Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling
Ride-hailing applications are becoming more and more popular for providing drivers and
passengers with convenient ride services, especially in metropolises like Beijing or New …
passengers with convenient ride services, especially in metropolises like Beijing or New …
Contextualized spatial–temporal network for taxi origin-destination demand prediction
Taxi demand prediction has recently attracted increasing research interest due to its huge
potential application in large-scale intelligent transportation systems. However, most of the …
potential application in large-scale intelligent transportation systems. However, most of the …
Real-time prediction of taxi demand using recurrent neural networks
Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the
wait-time for passengers and drivers. In this paper, we propose a sequence learning model …
wait-time for passengers and drivers. In this paper, we propose a sequence learning model …
The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms
Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle
taxis to passengers in need. To precisely balance the supply and the demand of taxis, online …
taxis to passengers in need. To precisely balance the supply and the demand of taxis, online …
Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing
services. However, predicting passenger demand over multiple time horizons is generally …
services. However, predicting passenger demand over multiple time horizons is generally …
Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
AO Al-Abbasi, A Ghosh… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The success of modern ride-sharing platforms crucially depends on the profit of the ride-
sharing fleet operating companies, and how efficiently the resources are managed. Further …
sharing fleet operating companies, and how efficiently the resources are managed. Further …
Short-term prediction of passenger demand in multi-zone level: Temporal convolutional neural network with multi-task learning
K Zhang, Z Liu, L Zheng - IEEE transactions on intelligent …, 2019 - ieeexplore.ieee.org
Accurate short-term passenger demand prediction contributes to the coordination of traffic
supply and demand. This paper proposes an end-to-end multi-task learning temporal …
supply and demand. This paper proposes an end-to-end multi-task learning temporal …
Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions
Advancements in sensing and the Internet of Things (IoT) technologies generate a huge
amount of data. Mobility on demand (MoD) service benefits from the availability of big data in …
amount of data. Mobility on demand (MoD) service benefits from the availability of big data in …
A residual spatio-temporal architecture for travel demand forecasting
G Guo, T Zhang - Transportation Research Part C: Emerging …, 2020 - Elsevier
This paper proposes a deep architecture called residual spatio-temporal network (RSTN) for
short-term travel demand forecasting. It comprises fully convolutional neural networks …
short-term travel demand forecasting. It comprises fully convolutional neural networks …
Predicting demand for air taxi urban aviation services using machine learning algorithms
S Rajendran, S Srinivas, T Grimshaw - Journal of Air Transport …, 2021 - Elsevier
This research focuses on predicting the demand for air taxi urban air mobility (UAM) services
during different times of the day in various geographic regions of New York City using …
during different times of the day in various geographic regions of New York City using …