827 research outputs found
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
Short-term passenger demand forecasting is of great importance to the
on-demand ride service platform, which can incentivize vacant cars moving from
over-supply regions to over-demand regions. The spatial dependences, temporal
dependences, and exogenous dependences need to be considered simultaneously,
however, which makes short-term passenger demand forecasting challenging. We
propose a novel deep learning (DL) approach, named the fusion convolutional
long short-term memory network (FCL-Net), to address these three dependences
within one end-to-end learning architecture. The model is stacked and fused by
multiple convolutional long short-term memory (LSTM) layers, standard LSTM
layers, and convolutional layers. The fusion of convolutional techniques and
the LSTM network enables the proposed DL approach to better capture the
spatio-temporal characteristics and correlations of explanatory variables. A
tailored spatially aggregated random forest is employed to rank the importance
of the explanatory variables. The ranking is then used for feature selection.
The proposed DL approach is applied to the short-term forecasting of passenger
demand under an on-demand ride service platform in Hangzhou, China.
Experimental results, validated on real-world data provided by DiDi Chuxing,
show that the FCL-Net achieves better predictive performance than traditional
approaches including both classical time-series prediction models and neural
network based algorithms (e.g., artificial neural network and LSTM). This paper
is one of the first DL studies to forecast the short-term passenger demand of
an on-demand ride service platform by examining the spatio-temporal
correlations.Comment: 39 pages, 10 figure
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Deep neural networks can be powerful tools, but require careful
application-specific design to ensure that the most informative relationships
in the data are learnable. In this paper, we apply deep neural networks to the
nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We
consider problems of estimating macroscopic quantities (e.g., the queue at an
intersection) at a lane level. First-principles modeling at the lane scale has
been a challenge due to complexities in modeling social behaviors like lane
changes, and those behaviors' resultant macro-scale effects. Following domain
knowledge that upstream/downstream lanes and neighboring lanes affect each
others' traffic flows in distinct ways, we apply a form of neural attention
that allows the neural network layers to aggregate information from different
lanes in different manners. Using a microscopic traffic simulator as a testbed,
we obtain results showing that an attentional neural network model can use
information from nearby lanes to improve predictions, and, that explicitly
encoding the lane-to-lane relationship types significantly improves
performance. We also demonstrate the transfer of our learned neural network to
a more complex road network, discuss how its performance degradation may be
attributable to new traffic behaviors induced by increased topological
complexity, and motivate learning dynamics models from many road network
topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation
System
A Transferable Intersection Reconstruction Network for Traffic Speed Prediction
Traffic speed prediction is the key to many valuable applications, and it is
also a challenging task because of its various influencing factors. Recent work
attempts to obtain more information through various hybrid models, thereby
improving the prediction accuracy. However, the spatial information acquisition
schemes of these methods have two-level differentiation problems. Either the
modeling is simple but contains little spatial information, or the modeling is
complete but lacks flexibility. In order to introduce more spatial information
on the basis of ensuring flexibility, this paper proposes IRNet (Transferable
Intersection Reconstruction Network). First, this paper reconstructs the
intersection into a virtual intersection with the same structure, which
simplifies the topology of the road network. Then, the spatial information is
subdivided into intersection information and sequence information of traffic
flow direction, and spatiotemporal features are obtained through various
models. Third, a self-attention mechanism is used to fuse spatiotemporal
features for prediction. In the comparison experiment with the baseline, not
only the prediction effect, but also the transfer performance has obvious
advantages.Comment: 14 pages, 12 figure
Study of real-time traffic state estimation and short-term prediction of signalized arterial network considering heterogeneous information sources
Compared with a freeway network, real-time traffic state estimation and prediction of a signalized arterial network is a challenging yet under-studied field. Starting from discussing the arterial traffic flow dynamics, this study proposes a novel framework for real-time traffic state estimation and short-term prediction for signalized corridors. Particle filter techniques are used to integrate field measurements from different sources to improve the accuracy and robustness of the model. Several comprehensive numerical studies based on both real world and simulated datasets showed that the proposed model can generate reliable estimation and short-term prediction of different traffic states including queue length, flow density, speed and travel time with a high degree of accuracy. The proposed model can serve as the key component in both ATIS (Advanced Traveler's Information System) and proactive traffic control system
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
Origin-destination~(OD) flow modeling is an extensively researched subject
across multiple disciplines, such as the investigation of travel demand in
transportation and spatial interaction modeling in geography. However,
researchers from different fields tend to employ their own unique research
paradigms and lack interdisciplinary communication, preventing the
cross-fertilization of knowledge and the development of novel solutions to
challenges. This article presents a systematic interdisciplinary survey that
comprehensively and holistically scrutinizes OD flows from utilizing
fundamental theory to studying the mechanism of population mobility and solving
practical problems with engineering techniques, such as computational models.
Specifically, regional economics, urban geography, and sociophysics are adept
at employing theoretical research methods to explore the underlying mechanisms
of OD flows. They have developed three influential theoretical models: the
gravity model, the intervening opportunities model, and the radiation model.
These models specifically focus on examining the fundamental influences of
distance, opportunities, and population on OD flows, respectively. In the
meantime, fields such as transportation, urban planning, and computer science
primarily focus on addressing four practical problems: OD prediction, OD
construction, OD estimation, and OD forecasting. Advanced computational models,
such as deep learning models, have gradually been introduced to address these
problems more effectively. Finally, based on the existing research, this survey
summarizes current challenges and outlines future directions for this topic.
Through this survey, we aim to break down the barriers between disciplines in
OD flow-related research, fostering interdisciplinary perspectives and modes of
thinking.Comment: 49 pages, 6 figure
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