Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph
Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations …
Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations …
A deep learning approach for detecting traffic accidents from social media data
This paper employs deep learning in detecting the traffic accident from social media data.
First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan …
First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan …
Improving traffic flow prediction with weather information in connected cars: A deep learning approach
A Koesdwiady, R Soua, F Karray - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Transportation systems might be heavily affected by factors such as accidents and weather.
Specifically, inclement weather conditions may have a drastic impact on travel time and …
Specifically, inclement weather conditions may have a drastic impact on travel time and …
Application of social sensors in natural disasters emergency management: a review
Natural disasters are public emergencies characterized by suddenness, universality, and
nonconventionality. Realizing the early warning, monitoring, and intervention of natural …
nonconventionality. Realizing the early warning, monitoring, and intervention of natural …
Forecasting the subway passenger flow under event occurrences with social media
Subway passenger flow prediction is strategically important in metro transit system
management. The prediction under event occurrences turns into a very challenging task. In …
management. The prediction under event occurrences turns into a very challenging task. In …
[HTML][HTML] From Twitter to traffic predictor: Next-day morning traffic prediction using social media data
The effectiveness of traditional traffic prediction methods, such as autoregressive or spatio-
temporal models, is often extremely limited when forecasting traffic dynamics in early …
temporal models, is often extremely limited when forecasting traffic dynamics in early …
Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace
Crowdsourcing has significantly motivated the development of meteorological services.
Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing‐driven …
Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing‐driven …
Forecasting current and next trip purpose with social media data and Google places
Trip purpose is crucial to travel behavior modeling and travel demand estimation for
transportation planning and investment decisions. However, the spatial-temporal complexity …
transportation planning and investment decisions. However, the spatial-temporal complexity …
Evaluating efficiency and safety of mixed traffic with connected and autonomous vehicles in adverse weather
G Hou - Sustainability, 2023 - mdpi.com
Connected and autonomous vehicles (CAVs) are expected to significantly improve traffic
efficiency and safety. However, the overall impacts of CAVs on mixed traffic have not been …
efficiency and safety. However, the overall impacts of CAVs on mixed traffic have not been …
Multi-crowdsourced data fusion for modeling link-level traffic resilience to adverse weather events
Climate change leads to more frequent and intense weather events, posing escalating risks
to road traffic. Crowdsourced data present new opportunities to monitor and investigate road …
to road traffic. Crowdsourced data present new opportunities to monitor and investigate road …