Design and analysis of a novel mathematical model towards realizing the need for intelligent transportation system in Indian cities
01 Nov 2017-pp 180-185
TL;DR: This paper provides an analytical model that serves as the backbone for developing ITS in India and formulation of objective function using road networks that reduces congestion by minimizing vehicular density under constraints of feasible paths and traffic flows.
Abstract: Intelligent Transportation System (ITS) is integral towards envisioning a truly mobile and digital India. While existing solutions like smart vehicles and autonomous driving are inapplicable for Indian cities, transport surveys in India have provided offline solutions through GPS data. Without analytical formulations, they are prone to failure in ever-changing Indian urban landscape. This paper addresses this contemporary problem and provides an analytical model that serves as the backbone for developing ITS in India. Contribution of this paper is the formulation of objective function using road networks that reduces congestion by minimizing vehicular density under constraints of feasible paths and traffic flows. Novelty of this work is further established by exhaustive studies that determine important traffic metrics and further establish relationships among critical congestion-queue parameters. Additionally, indepth studies of Indian cities under this model justify our motivation for ITS-enabled India. Finally, validation with theoretical model establishes the credibility of this work.
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TL;DR: The novel functionalities and current research challenges of the xG networks are explained in detail, and a brief overview of the cognitive radio technology is provided and the xg network architecture is introduced.
6,608 citations
"Design and analysis of a novel math..." refers background in this paper
...Firstly, from the communications perspective, the emerging Cognitive Radio Networks [12] may allow opportunistic access of licensed spectrum to these stakeholders at a nominal cost in place of a fixed licensed spectrum access policy....
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17 Aug 2012
TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
Abstract: Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
4,440 citations
01 Jan 2019
TL;DR: This chapter argues that the above characteristics make the Fog the appropriate platform for a number of critical internet of things services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
Abstract: Fog computing extends the cloud computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are 1) low latency and location awareness, 2) widespread geographical distribution, 3) mobility, 4) very large number of nodes, 5) predominant role of wireless access, 6) strong presence of streaming and real time applications, and 7) heterogeneity. In this chapter, the authors argue that the above characteristics make the Fog the appropriate platform for a number of critical internet of things (IoT) services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
2,384 citations
"Design and analysis of a novel math..." refers methods in this paper
...Secondly, from the computational and storage perspective, fog computing based framework [13] can be used to incorporate the proposed model which will certainly reduce the complexities and costs involved in the conventional approaches as prevalent in ITS....
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TL;DR: This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs).
Abstract: This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy schedule grids for all modeled vehicles. This combination allows an efficient classification of the existing vehicle-lane and vehicle- vehicle relations in traffic scenes and thus substantially improves the understanding of complex traffic scenes. Probabilities and variances within the network are propagated systematically which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, the network is able to classify a total of 27 driving maneuvers including merging and object following.
178 citations
"Design and analysis of a novel math..." refers methods in this paper
...Finally, a “smart” ITS solution backed by sensors, actuators and IoT framework is only possible through analytical validations and the same approach has been followed by notable works in literature [9], [10]....
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TL;DR: This paper model the decision making process of drivers by building a hierarchical Dynamic Bayesian Model that describes physical relationships as well as the driver's behaviors and plans and proposes an Expectation Maximization (EM) approach for learning the models integrated in the DBN from unlabeled observations.
Abstract: Estimating and predicting traffic situations over time is an essential capability for sophisticated driver assistance systems and autonomous driving. When longer prediction horizons are needed, e.g., in decision making or motion planning, the uncertainty induced by incomplete environment perception and stochastic situation development over time cannot be neglected without sacrificing robustness and safety. Building consistent probabilistic models of drivers interactions with the environment, the road network and other traffic participants poses a complex problem. In this paper, we model the decision making process of drivers by building a hierarchical Dynamic Bayesian Model that describes physical relationships as well as the driver's behaviors and plans. This way, the uncertainties in the process on all abstraction levels can be handled in a mathematically consistent way. As drivers behaviors are difficult to model, we present an approach for learning continuous, non-linear, context-dependent models for the behavior of traffic participants. We propose an Expectation Maximization (EM) approach for learning the models integrated in the DBN from unlabeled observations. Experiments show a significant improvement in estimation and prediction accuracy over standard models which only consider vehicle dynamics. Finally, a novel approach to tactical decision making for autonomous driving is outlined. It is based on a continuous Partially Observable Markov Decision Process (POMDP) that uses the presented model for prediction.
176 citations
"Design and analysis of a novel math..." refers methods in this paper
...Finally, a “smart” ITS solution backed by sensors, actuators and IoT framework is only possible through analytical validations and the same approach has been followed by notable works in literature [9], [10]....
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