Hasil untuk "Transportation and communications"

Haruka Kato, Kento Yoh

This study aimed to investigate the factors influencing older adults’ acceptance and use of demand-responsive transport (DRT) in an old New Town (old NT). The old NTs have a high density of older people, differing from urban or rural areas. The selected case was Senboku New Town (Senboku-NT), one of the largest old NTs in Japan. This study applied the unified theory of acceptance and use of technology model, moderating for age differences between older people and adults. Participants were recruited using a web-based questionnaire via a local smartphone application in Senboku-NT. As a result, we found a significant relationship between social influence and behavior intention for both older people (ßSI-BI = 0.347) and adults (ßSI-BI = 0.445). In addition, behavior intention was significantly correlated with performance expectancy for older people (ßPE-BI = 0.233) and with trust and safety for adults (ßTS-BI = 0.369). Regarding the importance of social influence, older people acquire face-to-face information via consultation meetings about DRT usage and referrals from family and friends. Therefore, our findings are directed to policymakers regarding the importance of emphasizing social influence to promote the use of DRT among older adults. The reason may be related to old NTs, which differ from urban and rural areas. These results provide interesting insights for generalizing our findings to DRT in old NTs.

Naser Zamani

Children are vulnerable road users. While many Australian parents are social media users and augmented reality effects are a huge trend in social media, there has been little research attention on the use of social media augmented reality for educating children in road safety. In this research, seven gamified social media augmented reality learning experiences were created about six road safety subjects to engage parents and educate children aged 5-9 years in the Australian Capital Territory region, Australia. The current research aimed to investigate the results of Facebook’s augmented reality educational interventions (advertisement campaigns) and analyse the perspectives of the parents who tried the learning experiences with their children. The results of Meta’s ads manager and Spark AR Hub analytics were used to evaluate the success of Facebook augmented reality advertisements. A questionnaire was developed to gather the perspectives of parents. Ten parents completed the questionnaire and were then interviewed to discuss their responses in more depth. Augmented reality Facebook advertisements reached 200,351 people and received 719,296 impressions and 3,218 clicks. All interviewees recommended the use of augmented reality gamified learning experiences to educate children about road safety. This study provides insights into how to use social media augmented reality to engage parents and educate children.

Panagiotis G. Tzouras, Lambros Mitropoulos, Christos Karolemeas et al.

Safety concerns expressed by micro-mobility users seem to practically constrain the use and flexibility of these new modes. This paper introduces a new, parametric agent-based modeling approach for simulating micro-mobility, using MATSim as a platform. Perceived safety is introduced as a factor, which affects travel behavior of micro-mobility modes in a car-dominated, heterogeneous, and perceived unsafe road network. To meet the research objectives, different scenarios are simulated, using the city of Athens, Greece as a test bed, to examine the model performance and applicability. A universal scoring function based on travel time, cost, and safety, is proposed. This study also discusses the concept of unsafe discontinuities. Simulation results show that an unsafe road segment of 500 m long is sufficient to induce evident behavioral variations and hinder the practical use of e-bikes and e-scooters in the base scenario. The establishment of a cycle-friendly road network leads to significant changes in the chosen routes and decreases the average trip distance for micro-mobility users. In this case, the simulation process is guided by perceived safety, primarily focusing on formulating safer paths for micro-mobility mode users. Nevertheless, the routing behavior of car users is not modified due to the spatial uniformity of safety perceptions in a car-dominated road environment.

Theodore L. Willke, Patcharinee Tientrakool, N. Maxemchuk

Shimaa A. Abdel Hakeem, Anar A. Hady, Hyungwon Kim

Rafał Burdzik, Wongelawit Chema, Ireneusz Celiński

Public transport during COVID-19 has been crucial in ensuring the safety and health of both passengers and staff while maintaining essential public transport services. Currently public transport is gradually resuming its operations, the pandemic's influence is expected to persist for a long time. The vast majority of studies in this aspect concern the likelihood of spreading the virus inside the means of transport during travel. Nevertheless, there exists a substantial body of articles addressing the manner in which passenger movement within public transport systems has been impacted by the safety concerns and altered satisfaction levels following the propagation of the pandemic. This paper presents a model that accurately represents how passengers move through different parts of a public transport system, such as a bus or train station and stops. This model takes into account factors like how long it takes for passengers to board and exit a vehicle, how they move through different parts of the stops, and how their movements are affected by factors like crowding and delays. To reduce the risk of transmission on public transport focused on bus stops areas, the research paper formulated a passenger flow model using simulation programs like PTV Vissim and FlexSim with assumptions on minimum distance and concept of area cross sections. These programs were used to simulate passenger exchange scenarios, using data collected from real data. The paper aimed to develop a passenger exchange model that could reduce the risk of infection. By understanding the passenger flow model and how passengers interact with the public transport system, we can implement effective measures to minimize the spread of COVID-19 and other infectious diseases.

Rajat Chaudhary, G. Aujla, S. Garg et al.

Industrial Internet of things (IIoT) is an emerging technology with a large number of smart connected devices having sensing, storage, and computing capabilities. IIoT is used in a wide range of applications such as transportation, healthcare, manufacturing, and energy management in smart grids. Most of the solutions reported in the literature for secure communications are not suitable for the aforementioned applications due to the usage of traditional TCP/IP-based network infrastructure. So, to handle this challenge, in this paper, a software-defined network (SDN) enabled multi-attribute secure communication model for an IIoT environment is designed. The proposed scheme works in three phases: 1) an SDN-IIoT communication model is designed using a cuckoo-filter-based fast-forwarding scheme, 2) an attribute-based encryption scheme is presented for secure data communication, and 3) a peer entity authentication scheme using a third party authenticator, Kerberos , is also presented. The proposed scheme has been evaluated using different parameters where the results obtained prove its effectiveness in comparison to the existing solutions.

Madhusudan Singh, Shiho Kim

Abstract Intelligent vehicle (IV) is an internet-enabled vehicle, commonly referred to as a self-driving car, which enables vehicles-to-everything communications. This communication environment is not secure and has several vulnerabilities. The major issues in IV communication are trustworthiness, accuracy, and security of received and broadcasted data in the communication channel. In this article, we introduce blockchain technology to build trust and reliability in peer-to-peer networks with topologies similar to IV communication. Further, we propose a blockchain-technology-enabled IV communication use case. Blockchain technology is used to build a secure, trusted environment for IV communication. This trusted environment provides a secure, distributed, and decentralized mechanism for communication between IVs, without sharing their personal information in the intelligent transportation system. Our proposed method comprises of a local dynamic blockchain (LDB) and main blockchain, enabled with a secure and unique crypto ID called intelligent vehicle trust point (IVTP). The IVTP ensures trustworthiness among vehicles. Vehicles use and verify the IVTP with the LDB to communicate with other vehicles. For evaluation, we simulated our proposed blockchain technology-based IV communication in a common intersection deadlock use case. The performance of the traditional blockchain is evaluated with emphasis on real-time traffic scenarios. We also introduce LDB branching, along with a branching and un-branching algorithm for automating the branching process for IV communication.

Bin Lin, Guoping Hu, Hao Zhou et al.

The traditional algorithm performing direction of arrival (DOA) estimation under the background of strong interference and colored noise has the problems of low estimation accuracy and small measurement targets. Based on the construction of a fourth-order cumulant (FOC) matrix to suppress colored noise, this paper adopts the extended noise subspace (ENS) algorithm and the fixed projection blocking (FPB) algorithm to estimate the DOA of weak targets. Firstly, a FOC matrix of the received signal vector is established to curb the noise component, and the eigenvalue decomposition is performed. Then, two approaches of weak signal DOA estimation are proposed. One approach is to merge the space where the strong interference steering vector lies into the noise subspace to construct an extended noise subspace, and then, the multisignal classification (MUSIC) algorithm is used to obtain the DOA estimation of the weak signal on the basis of the extended noise subspace. Another approach is to build the orthogonal projection matrix of the interference subspace as the interference blocking matrix, and the receiving array signal is preprocessed, and on the basis of it, the eigen decomposition is performed again to obtain the DOA information of the weak signal. Both algorithms make breakthroughs in the aperture limitation of the traditional algorithm, effectively expand the aperture, and promote the accuracy of estimation. The simulation tests the effectiveness of the proposed method.

Dominik Ziemke, Nico Kuehnel, Carlos Llorca et al.

Integrated land-use transport models are often accused of being too complex, too coarse or too slow. We tightly couple the microscopic land use model SILO (Simple Integrated Land Use Orchestrator) with the agent-based transport simulation model MATSim (Multi-Agent Transport Simulation). The integration of the two models is person-centric. It means, firstly, that travel demand is generated microscopically. Secondly, SILO agents can query individualized travel information to search for housing or jobs (and to choose among available modes). Consequently, travel time matrices (skim matrices) are not needed anymore. Travel time queries can be done for any time of the day (instead of for one or few time periods), any x/y coordinate (instead of a limited number of zones) and take into account properties of the individual. This way, we avoid aggregation issues (e.g., large zones that disguise local differences) and we can account for individual constraints (e.g., nighttime workers who cannot commute by public transport for lack of service). Therefore, the behavior of agents is represented realistically, which allows us to simulate their reaction to novel policies (e.g., emission-class-based vehicle restrictions) and to extract system-wide effects. The model is applied in two study areas: a toy scenario and the metropolitan region of Munich. We simulate various transport and land use policies to test the model capabilities, including public transport extensions, zones restricted for private cars and land use development regulations. The results demonstrate that the increase of the model resolution and model expressiveness facilitates the simulation of such policies and the interpretation of the results.

Chase P. Dowling, Thomas Maxner, Andisheh Ranjbari

This work applies an unsupervised clustering algorithm to blockface zoning data to identify typical curb configurations in a city. Data is obtained via the city of Seattle’s (Washington, USA) open data portal. To compare the distribution of blockfaces of varying length, all blockfaces are normalized where each zone type is presented as a percentage of the total blockface length in an order-preserving format. Common zone sequences are identified via k-modes clustering, where an optimal choice of k is cross-validated, quantifying the number of curb configurations to represent the majority of Seattle’s blockfaces. All documented code and data are open source and available at https://github.com/pnnl/curbclustering.

Jakob Geischberger, Norman Weik

Rail freight logistics is usually planned and analyzed using a macroscopic aggregated view on railway networks and train operations. As a result, disjoint tools have developed for simulating train operations which requires a detailed representation of track assets as well as the signaling architecture and supply chain networks in logistics analyzing the flow of goods where mode-specific capacity and traffic situations are incorporated in an aggregated manner. However, integrating the two areas could help evaluating railway-specific operative implications (such as conflicts and consequent delays) on the level of transport chains and thus single transport units instead of trains or network areas. The simulation tool SUMO is identified to meet criteria from both disciplines. It is shown how a respective methodology can be realized in SUMO to create such a simulation model. A use case of northwestern Germany shows by the means of exemplary container trajectories that the two simulative approaches can be merged.

Christoff A. Conradie, Leila L. Goedhals-Gerber, Frances E. van Dyk

Background: Fruit is an important export commodity for South Africa and accounts for 35% of its agricultural exports. South Africa is the second largest citrus exporter in the world, behind Spain. Maintaining the postharvest cold chain is key to ensuring that fruit quality meets export standards. Objectives: The main objectives of this research were to investigate the frequency, location, magnitude and duration of temperature deviations in the South African leg of the clementine and navel orange cold chain. Method: Temperature trials were conducted on two consignments of clementines and two consignments of navels. Each consignment contained 36 iButtons®, of which 18 measured pulp temperature and 18 measured ambient temperature. Data were successfully retrieved from 130 of the 144 iButtons®. Results: This research identified areas where the temperature went outside the prescribed range along the South African portion of the export cold chain of navel oranges and clementines from Citrusdal, South Africa to the Port of Newark, United States of America. Conclusion: The temperature incidents identified could result in a breach of the cold sterilisation (steri) protocols and quality defects. Recommendations were made to address these deficiencies to improve the South African citrus industry’s global competitiveness. Contribution: This research allowed the citrus industry to investigate and adjust current cold chain practices to improve the integrity of the entire export cold chain, potentially resulting in a higher quality product and increased revenue.

David Hummels, David Hummels, Dana Rapoport et al.

Nizar Msadek, Ridha Soua, T. Engel

Even in the face of strong encryption, the spectacular Internet of Things (IoT) penetration across sectors such as e-health, energy, transportation, and entertainment is expanding the attack surface, which can seriously harm users' privacy. We demonstrate in this paper that an attacker is able to disclose sensitive information about the IoT device, such as its type, by identifying specific patterns in IoT traffic. To perform the fingerprint attack, we train machine-learning algorithms based on selected features extracted from the encrypted IoT traffic. Extensive simulations involving the baseline approach show that we achieve not only a significant mean accuracy improvement of 18.5% and but also a speedup of 18.39 times for finding the best estimators. Obtained results should spur the attention of policymakers and IoT vendors to secure the IoT devices they bring to market.

G. Laporte, Frédéric Meunier, Roberto Wolfler Calvo

Nadine Farquharson, Joash Mageto, Hemisha Makan

Background: Evolution in global supply chain has created numerous complexities especially in the transportation of freight. Some of the complexities include ever increasing operational cost, long lead-times, limited end-to-end visibility and raw material supply disruptions because of adverse weather conditions. To improve processes and keep abreast of competition, it is vital for businesses to leverage their operations on current technologies. Internet of things (IoT) technology is one of the innovative technologies that can bring about radical transformations to freight transportation. Despite the promising capabilities of IoT, research on its application and effect on road freight sector in developing economies is scanty. Objectives: The purpose of this article is to establish the likely effect of IoT on the road freight sector. This article identifies IoT technologies used in road freight and establishes the relationship between the drivers and benefits of implementing IoT technologies. Method: Structured questionnaires were sent to employees working within the road freight industry within South Africa. The data were subjected to factor analysis for dimension reduction. Regression analysis helped to establish the relationship between drivers and benefits of IoT. Results: The benefits of IoT were operational effectiveness and improved decision making. The drivers of implementing IoT were identified as asset visibility and the need for real-time information sharing. The main effect of IoT on road freight sector is increased asset visibility. The challenges impeding implementation of IoT include high cost of installation, skills gap, fear of hacking and cyberattacks. Conclusion: Road freight transport managers are advised that IoT can be a strategic tool that uses smart sensor technologies that provide visibility of assets to reduce operational costs and improve decision making. The article contributes to logistics management literature by enumerating the IoT technologies used in the road freight sector in South Africa. It also highlights that IoT provides end-to-end visibility resulting in improved decision-making for optimal operations.

Qian Luo, Lin Zhang, Zhiwei Xing et al.

The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.

Qian Sun, Steven Chien, Dawei Hu et al.

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.

M. Barghamadi, A. Kapoor, C. Wen