979 research outputs found
A generalization of bounds for cyclic codes, including the HT and BS bounds
We use the algebraic structure of cyclic codes and some properties of the
discrete Fourier transform to give a reformulation of several classical bounds
for the distance of cyclic codes, by extending techniques of linear algebra. We
propose a bound, whose computational complexity is polynomial bounded, which is
a generalization of the Hartmann-Tzeng bound and the Betti-Sala bound. In the
majority of computed cases, our bound is the tightest among all known
polynomial-time bounds, including the Roos bound
Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?
Network-level traffic condition forecasting has been intensively studied for
decades. Although prediction accuracy has been continuously improved with
emerging deep learning models and ever-expanding traffic data, traffic
forecasting still faces many challenges in practice. These challenges include
the robustness of data-driven models, the inherent unpredictability of traffic
dynamics, and whether further improvement of traffic forecasting requires more
sensor data. In this paper, we focus on this latter question and particularly
on data from loop detectors. To answer this, we propose an uncertainty-aware
traffic forecasting framework to explore how many samples of loop data are
truly effective for training forecasting models. Firstly, the model design
combines traffic flow theory with graph neural networks, ensuring the
robustness of prediction and uncertainty quantification. Secondly, evidential
learning is employed to quantify different sources of uncertainty in a single
pass. The estimated uncertainty is used to "distil" the essence of the dataset
that sufficiently covers the information content. Results from a case study of
a highway network around Amsterdam show that, from 2018 to 2021, more than 80\%
of the data during daytime can be removed. The remaining 20\% samples have
equal prediction power for training models. This result suggests that indeed
large traffic datasets can be subdivided into significantly smaller but equally
informative datasets. From these findings, we conclude that the proposed
methodology proves valuable in evaluating large traffic datasets' true
information content. Further extensions, such as extracting smaller, spatially
non-redundant datasets, are possible with this method.Comment: 13 pages, 5 figure
A Data-driven and multi-agent decision support system for time slot management at container terminals: A case study for the Port of Rotterdam
Controlling the departure time of the trucks from a container hub is
important to both the traffic and the logistics systems. This, however,
requires an intelligent decision support system that can control and manage
truck arrival times at terminal gates. This paper introduces an integrated
model that can be used to understand, predict, and control logistics and
traffic interactions in the port-hinterland ecosystem. This approach is
context-aware and makes use of big historical data to predict system states and
apply control policies accordingly, on truck inflow and outflow. The control
policies ensure multiple stakeholders satisfaction including those of trucking
companies, terminal operators, and road traffic agencies. The proposed method
consists of five integrated modules orchestrated to systematically steer
truckers toward choosing those time slots that are expected to result in lower
gate waiting times and more cost-effective schedules. The simulation is
supported by real-world data and shows that significant gains can be obtained
in the system
Pattern retrieval of traffic congestion using graph-based associations of traffic domain-specific features
The fast-growing amount of traffic data brings many opportunities for
revealing more insightful information about traffic dynamics. However, it also
demands an effective database management system in which information retrieval
is arguably an important feature. The ability to locate similar patterns in big
datasets potentially paves the way for further valuable analyses in traffic
management. This paper proposes a content-based retrieval system for
spatiotemporal patterns of highway traffic congestion. There are two main
components in our framework, namely pattern representation and similarity
measurement. To effectively interpret retrieval outcomes, the paper proposes a
graph-based approach (relation-graph) for the former component, in which
fundamental traffic phenomena are encoded as nodes and their spatiotemporal
relationships as edges. In the latter component, the similarities between
congestion patterns are customizable with various aspects according to user
expectations. We evaluated the proposed framework by applying it to a dataset
of hundreds of patterns with various complexities (temporally and spatially).
The example queries indicate the effectiveness of the proposed method, i.e. the
obtained patterns present similar traffic phenomena as in the given examples.
In addition, the success of the proposed approach directly derives a new
opportunity for semantic retrieval, in which expected patterns are described by
adopting the relation-graph notion to associate fundamental traffic phenomena.Comment: 20 pages, 14 figure
Spatial and Temporal Characteristics of Freight Tours: A Data-Driven Exploratory Analysis
This paper presents a modeling approach to infer scheduling and routing
patterns from digital freight transport activity data for different freight
markets. We provide a complete modeling framework including a new
discrete-continuous decision tree approach for extracting rules from the
freight transport data. We apply these models to collected tour data for the
Netherlands to understand departure time patterns and tour strategies, also
allowing us to evaluate the effectiveness of the proposed algorithm. We find
that spatial and temporal characteristics are important to capture the types of
tours and time-of-day patterns of freight activities. Also, the empirical
evidence indicates that carriers in most of the transport markets are sensitive
to the level of congestion. Many of them adjust the type of tour, departure
time, and the number of stops per tour when facing a congested zone. The
results can be used by practitioners to get more grip on transport markets and
develop freight and traffic management measures
Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles
Car-Following (CF), as a fundamental driving behaviour, has significant
influences on the safety and efficiency of traffic flow. Investigating how
human drivers react differently when following autonomous vs. human-driven
vehicles (HV) is thus critical for mixed traffic flow. Research in this field
can be expedited with trajectory datasets collected by Autonomous Vehicles
(AVs). However, trajectories collected by AVs are noisy and not readily
applicable for studying CF behaviour. This paper extracts and enhances two
categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from
the open Lyft level-5 dataset. First, CF pairs are selected based on specific
rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the
raw CF data is corrected and enhanced via motion planning, Kalman filtering,
and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following
segments are obtained, with a total driving distance of 150k+ km. A diversity
assessment shows that the processed data cover complete CF regimes for
calibrating CF models. This open and ready-to-use dataset provides the
opportunity to investigate the CF behaviours of following AVs vs. HVs from
real-world data. It can further facilitate studies on exploring the impact of
AVs on mixed urban traffic.Comment: 6 pages, 9 figure
Rotor interaction in the annulus billiard
Introducing the rotor interaction in the integrable system of the annulus
billiard produces a variety of dynamical phenomena, from integrability to
ergodicity
- …