amount of time or money, more generally, the same amount of 'energy' into a travel according to the value attached to the purpose/objective of this travel. In this work, we test the assumption that it exists a relationship between the cost associated to a travel and the value given to its purpose by analyzing a credit card dataset contains information about 40 million bank card transactions made by customers of the Banco Bilbao Vizcaya Argentaria (BBVA) in the provinces of Madrid and Barcelona in 2011. We finally propose an individual human mobility patterns model which is able to explain and reproduce the properties observed in the data. Close

Abstract: Motor vehicle accidents contribute to over 1.2 million fatalities and are the leading cause of death for young people aged 15 to 29 years. The majority of accidents are caused by human error with an array of strategies implemented to mitigate such errors. The crash risk for young and novice drivers learning to negotiate the complexities of the road environment is considerable. In the absence of direct feedback, abhorrent driving behaviours (e.g., excess speeding, braking, acceleration) may become prevalent in the early stages of licensing and hence, prolonging the time over which they remain at high risk. The aim of this research is to prevent the adoption of early stage undesirable transitions in driving behaviour, and positively reinforce safer driving by providing direct feedback to drivers about their abhorrent behaviours. The presentation will present a novel way of using neural networks for the early identification of state transitions in an applied area of significant global importance, transport safety. Results of our research show that, using telematics data, driver behaviours can be mathematically represented by frequencies related to specific breaking, acceleration and speeding thresholds, providing a personalised profile of each driver’s use of the car’s throttle and brakes. The evolution of this profile is modelled using a Long Short-Term Memory recurrent neural network (LSTM). One LSTM per driver assesses whether a new data sample belongs to the current driver or not. Positive or negative behavioural change is identified by monitoring the prediction error and LSTM gate activity, which indicate whether the internal memory of the LSTM requires updating to accurately classify the latest instance of telematics data. Direct feedback to the driver can then be provided if the change in behaviour is negative, potentially preventing the adoption of the new behaviour and reducing deaths and injuries arising from crashes. Close

Abstract: The propagation of delays across airport networks, and more generally the resilience of such systems against perturbations such as bad weather, strikes or terrorist attacks, are problems of self-evident economic and social importance, as well as interesting from a theoretical point of view due to their rich complex behavior. Delay can be propagated from one flight to another when a dependency between the two flights exists because they use the same aircraft, passengers and crew members connect from one another, or more indirectly as the result of airport or airspace congestion. In this work, we characterize the delay propagation patterns in the US and European air transportation networks by following delays backwards in time to their possible original source(s). Using data obtained from the Bureau of Transportation Statics of the United States Department of Transportation and the Flightradar24 web-based service, we identify the initial sources of delay as well as the weak point of the network typically corresponding to airports affected early in the morning and in a recurrent way along the day. The analysis is performed at the flight and airport level for different seasons. Finally, the impact of intercontinental flights, which provide a coupling between the US and European networks, is also assessed. Close

Abstract: Each year, 1.25 million people are killed and a further 50 million are injured in road crashes. Approximately half of these deaths and injuries occur among vulnerable road users including pedestrians and cyclists. Separated cycling infrastructure that reduces physical interaction between cars and cyclists is one strategy employed by urban planners to improve safety. However, the effect of separated infrastructure on behavioural adaptation by drivers has not been extensively investigated. We constructed an agent-based model to explore the effects of introducing separated cycling infrastructure into a transportation network under conditions where simulated drivers demonstrated various levels of behavioural adaptation in responses to increased exposure to cyclists. We then compared the results generated by this model to that expected under a conventional mathematical model. The agent-based model showed that the introduction of low levels of separated cycling infrastructure provided little or no reduction in car vs cyclist crashes when behavioural adaptation was among drivers was also modelled. This finding differed from the conventional mathematical model, which predicts safety benefits at all levels of additional cycling infrastructure. The study demonstrates the importance of modelling behavioural mechanisms associated with cyclist and vehicle interaction when estimating potential safety benefits of separated cycling infrastructure. It suggests that if behavioural adaptation is a genuine mechanism contributing to cyclist safety, critical levels of separated infrastructure beyond those currently present in many highly motorized cities are likely to be required before observable reductions in cyclist deaths and injuries are realised. Close