Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Machine Learning for public transportation demand prediction: A Systematic Literature Review
Abstract Within the Intelligent Public Transportation Systems (IPTS) field, the prediction of
public transportation demand is a key point for enhancing the quality of the services. These …
public transportation demand is a key point for enhancing the quality of the services. These …
A systematic literature review on machine learning in shared mobility
J Teusch, JN Gremmel, C Koetsier… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Shared mobility has emerged as a sustainable alternative to both private transportation and
traditional public transport, promising to reduce the number of private vehicles on roads …
traditional public transport, promising to reduce the number of private vehicles on roads …
Physics-guided active sample reweighting for urban flow prediction
Urban flow prediction is a spatio-temporal modelling task that estimates the throughput of
transportation services like buses, taxis, and ride-sharing, where data-driven models have …
transportation services like buses, taxis, and ride-sharing, where data-driven models have …
Origin-destination travel time oracle for map-based services
Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD)
travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D …
travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D …
Topological graph representation of stratigraphic properties of spatial-geological characteristics and compression modulus prediction by mechanism-driven learning
M Wang, E Wang, X Liu, C Wang - Computers and Geotechnics, 2023 - Elsevier
The soil's compression modulus (Es) is one of the most critical mechanical parameters for
studying land subsidence in urban strata. Meanwhile, the vertical heterogeneity and lateral …
studying land subsidence in urban strata. Meanwhile, the vertical heterogeneity and lateral …
Regularized Spatial–Temporal Graph Convolutional Networks for Metro Passenger Flow Prediction
One of the challenging topics in Intelligent Transportation Systems (ITSs) is the metro
passenger flow prediction. It has great practical significance for the daily crowd management …
passenger flow prediction. It has great practical significance for the daily crowd management …
Diffusion probabilistic model for bike-sharing demand recovery with factual knowledge fusion
The mining of diverse patterns from bike flow has attracted widespread interest from
researchers and practitioners. Prior arts concentrate on forecasting the flow evolution from …
researchers and practitioners. Prior arts concentrate on forecasting the flow evolution from …
GNN-based passenger request prediction
AA Makhdomi, IA Gillani - Transportation Letters, 2024 - Taylor & Francis
Passenger request prediction is essential for operations planning, control, and management
in ride-hailing platforms. While the demand prediction problem has been studied …
in ride-hailing platforms. While the demand prediction problem has been studied …
Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles
The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have
catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In …
catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In …