Ed Manley

Measuring variability of mobility patterns from multiday smart-card data

Abstract The availability of large amounts of mobility data has stimulated the research in discovering patterns and understanding regularities. Comparatively, less attention has been paid to the study of variability, which, however, has been argued as equally important as regularities, since variability identifies diversity. In a transport network, variability exists from person to person, from place to place, and from day to day. In this paper, we present a set of measuring of variability at individual and aggregated levels using multi-day smart-card data. Statistical analysis, correlation matrix and network-based clustering methods are applied and potential use of measured results for urban applications are also discussed. We take Singapore as a case study and use one-week smart-card data for analysis. An interesting finding is that though the number of trips and mobility patterns varies from day to day, the overall spatial structure of urban movement always remains the same throughout a week. This finding showed that a systemic framework with well-organized analytical methods is indeed, necessary for extracting variability that may change at different levels and consequently for uncovering different aspects of dynamics, namely transit, social and urban dynamics. We consider this paper as a tentative work toward such generic framework for measuring variability and it can be used as a reference for other research work in such a direction.

Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data

To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable.

Identifying functional urban regions within traffic flow

In this work, community detection algorithms are applied to a topological representation of the road network formed through observed routing behaviour. The resulting communities provide an insight into how areas of the city may be grouped as functional regions, shaped purely on their shared usage.

Revisiting the past: Replicating fifty year old flow analysis using contemporary taxi flow data

Over sixty years ago, geography began its so-called quantitative revolution, where for the first time statistical methods were used to explain the spatial nature of geographic phenomena. Computers made some of this possible, but their limited power did not allow for more than relatively small analytic explorations and consequently many of these earlier ideas are now buried in the mists of time. Here we attempt to replicate one of these early analyses using taxi flow data collected in 1962 and originally used by Goddard (1970; then at the London School of Economics) to extract functional regions within Londons city center. Our experiment attempts to replicate Goddards methodology on a modern taxi flow data set, acquired through Global Positioning System tracking. We initially expected that our analysis would be directly comparable with Goddards, potentially providing insights into temporal change in the spatial structure of the city core. Attempts at replicating the original analysis have proved enormously difficult, however, for several reasons, including the many subjective choices made by the researcher in articulating and using the original method and the specific characteristics of contemporary taxi flow data. We therefore opt to replicate Goddards approach as closely and as logically as possible and to fill in gaps based on statistically informed choices. We have also run the analysis on two spatial scales—Central London and a wider area—to explore how scales of analyses that were beyond the capacities of Goddards early computations also help to shape our understanding of the results he obtained.

Spatiotemporal variation in travel regularity through transit user profiling

New smart card datasets are providing new opportunities to explore travel behaviour in much greater depth than anything accomplished hitherto. Part of this quest involves measuring the great array of regular patterns within such data and explaining these relative to less regular patterns which have often been treated in the past as noise. Here we use a simple method called DBSCAN to identify clusters of travel events associated with particular individuals whose behaviour over space and time is captured by smart card data. Our dataset is a sequence of three months of data recording when and where individual travellers start and end rail and bus travel in Greater London. This dataset contains some 640 million transactions during the period of analysis we have chosen and it enables us to begin a search for regularities at the most basic level. We first define measures of regularity in terms of the proportions of events associated with temporal, modal (rail and bus), and service regularity clusters, revealing that the frequency distributions of these clusters follow skewed distributions with different means and variances. The analysis then continues to examine how regularity relative to irregular travel across space, demonstrating high regularities in the origins of trips in the suburbs contrasted with high regularities in the destinations in central London. This analysis sets the agenda for future research into how we capture and measure the differences between regular and irregular travel which we discuss by way of conclusion.

Global Determinants of Navigation Ability

Human spatial ability is modulated by a number of factors, including age [1-3] and gender [4, 5]. Although a few studies showed that culture influences cognitive strategies [6-13], the interaction between these factors has never been globally assessed as this requires testing millions of people of all ages across many different countries in the world. Since countries vary in their geographical and cultural properties, we predicted that these variations give rise to an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis, we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people and sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-center clinical trials using cognitive testing.

Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions

The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC) framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.

Planetary-wide organization of human navigation ability

Human cognitive strategies vary across nations and cultures. However, it is unknown whether the cognitive strategies of nations are randomly distributed or whether groups of countries are clustered by similar cognitive profiles. Using a mobile-based virtual reality navigation task, we measured spatial navigation ability in more than 2.5 million people globally. Using a clustering approach, we find that navigation ability is not smoothly distributed globally but clustered into five distinct yet geographically related groups of countries. Furthermore, the economic wealth of a nation (Gross Domestic Product per capita) was predictive of the average navigation ability of its inhabitants and gender inequality (Gender Gap Index) was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally. This will not only inform cognitive assessment but also clinical assessment and educational approaches in the future.Countries vary in their geographical and cultural properties. We predicted that such variation impacts human cognition, resulting in an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people, sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-centre clinical trials using cognitive testing.

Planetary-wide organization of human cognition

Human cognitive strategies vary across nations and cultures. However, it is unknown whether the cognitive strategies of nations are randomly distributed or whether groups of countries are clustered by similar cognitive profiles. Using a mobile-based virtual reality navigation task, we measured spatial navigation ability in more than 2.5 million people globally. Using a clustering approach, we find that navigation ability is not smoothly distributed globally but clustered into five distinct yet geographically related groups of countries. Furthermore, the economic wealth of a nation (Gross Domestic Product per capita) was predictive of the average navigation ability of its inhabitants and gender inequality (Gender Gap Index) was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally. This will not only inform cognitive assessment but also clinical assessment and educational approaches in the future.Countries vary in their geographical and cultural properties. We predicted that such variation impacts human cognition, resulting in an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people, sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-centre clinical trials using cognitive testing.

Economic wealth and gender inequality shape the distribution of navigational ability in the world population

Human cognitive strategies vary across nations and cultures. However, it is unknown whether the cognitive strategies of nations are randomly distributed or whether groups of countries are clustered by similar cognitive profiles. Using a mobile-based virtual reality navigation task, we measured spatial navigation ability in more than 2.5 million people globally. Using a clustering approach, we find that navigation ability is not smoothly distributed globally but clustered into five distinct yet geographically related groups of countries. Furthermore, the economic wealth of a nation (Gross Domestic Product per capita) was predictive of the average navigation ability of its inhabitants and gender inequality (Gender Gap Index) was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally. This will not only inform cognitive assessment but also clinical assessment and educational approaches in the future.Countries vary in their geographical and cultural properties. We predicted that such variation impacts human cognition, resulting in an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people, sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-centre clinical trials using cognitive testing.