12 research outputs found
Discrete Event Simulation of Driver's Routing Behavior Rule at a Road Intersection
Several factors influence traffic congestion and overall traffic dynamics.
Simulation modeling has been utilized to understand the traffic performance
parameters during traffic congestions. This paper focuses on driver behavior of
route selection by differentiating three distinguishable decisions, which are
shortest distance routing, shortest time routing and less crowded road routing.
This research generated 864 different scenarios to capture various traffic
dynamics under collective driving behavior of route selection. Factors such as
vehicle arrival rate, behaviors at system boundary and traffic light phasing
were considered. The simulation results revealed that shortest time routing
scenario offered the best solution considering all forms of interactions among
the factors. Overall, this routing behavior reduces traffic wait time and total
time (by 69.5% and 65.72%) compared to shortest distance routing
Learning structure and schemas from heterogeneous domains in networked systems: a survey
The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible to manually organize such documents. Additionally, most of the document sexist in an unstructured form and do not follow any schemas. Therefore, research efforts in this direction are being dedicated to automatically infer structure and schemas. This is essential in order to better organize huge collections as well as to effectively and efficiently retrieve documents in heterogeneous domains in networked system. This paper presents a survey of the state-of-the-art methods for inferring structure from documents and schemas in networked environments. The survey is organized around the most important application domains, namely, bio-informatics, sensor networks, social networks, P2Psystems, automation and control, transportation and privacy preserving for which we analyze the recent developments on dealing with unstructured data in such domains.Peer ReviewedPostprint (published version
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
The increasing maturity of big data applications has led to a proliferation
of models targeting the same objectives within the same scenarios and datasets.
However, selecting the most suitable model that considers model's features
while taking specific requirements and constraints into account still poses a
significant challenge. Existing methods have focused on worker-task assignments
based on crowdsourcing, they neglect the scenario-dataset-model assignment
problem. To address this challenge, a new problem named the Scenario-based
Optimal Model Assignment (SOMA) problem is introduced and a novel framework
entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a
heterogeneous information framework that can integrate various types of
information to intelligently select a suitable dataset and allocate the optimal
model for a specific scenario. To comprehensively evaluate models, a new score
function that utilizes multi-head attention mechanisms is proposed. Moreover, a
novel memory mechanism named the mnemonic center is developed to store the
matched heterogeneous information and prevent duplicate matching. Six popular
traffic scenarios are selected as study cases and extensive experiments are
conducted on a dataset to verify the effectiveness and efficiency of SMAP and
the score function
A Survey on Causal Reinforcement Learning
While Reinforcement Learning (RL) achieves tremendous success in sequential
decision-making problems of many domains, it still faces key challenges of data
inefficiency and the lack of interpretability. Interestingly, many researchers
have leveraged insights from the causality literature recently, bringing forth
flourishing works to unify the merits of causality and address well the
challenges from RL. As such, it is of great necessity and significance to
collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL
methods, and investigate the potential functionality from causality toward RL.
In particular, we divide existing CRL approaches into two categories according
to whether their causality-based information is given in advance or not. We
further analyze each category in terms of the formalization of different
models, ranging from the Markov Decision Process (MDP), Partially Observed
Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment
Regime (DTR). Moreover, we summarize the evaluation matrices and open sources
while we discuss emerging applications, along with promising prospects for the
future development of CRL.Comment: 29 pages, 20 figure
Recommended from our members
Modeling and Optimizing Routing Decisions for Travelers and On-demand Service Providers
This thesis investigates the dynamic routing decisions for individual travelers and on-demand service providers (e.g., regular taxis, Uber, Lyft, etc).
For individual travelers, this thesis models and predicts route choice at two time-scales: the day-to-day and within-day. For day-to-day route choice, methodological development and empirical evidences are presented to understand the roles of learning, inertia and real-time travel information on route choices in a highly disrupted network based on data from a laboratory competitive route choice game. The learning of routing policies instead of simple paths is modeled when real-time travel information is available, where a routing policy is defined as a contingency plan that maps realized traffic conditions to path choices. Using data from a competitive laboratory experiment, prediction performance is then measured in terms of both one-step and full trajectory predictions. For within day route choice, a recursive logit model is formulated in a stochastic time-dependent (STD) network without sampling any choice sets. A decomposition algorithm is then proposed so that the model can be estimated in reasonable time. Estimation and prediction results of the proposed model are presented using a data set collected from a subnetwork of Stockholm, Sweden.
Taxis and ride-sourcing vehicles play an important role in providing on-demand mobility in an urban transportation system. Unlike individual travelers, they do not have a clear destination when there\u27s no passenger on board. The optimal routing of a vacant taxi is formulated as a Markov Decision Process (MDP) problem to maximize long-term profit over the full working period. Two approaches are proposed to solve the problem. One is the model-based approach where a model of the state transitions of the environment is obtained from queuing-theory based passenger arrival and competing taxi distribution processes. An enhanced value iteration for solving the MDP problem is then proposed making use of efficient matrix operations. The other is the model-free Reinforcement Learning (RL) approach, which learns the best policy directly from observed trajectory data. Both approaches are implemented and tested in a mega city transportation network with reasonable running time, and a systematic comparison of the two approaches is also provided