AIMC Topic: Transportation

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Optimization of two-passenger ride-pooling orders based on ST-GNN and path optimization.

PloS one
Urban dynamic ride-pooling faces significant challenges in achieving efficient real-time order matching and path planning, primarily due to the complex spatio-temporal coupling of passenger demand and traffic conditions. Traditional algorithms often ...

DSSA-TCN: Exploiting adaptive sparse attention and diffusion graph convolutions in temporal convolutional networks for traffic flow forecasting.

PloS one
Accurate traffic flow forecasting is essential for intelligent transportation systems, yet the nonlinear and dynamically evolving spatio-temporal dependencies in urban road networks make reliable prediction challenging. Existing graph-based and atten...

Multi-objective representation learning for road networks and trajectories with spatial-temporal fusion and contrastive signals.

PloS one
Modeling and learning representations for road networks and vehicle trajectories are crucial in enabling intelligent transportation systems, with applications ranging from traffic forecasting to many other downstream inference tasks. However, learnin...

Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective, Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Transportation insecurity is a known barrier to accessing eye care and is associated with poorer visual outcomes for patients. However, its mention is seldom captured in structured data fields in electronic health records, limiting effort...

Recognizing Skateboard and Kickboard Commuting Behaviors Using Activity Trackers: Feasibility Study Using Machine Learning Approaches.

JMIR formative research
BACKGROUND: Active commuting, such as skateboarding and kickboarding, is gaining popularity as an alternative to traditional modes of transportation such as walking and cycling. However, current activity trackers and smartphones, which rely on accele...

Empirical study of daily link traffic volume forecasting based on a deep neural network.

PloS one
Forecasting the daily link traffic volume is critical in transportation demand analysis in feasibility studies for planning transportation facilities. The high purchase and maintenance cost of commercial transport planning software poses a challenge ...

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction.

PloS one
Accurate traffic flow prediction is vital for intelligent transportation systems but presents significant challenges. Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal sc...

The urban physical environment and leisure-time physical activity in early midlife: a FinnTwin12 study.

Health & place
Under the exposome framework, this study examined the relationship between the urban physical environment and leisure-time physical activity during early midlife based on 394 participants (mean age: 37, range 34-40) from the FinnTwin12 cohort, residi...

Exploring the drivers of digital transformation in Chinese port and shipping enterprises: A machine learning approach.

PloS one
With the transition to a global green low-carbon economy, the urgency for digital transformation in the port and shipping industry has become increasingly prominent in making enterprises more efficient and sustainable. This study focuses on how Chine...

The analysis of rural revitalization serviceplatform in smart city under back propagation neural network.

PloS one
To achieve rural revitalization and enhance the development of rural tourism, this study employs a back propagation neural network (BPNN) to construct a rural revitalization development model. Additionally, the Grey Relation Analysis (GRA) algorithm ...