scholarly journals Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

2021 ◽  
Vol 13 (12) ◽  
pp. 6596
Author(s):  
Riccardo Ceccato ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi
Keyword(s):  
Travel Demand ◽  
Risk Mitigation ◽  
Great Part ◽  
Mitigation Measures ◽  
Sustainable Mobility ◽  
New Normal ◽  
Logistic Regression Models ◽  
Demand Prediction ◽  
Travel Decisions ◽  
The University

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

scholarly journals Impacts of the COVID-19 Pandemic on Active Travel Mode Choice in Bangladesh: A Study from the Perspective of Sustainability and New Normal Situation

2021 ◽  
Vol 13 (12) ◽  
pp. 6975
Author(s):  
Niaz Mahmud Zafri ◽  
Asif Khan ◽  
Shaila Jamal ◽  
Bhuiyan Alam
Keyword(s):  
Active Transport ◽  
Travel Behavior ◽  
Multinomial Logistic Regression ◽  
Well Being ◽  
Physical Distance ◽  
Sustainable Transportation ◽  
Sustainable Mobility ◽  
New Normal ◽  
Logistic Regression Models ◽  
Transport Modes

The COVID-19 pandemic has caused incredible impacts on people’s travel behavior. Recent studies suggest that while the demand for public transport has decreased due to passengers’ inability to maintain physical distance inside this mode, the demand for private automobile and active transport modes (walking and cycling) has increased during the pandemic. Policymakers should take this opportunity given by the pandemic and encourage people to use active transport more in the new normal situation to achieve sustainable transportation outcomes. This study explores the expected change in active transport mode usage in the new normal situation in Bangladesh based on the data from a questionnaire survey. The study finds that 56% and 45% of the respondents were expected to increase travel by walking and cycling, respectively, during the new normal situation. On the other hand, 19% of the respondents were expected to do the opposite. The study further identifies the factors influencing the expected change in travel by active transport modes during the new normal situation by developing multinomial logistic regression models. Finally, this study proposes policies to increase active transport use beyond the pandemic and ensure sustainable mobility for city dwellers and their well-being.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

Recent technical advancement in natural terrain landslide risk mitigation measures in Hong Kong

2018 ◽  
Vol 25 (2) ◽  
pp. 90-101 ◽  
Author(s):  
Julian S H Kwan ◽  
Harris W K Lam ◽  
Charles W W Ng ◽  
Nelson T K Lam ◽  
S L Chan ◽  
...  
Keyword(s):  
Hong Kong ◽  
Risk Mitigation ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Natural Terrain

Towards Climate Change Mitigation and Adaptation

2021 ◽  
Vol 20 (1) ◽  
pp. 138-158
Author(s):  
Umer Khayyam ◽  
Rida Bano ◽  
Shahzad Alvi
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Capital City ◽  
Mitigation Measures ◽  
Negative Consequences ◽  
Adaptation Measures ◽  
Natural Systems ◽  
Mitigation And Adaptation ◽  
Level Of Motivation ◽  
The University

Abstract Global climate change is one of the main threats facing humanity and the impacts on natural systems as well as humans are expected to be severe. People can take action against these threats through two approaches: mitigation and adaptation. However, mitigations and adaptations are contingent on the level of motivation and awareness, as well as socio-economic and environmental conditions. This study examined personal perception and motivation to mitigate and adapt to climate change among the university students in the capital city of Pakistan. We divided the respondents into social sciences, applied sciences and natural sciences, using logistic regression analysis. The results indicated that students who perceive severity, benefits from preparation, and have more information about climate change were 1.57, 4.98 and 1.63 times more likely to take mitigation and 1.47, 1.14 and 1.17 times more likely to take adaptation measures, respectively. Students who perceived self-efficacy, obstacles to protect from the negative consequences of climate change and who belonged to affluent families were more likely to take mitigation measures and less likely to take adaptation strategies. However, mitigation and adaptation were unaffected by age, gender and study discipline.

scholarly journals Quantifying the effectiveness of early warning systems for natural hazards

2016 ◽  
Vol 16 (1) ◽  
pp. 149-166 ◽  
Author(s):  
M. Sättele ◽  
M. Bründl ◽  
D. Straub
Keyword(s):  
Natural Hazards ◽  
Early Warning ◽  
Risk Mitigation ◽  
Early Warning Systems ◽  
Mitigation Measures ◽  
Management Approach ◽  
Warning Systems ◽  
Integrated Risk ◽  
Framework Approach

Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

scholarly journals STRATEGI LAYANAN PERPUSTAKAAN PERGURUAN TINGGI PADA MASA NEW NORMAL (STUDI KASUS UPT PERPUSTAKAAN UNIVERSITAS RIAU)

2021 ◽  
Vol 8 (2) ◽  
pp. 111-118
Author(s):  
Sastria Izprilla ◽  
Vita Amelia ◽  
Hadira Latiar
Keyword(s):  
Digital Libraries ◽  
Service Sector ◽  
Library Services ◽  
New Normal ◽  
University Library ◽  
Normal Period ◽  
It Department ◽  
Descriptive Approach ◽  
The University

This research is entitled strategy of university library services in the new normal period case study of the technical implementation unit (UPT) of the University of Riau library. The purpose of this study was to determine the service strategy taken by the technical implementing unit (UPT) of the Riau University library in the new normal era. The method used in this research is qualitative with a descriptive approach. The processing method uses data reduction, data presentation, and conclusions. The informants in this study were the head of the library, the head of the service sector, and the head of the IT department. The results of this study are that there are several strategies taken by the Riau University library, that is the development of digital libraries that must be accelerated and add supporting applications to ensure the smooth distribution of information to users.

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