Transportation network planning using simulation: Case study\ Al Mansour city

2017 ◽  
Author(s):  
Nada M. Abid ◽  
Saja S. Hussain
Keyword(s):  
Transportation Network ◽  
Network Planning

Evaluation of Nodes Importance in of Highway Transportation Hubs Planning Based on Fuzzy-AHP

2012 ◽  
Vol 253-255 ◽  
pp. 1181-1187
Author(s):  
Shen E Xi
Keyword(s):  
Evaluation System ◽  
Fuzzy Ahp ◽  
Transportation Network ◽  
Network Planning ◽  
Traffic Volume ◽  
Shandong Province ◽  
Highway Traffic ◽  
Highway Transportation ◽  
Made In

Nodes importance evaluation is the element of transportation network planning. From the aspects of society and economy, highway traffic volume, traffic location, an evaluation system on nodes importance and traffic location of Highway Transportation Junction is established. For the accuracy of evaluation, Fuzzy-AHP is applied to calculate weight of each indicator as fuzziness is existed when factors are compared mutually. Besides, case study is made in Shandong Province, results demonstrate that it is scientific and feasible for planning of highway transportation hubs.

An Oil Transportation Network Planning Method Based on Evolution

2012 ◽  
Vol 522 ◽  
pp. 915-920
Author(s):  
Guo Rong Chen ◽  
Xiao Bo Chen
Keyword(s):  
Growth Process ◽  
Transportation Network ◽  
Economic Effect ◽  
Network Planning ◽  
Clustering Coefficient ◽  
Planning Method ◽  
Oil Transportation ◽  
Designing Method

In order to resolve the problem of difficult planning the layout in oil transportation network, a new planning method based on evolution is put forward out in the paper. Firstly, the economic effect in the growth process of oil transportation network is analyzed, then, the attachment formula comes with the growth of the network; based on it, the growth process of adding nodes and edges is discussed, and the clustering coefficient is arrived to balance the degree of the nodes. Case study shows that the evolution designing method is reasonable.

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

scholarly journals Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1478
Author(s):  
Penugonda Ravikumar ◽  
Palla Likhitha ◽  
Bathala Venus Vikranth Raj ◽  
Rage Uday Kiran ◽  
Yutaka Watanobe ◽  
...  
Keyword(s):  
Real World ◽  
Pattern Mining ◽  
Transportation Network ◽  
Frequent Pattern Mining ◽  
Temporal Databases ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Temporal Database ◽  
First Case

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

scholarly journals Understanding the Usage Patterns of Bicycle-Sharing Systems to Predict Users’ Demand: A Case Study in Wenzhou, China

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaomei Xu ◽  
Zhirui Ye ◽  
Jin Li ◽  
Mingtao Xu
Keyword(s):  
Cluster Analysis ◽  
Comparative Analysis ◽  
Prediction Models ◽  
Transportation Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Demand Prediction ◽  
Usage Patterns ◽  
Accuracy Of Prediction

Bicycle-sharing systems (BSSs) have become a prominent feature of the transportation network in many cities. Along with the boom of BSSs, cities face the challenge of bicycle unavailability and dock shortages. It is essential to conduct rebalancing operations, the success of which largely depend on users’ demand prediction. The objective of this study is to develop users’ demand prediction models based on the rental data, which will serve rebalancing operations. First, methods to collect and process the relevant data are presented. Bicycle usage patterns are then examined from both trip-based aspect and station-based aspect to provide some guidance for users’ demand prediction. After that, the methodology combining cluster analysis, a back-propagation neural network (BPNN), and comparative analysis is proposed to predict users’ demand. Cluster analysis is used to identify different service types of stations, the BPNN method is utilized to establish the demand prediction models for different service types of stations, and comparative analysis is employed to determine if the accuracy of the prediction models is improved by making a distinction among stations and working/nonworking days. Finally, a case study is conducted to evaluate the performance of the proposed methodology. Results indicate that making a distinction among stations and working/nonworking days when predicting users’ demand can improve the accuracy of prediction models.

scholarly journals Discrete Robustness Optimization on Emergency Transportation Network Based on Prospect Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Zheng-kun Zhang ◽  
Chang-feng Zhu ◽  
Qing-rong Wang ◽  
Jia-shan Yuan
Keyword(s):  
Genetic Algorithm ◽  
Prospect Theory ◽  
Decision Maker ◽  
Transportation Network ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Decision Behavior ◽  
Bidirectional Search ◽  
Robustness Optimization

This paper focuses on the discrete robustness optimization of emergency transportation network with the consideration of timeliness and decision behavior of decision-maker under the limited rationality. Based on a situation that the nearer to disaster area, the higher probability of time delay, prospect theory is specially introduced to reflect the subjective decision behavior of decision-maker. Then, a discrete robustness optimization model is proposed with the purpose of the better timeliness and robustness. The model is based on the emergency transportation network with multistorage centers and multidisaster points. In order to obtain the optimal solution, an improved genetic algorithm is designed by introducing a bidirectional search strategy based on a newfangled path cluster to obtain specific paths that connect each storage centers and each disaster points. Finally, a case study is exhibited to demonstrate the reasonability of the model, theory, and algorithm. The result shows that the path cluster with the better timeliness and robustness can be well obtained by using the prospect theory and improved genetic algorithm. The analysis especially reveals that the robustness is correspondent to the risk aversion in prospect theory.

Hybrid optimization procedures applying for two-echelon urban underground logistics network planning: A case study of Beijing

2020 ◽  
Vol 144 ◽  
pp. 106452
Author(s):  
Wanjie Hu ◽  
Jianjun Dong ◽  
Bon-gang Hwang ◽  
Rui Ren ◽  
Zhilong Chen
Keyword(s):  
Network Planning ◽  
Hybrid Optimization ◽  
Logistics Network
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