Abstract Connected and automated vehicle (CAV) is a transformative technology that has great potential to change our daily life. Therefore, CAV related research has been advanced significantly in recent years. This paper does a comprehensive review on five selected subjects that lie in the heart of CAV research: (i) inter-CAV communications; (ii) security of CAVs; (iii) intersection control for CAVs; (iv) collision-free navigation of CAVs; and (v) pedestrian detection and protection. It is believed that these topics are essential to ensure the success of CAVs and need to be better understood. For inter-CAV communications, this paper focuses on both Dedicated Short Range Communications (DSRC) and the future 5G cellular technologies; for security of CAVs, this paper discusses both passive and active attacks and the existing solutions; for intersection control, this paper summarizes the pros and cons of both centralized and decentralized approaches; for collision avoidance, this paper concentrates on four subareas: maneuverability, vehicle networking, control confliction, and motorcycles; for pedestrian detection, this paper covers sensor, radar, and computer vision based approaches. Under each topic, this paper not only shows the state-of-the-art, but also unveils potential future research directions. By establishing connections among these subjects, this paper shows how they interact with each other and how they can be integrated into a seamless user experience. It is believed that the literature covered and conclusions drawn in this paper are very helpful to CAV researchers, application engineers, and policy makers.
Abstract The intelligent transportation system (ITS) is made possible and practical due to vehicular ad hoc networks (VANETs) that helps improve drivers safeties and traffic efficiency on road by interchanging traffic-related information among vehicles and infrastructures. However, due to the open wireless access medium, the security and privacy of this information become quite critical in VANETs. The attackers could capture, intercept, alter, replay and delete the traffic-related information and could compromise the security of VANETs. Therefore, to ensure security and privacy of the traffic-related information in VANETs is the hot research area of nowadays. In this context, lots of research works have been done to secure vehicular communications. However, these works did not address the security issues in terms of security requirements, security attacks, and efficiency in performance, properly. In this paper, several authentication and privacy schemes have been classified and discussed their mechanisms, strengths and limitations, security requirements, attacks, and performance parameters. Finally, we identified some open research challenges in the domain of VANETs security.
In January 2025, the United States Department of Transportation mandated that transportation funding prioritize communities with marriage and birth rates above national averages. This study analyzes 2019–2023 American Community Survey data to examine the relationship between these rates and transportation burden using multivariate regression at the state and county levels. Lower state-level birth rates were linked with longer commutes, higher poverty, and higher education, while higher county-level birth rates correlated with shorter commutes. Marriage rates were influenced by poverty and access to cars. Spatial patterns show higher rates in rural areas and lower rates in urban centers.
Transportation and communications, Urban groups. The city. Urban sociology
In the past decade, unmanned aerial vehicles (UAVs) technology has developed rapidly, while the flexibility and low cost of UAVs make them attractive in many applications. Path planning for UAVs is crucial in most applications, where the path planning for UAVs in unknown, while complex 3D environments has also become an urgent challenge to mitigate. In this paper, we consider the unknown 3D environment as a partially observable Markov decision process (POMDP) problem and we derive the Bellman equation without the introduction of belief state (BS) distribution. More explicitly, we use an independent emulator to model the environmental observation history, and obtain an approximate BS distribution of the state through Monte Carlo simulation in the emulator, which eliminates the need for BS calculation to improve training efficiency and path planning performance. Additionally, we propose a three-dimensional spatial information compression (3DSIC) algorithm to continuous POMDP environment that can compress 3D environmental information into 2D, greatly reducing the search space of the path planning algorithms. The simulation results show that our proposed 3D spatial information compression based deep deterministic policy gradient (3DSIC-DDPG) algorithm can improve the training efficiency by 95.9% compared to the traditional DDPG algorithm in unknown 3D environments. Additionally, the efficiency of combining 3DSIC with fast recurrent stochastic value gradient (FRSVG) algorithm, which can be considered as the most advanced state-of-the-art planning algorithm for the UAV, is 95% higher than that of FRSVG without 3DSIC algorithm in unknown environments.
Transportation engineering, Transportation and communications
Abstract This study holds significant theoretical and practical importance for enhancing the safety management and operational efficiency of railway systems. Currently, there is a notable gap in standardized safety-level measurement methods across diverse railway operating entities. Conventional safety assessment approaches predominantly rely on qualitative analysis frameworks, which often fail to comprehensively address the multifaceted risk factors inherent in complex railway operating environments. To address these limitations, this study leverages historical operational data from participating railway companies to propose an advanced integrated quantitative methodology: the Global Safety Index (GSI)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This innovative approach quantifies railway safety conditions by systematically analyzing incident and accident data, integrating statistical modeling frameworks, data imputation algorithms, and comprehensive analytical protocols. The method enables detailed examination and interpretation of large-scale operational datasets within railway systems. The implementation of this quantitative framework has demonstrated substantial improvements in the accuracy of safety performance metrics. Furthermore, it offers robust technical support for developing risk mitigation strategies and optimizing safety performance in the railway sector. By employing systematic risk factor identification and data-driven safety quantification, this approach facilitates accident prevention, enhances risk early-warning systems, and provides evidence-based decision-making support. As research progresses and the accessibility of Chinese railway safety data increases, the analytical precision of this methodology can be further refined. Future applications may include in-depth analyses of Chinese railway risk event datasets, thereby offering strong technical support for the continuous elevation of safety standards in China’s railway operations.
Transportation engineering, Transportation and communications
This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS.
Since the emergence of the Coronavirus and its declaration as a global pandemic, the world has changed all sectors radically. Covid-19 has caused a very strong effect on daily life and adaptation to new ways of learning, working, and communicating. The smart city is one of the most important solutions that enable us to go on with daily life during the pandemic and beyond. The smart city employs the current technology and smart solutions to serve the community, and improve the services provided to the people, particularly in vital sectors such as health, education, electricity, transportation, communications, and others. This article deals with two innovative applications for the Internet of things in smart cities: The first refers to designing an intelligent health monitoring system, which aims to reduce the spread of Coronavirus infection from people to the medical staff, as well as reduce work pressure on the medical staff. The second application refers to monitoring electrical energy consumed by measuring and monitoring electrical parameters and energy consumption. In addition, it helps us to know the power line trespasser. Both systems are implemented by using sensors to gather data in real-time and then transmit it to the server.
Abstract Vehicular Ad-hoc Network (VANET) is the fundamental of intelligent transportation systems. Security and privacy are the important issues needed to be addressed. Existing schemes for privacy-preserving vehicular communications face many challenges, such as strong assumption on ideal tamper-proof device (TPD) and reducing the cost of computation and communication. In order to overcome the challenge, we propose a privacy-preserving authentication scheme with full aggregation in VANET, using certificateless aggregate signature to achieve secure vehicle-to-infrastructure (V2I) communications. The technique of aggregate signature can achieve message authentication and greatly save the bandwidth and computation resources. In addition, we use pseudonym to realize conditional privacy preserving and a trace authority (TRA) is responsible for generating pseudonym and tracking the real identity during the communication if it is necessary. When a vehicle enters an area under a new road side unit (RSU)’s coverage, we pre-calculate some data for once, thus the computation cost in sign phase can be reduced. The length of aggregated signature is constant which reduces the communication and storage overhead.
Airline ticket pricing is a complex and dynamic process influenced by various factors, including demand fluctuations, seasonal variations, and competitive strategies. Accurate price prediction is crucial for both airlines, to maximize revenue, and customers, to secure the best deals. Traditional methods often fall short of capturing the intricate and rapidly changing patterns of airfare pricing. With the advent of machine learning algorithms, there is a growing potential to enhance the accuracy and reliability of ticket price predictions. This paper aims to predict ticket prices based on airline flight data using ML algorithms and to compare the performance of ML algorithms. The secondary objective of this paper is to identify the main factors affecting airline ticket prices. The flight and ticket price datasets of THY and PGS that were obtained from open-access sources are used in this paper. The final dataset consists of 962 records for three months from June 1st, 2022 to August 30th, 2022 and includes 19 different variables. Statistical tests and ML algorithms were applied to the final dataset. This paper compares various ML models to predict airline ticket prices, considering performance metrics such as MAE, MSE, RMSE, and R2 during training and test phases. According to the model training and test results, the best algorithm is GPR with R2: 0.86 (training) and R2: 0.90 (test). The findings are consistent with existing literature, further validating the superior efficacy of certain models in specific contexts and demonstrating significant progress in the field. This paper contributes to the literature by comparing the effectiveness of various machine learning algorithms in predicting airline ticket prices, providing new and valuable insights into model performance and key price-determining factors.
Ashu Kedia, Dana Abudayyeh, Diana Kusumastuti
et al.
<i>Background</i>: Due to the remarkable growth in online retail sales in New Zealand, a large number of parcels are needed to be delivered to consumers’ doorsteps. Home deliveries in major New Zealand cities (e.g., Christchurch) typically occur between 9 a.m. and 6 p.m. on weekdays, when many home delivery attempts fail. This leads to adverse effects, such as vehicular traffic in residential areas and greater air pollution per parcel delivered. However, home deliveries outside of typical business hours (i.e., before 9 a.m. and after 5 p.m.) might be worthwhile to help subside the above issues. Therefore, this study investigated consumers’ preferences for receiving home deliveries during various times, such as early morning, morning, afternoon, late afternoon, and evening. <i>Methods</i>: The data used in this study were obtained via an online survey of 355 residents of Christchurch city. Non-parametric tests, namely the Friedman test, Wilcoxon signed-rank test, and ordinal logistic regression, were carried out to examine consumer preferences for the above time slots. <i>Results</i>: The results showed that consumers preferred the late afternoon (3 p.m. to 6 p.m.) time slot the most for receiving home deliveries. <i>Conclusion</i>: It appeared that the off-peak delivery option is less likely to draw the desired consumer patronage and is thus less likely to assist in lowering the number of unsuccessful home deliveries, the transportation costs incurred by service providers, traffic congestion, and pollution in urban areas.
Transportation and communication, Management. Industrial management
Anil Kumar Sutrala, M. Obaidat, Sourav Saha
et al.
With the tremendous growth of Information and Communications Technology (ICT), Cyber Physical Systems (CPS) have opened the door for many potential applications ranging from smart grids and smart cities to transportation, retail, public safety and networking, healthcare and industrial manufacturing. However, due to communication via public channel occurring among various entities in an industrial CPS (ICPS) with the help of the 5G technology and Software-Defined Networking (SDN), it poses several potential security threats and attacks. To mitigate these issues, we propose a new three-factor user authentication and key agreement scheme (UAKA-5GSICPS) for 5G-enabled SDN based ICPS environment. UAKA-5GSICPS allows an authorized user to access the real-time data directly from some designated Internet of Things (IoT)-based smart devices provided that a successful mutual authentication among them is executed via their controller node in the SDN network. It is shown to be robust against various potential attacks through detailed security analysis including the simulation-based formal security verification. A detailed comparative study with the help of experimental results shows that UAKA-5GSICPS achieves better trade-off among security and functionality features, communication and computation overheads as compared to other existing competing schemes.
This paper focuses on the routing algorithm for the communications between vehicles and places in urban vehicular ad hoc networks. As one of the basic transportation facilities in an urban setting, buses periodically run along their fixed routes and cover many city streets. The trajectory of bus lines can be seen as a submap of a city. Based on the characters of bus networks, we propose a bus-trajectory-based street-centric (BTSC) routing algorithm, which uses buses as the main relay to deliver messages. In BTSC, we build a routing graph based on the trajectories of bus lines by analyzing the probability of bus appearing on every street. We propose two novel concepts, i.e., the probability of street consistency and the probability of path consistency, which are used as metrics to determine routing paths for message delivery. This aims to choose the best path with higher density of busses and lower probability of transmission direction deviating from the routing path. In order to improve the bus forwarding opportunity, we design a bus-based forwarding strategy with ant colony optimization to find a reliable and a steady multihop link between two relay buses in order to decrease the end-to-end delay. The BTSC makes improvements in the selection of routing paths and a strategy of message forwarding. Simulation results show that our proposed routing algorithm has a better performance in terms of the transmission ratio, transmission delay, and adaptability to different networks.