scholarly journals A Segmented Signal Progression Model for the Modern Streetcar System

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Baojie Wang ◽  
Wei Wang ◽  
Xiaojian Hu ◽  
Xiaowei Li
Keyword(s):  
Historical Data ◽  
Control Unit ◽  
Vehicular Traffic ◽  
System Operation ◽  
Arrival Times ◽  
Progression Model ◽  
Proposed Model ◽  
Control Concept ◽  
New City ◽  
Negative Impacts

This paper is on the purpose of developing a segmented signal progression model for modern streetcar system. The new method is presented with the following features: (1) the control concept is based on the assumption of only one streetcar line operating along an arterial under a constant headway and no bandwidth demand for streetcar system signal progression; (2) the control unit is defined as a coordinated intersection group associated with several streetcar stations, and the control joints must be streetcar stations; (3) the objective function is built to ensure the two-way streetcar arrival times distributing within the available time of streetcar phase; (4) the available time of streetcar phase is determined by timing schemes, intersection structures, track locations, streetcar speeds, and vehicular accelerations; (5) the streetcar running speed is constant separately whether it is in upstream or downstream route; (6) the streetcar dwell time is preset according to historical data distribution or charging demand. The proposed method is experimentally examined in Hexi New City Streetcar Project in Nanjing, China. In the experimental results, the streetcar system operation and the progression impacts are shown to affect transit and vehicular traffic. The proposed model presents promising outcomes through the design of streetcar system segmented signal progression, in terms of ensuring high streetcar system efficiency and minimizing negative impacts on transit and vehicular traffic.

scholarly journals Lane Allocation Optimization in Container Seaport Gate System Considering Carbon Emissions

2021 ◽  
Vol 13 (7) ◽  
pp. 3628
Author(s):  
Zhihong Jin ◽  
Xin Lin ◽  
Linlin Zang ◽  
Weiwei Liu ◽  
Xisheng Xiao
Keyword(s):  
Branch And Bound ◽  
Carbon Emissions ◽  
Integer Program ◽  
Arrival Times ◽  
Proposed Model ◽  
Gate System ◽  
Appointment System ◽  
Truck Appointment System ◽  
Gate Congestion ◽  
Allocation Decisions

Long queues of arrival trucks are a common problem in seaports, and thus, carbon emissions generated from trucks in the queue cause environmental pollution. In order to relieve gate congestion and reduce carbon emissions, this paper proposes a lane allocation framework combining the truck appointment system (TAS) for four types of trucks. Based on the distribution of arrival times obtained from the TAS, lane allocation decisions in each appointment period are determined in order to minimize the total cost, including the operation cost and carbon emissions cost. The resultant optimization model is a non-linear fractional integer program. This model was firstly transformed to an equivalent integer program with bilinear constraints. Then, an improved branch-and-bound algorithm was designed, which includes further transforming the program into a linear program using the McCormick approximation method and iteratively generating a tighter outer approximation along the branch-and-bound procedure. Numerical studies confirmed the validity of the proposed model and algorithm, while demonstrating that the lane allocation decisions could significantly reduce carbon emissions and operation costs.

Feeder Bus Timetable Design and Vehicle Size Setting in Peak Hour Demand Conditions

2019 ◽  
Vol 2673 (10) ◽  
pp. 321-332 ◽  
Author(s):  
Xueping Dou ◽  
Qiang Meng
Keyword(s):  
Mixed Integer ◽  
Arrival Times ◽  
Non Linear Programming ◽  
Bee Colony ◽  
Proposed Model ◽  
Feeder Bus ◽  
Minlp Model ◽  
Failure Cost ◽  
Set Up ◽  
The Given

This study proposes a solution to the feeder bus timetabling problem, in which the terminal departure times and vehicle sizes are simultaneously determined based on the given transfer passengers and their arrival times at a bus terminal. The problem is formulated as a mixed integer non-linear programming (MINLP) model with the objective of minimizing the transfer waiting time of served passengers, the transfer failure cost of non-served passengers, and the operating costs of bus companies. In addition to train passengers who plan to transfer to buses, local passengers who intend to board buses are considered and treated as passengers from virtual trains in the proposed model. Passenger attitudes and behaviors toward the waiting queue caused by bus capacity constraints in peak hour demand conditions are explicitly embedded in the MINLP model. A hybrid artificial bee colony (ABC) algorithm is developed to solve the MINLP model. Various experiments are set up to account for the performance of the proposed model and solution algorithm.

Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 2050017
Author(s):  
Nghiem Van Tinh
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Pso Algorithm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
Universe Of Discourse

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

scholarly journals A Novel Discrete Grey Model and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Tensile Strength ◽  
Arbitrary Number ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Prediction Performance ◽  
Grey Model ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Future Data

This paper aims to further increase the prediction accuracy of the grey model based on the existing discrete grey model, DGM(1,1). Herein, we begin by studying the connection between forecasts and the first entry of the original series. The results comprehensively show that the forecasts are independent of the first entry in the original series. On this basis, an effective method of inserting an arbitrary number in front of the first item of the original series to extract messages is applied to produce a novel grey model, which is abbreviated as FDGM(1,1) for simplicity. Incidentally, the proposed model can even forecast future data using only three historical data. To demonstrate the effectiveness of the proposed model, two classical examples of the tensile strength and life of the product are employed in this paper. The numerical results indicate that FDGM(1,1) has a better prediction performance than most commonly used grey models.

scholarly journals Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach

2018 ◽  
Vol 7 (4.30) ◽  
pp. 281
Author(s):  
Nazirah Ramli ◽  
Siti Musleha Ab Mutalib ◽  
Daud Mohamad
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Fuzzy Numbers ◽  
Historical Data ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Forecasting Error ◽  
Proposed Model ◽  
Fuzzy Forecast

This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.

scholarly journals Optimal Pricing in Recycling and Remanufacturing in Uncertain Environments

2020 ◽  
Vol 12 (8) ◽  
pp. 3199
Author(s):  
Guangzhou Yan ◽  
Yaodong Ni ◽  
Xiangfeng Yang
Keyword(s):  
Historical Data ◽  
Programming Model ◽  
Optimal Pricing ◽  
Market Demand ◽  
Pricing Model ◽  
Uncertain Environments ◽  
Uncertainty Distribution ◽  
Equivalent Models ◽  
Fashion Products ◽  
Proposed Model

With the increasing awareness of environmental protection, firms pay much more attention to the recycling and remanufacturing of used products. This paper addresses the problem of the optimal pricing in recycling and remanufacturing in uncertain environments. We consider two strategies of remanufacturing products, by which a recycled product can be repaired and sold as a second-hand product or dissembled into materials for production of new products according to its quality. As the market demand for products and the quantities of recycled products, such as fashion products and mobile phones, usually lack historical data, this paper adopts uncertainty theory to depict uncertainty in establishing the pricing model. An uncertain programming model and a series of crisp equivalent models are proposed under the assumptions of particular uncertainty distribution. Finally, numerical experiments are performed to show how various parameters influence the results of the proposed model.

scholarly journals A Strategies Alignment Approach to Manage Disruptive Events in Collaborative Networks

2020 ◽  
Vol 12 (7) ◽  
pp. 2641 ◽  
Author(s):  
Beatriz Andres ◽  
Giulio Marcucci
Keyword(s):  
Network Performance ◽  
Negative Impact ◽  
Positive Impact ◽  
Performance Loss ◽  
Turbulent Environments ◽  
Conflict Situations ◽  
Proposed Model ◽  
Wide Range ◽  
Negative Impacts ◽  
Disruption Recovery

Enterprises of the supply chain are currently embedded in dynamic and turbulent environments, having to deal with the appearance of disruptive events. When an enterprise is affected by a disruptive event, the consequences of the disruption not only impact in the enterprise itself, but also influences on the other partners of the network to which it belongs. Thus, disruptive events exceed the capability of individual actors, impacting on the network performance. Consequently, network partners have to collaboratively make decisions to soften the negative impacts on the performance. In this regard, after a disruption takes place, network enterprises should be aware of activating a set of sustainable and resilience strategies that attenuate the performance loss and reduce the disruption recovery time. Nevertheless, the diverse nature of disruptions means that a wide range of varied and sometimes contradictory strategies can be formulated, resulting in conflict situations among the collaborative network (CN) partners. The current paper proposes an approach that makes it possible to collaboratively manage the strategies to activate when a disruptive event occurs, so that the selected strategies are aligned. The strategies alignment approach, proposed in the paper, makes it possible to select those strategies that have a positive impact, or a minimum negative impact, on the objectives defined, not only in the enterprise itself, but also in the objectives defined by the rest of CN partners. The alignment of strategies makes it possible to reduce the performance level loss when a disruption takes place. Thus, the strategies alignment approach aims at activating those strategies that maximize the performance of the CN, achieving levels of performance equal or higher than the levels previous to the disruption, limiting the adverse effects produced by the disruptive events, and contributing to a more sustainable–resilient CN. Finally, in order to validate the proposal, a case study is presented. The proposed model is validated to deal with a drop in demand due to a political embargo, in a textile CN.

Macrolevel Classification Yard Capacity Modeling

2017 ◽  
Vol 2608 (1) ◽  
pp. 125-133 ◽  
Author(s):  
Licheng Zhang ◽  
Mingzhou Jin ◽  
Zhirui Ye ◽  
Haodong Li ◽  
David B. Clarke ◽  
...  
Keyword(s):  
Dwell Time ◽  
Historical Data ◽  
Freight Transportation ◽  
The United States ◽  
Highway Capacity ◽  
Proposed Model ◽  
Capacity Modeling ◽  
Capacity Data ◽  
Classification Yard ◽  
Rail Yards

Classification yards play a significant role in railroad freight transportation and are often considered bottlenecks for railroad networks. Based on a generic yard simulation model, the model in the presented study fits the Bureau of Public Roads function, which is widely used in highway capacity to represent the volume–dwell time relationship. The proposed analytical model incorporates major features of rail yards, such as the number and capacity of tracks in each area, the number of engines and humps, the humping speed, and the assemble rate. The model is validated by historical data from 16 classification yards of Class I railroads in the United States. The results show that the proposed model can generate precise capacity data of rail yard, as well as the dwell time of rail cars in yards. The dwell time increases sharply when the volume is greater than the capacity of a rail yard. The identified relationship may help a railroad analyze its network at the macro level and therefore improve the systemwide capacity and efficiency.

scholarly journals Possibility of using Matlab application to propose fuzzy computer model

2021 ◽  
Vol 1199 (1) ◽  
pp. 012020
Author(s):  
S Hrehova ◽  
J Husár ◽  
V Hladký
Keyword(s):  
Computer Model ◽  
Industry 4.0 ◽  
Fuzzy Logic Controller ◽  
Historical Data ◽  
Computer Models ◽  
Fuzzy Approach ◽  
Software Applications ◽  
Proposed Model ◽  
The Creation ◽  
The Individual

Abstract More and more organizations in various fields apply the principles of the Industry 4.0 philosophy. The result, among other benefits, is the acquisition of a large amount of data. Data can be of great importance to them in terms of decision support, analysis and, last but not least, as a resource for simulations and computer models. Currently, various approaches and software applications can be used to create models. One of the applications that allows the creation of computer models in various fields is Matlab. Diversity of use is ensured by different sets of tools, which are specifically focused on individual areas and thus provide the necessary tools. In the presented paper we focus on the possibilities of using fuzzy approach in the design of a computer model in the field of heating with the tools of the Fuzzy Logic Controller toolbox. The basis for creating the model will be historical data obtained from the real object. The individual tools of the toolbox, the creation and presentation of rules will be described, as well as the connection of the proposed model with the Simulink environment.

scholarly journals A Two-Stage Short-Term Load Forecasting Method Using Long Short-Term Memory and Multilayer Perceptron

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5873
Author(s):  
Yuhong Xie ◽  
Yuzuru Ueda ◽  
Masakazu Sugiyama
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Historical Data ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Long Short Term Memory

Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement.

Sign in / Sign up

Export Citation Format

Share Document