Search Results (28)

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities. Full article

While studies have typically examined the driving habits of young drivers living in large urban cities, few have examined the habits of young drivers living in smaller cities with large rural surrounding areas. Three surveys were disseminated to 193 young drivers, 65 police officers, and 62 driving instructors to examine the driving habits and challenging driving situations young drivers experience. Almost a fifth (18.1%) reported consuming alcohol prior to driving; alcohol consumption prior to driving was significantly associated with eating food/drinking beverages while driving, cellphone use, and speeding. The most challenging situations young drivers reported were night driving, encountering wild animals on the road, and driving in extreme weather conditions (e.g., ice, snow). Driving instructors reported that young drivers had challenges with lane positioning, speed control, and navigating traffic signs and signals. Additionally, police officers reported issuing tickets to young drivers primarily for failure to stop, distracted driving, impaired driving, and speeding. Young drivers living in smaller cities and rural communities have unique challenges, including interactions with wildlife, driving on gravel roads, and driving in poor weather and road conditions (e.g., ice, snow). Opportunities for young drivers to be exposed to these scenarios during driver training are critical for increasing awareness of these conditions and reducing crash risk. Full article

Tatra National Park (TNP) is one of the most popular national parks in Poland. The purpose of this study was to examine changes in the number of tourists visiting the Park each year from 1993 with a special focus on the COVID-19 period. The main part of this study focused on tourist traffic data for the period from 1993 to 2023. Daily, monthly, and annual data were examined. The source of most of the data is park entry ticket sales. The largest number of tourists entering TNP in the period of 1993–2022 was recorded in 2021 at 4,788,788. Tourist traffic in TNP is concentrated on so-called long weekends in May and June. An examination of data from 2010, 2015, and 2021 shows that tourist volumes on the long weekend of 1–3 May be up to 40 times larger than those on other weekends in May. On the other hand, long weekends in June can attract eight times more tourists relative to the average other weekends in June. The number of tourists engaging in hiking, climbing, spelunking, and ski touring declined during the COVID-19 pandemic in 2020. However, the number of ski tourers in TNP in 2021 was about four times larger than the total between 2015–2022. Data on traffic patterns are key in designing, implementing, and measuring the efficiency of solutions for sustainable management for both the peak usage periods and future patterns in tourism. Full article

Transportation is fundamental, granting access to goods, services, and economic opportunities. Ensuring sustainable transportation, especially in vehicular modes, is crucial for the pillars of social, economic, and environmental sustainability. High-traffic countries, like the United Arab Emirates (UAE), grapple with significant challenges to this end. This study delves into the repercussions of traffic-related incidents on UAE road users and their intricate links to the social and economic dimensions of sustainability. To achieve this, this work examines the influential demographic factors contributing to incidents, utilizing artificial neural network models to predict the likelihood of individuals experiencing traffic tickets and accidents. Findings reveal associations between gender, driving frequency, age, nationality, and reported incident frequency. Men experience more accidents and tickets than women. Age exhibits a negative linear relationship with incident occurrence, while driving experience shows a positive linear relationship. Nationalities and cultural backgrounds influence road users’ adherence to traffic rules. The predictive models in this study demonstrate their high accuracy, with 93.7% precision in predicting tickets and 95.8% in predicting accidents. These insights offer valuable information for stakeholders, including government entities, road users, contractors, and designers, contributing to the enhancement of the social and economic aspects of road sustainability. Full article

For smart cities, the issue of how to solve traffic chaos has always attracted public attention. Many studies have proposed various solutions for traffic flow prediction, such as ARIMA, ANN, and SVM. With the breakthrough of deep learning technology, the evolutionary models of RNN, such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) models, have been proven to have excellent performance in traffic flow prediction. By using LSTM and GRU models, we explore more features and multi-layer models to increase the accuracy of traffic flow prediction. We compare the prediction accuracy of LSTM and GRU models in urban traffic flow prediction. The data collected in this study are divided into three categories, namely “regular traffic flow data”, “predictable episodic event data”, and “meteorological data”. The regular traffic flow data source is the “Vehicle Detector (VD) data of Taipei Open Data Platform”. Predictable episodic event data are predictable as non-routine events such as concerts and parades. We use a crawler program to collect this information through ticketing systems, tourism websites, news media, social media, and government websites and the meteorological data from the Central Meteorological Bureau. Through these three types of data, the accuracy in predicting traffic flow is enhanced to predict the degree of traffic congestion that may be affected. Full article

Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, incorporating edge computing and knowledge distillation to address these challenges. Recognizing the intricate relationship between the accuracy of deep learning algorithms and their complexity, our model employs knowledge distillation to compress deep learning. Teacher–student models were initially chosen and constructed in the cloud, focusing on developing structurally complex teacher models that incorporate passenger and production characteristics. The knowledge acquired from these models was then transferred to a student model, characterized by weaker learning capabilities and a simpler structure, facilitating the compression and acceleration of an intelligent ranking model. Following this, the student model underwent segmentation, and certain computational tasks were shifted to end devices, aligning with edge computing principles. This collaborative approach between the cloud and end devices enabled the realization of an intelligent ranking for product listings. Finally, a random selection of the passengers’ travel records from the last five years was taken to test the accuracy and performance of the proposed model, as well as to validate the intelligent ranking of the remaining tickets. The results indicate that, on the one hand, an intelligent recommendation system based on knowledge distillation and edge computing successfully achieved the concurrency and timeliness of the existing remaining ticket queries. Simultaneously, it guaranteed a certain level of accuracy, and reduced computing resource and traffic load on the cloud, showcasing its potential applicability in highly concurrent recommendation service scenarios. Full article

The intelligent transport system (ITS) has transformed urban transportation, enhancing daily commutes with services like congestion management, vehicle crash prevention, traffic control, roadside safety, breakdown assistance, ticket booking, vehicle registration, and insurance. However, in urban bus transportation, the ITS faces security threats, such as data forgery and manipulation. To counter these challenges, a blockchain-based framework for bus transportation approval is proposed, ensuring data integrity and security. The framework’s performance is evaluated based on processing time, central processing unit (CPU), graphical processing unit (GPU), cloud usage, and memory consumption, and compared to Ethereum and Aurora testnet, in terms of gas cost, security, and performance. Stochastic algorithms, including the genetic algorithm and Tabu search, are used for time complexity analysis, to obtain an optimized solution. The decision-making trial and evaluation laboratory (DEMATEL) analysis is also performed to assess factors like transaction costs, execution time, memory consumption, and security. The results show that execution time, memory consumption, and processing time are crucial, while transaction cost, reliability, and transparency positively impact the system’s effectiveness. By reducing the risk of false data presentation and ensuring accurate records, the proposed framework contributes to a more efficient and reliable transportation system. Full article

Gate machines, such as ticket gates in stations and secure gates in office buildings, are very common in people’s daily lives. On the one hand, the passage between the gates is not wide enough for pedestrians to pass through, which may affect the traffic efficiency of the crowd; on the other hand, the gates make pedestrians move more orderly and smooth and may speed up evacuation. Whether the gates benefit or hinder the movement and evacuation of a crowd is not clear for now. This paper studies the effects of gate machines on crowd traffic based on simulations using the modified social force model. Three simulation scenarios are considered, including the absence of any gate machines, the presence of gate machines without invisible walls, and the presence of gate machines with invisible walls. Normal and evacuation situations are distinguished by whether or not a pedestrian pauses for a while in front of the gates. The influences of factors such as the number of passages, exit width, and the number of pedestrians on crowd traffic are analyzed. Simulation results show that for different exit widths, there is a corresponding optimal number of passages to make the evacuation efficiency of the crowd the highest. The conclusions of this paper can provide some suggestions for the setting of the gate machines and the development of evacuation strategies. Full article

Despite an observed daytime front-seat seat belt use that exceeds 90%, nearly half of motor vehicle occupants who die in New York State (NYS) each year are not wearing a seat belt. Crash outcomes were examined by occupant, vehicle, environmental and traffic enforcement patterns related to the annual Click It or Ticket high visibility seat belt enforcement campaign. Three periods of enforcement were examined: pre-enforcement, peri-enforcement (during/immediately after), and post-enforcement. Of the 14.4 million traffic citations, 713,990 (5.0%) were seat belt violations. Relative risk with 95% CI was assessed using deaths from the Fatality Analysis Reporting System (FARS) and SAS Glimmix 9.4 software. Mortality was lower peri-enforcement (32.9%) compared to pre- (40.9%) or post-enforcement (37.1%) (p < 0.001) and tended to be elevated in low enforcement response areas (43.6%). Fatalities were 30% lower (0.7, 95% CI 0.6–0.9) during peri-enforcement in models adjusted for demographics, law coverage, enforcement response, rural, weekend, impairment, speeding, and vehicle type. Adjusted mortality was higher in rural (1.9, 1.6–2.6), alcohol-involved (1.8, 1.4–2.9), and speeding-involved (2.0, 1.7–2.5) crashes. Peri-enforcement alcohol- and speed-involved fatalities tended to be lower in restrained, unrestrained and occupants missing belt status. The finding of lower mortality in both belted and unbelted occupant’s peri-enforcement—in the context of fewer fatal speed and alcohol-involved crashes—suggests that the mechanism(s) through which high visibility seat belt enforcement lowers mortality is through impacting multiple risky driving behaviors. Full article

A smart parking system (SPS) is an integral part of smart cities where Internet of Things (IoT) technology provides many innovative urban digital solutions. It offers hassle-free parking convenience to the city dwellers, metering facilities, and a revenue source for businesses, and it also protects the environment by cutting down drive-around emissions. The real-time availability information of parking slots and the duration of occupancy are valuable data utilized by multiple sectors such as parking management, charging electric vehicles (EV), car servicing, urban infrastructure planning, traffic regulation, etc. IPv6 wireless mesh networks are a good choice to implement a fail-safe, low-power and Internet protocol (IP)-based secure communication infrastructure for connecting heterogeneous IoT devices. In a smart parking lot, there could be a variety of local IoT devices that consume the occupancy data generated from the parking sensors. For instance, there could be a central parking management system, ticketing booths, display boards showing a count of free slots and color-coded lights indicating visual clues for vacancy. Apart from this, there are remote user applications that access occupancy data from browsers and mobile phones over the Internet. Both the types of data consumers need not collect their inputs from the cloud, as it is beneficial to offer local data within the network. Hence, an SPS with multiple data consumers needs an efficient communication model that provides reliable data transfers among producers and consumers while minimizing the overall energy consumption and data transit time. This paper explores different SPS communication models by varying the number of occupancy data collators, their positions, hybrid power cycles and data aggregation strategies. In addition, it proposes a concise data format for effective data dissemination. Based on the simulation studies, a multi-collator model along with a data superimposition technique is found to be the best for realizing an efficient smart parking system. Full article

To design a more suitable scheme under different conditions so that subway stations can play their role better, this study investigated the construction elements of subway stations in residential areas. Metro stations in residential areas are generally located at the intersection of urban roads. As the “handover space” of the city, the construction principle should be based on people’s experience. As the core basis for traffic planning and future operation management, construction of subways in residential areas should take into consideration factors such as trip volume, distribution, and mode selection of residents, and be determined based on such mathematical models as the unit factor method, gravity model, and disaggregated analysis method. Station site selection is based on the point, line, and surface elements, and the importance of a station in the line network is judged by degree and betweenness centrality; the accessibility of the line network is determined by connectivity. In this study, the influencing factors of residential area subway station construction are divided into construction elements and site selection. Internal construction elements of stations include station entrances and exits, escalators, ticket machines, and transfer routes, and the conclusion of the mathematical model is used to select or give opinions about the internal construction elements of the subway. The point, line, and surface elements and the connection relationship between the subway and buses are used to determine the site selection of the subway. Furthermore, this paper discusses the three elements that affect the construction of the subway, comprehensively considers the functional requirements of the subway, and makes reasonable adjustments to each element. Finally, the requirements for the elements of the subway construction are determined. Full article

With China’s urban renewal, parks have developed into significant green recreational areas in cities. This paper analyzed social media texts and compared the evaluation outcomes of the 50 most popular urban parks in Beijing from various perspectives, such as the characteristics of various groups of people, park types, and the spatial and temporal distribution characteristics of recreational activities. The importance–performance analysis method was used to analyze the main factors affecting visitors’ satisfaction with parks. The research found the following: (1) Positive evaluation of parks was related to environmental construction, event organization, etc., and negative evaluations focused on ticket supply, consumer spending, etc. (2) Visitors of different genders and from different regions focused on different aspects of parks. (3) In terms of traffic accessibility, historical and cultural display, parent–child activity organization, and ecological environment experience, people had diverse demands from various types of parks. (4) People were more likely to visit parks located within the range of all green belts in springs and parks located in the second green isolation belt in the fall. (5) The number of non-holiday reviews of parks was higher than that of holiday reviews. (6) Managers could improve visitor satisfaction by improving the infrastructure and management of parks. Full article

The air transport market has been exposed to the biggest crisis in connection with the COVID-19 pandemic over the last two years. Many airlines have tried to stay in the market, but the impact of various factors was so strong that some airlines were forced to stop of the operation. In this way, the pandemic verified the fundamental pillars of airline business models and at the same time pointed out weaknesses in the entire air transport system. Flexibility has become one of the most important features for sustaining any business. The article is focused on the complex processing of the issue of the virtual airline and proposes the concept of a virtual air carrier, considering the current starting points of the market, on which it will have sufficient potential to establish itself, at the level of EU. At the same time, it proposes possibilities for the development of air transport in the Slovak Republic through the introduction of a virtual air carrier as a tool for revitalizing the air transport market in the Slovak Republic. Based on the results of the research, the article defines how and under what conditions a potential virtual carrier could operate, from ticket sales to possible cooperation with other airlines. The aim of the article is to demonstrate that the model of virtual air carrier is a solution for maintaining a stable level of air traffic in several countries in the EU that have lost their air carriers during the pandemic. Full article

Traveling during off-peak season can mean cheaper flights, cheaper hotels, and the chance to see a destination at a less frenetic time of year. To alleviate the congestion of roads and tourist attractions, a better demand management plan is needed to guide tourists to avoid travel during holidays. This study takes holiday tourists’ peak travel avoidance behavior as the research object, and a Nested Logit (NL) model of travel time and destination joint decisions was established based on Utility Maximization Theory. Model calibration and elastic analysis were carried out using Revealed Preference/Stated Preference (RP/SP) survey data. Results show that tourist attributes such as the number of tourists traveling together, travel companion, duration of the visit, the number of previous visits, tourism motivation, type of tourist attraction, quality grade of tourist attraction, and degree of congestion significantly influence destination decisions. Travel scope, travel duration, age, and other factors significantly influence travel time decisions. The traffic congestion around tourist attractions, holiday admission ticket prices, and non-holiday admission ticket prices significantly influence travel time and destination decisions. Holiday admission ticket price increases have a strong impact on the decision to change the travel destination, while non-holiday admission ticket discounts have a weak impact on travel time decision behavior. The findings of this study offer a theoretical basis for holiday travel management and tourism management. It is practical and significant to reasonably guide tourists to travel during the off-peak season and to understand the travel needs and characteristics of holiday tourists, thus adjusting the distribution of holiday tourist flow. Full article

Light rail transit (LRT) systems are vital aspects of the worldwide endeavor to achieve transport sustainability and have been essential in enhancing the economies of urban areas. Issues such as pollution, the risk of road accidents, and traffic congestion could be resolved using this progressive alternative. The statistics showed that only 20% of the commuters in Malaysia use public transport, including LRT, and 80% use private transportation. It is relatively low compared to other Asian countries. High-quality service is essential to improve users’ perceived satisfaction with the provided services and increase LRT ridership. The objective of the present study is to acquire an understanding of which factors are crucially influential on users’ perceptions of satisfaction. In-person questionnaires were utilized to obtain the information for this paper, with a total of 417 LRT riders in Malaysia’s Klang Valley surveyed. This study adopted the factor analysis, correlation test, and artificial neural network (ANN) model. Eight elements related to the quality of service were extracted to ascertain how they influenced the perceived satisfaction of users: information signs, ticket-based services, amenities, safety, employee performance, speed, comfort, and the service details available to riders. Each factor was significantly related to the perceptions of satisfaction, according to the correlation test. Finally, the ANN model shows that the dominant factors determining the LRT users’ perceived satisfaction are the signage, amenities, and provision of information. The findings of this research should benefit the providers of services, policy makers, and planning departments by enabling them to formulate successful approaches that ensure user satisfaction is enhanced and the number of riders on the LRT increases. Full article