From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data

This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM’s abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making

The Tell-Tale Tweet: extracting, enriching, and modelling people’s activity and movement from social media data

Social media data have been used in many studies in the recent years. Compared to the traditional household travel survey data, social media data have a lower cost, and they can be obtained from abundant sources. However, several pre-processing tasks are required before social media data can be used for mobility analyse. For instance, distinguishment needs to be made between location and activity. In this thesis, text analytics, machine learning, and “Tweet Block” are applied in order to differentiate between location and activity and to extract more accurate information from social media data which can then be used for mobility analyse. In Section 3, the focus is to extract and analyse users’ movement and lifestyle. Unlike the state-of-the-practice, this research clearly distinguishes between location and activity. Text mining technique was applied to identify location and activity information respectively, and a clustering algorithm was applied to analyse the lifestyle of users. The strict distinguish between activity and location led to a result that the identified data is limited compared to traditional ways of labelling. To solve this problem, the information extracted from data was enriched by applying a method called “Tweet Block”. Tweet Block enable to identify 1,745 location and 98 activity which were not identified in text mining process. With the enriched data in hand, a method was purposed to infer information of user’s movement from the data point that is previously unusable (i.e. a single record from a day.) The average generated trip rate using this method was increased by 26%-50% compared to the method used in previous research. Travelling track was also generated to analyse the movement of these users. In Section 4, the primary purpose is to build a valid activity prediction model from the data. Machine learning algorithms were applied to build an activity prediction model from the data. Land use data were overlapped to the original data set, which acted as a supportive data to location information. Random Forest (RF) and Neural Network (NN) algorithms were used to build models and NN models were kept after model selection. A Stratified K-fold cross-validation was used to validate the model