|
accessibility |
4 |
|
big data |
4 |
|
commute |
4 |
|
covid-19 |
4 |
|
land use |
4 |
|
smartcard data |
4 |
|
china |
3 |
|
excess commuting |
3 |
|
human mobility |
3 |
|
intentional travel groups |
3 |
|
metro |
3 |
|
metro system |
3 |
|
node-place model |
3 |
|
node–place model |
3 |
|
social contact |
3 |
|
transport |
3 |
|
age-friendly public transport |
2 |
|
beijing |
2 |
|
belt and road initiative |
2 |
|
brisbane |
2 |
|
broken highways |
2 |
|
bus |
2 |
|
change |
2 |
|
chinese urbanization |
2 |
|
city-regions |
2 |
|
complement |
2 |
|
connectivity |
2 |
|
covid 19 |
2 |
|
distance-based fare |
2 |
|
equity |
2 |
|
europe |
2 |
|
event |
2 |
|
extreme transit behavior |
2 |
|
high-speed rail |
2 |
|
implication |
2 |
|
intercity transport planning |
2 |
|
jobs-housing balance |
2 |
|
jobs-housing relationship |
2 |
|
jobs-housing separation |
2 |
|
land use and transportation |
2 |
|
metro station |
2 |
|
mode choice |
2 |
|
non-traditional data |
2 |
|
non-work trips |
2 |
|
older adults |
2 |
|
open data |
2 |
|
ordered probit model |
2 |
|
passenger flows |
2 |
|
personal travel |
2 |
|
population growth |
2 |
|
productivity effect |
2 |
|
public transit |
2 |
|
smart card data (scd) |
2 |
|
smartphone |
2 |
|
space |
2 |
|
substitute |
2 |
|
telecommuting |
2 |
|
tod attributes |
2 |
|
tod outcomes |
2 |
|
transit |
2 |
|
transit-oriented development |
2 |
|
transport infrastructures |
2 |
|
travel behaviour |
2 |
|
travel demand management |
2 |
|
travel survey |
2 |
|
travel time |
2 |
|
trip production/attraction |
2 |
|
urban analytics |
2 |
|
urban dynamics |
2 |
|
urban spatial structure |
2 |
|
2sfca |
1 |
|
access/accessibility |
1 |
|
adaptive travel behaviors |
1 |
|
air quality |
1 |
|
alternative modes to solo-driving |
1 |
|
assignment modelling |
1 |
|
bicycle volume |
1 |
|
big and/or open data |
1 |
|
big data era |
1 |
|
built environment |
1 |
|
bundled services |
1 |
|
car dependence |
1 |
|
car pooling |
1 |
|
car-sharing |
1 |
|
carshare |
1 |
|
carsharing |
1 |
|
case |
1 |
|
cellular network data |
1 |
|
china's high-speed rail network |
1 |
|
choice set |
1 |
|
co emissions 2 |
1 |
|
college town |
1 |
|
commute efficiency |
1 |
|
commuter benefits |
1 |
|
commuting |
1 |
|
commuting behavior |
1 |
|
commuting frequency |
1 |
|
commuting pattern |
1 |
|
comparative study |
1 |
|
competition |
1 |
|
complex network |
1 |
|
confirmatory framework |
1 |
|
construction and planning |
1 |
|
consumption |
1 |
|
coronavirus |
1 |
|
crime |
1 |
|
cumulative opportunity |
1 |
|
data sources |
1 |
|
death |
1 |
|
determinant |
1 |
|
discounted transit pass |
1 |
|
distribution |
1 |
|
eating out |
1 |
|
employee |
1 |
|
energy |
1 |
|
environmental justice |
1 |
|
evolution process |
1 |
|
excessive commute |
1 |
|
familiar stranger |
1 |
|
feature |
1 |
|
free trial |
1 |
|
freight transportation |
1 |
|
generalized linear mixed model |
1 |
|
genetic algorithm |
1 |
|
geo-visualisation |
1 |
|
geovisualization |
1 |
|
gradient boosting decision tree |
1 |
|
gravity method |
1 |
|
gravity model |
1 |
|
group travel behaviors |
1 |
|
hedonic pricing model |
1 |
|
highway transportation |
1 |
|
historic district |
1 |
|
hong kong |
1 |
|
housing dynamics |
1 |
|
housing location |
1 |
|
human movement |
1 |
|
implications |
1 |
|
infill |
1 |
|
influence factors |
1 |
|
influencing factors |
1 |
|
intra-metropolitan |
1 |
|
intrinsic value |
1 |
|
job accessibility |
1 |
|
job dynamics |
1 |
|
jobs/housing balance |
1 |
|
life |
1 |
|
limited data |
1 |
|
local accessibility |
1 |
|
locale |
1 |
|
longitudinal data analysis |
1 |
|
los angeles |
1 |
|
losers |
1 |
|
marginal effects |
1 |
|
measurement |
1 |
|
metro investment |
1 |
|
metropolitan areas |
1 |
|
migrants and locals |
1 |
|
mobility group |
1 |
|
mode of travel |
1 |
|
models |
1 |
|
multi-modal |
1 |
|
multimodal public transport system |
1 |
|
multiscale |
1 |
|
network characteristics |
1 |
|
odds |
1 |
|
open and big data |
1 |
|
outcomes |
1 |
|
pedestrian crash frequency |
1 |
|
pedestrian crash risk |
1 |
|
pedestrian volume |
1 |
|
performance |
1 |
|
performance evaluation |
1 |
|
planning |
1 |
|
planning and analysis |
1 |
|
policy |
1 |
|
policy implications |
1 |
|
policy scenario |
1 |
|
predictor |
1 |
|
program |
1 |
|
property price |
1 |
|
property pricespatial autocorrelation |
1 |
|
public policy/town and city planning |
1 |
|
public service |
1 |
|
public transport |
1 |
|
public transportation |
1 |
|
rail plus property |
1 |
|
regional accessibility |
1 |
|
regional planning |
1 |
|
relationships |
1 |
|
research |
1 |
|
residence |
1 |
|
resilience |
1 |
|
respiratory health |
1 |
|
restaurants |
1 |
|
road network |
1 |
|
route choice |
1 |
|
safety |
1 |
|
self-quarantine |
1 |
|
serendipitous contacts |
1 |
|
shapley additive explanations |
1 |
|
shenzhen |
1 |
|
smart card data |
1 |
|
smartcard records |
1 |
|
spatial autocorrelation |
1 |
|
spatial pattern |
1 |
|
sprawl |
1 |
|
subscription |
1 |
|
subsidy |
1 |
|
survey |
1 |
|
survey data |
1 |
|
sustainability |
1 |
|
suzhou industry park |
1 |
|
system optimal |
1 |
|
the market |
1 |
|
tod |
1 |
|
todness |
1 |
|
todtod-ness |
1 |
|
traffic analysis zone (taz) |
1 |
|
traffic congestion |
1 |
|
trajectory |
1 |
|
transit ridership |
1 |
|
transit-oriented developments |
1 |
|
transit-served area |
1 |
|
transport equity |
1 |
|
transportation |
1 |
|
transportation planning analysis and application |
1 |
|
travel |
1 |
|
travel behavior |
1 |
|
trip frequency |
1 |
|
two-step floating catchment area (2sfca) method |
1 |
|
university |
1 |
|
university commuters |
1 |
|
university student |
1 |
|
university students |
1 |
|
urban china |
1 |
|
urban geography |
1 |
|
urban sensor data |
1 |
|
urban traffic |
1 |
|
urban transit system |
1 |
|
urban vibrancy |
1 |
|
urbanization |
1 |
|
variance |
1 |
|
visual |
1 |
|
visualisation |
1 |
|
visualization |
1 |
|
walk score |
1 |
|
walking accessibility |
1 |
|
work |
1 |
|
xi'an |
1 |
|
xiamen city |
1 |
|
zipcar |
1 |
