From Twitter to traffic predictor: Next-day morning traffic prediction using social media data

Yao, Weiran; Qian, Sean

doi:10.1016/j.trc.2020.102938

Cited by 44 publications

(22 citation statements)

References 49 publications

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“…Wang et al [ 32 ] predicted the travel time based on the improved KNN, using cross validation to determine the selection of the k value. Yao et al [ 33 ] selected the training feature and the most similar neighbor days through the classification models of random forest (RF) and KNN and then used the regression model of RF and KNN to predict the time of traffic congestion. Traditional machine learning is the simple linear regression model, which fails to capture the complex nonlinear spatial-temporal correlations.…”

Section: Related Workmentioning

confidence: 99%

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

Luo

Zou

Zhang

et al. 2022

Entropy

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The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide owners with dynamic and accurate travel time prediction services to assist the owners to formulate more reasonable travel plans. However, there are still some problems in the current travel time prediction research (e.g., different types of vehicles are not processed separately, the proximity of the road network is not considered, and the capture of important information in the spatial-temporal perspective is not considered in depth). In this paper, we propose a Multi-View Travel Time Prediction (MVPPT) model. First, the travel times of different types of vehicles of each section in the expressway are analyzed, and the main differences in the travel times of different types of vehicles are obtained. Second, multiple travel time features are constructed, which include a novel spatial proximity feature. On this basis, we use CNN to capture the spatial correlation and the spatial attention mechanism to capture key information, the BiLSTM to capture the time correlation of time series, and the time attention mechanism capture key time information. Experiments on large-scale real traffic data demonstrate the effectiveness of our proposal over state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

Luo

Zou

Zhang

et al. 2022

Entropy

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“…A technique to analyze different patterns associated to work and rest activities and how traffic congestion influences these states during different moments of the day is described in [ 20 ]. The authors proposed to determine these patterns by extracting information from Twitter.…”

Section: Related Workmentioning

confidence: 99%

Traffic Congestion Analysis Based on a Web-GIS and Data Mining of Traffic Events from Twitter

Salazar-Carrillo

Torres-Ruiz

Davis

et al. 2021

Sensors

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Smart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic-related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web-GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.

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“…Researchers also have focused on detecting effective social media users and explored their network features to understand the spread of targeted information in major disasters [30,31]. There are few recent studies that showed the potentiality of Twitter data at a traffic predictor [32][33][34]. This made Twitter a promising source for real-time traffic management and potentially extended for traffic prediction at any time of day.…”

Section: Background and Related Workmentioning

confidence: 99%

Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions

Alam

Sadri

Jin

2021

Future Transportation

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The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.

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From Twitter to traffic predictor: Next-day morning traffic prediction using social media data

Cited by 44 publications

References 49 publications

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data

Traffic Congestion Analysis Based on a Web-GIS and Data Mining of Traffic Events from Twitter

Identifying Public Perceptions toward Emerging Transportation Trends through Social Media-Based Interactions

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