Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach

Abstract

This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle largescale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.INDEX TERMS Social media, urban mobility, text classification, sentiment analysis, framework.

A Systematic Literature Review on Machine Learning in Shared Mobility

“…This understanding is pivotal for service providers. Analyzing user sentiment not only pinpoints areas for enhancement but also helps tailor services to user needs [81]. While analyzing existing customers can foster loyalty by catering to their specific needs [44], insights from social media discussions about the services can guide targeted customer acquisition strategies.…”

Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

“…To mitigate this dataset imbalance, they used undersampling, which removes certain instances from over-represented classes, and oversampling, which generates new instances for under-represented classes. In other aspects, the NLP technique has also been used to identify the traffic situation based on the social media data in various traffic studies: identifying the traffic congestion situations [36], analyzing the mobility environment [37], incident detection [38], and designing the traffic management systems [39].…”