ABSTRACT
During the COVID-19 pandemic, significant changes occurred in customer behavior, especially in traffic and urban transmission systems. In this context, there is a need for more scientific research and managerial approaches to develop behavior-based smart transportation solutions to deal with recent changes in customers, drivers, and traffic behaviors, including the volume of traffic and traffic routes. This research has tried to find a comprehensive view of novel travel behavior in different routes using a new social network analysis method. Our research is rooted in graph theory/network analysis and application of centrality concepts in social network analysis, particularly in the ride-hailing transportation systems under monumental competition. In this study, a big city, with near to ten million habitants (Tehran), is considered. All city areas were studied and clustered based on the primary measures of centrality, including degree centrality, Katz centrality, special vector centrality, page rank centrality, proximity centrality, and intermediate centrality. Our data were the trips of this system in Tehran, where the nodes in this network represent Tehran's districts, and the connection between the two districts indicates the trips made between those two districts. Also, each link's weight is the number of trips between the two nodes (district). The districts of Tehran were ranked in the smart transportation network based on six criteria: degree centrality, degree centrality of input, degree centrality of output, special vector centrality, hub, and reference points. Finally, according to comprehensive data-driven analysis, the studied company was suggested to create shared value and sustainability through the platform to perform a legitimate system to meet the new challenges. Our proposed system can help managers and governments to develop a behavior-based smart transportation system for big cities. © 2023 by the authors.
ABSTRACT
This chapter presents the fruit of our research by merging smart transportation and smart health to provide IoT transportation solutions based on green smart city intelligence and safety to fight against the COVID-19 pandemic. For this, we have realized a model that allows transporting citizens via a means of transportation based on electric mobility to reduce energy consumption, reduce CO 2 emission and cost by searching the optimal path that this vehicle will be used. And to transport people, we need a system that allows us to check the health situation of citizens to avoid and prevent the spread of the COVID-19 pandemic. In order to find solutions to this work, we have proposed approaches to calculate the most optimal path that meets our needs, as well as to propose scenarios that allow checking the situation of the citizens. And to complete this work with minimum consumption of memory and time, we made a comparative study on the nodes used for the different IoT network topologies to choose the best one for our platform. Concerning the communication, we chose to use the CoAP protocol to ensure the communication between the nodes, and we used the AES-SHA256 encryption algorithm to compare it with RSA-SHA256 to ensure the elements of security and protection the data from any intrusion. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The purpose of this study is to explore, after the epidemic, the intelligent traffic management system, which is the key to creating a green leisure tourism environment in the move towards sustainable urban development. First, quantitative research, snowballing, and convenience sampling methods are used to analyze 750 questionnaires with a basic statistical test, t-test, ANOVA test, and the Pearson product–moment correlation coefficient (PPMCC) method. Qualitative research and a semi-structured interview method are used to collect the opinions of six experts on the data results. Finally, the results are discussed with the multivariate inspection method. Although the current electric bicycle system is convenient, the study found that the service quality of the airport is sufficient;that the fare of the subway is low and popular with students if the system can ease the crowd during peak hours;and that the login and security check time can be shortened, which can help improve the operating convenience of the system interface and link the information of leisure and tourism activities. On the other hand, adjusting fares, increasing seats, planning for women-only ticketing measures and travel space, providing disinfection or cleaning facilities in public areas, and improving passenger’s public health literacy and epidemic prevention cooperation will further enhance the student travel experience, improve the smart city and green tourism network, and help achieve sustainable urban tourism.
ABSTRACT
T Public bus transportation in India plays a major role in short and long-distance. The majority of the population in India depends on the bus daily for going to work, pursuing studies and many other essential purposes. Owing to this the congestion inside the bus increases. In the pandemic situation like COVID social distancing should be maintained and avoid the exchange of money and ticket plays an important factor. Further, the number of passengers within the bus has to fix with a limit that in turn has to plan for increased inefficient transportation. The proposed work implies that upon making the paper-based ticketing system to application-oriented ticket process exchange of money and the ticket can be avoided. Ticket payment by RFID card or scanning QR code can be done that makes travelling by bus much more comfortable during the pandemic situation. Machine learning helps in analyzing the dynamic count of passengers within the bus based on the source and destination a passenger enters inside the bus. Thus the congestion inside the bus predicted to plan the travel.
ABSTRACT
This paper explores the impact of the future web geoinformation utilities for sustainable Smart Cities. This paper proposes a smart cities innovation roadmap framework and recommendations for urban development enabled by efficient web geoinformation utilities for an integrated sustainability and public health protection at smart cities. The roadmap framework aims to support the innovation policies and sustainable development strategies of cities towards becoming “smart” for sustainability and public health protection. An innovative health policy roadmap is presented for efficient solutions in sustainable designs, and public health protection at smart cities using proper web geoinformation utilities. The latter could be useful in project management of particular services and activities that promote health within smart cities, like health urban tourism and safe operational management of integrated community health centres using proper web utilities. In this way could be achieved a quality assurance of fundamental efficient designs for a geo-health intelligence of sustainable smart cities embedded on future web technologies and user-driven innovation in future smart city ecosystems. Useful web utilities are presented for stakeholders achieving a better project management in terms of an integrated environmental health policy and safety for sustainable development at future smart cities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Suggesting tourists/residents about the pollution-free locations and controlling the number of passengers in a shareable vehicle have become crucial tasks to smart city officials as they plummet health issues such as asthma or COVID-19. Recently, city authorities, transport logistic designers, and policymakers have tasked researchers/entrepreneurs to innovate in shared mobility systems. This paper proposes a Blockchain-Enabled Shared Mobility (BESM) architecture that allocates seats to residents/tourists in a shareable vehicle based on air quality and COVID-19 information of traveling locations. BESM involves smart city authorities, vehicle owners, hospital authorities, and residents using permissioned-blockchains to collaboratively decide on allocating travel seats. Experiments were carried out at the IoT Cloud research laboratory to manifest the allocation of seats. For instance, BESM excluded in allocating seats to asthma patients and limited the number of travelers in the cities where COVID-19 cases or pollution levels were higher in numbers using BESM. The pollution levels of cities were monitored using air quality monitoring sensors or predicted using a few prediction algorithms such as Random Forests (RF), Linear Regression (LR), Quantile Regression (QR), Ridge Regression (RR), Lasso Regression (LaR), ElasticNet Regression (ER), Support Vector Machine (SVM), and Recursive Partitioning (RP). In succinct, the article unfolded the primordial importance of the proposed BESM architecture for promoting efficient shared mobility aspects in smart cities.