Developing an optimised activity type annotation method based on classification accuracy and entropy indices

The generation of substantial amounts of travel and mobility related data has spawned the emergence of the era of big data. However, this data generally lacks activity-travel information such as trip purpose. This deficiency led to the development of trip purpose inference (activity type imputation / annotation) techniques, of which the performance depends on the available input data and the (number of) activity type classes to infer. Aggregating activity types strongly increases the inference accuracy and is usually left to the discretion of the researcher. As this is open for interpretation, it undermines the reported inference accuracy. This study developed an optimised classification methodology by identifying classes of activity types with an optimal balance between improving model accuracy, and preserving activity information from the original data set. A sensitivity analysis was performed. Additionally, several machine learning algorithms are experimented with. The proposed method may be applied to any study area

Investigating Pedestrian Walkability using a Multitude of Seoul Data Sources

Currently walking is a multidisciplinary and emerging point of attention for urban sustainability and for ensuring the quality of pedestrian environments. In order to understand pedestrian behaviour, walkability researches estimate the factors which affect the level of pedestrian satisfaction. Past studies focused on the relationship between environmental factors and pedestrian behavioural outcomes. In this study, we developed pedestrian satisfaction multinomial logit models using various datasets, examining the relative impact of five differently themed sets of attributes: personal, walk-facilities, land-use, pedestrian volumes, and weather-related variables. The results show that the personal variability attributes were selected as most significant. We investigated effects of personal variability, such as the spatial cognition level and travel purpose, and detailed effects of environmental features. In addition, crowdedness, land-use types, and residential information were investigated. The results from this study offer contributions by providing evidence of the importance of personal and contextual variables in influencing the pedestrian walkability

The influence of intra-daily activities and settings upon weekday violent crime in public space in Manchester, UK

People ebb and flow across the city. The spatial and temporal patterning of crime is, in part, reflective of this mobility, of the scale of the population present in any given setting at a particular time. It is also a function of capacity of this population to perform an active role as an offender, victim or guardian in any specific crime type, itself shaped by the time-variant activities undertaken in, and the qualities of, particular settings. To this end, this paper explores the intra-daily influence of activities and settings upon the weekday spatial and temporal patterning of violent crime in public spaces. This task is achieved through integrating a transient population dataset with travel survey, point-of-interest and recorded crime data in a study of Great Manchester (UK). The research deploys a negative binomial regression model controlling for spatial lag effects. It finds strong and independent, but time-variant, associations between leisure activities, leisure settings and the spatial and temporal patterning of violent crime in public space. The paper concludes by discussing the theoretical and empirical implications of these findings

Developing an optimised activity type annotation method based on classification accuracy and entropy indices

The generation of substantial amounts of travel- and mobility-related data has spawned the emergence of the era of big data. However, this data generally lacks activity-travel information such as trip purpose. This deficiency led to the development of trip purpose inference (activity type imputation/annotation) techniques, of which the performance depends on the available input data and the (number of) activity type classes to infer. Aggregating activity types strongly increases the inference accuracy and is usually left to the discretion of the researcher. As this is open for interpretation, it undermines the reported inference accuracy. This study developed an optimised classification methodology by identifying classes of activity types with an optimal balance between improving model accuracy, and preserving activity information from the original data set. A sensitivity analysis was performed. Additionally, several machine learning algorithms are experimented with. The proposed method may be applied to any study area