An interdisciplinary survey on origin-destination flows modeling: Theory and techniques

C Rong, J Ding, Y Li - ACM Computing Surveys, 2024 - dl.acm.org
Origin-destination (OD) flow modeling is an extensively researched subject across multiple
disciplines, such as the investigation of travel demand in transportation and spatial …

Context-aware machine learning for intelligent transportation systems: A survey

GL Huang, A Zaslavsky, SW Loke… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Context awareness adds intelligence to and enriches data for applications, services and
systems while enabling underlying algorithms to sense dynamic changes in incoming data …

Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach

Y Liang, G Huang, Z Zhao - Transportation research part C: emerging …, 2022 - Elsevier
Dynamic demand prediction is crucial for the efficient operation and management of urban
transportation systems. Extensive research has been conducted on single-mode demand …

An origin–destination passenger flow prediction system based on convolutional neural network and passenger source-based attention mechanism

S Lv, K Wang, H Yang, P Wang - Expert Systems with Applications, 2024 - Elsevier
An accurate origin–destination (OD) passenger flow prediction system is crucially important
for urban metro operation and management. However, there are still lacking targeted …

[HTML][HTML] Bi-level model predictive control for metro networks: Integration of timetables, passenger flows, and train speed profiles

X Liu, A Dabiri, J Xun, B De Schutter - Transportation Research Part E …, 2023 - Elsevier
This paper deals with the train scheduling problem for metro networks taking into account
time-dependent passenger origin–destination demands and train speed profiles. The aim is …

Decomposition and approximate dynamic programming approach to optimization of train timetable and skip-stop plan for metro networks

Y Yuan, S Li, R Liu, L Yang, Z Gao - Transportation Research Part C …, 2023 - Elsevier
Carefully coordinating train timetables of different operating lines can help reduce transfer
delays, which in turn reduces station crowding and improves overall service quality. This …

Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems

W Jiang, Z Ma, HN Koutsopoulos - Neural Computing and Applications, 2022 - Springer
Short-term origin–destination (OD) flow prediction is vital for operations planning, control,
and management in urban railway systems. While the entry and exit passenger demand …

Estimating intercity heavy truck mobility flows using the deep gravity framework

Y Yang, B Jia, XY Yan, Y Chen, D Song, D Zhi… - … Research Part E …, 2023 - Elsevier
Accurate estimation of intercity heavy truck mobility flows is of vital importance to urban
planning, transportation management and logistics operations. The inaccessibility of big …

Deep demand prediction: An enhanced conformer model with cold-start adaptation for origin–destination ride-hailing demand prediction

H Lin, Y He, Y Liu, K Gao, X Qu - IEEE Intelligent Transportation …, 2023 - ieeexplore.ieee.org
In intelligent transportation systems, one key challenge for managing ride-hailing services is
the balancing of traffic supply and demand while meeting passenger needs within vehicle …

DEASeq2Seq: An attention based sequence to sequence model for short-term metro passenger flow prediction within decomposition-ensemble strategy

H Huang, J Mao, W Lu, G Hu, L Liu - Transportation Research Part C …, 2023 - Elsevier
Short-term passenger flow prediction has practical significance for metro management and
operation. However, the complex nonlinear and non-stationary characteristics make it …