|
ai-generated content (aigc) |
7 |
|
generative ai |
7 |
|
generative method |
7 |
|
generative urban design |
7 |
|
human-machine collaboration |
7 |
|
urban form generation |
7 |
|
shared mobility |
4 |
|
covid-19 |
3 |
|
human mobility |
3 |
|
intentional travel groups |
3 |
|
land use |
3 |
|
metro system |
3 |
|
node-place model |
3 |
|
node–place model |
3 |
|
smartcard data |
3 |
|
social contact |
3 |
|
spatiotemporal analysis |
3 |
|
transport |
3 |
|
bike sharing |
2 |
|
data sparsity |
2 |
|
demand forecasting |
2 |
|
discrete choice modelling |
2 |
|
graph neural network |
2 |
|
non-linear relationship |
2 |
|
origin–destination matrix |
2 |
|
ridesplitting |
2 |
|
ridesplitting service |
2 |
|
trip shareability |
2 |
|
willingness to share |
2 |
|
2d convnets |
1 |
|
activity discovery |
1 |
|
activity-based modeling |
1 |
|
activity-based travel demand model |
1 |
|
adaptation models |
1 |
|
adversarial learning |
1 |
|
agent-based simulation |
1 |
|
battery electric buses |
1 |
|
bayesian inference |
1 |
|
bayesian n-gram model |
1 |
|
bayesian online changepoint detection |
1 |
|
behavior change |
1 |
|
big data |
1 |
|
bike sharing planning |
1 |
|
bus fleet transition |
1 |
|
car ownership |
1 |
|
car pride |
1 |
|
car use |
1 |
|
car-following gan |
1 |
|
charging facility planning |
1 |
|
conflict analysis |
1 |
|
data and data science |
1 |
|
data mining |
1 |
|
deep learning |
1 |
|
deep reinforcement learning |
1 |
|
demand prediction |
1 |
|
driver behavior randomness |
1 |
|
e-bike |
1 |
|
e-scooter |
1 |
|
entropy rate |
1 |
|
excess journey time |
1 |
|
explainable artificial intelligence |
1 |
|
feature extraction |
1 |
|
fleet management |
1 |
|
graph neural networks |
1 |
|
gtfs |
1 |
|
heterogeneous effect |
1 |
|
heterogeneous graph neural network |
1 |
|
human mobility prediction |
1 |
|
individual mobility |
1 |
|
intelligent transportation systems |
1 |
|
inter-modal relationships |
1 |
|
interpretable machine learning |
1 |
|
intrapersonal variability |
1 |
|
inverse reinforcement learning |
1 |
|
knowledge graph |
1 |
|
large kernel attention |
1 |
|
large language models |
1 |
|
location-based services |
1 |
|
london overground |
1 |
|
market competition |
1 |
|
memory networks |
1 |
|
metro systems |
1 |
|
mobile signaling data |
1 |
|
multi-task learning |
1 |
|
network analysis |
1 |
|
network effect |
1 |
|
network expansion |
1 |
|
next location prediction |
1 |
|
next trip prediction |
1 |
|
noise enhancement |
1 |
|
online shopping |
1 |
|
operations |
1 |
|
parking management |
1 |
|
passenger incidence behavior |
1 |
|
pattern change detection |
1 |
|
pedestrian exposure |
1 |
|
pedestrian safety |
1 |
|
policy making |
1 |
|
predictive models |
1 |
|
public events |
1 |
|
public transit |
1 |
|
public transportation |
1 |
|
regularity |
1 |
|
ride-hailing service |
1 |
|
ridership dynamics |
1 |
|
road network representation |
1 |
|
route prediction |
1 |
|
service quality |
1 |
|
shanghai |
1 |
|
shanghai naturalistic driving study (sh-nds) |
1 |
|
shared autonomous vehicles |
1 |
|
shared transport |
1 |
|
smart card data |
1 |
|
spatial analysis |
1 |
|
spatial autoregressive quantile regression |
1 |
|
spatial regression |
1 |
|
spatio-temporal data |
1 |
|
spatiotemporal pattern |
1 |
|
spatiotemporal phenomena |
1 |
|
squeeze-and-excitation mechanism |
1 |
|
street design |
1 |
|
street environment |
1 |
|
street view images |
1 |
|
structural equation model |
1 |
|
system expansion |
1 |
|
text data mining |
1 |
|
topic model |
1 |
|
traffic information |
1 |
|
transformative trends in transit data |
1 |
|
transit smart card |
1 |
|
transportation network company |
1 |
|
travel behavior |
1 |
|
travel demand modeling |
1 |
|
travel distance |
1 |
|
travel frequency |
1 |
