27 research outputs found
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Taxi demand prediction is an important building block to enabling intelligent
transportation systems in a smart city. An accurate prediction model can help
the city pre-allocate resources to meet travel demand and to reduce empty taxis
on streets which waste energy and worsen the traffic congestion. With the
increasing popularity of taxi requesting services such as Uber and Didi Chuxing
(in China), we are able to collect large-scale taxi demand data continuously.
How to utilize such big data to improve the demand prediction is an interesting
and critical real-world problem. Traditional demand prediction methods mostly
rely on time series forecasting techniques, which fail to model the complex
non-linear spatial and temporal relations. Recent advances in deep learning
have shown superior performance on traditionally challenging tasks such as
image classification by learning the complex features and correlations from
large-scale data. This breakthrough has inspired researchers to explore deep
learning techniques on traffic prediction problems. However, existing methods
on traffic prediction have only considered spatial relation (e.g., using CNN)
or temporal relation (e.g., using LSTM) independently. We propose a Deep
Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial
and temporal relations. Specifically, our proposed model consists of three
views: temporal view (modeling correlations between future demand values with
near time points via LSTM), spatial view (modeling local spatial correlation
via local CNN), and semantic view (modeling correlations among regions sharing
similar temporal patterns). Experiments on large-scale real taxi demand data
demonstrate effectiveness of our approach over state-of-the-art methods.Comment: AAAI 2018 pape
Path cost distribution estimation using trajectory data
With the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs in a road network, including travel time and fuel consumption. The current paradigm represents a road network as a weighted graph; it blasts trajectories into small fragments that fit the under-lying edges to assign weights to edges; and it then applies a routing algorithm to the resulting graph. We propose a new paradigm, the
hybrid graph
, that targets more accurate and more efficient path cost distribution estimation. The new paradigm avoids blasting trajectories into small fragments and instead assigns weights to paths rather than simply to the edges.
We show how to compute path weights using trajectory data while taking into account the travel cost dependencies among the edges in the paths. Given a departure time and a query path, we show how to select an optimal set of weights with associated paths that cover the query path and such that the weights enable the most accurate joint cost distribution estimation for the query path. The cost distribution of the query path is then computed accurately using the joint distribution. Finally, we show how the resulting method for computing cost distributions of paths can be integrated into existing routing algorithms. Empirical studies with substantial trajectory data from two different cities offer insight into the design properties of the proposed method and confirm that the method is effective in real-world settings.</jats:p
Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
We are witnessing increasing interests in the effective use of road networks.
For example, to enable effective vehicle routing, weighted-graph models of
transportation networks are used, where the weight of an edge captures some
cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions
or travel time. It is a precondition to using a graph model for routing that
all edges have weights. Weights that capture travel times and GHG emissions can
be extracted from GPS trajectory data collected from the network. However, GPS
trajectory data typically lack the coverage needed to assign weights to all
edges. This paper formulates and addresses the problem of annotating all edges
in a road network with travel cost based weights from a set of trips in the
network that cover only a small fraction of the edges, each with an associated
ground-truth travel cost. A general framework is proposed to solve the problem.
Specifically, the problem is modeled as a regression problem and solved by
minimizing a judiciously designed objective function that takes into account
the topology of the road network. In particular, the use of weighted PageRank
values of edges is explored for assigning appropriate weights to all edges, and
the property of directional adjacency of edges is also taken into account to
assign weights. Empirical studies with weights capturing travel time and GHG
emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark)
offer insight into the design properties of the proposed techniques and offer
evidence that the techniques are effective.Comment: This is an extended version of "Using Incomplete Information for
Complete Weight Annotation of Road Networks," which is accepted for
publication in IEEE TKD