10,529 research outputs found
Spatial inference of traffic transition using micro-macro traffic variables
This paper proposes an online traffic inference algorithm for road segments in which local traffic information cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of three main operations. First, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the same unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the segment. Using real-world closed-circuit television data, it is shown that the proposed algorithm outperforms previously proposed methods in the literature
Data Assimilation Based on Sequential Monte Carlo Methods for Dynamic Data Driven Simulation
Simulation models are widely used for studying and predicting dynamic behaviors of complex systems. Inaccurate simulation results are often inevitable due to imperfect model and inaccurate inputs. With the advances of sensor technology, it is possible to collect large amount of real time observation data from real systems during simulations. This gives rise to a new paradigm of Dynamic Data Driven Simulation (DDDS) where a simulation system dynamically assimilates real time observation data into a running model to improve simulation results. Data assimilation for DDDS is a challenging task because sophisticated simulation models often have: 1) nonlinear non-Gaussian behavior 2) non-analytical expressions of involved probability density functions 3) high dimensional state space 4) high computation cost. Due to these properties, most existing data assimilation methods fail to effectively support data assimilation for DDDS in one way or another.
This work develops algorithms and software to perform data assimilation for dynamic data driven simulation through non-parametric statistic inference based on sequential Monte Carlo (SMC) methods (also called particle filters). A bootstrap particle filter based data assimilation framework is firstly developed, where the proposal distribution is constructed from simulation models and statistical cores of noises. The bootstrap particle filter-based framework is relatively easy to implement. However, it is ineffective when the uncertainty of simulation models is much larger than the observation model (i.e. peaked likelihood) or when rare events happen. To improve the effectiveness of data assimilation, a new data assimilation framework, named as the SenSim framework, is then proposed, which has a more advanced proposal distribution that uses knowledge from both simulation models and sensor readings. Both the bootstrap particle filter-based framework and the SenSim framework are applied and evaluated in two case studies: wildfire spread simulation, and lane-based traffic simulation. Experimental results demonstrate the effectiveness of the proposed data assimilation methods. A software package is also created to encapsulate the different components of SMC methods for supporting data assimilation of general simulation models
Does Infrastructure Investment Lead to Economic Growth or Economic Fragility? Evidence from China
The prevalent view in the economics literature is that a high level of
infrastructure investment is a precursor to economic growth. China is
especially held up as a model to emulate. Based on the largest dataset of its
kind, this paper punctures the twin myths that, first, infrastructure creates
economic value, and, second, China has a distinct advantage in its delivery.
Far from being an engine of economic growth, the typical infrastructure
investment fails to deliver a positive risk adjusted return. Moreover, China's
track record in delivering infrastructure is no better than that of rich
democracies. Where investments are debt-financed, overinvesting in unproductive
projects results in the buildup of debt, monetary expansion, instability in
financial markets, and economic fragility, exactly as we see in China today. We
conclude that poorly managed infrastructure investments are a main explanation
of surfacing economic and financial problems in China. We predict that, unless
China shifts to a lower level of higher-quality infrastructure investments, the
country is headed for an infrastructure-led national financial and economic
crisis, which is likely also to be a crisis for the international economy.
China's infrastructure investment model is not one to follow for other
countries but one to avoid
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data
The increased availability of large-scale trajectory data around the world
provides rich information for the study of urban dynamics. For example, New
York City Taxi Limousine Commission regularly releases source-destination
information about trips in the taxis they regulate. Taxi data provide
information about traffic patterns, and thus enable the study of urban flow --
what will traffic between two locations look like at a certain date and time in
the future? Existing big data methods try to outdo each other in terms of
complexity and algorithmic sophistication. In the spirit of "big data beats
algorithms", we present a very simple baseline which outperforms
state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs
permit large scale experimentation). Such a travel time estimation baseline has
several important uses, such as navigation (fast travel time estimates can
serve as approximate heuristics for A search variants for path finding) and
trip planning (which uses operating hours for popular destinations along with
travel time estimates to create an itinerary).Comment: 12 page
A Macro-Micro Approach to Reconstructing Vehicle Trajectories on Multi-Lane Freeways with Lane Changing
Vehicle trajectories can offer the most precise and detailed depiction of
traffic flow and serve as a critical component in traffic management and
control applications. Various technologies have been applied to reconstruct
vehicle trajectories from sparse fixed and mobile detection data. However,
existing methods predominantly concentrate on single-lane scenarios and neglect
lane-changing (LC) behaviors that occur across multiple lanes, which limit
their applicability in practical traffic systems. To address this research gap,
we propose a macro-micro approach for reconstructing complete vehicle
trajectories on multi-lane freeways, wherein the macro traffic state
information and micro driving models are integrated to overcome the
restrictions imposed by lane boundary. Particularly, the macroscopic velocity
contour maps are established for each lane to regulate the movement of vehicle
platoons, meanwhile the velocity difference between adjacent lanes provide
valuable criteria for guiding LC behaviors. Simultaneously, the car-following
models are extended from micro perspective to supply lane-based candidate
trajectories and define the plausible range for LC positions. Later, a
two-stage trajectory fusion algorithm is proposed to jointly infer both the
car-following and LC behaviors, in which the optimal LC positions is identified
and candidate trajectories are adjusted according to their weights. The
proposed framework was evaluated using NGSIM dataset, and the results indicated
a remarkable enhancement in both the accuracy and smoothness of reconstructed
trajectories, with performance indicators reduced by over 30% compared to two
representative reconstruction methods. Furthermore, the reconstruction process
effectively reproduced LC behaviors across contiguous lanes, adding to the
framework's comprehensiveness and realism
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