[HTML][HTML] Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights
Accurate traffic state (ie, flow, speed, density, etc.) on an urban road network is important
information for urban traffic control and management strategies. However, due to the …
information for urban traffic control and management strategies. However, due to the …
Compressive sensing-based IoT applications: A review
The Internet of Things (IoT) holds great promises to provide an edge cutting technology that
enables numerous innovative services related to healthcare, manufacturing, smart cities and …
enables numerous innovative services related to healthcare, manufacturing, smart cities and …
Personalized route recommendation using big trajectory data
When planning routes, drivers usually consider a multitude of different travel costs, eg,
distances, travel times, and fuel consumption. Different drivers may choose different routes …
distances, travel times, and fuel consumption. Different drivers may choose different routes …
STGNN-TTE: Travel time estimation via spatial–temporal graph neural network
Estimating the travel time of urban trajectories is a basic but challenging task in many
intelligent transportation systems, which is the foundation of route planning and traffic …
intelligent transportation systems, which is the foundation of route planning and traffic …
Stochastic weight completion for road networks using graph convolutional networks
Innovations in transportation, such as mobility-on-demand services and autonomous driving,
call for high-resolution routing that relies on an accurate representation of travel time …
call for high-resolution routing that relies on an accurate representation of travel time …
Travel cost inference from sparse, spatio temporally correlated time series using markov models
The monitoring of a system can yield a set of measurements that can be modeled as a
collection of time series. These time series are often sparse, due to missing measurements …
collection of time series. These time series are often sparse, due to missing measurements …
Stochastic skyline route planning under time-varying uncertainty
Different uses of a road network call for the consideration of different travel costs: in route
planning, travel time and distance are typically considered, and green house gas (GHG) …
planning, travel time and distance are typically considered, and green house gas (GHG) …
Learning to route with sparse trajectory sets
Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route,
a comprehensive trajectory-based routing solution. Specifically, we first construct a graph …
a comprehensive trajectory-based routing solution. Specifically, we first construct a graph …
PACE: a PAth-CEntric paradigm for stochastic path finding
With the growing volumes of vehicle trajectory data, it becomes increasingly possible to
capture time-varying and uncertain travel costs, eg, travel time, in a road network. The …
capture time-varying and uncertain travel costs, eg, travel time, in a road network. The …
Fast stochastic routing under time-varying uncertainty
Data are increasingly available that enable detailed capture of travel costs associated with
the movements of vehicles in road networks, notably travel time, and greenhouse gas …
the movements of vehicles in road networks, notably travel time, and greenhouse gas …