An Open Transportation Network Resilience Analytics Platform for Large-Scale Urban Accessibility Analysis

2018 ◽  
Author(s):  
Edgar Castro ◽  
Qi Wang ◽  
Armin Akhavan
Keyword(s):  
Large Scale ◽  
Transportation Network ◽  
Network Resilience ◽  
Accessibility Analysis ◽  
Urban Accessibility

scholarly journals Challenges and Prospects of Entrepreneurship in Bhutan from the Lens of Business Educators

2020 ◽  
pp. 63-71
Author(s):  
Dhanapati Sharma ◽  
Khem Prasad Gautam
Keyword(s):  
Large Scale ◽  
Transportation Network ◽  
Structured Interview ◽  
Good Prospect ◽  
Scale Production ◽  
Entrepreneurial Ventures ◽  
Technological Support ◽  
Business Educators ◽  
Goods And Services ◽  
Undue Advantage

Entrepreneurship plays an important role in generating employment opportunities, enhancing public income and promoting societal change, particularly in a budding economy like Bhutan. Given its importance to the country, this paper investigates the challenges faced by aspiring entrepreneurs and entrepreneurial ventures, and their future prospects in Bhutan. The paper adopts qualitative research methodology to study the perspective of business educators through a semi-structured interview with fifteen participants from Gedu College of Business Studies located at Gedu, Bhutan. The study reveals that, due to a range of challenges the aspiring entrepreneurs and the entrepreneurial ventures face today, the Bhutanese entrepreneurs have a long way to go before they can effectively drive changes in the economy. However, the findings suggest that there is a good prospect for the entrepreneurial ventures in the country, and the ventures will have an undue advantage if it can leverage on the technological support from other countries. To ease the way for entrepreneurs in the country, the study finds it important to encourage the consumption of indigenous products by discouraging the import of the goods and services that can be produced within the country, regulate the price of the home made product, explore market and marketing facilities beyond the country to encourage large scale production, initiate discussion about entrepreneurship and the associated benefits at school levels and also take adequate infrastructural facilities (water, electricity and transportation network) across all parts of the country.

scholarly journals Analyzing the Formation Mechanism of Cross-City Transportation Network Resilience

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Liang Wang ◽  
Xiaolong Xue ◽  
Weirui Xue ◽  
Beile Zhao
Keyword(s):  
Theoretical Model ◽  
Formation Mechanism ◽  
Transportation Network ◽  
Network Resilience ◽  
Railway Network ◽  
Network Nodes ◽  
General Meaning ◽  
Important Position ◽  
And Function ◽  
Constituent Elements

The formation mechanism of cross-city transportation network resilience occupies an important position in cross-city transportation network resilience management. This study analyzes the constituent elements of the cross-city transportation network and their interrelationships, and the connotation of cross-city transportation network resilience is defined from the general meaning of system resilience. Combining with the connotation of cross-city transportation network resilience, the specific formation process of cross-city transportation network resilience is analyzed and summarized from three stages, including resisting disturbance, absorbing disturbance, and function recovery. Taking cross-city transportation network nodes and systems as specific objects, the static and dynamic formation path of cross-city transportation network resilience is condensed. Based on the standard linear solid model, a theoretical model is constructed and solved for revealing the formation mechanism of cross-city transportation network resilience. Finally, the theoretical model of cross-city transportation network resilience proposed in this study is used for analyzing the China railway network resilience.

scholarly journals A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Seungkyu Ryu ◽  
Anthony Chen ◽  
Songyot Kitthamkesorn
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Transportation Network ◽  
User Equilibrium ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Nested Logit ◽  
Two Phase ◽  
Phase Gradient ◽  
Modal Split

This study provides a gradient projection (GP) algorithm to solve the combined modal split and traffic assignment (CMSTA) problem. The nested logit (NL) model is used to consider the mode correlation under the user equilibrium (UE) route choice condition. Specifically, a two-phase GP algorithm is developed to handle the hierarchical structure of the NL model in the CMSTA problem. The Seoul transportation network in Korea is adopted to demonstrate an applicability in a large-scale multimodal transportation network. The results show that the proposed GP solution algorithm outperforms the method of the successive averages (MSA) algorithm and the classical Evan’s algorithm.

scholarly journals Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data

2020 ◽  
Vol 55 ◽  
pp. 102053 ◽  
Author(s):  
Xinwu Qian ◽  
Tian Lei ◽  
Jiawei Xue ◽  
Zengxiang Lei ◽  
Satish V. Ukkusuri
Keyword(s):  
Large Scale ◽  
Transportation Network ◽  
Trajectory Data ◽  
Transportation Network Companies

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

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