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In order to create a sustainable future for the urban environment in s=Smart cities, it is necessary to develop a concept of urban transport, partially reduce the use of traditional transport, primarily cars, as well as the environmental pressure on society, which is essential to move to a sustainable urban future. In the latest discussions on the future of the urban transport system, the quality of the environment, and the possibility of its improvement are discussed, this issue became especially relevant with the onset of the pandemic, when the lockdowns were introduced. The problem of sustainable transport in urban areas has been recognized in academic studies, searching for appropriate models and solutions. The article presents the latest literature review and illustrates the newest trends with several examples. VOS Viewer software has been used to classify the different keywords, according to their co-citation, following clustering techniques. By analyzing the research conducted by other researchers, it has been possible to structure the ecosystem and trends in the Urban Transportation Concept, also mentioning likely future trends. Based on the literature analysis of the Sustainable Urban Transport, the authors of the study found that a large group of researchers deal with technical solutions and innovative business models, while the essential behavioral aspects are examined in less detail. Extensive literature analysis allowed the authors to select several solutions to achieve the transformation towards sustainable transportation in urban areas: new vehicle technologies and their environmental factors' analysis, geographic information systems, the analytic hierarchy process method, the time series analysis of road traffic accidents using multiplicative models, electrification and use of Friedman Analysis of Variance by Ranks, as well as innovations in sharing mobility.
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Today, many cities around the globe are interested in developing or adopting smart city policy frameworks;however, the complexity of the smart city concept combined with complicated urban issues makes it a highly challenging task. Moreover, there are limited studies to consolidate our understanding of smart city policymaking. The aim of this study was to bridge this knowledge gap by placing a set of official smart city policy frameworks under the policy analysis microscope. The study approached the analysis by, firstly, internationally collating the smart city policy frameworks of 52 local governments from 17 countries. The methodology then progressed to a deductive content analysis of the identified policies with a thematic data analysis software. The investigation employed the main themes to identify common urban issues in smart city policies—i.e., smart economy, smart environment, smart governance, smart living, smart mobility, and smart people. The results revealed the targeted key planning issues, goals, and priorities, and the ways that smart city policies address these key planning issues, goals, and priorities. The study findings inform policymakers, planners and practitioners on the smart city policy priorities and provide insights for smart city policymaking.
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Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous information and environmental noise level around them. This research studies the spatio-temporal behavior of people and noise pollution in the campus as a short-scale Smart City, i.e., a Smart Campus. Applying specific machine learning algorithms, we have analyzed two months of captured data (61 days). The main findings from the analysis show that most university community members move through the campus at similar hours, generating congestion problems. In addition, the campus suffers from acoustic pollution according to regulations; therefore, we conclude that the proposed system is useful for gathering helpful information for the university community members and managers. Thanks to its low cost, it can be easily extended and even used in other similar environments, allowing democratic access to Smart City services as an excellent added value.
Subject(s)
Pedestrians , Algorithms , Humans , Monitoring, Physiologic , Noise , SpainABSTRACT
In times of ongoing pandemic outbreak, public transportation systems organisation and operation have been significantly affected. Among others, the necessity to implement in-vehicle social distancing has fostered new requirements, such as the possibility to know in advance how many people will likely be on a public bus at a given stop. This is very relevant for both potential passengers waiting at a stop, and for decision makers of a transit company, willing to adapt the operational planning. Within the domain of data-driven Intelligent Transportation Systems (ITS), some research activities are being conducted towards Bus Passenger Load (BPL) predictions, with contrasting results. In this paper we report on an academic/industrial experience we conducted to predict BPL in a major Italian city, using real-world data. In particular, we describe the difficulties and challenges we had to face in the data processing and mining steps, due to multiple data sources, with noisy data. As a consequence, in this paper we highlight to the ITS community the need of more advanced techniques and approaches suitable to support the instantiation of a data analytic pipeline for BPL prediction. © 2022, Springer Nature Switzerland AG.
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The term Smart associated to cities and communities is commonly referred to a broad concept involving vehicles, humans, environment and services. In this paper we focus on a Smart solution dedicated to a specific reality, where humans are the main actors. We propose LiveSmart-CAMPUS, an intelligent system whose main goal is to improve the life quality of students, collaborators and visitors within a physical university campus. The system will enable the management of physical distancing and the optimization of shared spaces occupancy, for a safe return to work and academic environment, during the Covid post-pandemic time. We present in this paper the LiveSmart-CAMPUS application, a mobile application developed on top of the LiveSmart global solution, and envisioned for the campus community and visitors which allows them to obtain location based, calendar and time based, and users interests' based information. The presented application will give the possibility to the LiveSmart system to collect users mobility data, allowing the monitoring and the prediction of no latency information on expected rooms and corridors occupancy levels. People who tested the applications reported its usefulness in improving their comfort and their life quality within the campus spaces. © 2021 Copyright for this paper by its authors
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Traffic prediction and analysis is an essential task towards intelligent mobility, particularly for path planning and navigation. When the traffic flow starts after the COVID-19 pandemic is subsided, the mobility patterns changes and may become unpredictable or challenging. This problem may be crucial, particularly if many people hurry to single occupancy transport mode. Notably, the rapid development in machine learning with new methods and the emergence of new data sources make it possible to evaluate and predict traffic conditions in smart cities more quickly and precisely. The proposed work is modeled in two-fold manner to investigate the impact of COVID shift in regular urban traffic movements given the particular period of the pre, during, and post lockdown phases. Firstly, the investigation is carried out for time series analysis considering the three phases of lockdown. Secondly, the real-time spatial information is analyzed for different time zones in a day. Notably, this requires a detailed analysis of the heterogeneous and complex input traffic data. Machine learning and advanced deep learning methodologies such as regression models, RNN, variants of LSTM, and GRU is used for analysis in this proposed traffic modeling. Significantly, the least error scores with Root Mean Square Error (RMSE) loss of 1.82 is observed for the RNN and GRU models, and 0.058 with the Gradient Boosting regression analysis, respectively. © 2022 IEEE.
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Many concepts and innovations aim to improve transport and mobility, while helping to decrease the externalities that transport imposes on society. Research and innovation monitoring tools are important to assess the current state of development so that research funding and policy making efforts can be aligned optimally. This paper presents a comprehensive approach which links technological developments in the transport sector in Europe to the objectives of the most recent policy developments, in particular, the 2020 European Sustainable and Smart Mobility Strategy. It does so by identifying and evaluating technologies from European Union-funded projects between 2007 and 2020, by means of a technology taxonomy. Information is provided at an aggregated level on funding characteristics of both projects and the technologies, while at the same time, the level of maturity of researched technologies in the most recent projects is identified. This study can aid policy makers to support the future development of transport technologies as part of pertinent policy strategies and identify research gaps.
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Increasing population in cities implies growth infrastructure, basic services, transportation, employment, dwelling and those additional resources that allow them to improve their quality of life, which makes a necessary change in conventional urban planning models, that are insufficient to attend the new requirements. The 21st planning cities processes tend to consider an inclusive approach, where citizen participation takes a special interest;therefore, it contributes with the purpose to obtain receptive cities which are focused on the citizen. About mobility, the conventional planning mobility approach has been changing toward sustainable smart mobility to guarantee the participation of all social groups and reduce the effects associated with transport such as energy consumption, CO2 release, air quality, wasted space in the streets or impact on public health. Due to the current conditions associated with the Covid-19 pandemic, mobility meaning has been changed from transport modes, their needs, accessibility, and affordability that constitutes a forced and disruptive adaptation process of existing models to plan, manage and implement sustainable mobility systems. Under this consideration, the mobility evaluation in the current context is based on the pillars of sustainability: social, economic, and environmental issues;furthermore, in the receptive cities' scenario, it is necessary to articulate an additional component: planning and governance, where the technology integration, knowledge and people participation will make it possible to achieve the goals of sustainable and smart mobility. To achieve a participatory evaluation proposal for sustainable and smart mobility, it is relevant to bear in mind the fundamental principles of citizen participation and determine the desired level of participation, that in receptive cities the greatest citizen commitment is expected, so, they can contribute with their knowledge and life experiences to achieve more effective, efficient, relevant, and sustainable results over time. Finally, some of the aspects registered in this proposal for the urban mobility evaluation are related to the invert mobility priorities, the global context and environmental quality, the built environment, connectivity, urban design, and transport-oriented sustainable communities, financial management, the use of technology in the city, among others, to contribute to the proposed objective. © 2021 Elsevier B.V.. All rights reserved.
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When a disaster such as pandemic Covid-19, flood and landslide struct, essential services and aid must reach the disaster area promptly. A mechanism that enables smooth coordination for people and vehicles especially for movement during a disaster is vital. This paper aims to present the conceptual model for personalised smart mobility for smart movement during a disaster such as Covid-19 that includes smart vehicle mobility profile and smart people mobility profile. The model was formulated by applying the smart city concept in urban Malaysia and focusing on smartphones and various IoT sensors as the enabler technologies and foundation of data gathering to be utilized for decision making in multiple circumstances. In the process, we reviewed recent advances based on Smart City and disaster management policy for Malaysia and outline relevant directions for future research of smart movement control and decision-making during a disaster including pandemic outbreaks such as Covid-19 for the Malaysia case. The focus is on a fundamental topic in the application of telematics to assist the authority and community for citizens and vehicles mobility when disaster strike. © 2021, Springer Nature Switzerland AG.
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COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.