scholarly journals Short-Term IoT Data Forecast of Urban Public Bicycle Based on the DBSCAN-TCN Model for Social Governance

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Dazhou Li ◽  
Chuan Lin ◽  
Wei Gao ◽  
Guangbao Yu ◽  
Jian Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Public Transportation ◽  
Clustering Algorithm ◽  
Smart Cities ◽  
Dropout Rate ◽  
Urban Transport ◽  
Transport Systems ◽  
Social Governance ◽  
Shared Bicycle

Internet of Things will play a vital role in the public transport systems to achieve the concepts of smart cities, urban brains, etc., by mining continuously generated data from sensors deployed in public transportation. In this sense, smart cities applied artificial intelligence techniques to offload data for social governance. Bicycle sharing is the last mile of urban transport. The number of the bike in the sharing stations, to be rented in future periods, is predicted to get the vehicles ready for deployment. It is an important tool for the implementation of smart cities using artificial intelligence technologies. We propose a DBSCAN-TCN model for predicting the number of rentals at shared bicycle stations. The proposed model first clusters all shared bicycle stations using the DBSCAN clustering algorithm. Based on the results of the clustering, the data on the number of shared bicycle rentals are fed into a TCN neural network. The TCN neural network structure is optimized. The effects of convolution kernel size and Dropout rate on the model performance are discussed. Finally, the proposed DBSCAN-TCN model is compared with the LSTM model, Kalman filtering model, and autoregressive moving average model. Through experimental validation, the proposed DBSCAN-TCN model outperforms the traditional three models in terms of two metrics, root mean squared logarithmic error, and error rate, in terms of prediction performance.

scholarly journals The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2581 ◽  
Author(s):  
Shu Shen ◽  
Zhao-Qing Wei ◽  
Li-Juan Sun ◽  
Yang-Qing Su ◽  
Ru-Chuan Wang ◽  
...  
Keyword(s):  
Public Transportation ◽  
Technology Development ◽  
Smart Cities ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Transportation Mode ◽  
Technical Problems ◽  
Intelligent Transport ◽  
Shared Bicycle ◽  
First Time

With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB’s implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB’s potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.

scholarly journals Smart Cities in Turkey: Approaches, Advances and Applications with Greater Consideration for Future Urban Transport Development

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2308 ◽  
Author(s):  
Can Bıyık
Keyword(s):  
Urban Areas ◽  
Planning Process ◽  
Smart Cities ◽  
Radical Change ◽  
Urban Transport ◽  
Transport Systems ◽  
Structured Interviews ◽  
Policy Makers ◽  
Comprehensive Survey ◽  
Future Vision

The smart city transport concept is viewed as a future vision aiming to undertake investigations on the urban planning process and to construct policy-pathways for achieving future targets. Therefore, this paper sets out three visions for the year 2035 which bring about a radical change in the level of green transport systems (often called walking, cycling, and public transport) in Turkish urban areas. A participatory visioning technique was structured according to a three-stage technique: (i) Extensive online comprehensive survey, in which potential transport measures were researched for their relevance in promoting smart transport systems in future Turkish urban areas; (ii) semi-structured interviews, where transport strategy suggestions were developed in the context of the possible imaginary urban areas and their associated contextual description of the imaginary urban areas for each vision; (iii) participatory workshops, where an innovative method was developed to explore various creative future choices and alternatives. Overall, this paper indicates that the content of the future smart transport visions was reasonable, but such visions need a considerable degree of consensus and radical approaches for tackling them. The findings offer invaluable insights to researchers inquiring about the smart transport field, and policy-makers considering applying those into practice in their local urban areas.

scholarly journals IMPROVING OF URBAN PUBLIC TRANSPORTATION QUALITY VIA OPERATOR SCHEDULE OPTIMIZATION

2019 ◽  
pp. 23-33
Author(s):  
Andrii Galkin ◽  
Yurii Davidich ◽  
Yevhen Kush ◽  
Nataliia Davidich ◽  
Iryna Tkachenko
Keyword(s):  
Public Transportation ◽  
Activity Index ◽  
Idle Time ◽  
Urban Transport ◽  
Transport Systems ◽  
Standing Time ◽  
Schedule Optimization ◽  
Regulatory Systems ◽  
Urban Public Transport

The functioning of passenger transport systems should provide necessary quality of passenger service. The results of this research have shown possibility to increase the quality of urban public transport via influence on the driver’s state due to the rational schedule planning. The state change patterns of drivers during the idle time on the final and intermediate bus stops were formalized, based on field observations. The following conclusion was made: decreasing of driver’s body stress takes place during the idle time on the route stops. The intensity of decreasing of driver’s body stress is inversely proportional to the meaning of activity index of driver’s regulatory systems before the start of standing time. Consequently, the duration of idle time must be differentiated depending on the value of the indicator of activity of driver’s regulatory systems before the start of standing time, which is influenced by the working conditions. ECG method was used for assessing driver’s fatigue in elements of transportation process. Comparative analysis of driver’s state changes during the different types of idle time shows the comparability of the results of the study. Transportation management experts can use the research results in urban transport schedule planning and monitoring.

scholarly journals Two Advanced Models of the Function of MRT Public Transportation in Taipei

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1048
Author(s):  
You-Shyang Chen ◽  
Chien-Ku Lin ◽  
Su-Fen Chen ◽  
Shang-Hung Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Public Transportation ◽  
Smart Cities ◽  
Regression Function ◽  
Traffic Demand ◽  
Step Procedure ◽  
Study Results ◽  
Artificial Neural

Tour traffic prediction is very important in determining the capacity of public transportation and planning new transportation devices, allowing them to be built in accordance with people’s basic needs. From a review of a limited number of studies, the common methods for forecasting tour traffic demand appear to be regression analysis, econometric modeling, time-series modeling, artificial neural networks, and gray theory. In this study, a two-step procedure is used to build a predictive model for public transport. In the first step of this study, regression analysis is used to find the correlations between two or more variables and their associated directions and strength, and the regression function is used to predict future changes. In the second step, the regression analysis and artificial neural network methods are assessed and the results are compared. The artificial neural network is more accurate in prediction than regression analysis. The study results can provide useful references for transportation organizations in the development of business operation strategies for managing sustainable smart cities.

scholarly journals Analysis of Aerial Ropeways by following various Indian and International Codes for Urban Mobility with some Structural Modifications in Monocable Ropeways to make it Safe, Secure, Economical and all Weather Durable

2021 ◽  
Vol 9 (VI) ◽  
pp. 1539-1548
Author(s):  
Bhat Omair Bashir
Keyword(s):  
Public Transportation ◽  
Urban Areas ◽  
Public Transit ◽  
Transportation Systems ◽  
Urban Transport ◽  
Transport Systems ◽  
Transit Systems ◽  
Tourist Attractions ◽  
Structural Modifications ◽  
Transit Service

Cable-propelled transit’ (CPT), in particular detachable aerial ropeways are widely employed as transportation systems in alpine areas. In recent years, these transport systems have also been increasingly used in urban areas and are no longer a niche public transportation technology (Hoffmann 2006, Alshalalfah, Shalaby, and Dale 2014). Cable cars systems compete with performance characteristics of other more common urban transport technologies and have the potential to enhance the existing transport provision in cities (O'Connor and Dale 2011). While many applications can be found as transportation systems in airport facilities, and to provide access to tourist attractions, several metropolitan areas have even incorporated gondolas and aerial tramways into their public transport networks. This paper focuses on aerial ropeway systems that operate as a mass transit service (similar to buses, BRT, LRT, etc.) and are part of the public transit systems in their respective cities. Therefore, the analysis and case studies presented in the paper concern systems that are used as a public transit service

scholarly journals An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 435
Author(s):  
Emanuele Bellini ◽  
Pierfrancesco Bellini ◽  
Daniele Cenni ◽  
Paolo Nesi ◽  
Gianni Pantaleo ◽  
...  
Keyword(s):  
Communication Networks ◽  
Smart City ◽  
Structural Information ◽  
Smart Cities ◽  
Urban Transport ◽  
Multimedia Data ◽  
Urban Systems ◽  
Transport Systems ◽  
Big Multimedia Data ◽  
The City

Today, the complexity of urban systems combined with existing and emerging threats constrains administrations to consider smart technologies and related huge amounts of data generated as a means to take timely and informed decisions. The smart city needs to be prepared for both expected and unexpected situations, and the possibility to mitigate the effect of the uncertainty behind the causes of disruptions through the analysis of all the possible data generated by the city open new possibility for resilience operationalization. This article aims at introducing a new conceptualization for resilience and presenting an innovative full stack solution to exploit Internet of Everything (IoE) and big multimedia data in smart cities to manage resilience of urban transport systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to resilience engineering and functional resonance analysis method (FRAM), to understand and model an UTS in the context of smart cities and to support evidence driven decision making. The paper proposes an architecture taking into account: (a) different kinds of available data generated in the smart city, (b) big data collection and semantic aggregation and enrichment; (c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; (d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Florence metropolitan city in the context of RESOLUTE H2020 research project of the European Commission.

scholarly journals ICONet: A Lightweight Network with Greater Environmental Adaptivity

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2119
Author(s):  
Wei He ◽  
Yanmei Huang ◽  
Zanhao Fu ◽  
Yingcheng Lin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Complex Environments ◽  
Illumination Condition ◽  
Fuzzy C Means Clustering ◽  
Fatigue Driving ◽  
Reduced Time

With the increasing popularity of artificial intelligence, deep learning has been applied to various fields, especially in computer vision. Since artificial intelligence is migrating from cloud to edge, deep learning nowadays should be edge-oriented and adaptive to complex environments. Aiming at these goals, this paper proposes an ICONet (illumination condition optimized network). Based on OTSU segmentation algorithm and fuzzy c-means clustering algorithm, the illumination condition classification subnet increases the environmental adaptivity of our network. The reduced time complexity and optimized size of our convolutional neural network (CNN) model enables the implementation of ICONet on edge devices. In the field of fatigue driving, we test the performance of ICONet on YawDD and self-collected datasets. Our network achieves a general accuracy of 98.56% and our models are about 590 kilobytes. Compared to other proposed networks, the ICONet shows significant success and superiority. Applying ICONet to fatigue driving detection is helpful to solve the symmetry of the needs of edge-oriented detection under complex illumination condition environments and the scarcity of related approaches.

The impact of climate change on urban transport resilience in a changing world

2014 ◽  
Vol 38 (4) ◽  
pp. 448-463 ◽  
Author(s):  
David Jaroszweski ◽  
Elizabeth Hooper ◽  
Lee Chapman
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Smart Cities ◽  
Urban Transport ◽  
Transport Systems ◽  
Future Climate Change ◽  
Climate Projections ◽  
Impact Of Climate Change ◽  
Change Impact ◽  
The Impact

The assessment of the potential impact of climate change on transport is an area of research very much in its infancy, and one that requires input from a multitude of disciplines including geography, engineering and technology, meteorology, climatology and futures studies. This paper investigates the current state of the art for assessments on urban surface transport, where rising populations and increasing dependence on efficient and reliable mobility have increased the importance placed on resilience to weather. The standard structure of climate change impact assessment (CIA) requires understanding in three important areas: how weather currently affects infrastructure and operations; how climate change may alter the frequency and magnitude of these impacts; and how concurrent technological and socio-economic development may shape the transport network of the future, either ameliorating or exacerbating the effects of climate change. The extent to which the requisite knowledge exists for a successful CIA is observed to decrease from the former to the latter. This paper traces a number of developments in the extrapolation of physical and behavioural relationships on to future climates, including a broad move away from previous deterministic methods and towards probabilistic projections which make use of a much broader range of climate change model output, giving a better representation of the uncertainty involved. Studies increasingly demand spatially and temporally downscaled climate projections that can represent realistic sub-daily fluctuations in weather that transport systems are sensitive to. It is recommended that future climate change impact assessments should focus on several key areas, including better representation of sub-daily extremes in climate tools, and recreation of realistic spatially coherent weather. Greater use of the increasing amounts of data created and captured by ‘intelligent infrastructure’ and ‘smart cities’ is also needed to develop behavioural and physical models of the response of transport to weather and to develop a better understanding of how stakeholders respond to probabilistic climate change impact projections.

scholarly journals The Role of Culture in Urban Travel Patterns: Quantitative Analyses of Urban Areas Based on Hofstede’s Culture Dimensions

2019 ◽  
Vol 8 (8) ◽  
pp. 227 ◽  
Author(s):  
Ali Enes Dingil ◽  
Federico Rupi ◽  
Joerg Schweizer ◽  
Zaneta Stasiskiene ◽  
Kasra Aalipour
Keyword(s):  
Public Transportation ◽  
Urban Areas ◽  
Urban Transport ◽  
Transport Systems ◽  
Travel Patterns ◽  
Urban Travel ◽  
Culture Dimensions ◽  
Per Capita ◽  
The Relationship

Introduction—culture is an interpretation code of societies, which may explain common preferences in a place. Prediction of alternative transport systems, which could be adopted in a city at peace can help urban transport planners and policy makers adjust urban environments in a more sustainable manner. This paper attempts to investigate the role of Hofstede’s culture dimensions (HCD) on urban travel patterns in 87 urban areas and 41 countries. Analysis—this is the first, systematic analysis investigating the effect of culture on urban travel patterns with open source data from different urban areas around the world. The relationship between HCD and some urban travel patterns such as mode choices (individual transportation and public transportation), car ownership, and infrastructure accessibility (road infrastructure per capita) was demonstrated. In addition, the relationship between culture and some demographic indicators (population density and GDP per capita) closely associated with travel choices are checked. The relations between indicators were identified through correlations and regression models, and calibrated to quantify the relation between indicators. Results and Conclusions—good correlation values between Hofstede’s fundamental culture dimension: individualism/collectivism (IND/COL) and urban travel patterns were demonstrated with a reasonably good fit. The analysis showed that countries with higher individualism build more individualistic transport-related environments, which in turn result in more driving. On the other hand, collective nations tend to use more public transportation. There is significant evidence that, in the case of nations, an increase in tree culture dimensions: collectivism, uncertainty, and masculinity, results in greater usage of public transport.

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