Deep demand prediction: An enhanced conformer model with cold-start adaptation for origin–destination ride-hailing demand prediction
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 …
the balancing of traffic supply and demand while meeting passenger needs within vehicle …
A Reverse Auction-Based Individualized Incentive System for Transit Mobility Management
W Jiang, HN Koutsopoulos, Z Ma - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Urban rail transit systems in many cities are experiencing crowding during peak periods due
to rapid population growth. Incentive-based demand management strategies aim to better …
to rapid population growth. Incentive-based demand management strategies aim to better …
[HTML][HTML] Resilience assessment of intercity transport in a two-city system
For resilience analysis, studies in the transport field have focused on the short term, while
attention in the spatial and economic field has been paid to the long term. No resilience …
attention in the spatial and economic field has been paid to the long term. No resilience …
Full-scale spatio-temporal traffic flow estimation for city-wide networks: A transfer learning based approach
The full-scale spatio-temporal traffic flow estimation/prediction has always been a hot spot in
transportation engineering. The low coverage rate of detectors in transport networks brings …
transportation engineering. The low coverage rate of detectors in transport networks brings …
IG-Net: An interaction graph network model for metro passenger flow forecasting
The urban metro system accommodates significant travel demand and alleviates traffic
congestion. Improving metro operational efficiency can increase the metro operator revenue …
congestion. Improving metro operational efficiency can increase the metro operator revenue …
How do you go where? improving next location prediction by learning travel mode information using transformers
Predicting the next visited location of an individual is a key problem in human mobility
analysis, as it is required for the personalization and optimization of sustainable transport …
analysis, as it is required for the personalization and optimization of sustainable transport …
Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction
Accurate and fine-grained traffic state prediction has always been an important research
field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature …
field. For long-term traffic flow prediction, the high-dimensional and coupled traffic feature …
[HTML][HTML] Context-aware multi-head self-attentional neural network model for next location prediction
Accurate activity location prediction is a crucial component of many mobility applications and
is particularly required to develop personalized, sustainable transportation systems. Despite …
is particularly required to develop personalized, sustainable transportation systems. Despite …
A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China
Urban public transport systems, characterised by their complexity, generate vast data sets
that pose challenges to traditional analytical methods. To address this issue, our research …
that pose challenges to traditional analytical methods. To address this issue, our research …
A holistic data-driven framework for developing a complete profile of bus passengers
User profiles, considered as one of the fundamental inputs of recommendation systems and
customized services, can be rationally applied in the public transport domain to represent …
customized services, can be rationally applied in the public transport domain to represent …