Search Results (24)

This study presents a comprehensive multi-objective optimization framework for optimal placement and sizing of distributed generation (DG) units in electric bus (E-bus) transit systems integrated with a high-power flash charging infrastructure. An enhanced Multi-Objective Grey Wolf Optimizer (MOGWO), utilizing Euclidean distance-based Pareto ranking, is developed to minimize power loss, voltage deviation, and voltage violations. The framework incorporates realistic E-bus operation characteristics, including a 31-stop, 62 km route, 600 kW pantograph flash chargers, and dynamic load profiles over a 90 min simulation period. Statistical evaluation on IEEE 33-bus and 69-bus distribution networks demonstrates that MOGWO consistently outperforms MOPSO and NSGA-II across all DG deployment scenarios. In the three-DG configuration, MOGWO achieved minimum power losses of 0.0279 MW and 0.0179 MW, and voltage deviations of 0.1313 and 0.1362 in the 33-bus and 69-bus systems, respectively, while eliminating voltage violations. The proposed method also demonstrated superior solution quality with low variance and faster convergence, requiring under 7 h of computation on average. A five-method compromise solution strategy, including TOPSIS and Lp-metric, enabled transparent and robust decision-making. The findings confirm the proposed framework’s effectiveness and scalability for enhancing distribution system performance under the demands of electric transit electrification and smart grid integration. Full article

This study proposes an eXtended Reality (XR) glasses-based walking assistance system to support independent and safe outdoor walking for visually impaired people. The system leverages the YOLOv8n deep learning model to recognize walkable areas, public transport facilities, and obstacles in real time and provide appropriate guidance to the user. The core components of the system are Xreal Light Smart Glasses and an Android-based smartphone, which are operated through a mobile application developed using the Unity game engine. The system divides the user’s field of vision into nine zones, assesses the level of danger in each zone, and guides the user along a safe walking path. The YOLOv8n model was trained to recognize sidewalks, pedestrian crossings, bus stops, subway exits, and various obstacles on a smartphone connected to XR glasses and demonstrated an average processing time of 583 ms and an average memory usage of 80 MB, making it suitable for real-time use. The experiments were conducted on a 3.3 km route around Bokjeong Station in South Korea and confirmed that the system works effectively in a variety of walking environments, but recognized the need to improve performance in low-light environments and further testing with visually impaired people. By proposing an innovative walking assistance system that combines XR technology and artificial intelligence, this study is expected to contribute to improving the independent mobility of visually impaired people. Future research will further validate the effectiveness of the system by integrating it with real-time public transport information and conducting extensive experiments with users with varying degrees of visual impairment. Full article

Dockless bike-sharing (DBS) plays a crucial role in solving the “last-mile” problem for metro trips. However, bike–metro transfer usage varies by time and transfer flows. This study explores the nonlinear relationship between the built environment and bike–metro transfer in Shenzhen, considering different times and transfer flows while incorporating spatial dependence to improve model accuracy. We integrated smart card records and DBS data to identify transfer trips and categorized them into four types: morning access, morning egress, evening access, and evening egress. Using random forest and gradient boosting decision tree models, we found that (1) introducing spatial lag terms significantly improved model accuracy, indicating the importance of spatial dependence in bike–metro transfer; (2) the built environment’s impact on bike–metro transfer exhibited distinct nonlinear patterns, particularly for bus stop density, house prices, commercial points of interest (POI), and cultural POI, varying by time and transfer flow; (3) SHAP value analysis further revealed the influence of urban spatial structure on bike–metro transfer, with residential and employment areas displaying different transfer patterns by time and transfer flow. Our findings underscore the importance of considering both built environment factors and spatial dependence in urban transportation planning to achieve sustainable and efficient transportation systems. Full article

Bus transit plays a vital role in urban public transportation but often struggles to provide accurate and reliable departure times. This leads to delays, passenger dissatisfaction, and decreased ridership, particularly in transit-dependent areas. A major challenge lies in the discrepancy between actual and scheduled bus departure times, which disrupts timetables and impacts overall operational efficiency. To address these challenges, this paper presents a neural network-based approach for real-time bus departure time prediction tailored for smart IoT public transit applications. We leverage AI-driven models to enhance the accuracy of bus schedules by preprocessing data, engineering relevant features, and implementing a fully connected neural network that utilizes historical departure data to predict departure times at subsequent stops. In our case study analyzing bus data from Boston, we observed an average deviation of nearly 4 minutes from scheduled times. However, our model, evaluated across 151 bus routes, demonstrates a significant improvement, predicting departure time deviations with an accuracy of under 80 s. This advancement not only improves the reliability of bus transit schedules but also plays a crucial role in enabling smart bus systems and IoT applications within public transit networks. By providing more accurate real-time predictions, our approach can facilitate the integration of IoT devices, such as smart bus stops and passenger information systems, that rely on precise data for optimal performance. Full article

Currently, cities are the most vulnerable places on the planet to the effects of global change, both anthropogenic and climate-related, and this is not compatible with harmony and well-being regarding the economy, nature, and future generations. Young people have a unique potential to catalyze the transformative sustainable change that the planet needs now, as they are the first generation to grow up with tangible impacts of climate change. We tested a new strategy to empower young people to foster carbon neutrality in cities by engaging them in ecosystem services quantification and technological innovation to increase CO₂ sequestration in two Portuguese cities. The species with best performance for carbon sequestration were M. exelsa in Porto and O. europea in Loulé, and for air pollutant removal and hydrological regulation were P. hispanica in Porto and P. pinea in Loulé. Through the innovative advanced summer program SLI, a nature-based learning experience, young people developed two new concepts of technological solutions to accelerate city decarbonization by designing a hedge for air pollution hotspots and a biodevice to be placed at bus stops using autochthonous shrubs and mosses. Initiatives like SLI contribute to a greater awareness among young people about the drivers that brought us to the current climate emergency, motivating them towards more balanced lifestyles and creating innovative nature-based solutions towards a smart and sustainable city. Full article

With the level of development of the smart city, there are more and more research sub-areas in which the latest material and technological solutions are used, enabling the proper management and functioning of these cities. On the one hand, the introduced materials and technologies are designed to facilitate the functioning of residents both in the urban space and at home; on the other hand, the implemented solutions strive to be consistent with the principles of sustainable development. As shown in this article, reports on new technical and technological solutions and their positive and negative effects are strongly emphasized in publications on the development of smart cities. The most highlighted materials research in the smart city area concerns smart materials and their characteristics and applications. A research gap in this area is in the presentation of material solutions, particularly materials intended for the load-bearing structures of vehicles (electric vehicles, flying vehicles) or infrastructure elements (buildings, shelters, etc.) designed to increase the durability of the structure while reducing its weight. This paper aims to comprehensively present the most important research areas related to the functioning of smart cities in light of previous research, with particular emphasis on new material solutions used for thin-walled load-bearing structures in smart cities made of AHSS (advanced high-strength steel). These solutions are very essential for smart cities because their use allows for the installation of additional devices, sensors, transmitters, antennas, etc., without increasing the total weight of the structure; they reduce the number of raw materials used for production (lighter and durable thin structures), ensure lower energy consumption (e.g., lighter vehicles), and also increase the passive safety of systems or increase their lifting capacity (e.g., the possibility of transporting more people using transports at the same time; the possibility of designing and arranging, e.g., green gardens on buildings; etc.). AHSS-welded joints are usually characterized by too-low strength in the base material or a tendency to crack. Thus, the research problem is producing a light and durable AHSS structure using welding processes. The research presented in this article concerns the possibility of producing welded joints using the Metal Active Gas (MAG) process. The test methods include the assessment of the quality of joints, such as through visual examination (VT); according to the requirements of PN-EN ISO 17638; magnetic particle testing (MT); according to PN-EN ISO 17638; and the assessment of the selected mechanical properties, such as tensile strength tests, bending tests, and fatigue strength checks. These methods enable the selection of the correct joints, without welding defects. The results have a practical implication; advanced production technology for obtaining AHSS joints can be used in the construction of the load-bearing elements of mobile vehicles or parts of point infrastructure (shelters, bus stops). The obtained joint is characterized by adequate strength for the production of the assumed structures. The originality of the manuscript is the presentation of a new, cheaper, and uncomplicated solution for obtaining an AHSS joint with good mechanical properties. The application of the presented solution also contributes to sustainable development (lower fuel and material consumption use by mobile vehicles) and may contribute to increasing the load capacity of mobile vehicles (the possibility of transporting more people). Full article

In this paper we assess the transit quality of service (QoS) from a user’s standpoint, using smart data. A number of bus lines with different characteristics, operating in the Metropolitan Area of Athens, were chosen as a case study. The data used were gathered by an Automatic Passenger Counting (APC) system. APC technologies provide exact temporalized passenger counting along the line for each service, thus assisting to better understand causalities of delays and avoid operational problems. By employing archived APC data from buses running on crosstown routes between 15 January 2019 and 15 April 2019 we conducted a statistical analysis to explore occupancies and assess QoS, including under a social distancing scenario. The passenger distribution along the stops, the bus’s occupancy level, the stops that are maximum occupancy points and their rate of occurrence and, lastly, the passenger’s average trip length during the day and the week are examined. Full article

In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population. Full article

This work presents a real-life demonstration of 23 heavy-duty (HD) public electric buses (e-buses) in Jaworzno, Poland, with three lengths: 8.9 m, 12 m, and 18 m. The e-bus demo is based on the development of baseline e-buses to optimize the operational cost based on technical optimization. The demo aims to switch public transportation from internal combustion engine vehicles (ICEVs) to electric ones to minimize CO₂ emissions. The e-buses are equipped with standard charging solutions, which are plug-in charging with Combined Charging System Type 2 (CCS2, Combo 2) and pantograph-up (Type B). The CCS2 solution is used for overnight slow/normal charging (NC) in the depot of the e-bus operator, whereas the pantograph charging solutions are installed along the e-buses routes and used for fast charging (FC) when the e-buses are stopped for a short time. In Jaworzno, there are 20 chargers with CCS2 in the depot of the e-bus operator and 12 pantograph-up (Type B solution) fast-charging stations. This work studies the technical operations and operational costs of the e-bus fleet, and the impact of the NC and FC solutions on the Li-ion battery packs and on the grid. The uncoordinated/standard and coordinated charging (smart charging) based on load shifting were investigated to study the impact of e-bus fleet integration on the distribution grid. The exploited data in this study were collected from the data logger devices, which are installed on the e-buses and record over 46 signals. Data from over one year were collected, and some sample data were processed and analyzed to study the technical and economic operations of the e-bus fleet. Full article

Over the past decades, industrialization has resulted in radical economic development in Korea. The resulting urban sprawl and unsustainable development have led to considerable air pollution. In this study, using spatial regression models, we examine the effects of the physical and socioeconomic characteristics of neighborhoods on particulate matter (PM₁₀, PM_2.5), NO₂, CO, and SO₂ concentrations in the Daegu Metropolitan area. Results reveal the following: (i) the socioeconomic characteristics were not statistically significant regardless of the air pollutant type; (ii) the effects of the built environment characteristics of the neighborhoods were different for each air pollutant. Compared with other pollutants, PM_2.5 was affected more by the built environment. Concerning the neighborhoods’ main roads, the SO₂ concentration was higher, that of PM_2.5 was higher in neighborhoods with more bus stops, and those of CO and PM_2.5 were possibly higher in the neighborhood of industrial zones. In neighborhoods with parks and green areas, air pollutant concentrations are likely to be lower. When the total used surface of residential buildings was higher, the air pollutant concentrations were lower. Contextually, similar neighborhoods with more single-family houses seemed to have high pollution levels. Overall, this study is expected to guide policymakers and planners in making smart decisions for eco-friendly and healthy cities. Full article

A reliable estimation of public bicycle trip characteristics, especially trip distribution and duration, can help decision-makers plan for the relevant transport infrastructures and assist operators in addressing issues related to bicycle imbalance. Past research studies have attempted to understand the relationship between public bicycle trip generation, trip attraction and factors such as built environment, weather, population density, etc. However, these studies typically did not include trip distribution, duration, and detailed information on the built environment. This paper aims to estimate public bicycle daily trip characteristics, i.e., trip generation, trip attraction, trip distribution, and duration using points of interest and smart card data from Nanjing, China. Negative binomial regression models were developed to examine the effect of built environment on public bicycle usage. Totally fifteen types of points of interest (POIs) data are investigated and factors such as residence, employment, entertainment, and metro station are found to be statistically significant. The results showed that 300 m buffer POIs of residence, employment, entertainment, restaurant, bus stop, metro station, amenity, and school have significantly positive effects on public bicycle generation and attraction, while, counterintuitively, 300 m buffer POIs of shopping, parks, attractions, sports, and hospital have significantly negative effects. Specifically, an increase of 1% in the trip distance leads to a 2.36% decrease in the origin-destination (OD) trips or a 0.54% increase of the trip duration. We also found that a 1% increase in the number of other nearby stations can help reduce 0.19% of the OD trips. The results from this paper can offer useful insights to operators in better estimating public bicycle usage and providing reliable services that can improve ridership. Full article

In recent years, there has been a big data revolution in smart cities dues to multiple disciplines such as smart healthcare, smart transportation, and smart community. However, most services in these areas of smart cities have become data-driven, thus generating big data that require sharing, storing, processing, and analysis, which ultimately consumes massive amounts of energy. The accumulation process of these data from different areas of a smart city is a challenging issue. Therefore, researchers have started aiming at the Internet of vehicles (IoV), in which smart vehicles are equipped with computing and storage capabilities to communicate with surrounding infrastructure. In this paper, we propose a subcategory of IoV as the Internet of buses (IoB), where public buses enable a service as a data carrier in a smart city by introducing a neural network-based sustainable data dissemination system (NESUDA), where opportunistic sensing comprises delay-tolerant data collection, processing and disseminating from one place to another place around the city. The objective was to use public transport to carry data from one place to another and to reduce the traffic from traditional networks and energy consumption. An advanced neural network (NN) algorithm was applied to locate the realistic arrival time of public buses for data allocation. We used the Auckland transport (AT) buses data set from the transport agency to validate our model for the level of accuracy in predicted bus arrival time and scheduled arrival time to disseminate data using bus services. Data were uploaded onto buses as per their dwelling time at each stop and terminals within the coverage area of deployed RSU. The offloading capacity of our proposed data dissemination system showed that it could be utilized to effectively complement traditional data networks. Moreover, the maximum offloading capacity at each parent stop could reach up to 360 GB with a huge saving of energy consumption. Full article

Seoul Metropolitan City’s buses cater to more than 50% of the average daily public transportation use, and they are the most important transportation mode in Korea, together with the subway. Since 2004, all public transportation records of passengers have been stored in Seoul, using smart transportation cards. This study explores the environmental and psychological factors in implementing a smart transportation system. We analyze the switching behavior of traffic users according to traffic congestion time and number of transfers based on public transportation data and show that bus-use behavior differs according to the traffic information of users and the degree of traffic congestion. Information-based switching behavior of people living near bus stops induces people to change routes during traffic congestion. However, in non-congested situations, the original routes are used. These results can guide the formulation of policy measures on bus routes. We made it possible to continuously change the routes for certain buses, which were temporarily implemented due to traffic congestion. Moreover, we added a service that posts the estimated arrival time to major stops while reflecting real-time traffic conditions in addition to the bus location and arrival time information through the global positioning system. Full article

The 28 GHz band is one of the available bands in Frequency Range 2 (FR2), above 6 GHz, for fifth generation (5G) communications. The propagation characteristics at this frequency band, together with the bandwidth requirements of 5G communications, make it suitable for ultra-dense smart cell networks. In this paper, we investigate the performance of a radio channel in the presence of moving, scattering sources for a small cell at 28 GHz, located at a bus stop. To do so, measurements of the channel complex impulse response with a sweep time delay cross-correlation sounder were made and then used to examine the distribution of multipath components. Besides analyzing the delay spread caused by the channel, we also evaluate the impact on the Doppler spectrum (DS) caused by the vehicles passing near the bus stop. We show that delay components are grouped in clusters exhibiting exponential decay power. We also show that the DS varies with time as vehicles pass by, so the channel cannot be considered stationary. We propose an empirical DS model, where the model parameter should change with time to describe the non-stationary nature of the radio channel. We have also found that the DS with maximum spread is similar for channel contributions in different delay clusters. Full article

The COVID–19 pandemic led to restrictions on activities and mobility in many parts of the world. After the main peak of the crisis, restrictions were gradually removed, returning to a new normal situation. This process has impacted urban mobility. The limited information on the new normal situation shows changes that can be permanent or reversible. The impact on the diverse urban transport modes varies. This study analyzes the changes in transit ridership by line, the use of stops, the main origin–destination flows, changes in transit supply, operation time, and reliability of the city bus network of A Coruña. It is based on data from automatic vehicle location, bus stop boarding, and smart card use. Data from the first half of 2020 were compared to similar data in 2017–2019, defining suitable baselines for each analysis to avoid seasonal and day of week effects. The impact on transit ridership during the lockdown process was more significant than that on general traffic. In the new normal situation, the general traffic and the shared bike system recovered a higher percentage of their previous use than the bus system. These impacts are not uniform across the bus network. Full article