User and Operator Perspectives in Public Transport Timetable Synchronization Design

2017 ◽  
Vol 2667 (1) ◽  
pp. 154-163
Author(s):  
Tao Liu ◽  
Avishai (Avi) Ceder
Keyword(s):  
Public Transport ◽  
Traffic Congestion ◽  
Large Scale ◽  
Systems Approach ◽  
Programming Model ◽  
Efficient Solutions ◽  
Linear Constraints ◽  
Nonlinear Integer Programming ◽  
Modeling Framework ◽  
Objective Model

Increased traffic congestion and the adverse environmental effect of private cars have resulted in an increasingly pressing need for an integrated public transport (PT) system that is more attractive than private car use. The intelligent PT timetable synchronization design is one way to improve the integration and service quality of a PT system with increased connectivity, synchronization, and attractiveness toward far more user-oriented, system-optimal, smart, and sustainable travel. This paper proposes a new multicriteria optimization modeling framework with a systems approach for the PT timetable synchronization design problem. A new bi-objective model is proposed; it takes PT user and operator interests into account. The nature of the overall mathematical formulations of the new model is bi-objective nonlinear integer programming with linear constraints. On the basis of the characteristics of the model, a novel deficit function (DF)–based sequential search method is proposed to solve the problem so as to obtain Pareto-efficient solutions. The visual nature of the proposed DF and the two-dimensional fleet-cost space graphical techniques can facilitate the decision-making process of PT schedulers for finding a desired solution. Numerical results from a small PT network demonstrate that the proposed mathematical programming model and solution method are effective in practice and have the potential to be applied in large-scale and realistic networks.

Decision-Making Framework to Allocate Real-Time Passenger Information Signs at Bus Stops: Model Application in Athens, Greece

2017 ◽  
Vol 2647 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Styliani Papagianni ◽  
Christina Iliopoulou ◽  
Konstantinos Kepaptsoglou ◽  
Antony Stathopoulos
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Public Transport ◽  
Ad Hoc ◽  
Programming Model ◽  
Urban Transport ◽  
Transport Systems ◽  
Modeling Framework ◽  
Bus Stops ◽  
Bus Networks

The use of intelligent transport systems for the provision of real-time passenger information is an important incentive in efforts to strengthen the role of public transport and improve livability in large cities. Electronic signs installed at bus stops to disseminate information on bus arrivals are an important component of these systems with a significant capital cost. Nonetheless, to the best of the authors’ knowledge, there is no systematic approach for the selection of location sites for deployment of dynamic message signs (DMS). Public transport authorities often follow ad hoc procedures that are based on various location criteria—namely, passenger boardings, availability of power, and number of routes served at bus stops—to derive a set of candidate location sites. This was the case with the methodology implemented by the Athens Urban Transport Organization in Athens, Greece. With data from Athens, this paper proposes a modeling framework for the decision-making process regarding DMS locations in bus networks. The framework is formulated as a linear programming model, and the results show that the proposed model constitutes a systematic and transferable approach to tackle the problem at hand.

scholarly journals A Fuzzy Interactive BI-objective Model for SVM to Identify the Best Compromise Solution for Measuring the Degree of Infection With Corona Virus Disease (Covid-19)

2021 ◽  
Vol 11 (1) ◽  
pp. 13-22
Author(s):  
Mohammed Zakaria Moustafa ◽  
Hassan Mahmoud Elragal ◽  
Mohammed Rizk Mohammed ◽  
Hatem Awad Khater ◽  
Hager Ali Yahia
Keyword(s):  
Quadratic Programming ◽  
Programming Model ◽  
Virus Disease ◽  
Fuzzy Model ◽  
Efficient Solutions ◽  
Support Vector ◽  
Compromise Solution ◽  
Weighting Method ◽  
Quadratic Programming Problems ◽  
Objective Model

A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).

A weighted K-means clustering approach to solve the redundancy allocation problem of systems having components with different failures

2019 ◽  
Vol 233 (6) ◽  
pp. 925-942
Author(s):  
Behzad Karimi ◽  
Seyed Taghi Akhavan Niaki ◽  
Seyyed Masih Miriha ◽  
Mahsa Ghare Hasanluo ◽  
Shima Javanmard
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Programming Model ◽  
Search Algorithm ◽  
Harmony Search ◽  
Nonlinear Integer Programming ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Redundancy Allocation ◽  
Solution Algorithms

A nonlinear integer programming model is developed in this article to solve redundancy allocation problems with multiple components having different failure rates in the series–parallel configuration using an active strategy. The main objective of this research is to select the number and the type of each component in subsystems so as the reliability of the system under certain constraints is maximized. To this aim, a weighted K-means clustering method is proposed, in which the analytical network process is employed to assign weights to the components of each cluster. As the proposed model belongs to the class of nondeterministic polynomial-time hardness problems, precise solution methods cannot solve it in large scale. Therefore, an invasive weed optimization algorithm, due to its proven high efficiency, is utilized to solve the problem. As there is no benchmark available in the literature, a harmony search algorithm and a genetic algorithm are employed as well to validate the results obtained. In order to find better solutions, response surface methodology is used to tune the parameters of the solution algorithms. Some numerical illustrations are solved in the end to not only show the application of the proposed methodology but also to validate the solution obtained and to compare the performance of the three solution algorithms. Experimental results are generally in favor of the invasive weed optimization.

scholarly journals A Fuzzy BI-Objective Model for SVM with an Interactive Procedure to Identify the Best Compromise Solution

2020 ◽  
Author(s):  
Hager Ali Yahia ◽  
Mohammed Zakaria Moustafa ◽  
Mohammed Rizk Mohammed ◽  
Hatem Awad Khater
Keyword(s):  
Quadratic Programming ◽  
Programming Model ◽  
Fuzzy Model ◽  
Efficient Solutions ◽  
Support Vector ◽  
Compromise Solution ◽  
Weighting Method ◽  
Quadratic Programming Problems ◽  
Objective Model ◽  
Fuzzy Parameters

A support vector machine (SVM) learns the decision surface from two different classes of the input points. In many applications, there are misclassifications in some of the input points and each is not fully assigned to one of these two classes. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. An important contribution will be added for the proposed fuzzy bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The experimental results show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 991
Author(s):  
Peidong Zhu ◽  
Peng Xun ◽  
Yifan Hu ◽  
Yinqiao Xiong
Keyword(s):  
Smart Grid ◽  
Collective Behavior ◽  
Physical System ◽  
Large Scale ◽  
Public Utility ◽  
Modeling Framework ◽  
Vast Number ◽  
Attack Model ◽  
The Social ◽  
Social Collective

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

scholarly journals Finite cell method for functionally graded materials based on V-models and homogenized microstructures

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Benjamin Wassermann ◽  
Nina Korshunova ◽  
Stefan Kollmannsberger ◽  
Ernst Rank ◽  
Gershon Elber
Keyword(s):  
Functionally Graded Materials ◽  
Large Scale ◽  
Functionally Graded ◽  
Material Behavior ◽  
Modeling Framework ◽  
Finite Cell Method ◽  
Geometric Framework ◽  
Graded Materials ◽  
Cell Method ◽  
Finite Cell

AbstractThis paper proposes an extension of the finite cell method (FCM) to V-rep models, a novel geometric framework for volumetric representations. This combination of an embedded domain approach (FCM) and a new modeling framework (V-rep) forms the basis for an efficient and accurate simulation of mechanical artifacts, which are not only characterized by complex shapes but also by their non-standard interior structure. These types of objects gain more and more interest in the context of the new design opportunities opened by additive manufacturing, in particular when graded or micro-structured material is applied. Two different types of functionally graded materials (FGM) are considered: The first one, multi-material FGM is described using the inherent property of V-rep models to assign different properties throughout the interior of a domain. The second, single-material FGM—which is heterogeneously micro-structured—characterizes the effective material behavior of representative volume elements by homogenization and performs large-scale simulations using the embedded domain approach.

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