313 research outputs found
Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
Big data has been used widely in many areas including the transportation
industry. Using various data sources, traffic states can be well estimated and
further predicted for improving the overall operation efficiency. Combined with
this trend, this study presents an up-to-date survey of open data and big data
tools used for traffic estimation and prediction. Different data types are
categorized and the off-the-shelf tools are introduced. To further promote the
use of big data for traffic estimation and prediction tasks, challenges and
future directions are given for future studies
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network
Traffic volume is an indispensable ingredient to provide fine-grained
information for traffic management and control. However, due to limited
deployment of traffic sensors, obtaining full-scale volume information is far
from easy. Existing works on this topic primarily focus on improving the
overall estimation accuracy of a particular method and ignore the underlying
challenges of volume estimation, thereby having inferior performances on some
critical tasks. This paper studies two key problems with regard to traffic
volume estimation: (1) underdetermined traffic flows caused by undetected
movements, and (2) non-equilibrium traffic flows arise from congestion
propagation. Here we demonstrate a graph-based deep learning method that can
offer a data-driven, model-free and correlation adaptive approach to tackle the
above issues and perform accurate network-wide traffic volume estimation.
Particularly, in order to quantify the dynamic and nonlinear relationships
between traffic speed and volume for the estimation of underdetermined flows, a
speed patternadaptive adjacent matrix based on graph attention is developed and
integrated into the graph convolution process, to capture non-local
correlations between sensors. To measure the impacts of non-equilibrium flows,
a temporal masked and clipped attention combined with a gated temporal
convolution layer is customized to capture time-asynchronous correlations
between upstream and downstream sensors. We then evaluate our model on a
real-world highway traffic volume dataset and compare it with several benchmark
models. It is demonstrated that the proposed model achieves high estimation
accuracy even under 20% sensor coverage rate and outperforms other baselines
significantly, especially on underdetermined and non-equilibrium flow
locations. Furthermore, comprehensive quantitative model analysis are also
carried out to justify the model designs
Optimizing city-scale traffic through modeling observations of vehicle movements
The capability of traffic-information systems to sense the movement of
millions of users and offer trip plans through mobile phones has enabled a new
way of optimizing city traffic dynamics, turning transportation big data into
insights and actions in a closed-loop and evaluating this approach in the real
world. Existing research has applied dynamic Bayesian networks and deep neural
networks to make traffic predictions from floating car data, utilized dynamic
programming and simulation approaches to identify how people normally travel
with dynamic traffic assignment for policy research, and introduced Markov
decision processes and reinforcement learning to optimally control traffic
signals. However, none of these works utilized floating car data to suggest
departure times and route choices in order to optimize city traffic dynamics.
In this paper, we present a study showing that floating car data can lead to
lower average trip time, higher on-time arrival ratio, and higher
Charypar-Nagel score compared with how people normally travel. The study is
based on optimizing a partially observable discrete-time decision process and
is evaluated in one synthesized scenario, one partly synthesized scenario, and
three real-world scenarios. This study points to the potential of a "living
lab" approach where we learn, predict, and optimize behaviors in the real
world
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