Human Artificial Intelligence Teaming (HAIT) is a significant topic that is dominating different research domains. One of these domains is the automotive industry, whereby automation is suggested to certain aspects of driving, while the driver can intervene and be aware of the decisions. Trust is a major issue; hence the AI collaborates with the human towards making a decision regarding different aspects of driving. The Internet of Vehicles (IoV) is a topic that can use HAIT in many of its applications. A major point of the HAIT application is the increase in the transparency of the AI process and trust is being built between the two teammates. In this paper, the goal is to offer a comprehensive review of HAIT and its significance, going deep into various representations to facilitate the development of automated vehicles systems. HAIT seeks to promote trust in automated automotive systems, particularly regarding data sourced from vehicle sensors. The human roles “in,” “on,” and “over” the loop within HAIT is provided, elucidating their pivotal contributions. Furthermore, ongoing academic contributions are reviewed integrating HAIT into the automotive sector, emphasizing the symbiosis between IoV and AI to forge unified solutions. The solutions have been separated according to their functionality and models used comprising Reinforcement Learning, Hidden Markov Models, Deep Learning and experiments as well as simulation based methods. The use of HAIT in automotive applications will pave the way to its utilisation in other disciplines such as aviation and maritime.

The logistics sector is overburdened trying to keep up with the demand for package shipments due to the impact of the growth of online sales on various platforms that make it easier for people to shop. However, there are still a few manual parts involves measuring and calculating the cargo volume. This research proposes a solution with a three-dimensional package measurement approach based on the Ultrasonics HC-SR04 sensor, Arduino, and DC motor to make volume calculation easy, cheap, and automatic. Volume measurement is equipped with a moving arm mechanism from 3 axes simultaneously. The system's ability was tested using a variety of package shape measurement scenarios. According to the measurement results, it can measure package dimensions and volume with an overall success rate of 82.66%, a flat box-shaped package success rate of 93.35%, a cylindrical shape success rate of 96.65%, and an irregular shape success rate of 33.33%. According to the test findings, it can be concluded that this method is highly effective to contribute to calculating the volume of regular-shaped package objects. This is because over 90% of the package shapes received are regular-shaped. However, measuring irregular shapes requires more enhancement to achieve accurate results.

The study evaluates the accuracy of LiDAR and CCTV technologies for vehicle and pedestrian count collection at a signalized intersection under varied weather conditions. Data collection occurred over a two-hour period during peak morning and evening hours using both technologies. The trajectory identification, entry and exit point determination, and anomaly filtering were utilized to analyze the vehicle counts. The pedestrian counts were carefully analyzed using LiDAR point cloud data and CCTV footage to monitor movements, in areas. Analysis of the data showed differences in vehicle and pedestrian counts depending on the weather conditions. Rainy weather had the variations while sunny conditions also showed differences with snowy weather having the least discrepancies. Interestingly the southbound through and eastbound right movements exhibited the variations in both vehicle and pedestrian counts. Despite challenges like spots and weather impacts, both LiDAR and CCTV technologies hold promise for collecting traffic data. It is vitally important that this study focuses on the limitations of current traffic control systems. The integrity of current systems and improving them is essential for traffic monitoring and enhancing safety measures at signalized intersections.

In order to facilitate effective communication between the V2V infrastructure, Vehicular Ad hoc Networks (VANETs) are utilized. Problems with routing, security, and node management are now plaguing VANETs that use vehicle-to-vehicle communication. New avenues for investigation into VANET routing, security, and mobility management have opened up because to intelligent transportation systems. Optimal routing for targeted traffic scenarios is one of the main issues in VANETs. Because VANET vehicles are constantly moving at high speeds, traditional protocols like AODV, OLSR, and DSDV are not suitable for this network. In a similar vein, swarm intelligence routing algorithms like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have had some success in optimizing routing in VANET scenarios involving dense, sparse, and realistic traffic. Furthermore, most metaheuristics methods have issues with slow convergence speed, premature convergence, and becoming stuck in local optima. Hence, a new metaheuristic approach to selecting the cluster head is suggested in the study, which employs an improved wild horse optimization algorithm (IWHO). The social behaviour of wild horses served as an inspiration for the development of IWHO. The ethics of the horse informed the proposed approach. The next step is to cluster the vehicles according to the reliability of linkages criteria. Subsequently, a maintenance phase is suggested for the purpose of redistributing vehicles within the clusters and updating the cluster heads. Lastly, a MATLAB simulation is run on a real-life urban setting to assess the efficacy of the proposed strategy. A 76% decrease in change rate is indicative of improved stability, while a 37% rise in throughput and a 19% decrease in average latency are indicators of improved performance.

This research article presents a comprehensive comparative analysis of driving patterns in Kuala Terengganu during various peak hours, shedding light on the dynamic nature of urban traffic flows. The study aims to provide valuable insights into the temporal variations in vehicular behaviour within this Malaysian city, with the ultimate goal of informing transportation planning and policy decisions. To achieve this objective, a diverse dataset of vehicle trajectories, collected through GPS tracking systems, was meticulously analysed. The data encompassed two different peak hours, including morning and evening peaks which is go-to-work time and back-from-work time. Several key parameters such as speed, acceleration, deceleration, and others were meticulously extracted and statistically compared across different timeframes. The findings of this study reveal striking disparities in driving behaviour during distinct peak hours. Evening peak hours, characterized by rush hour congestion, displayed significantly lower average speeds, higher traffic density, and increased instances of abrupt acceleration and deceleration. In contrast, morning peaks exhibited more fluid traffic conditions with higher average speeds and reduced congestion. This research provides a comprehensive understanding of the nuances of driving patterns in Kuala Terengganu, shedding light on the temporal dynamics of urban traffic. Finally, the insights generated by this comparative study could be useful for urban planners, traffic control bodies, and policy-makers to minimize peak-hour traffic by using the existing transportation infrastructure more effectively. Moreover, the methodology followed in this research could be useful as a model study approach for similar research in other urban areas, resulting in normalized and efficient urban transportation.

The cost of maritime intermodal freight transport is competitive against that of road transport on long corridors. The length of the major corridor in Java Island is medium. The land distance of the transport corridor in the island is relatively equal to the maritime distance. The objective of this research is to compare the cost of freight transport using maritime intermodal transport with the one using road transport in Java Island. The commodities, origin, destination, and potential freight flow are decided based on the secondary data analysis and the field surveys. The transport costs are estimated using secondary and survey data. The maritime intermodal transport is competitive on the time and distance related costs, while the road mode transport is competitive on the node charges and the first and last mile costs. There is a relatively close cost difference between the maritime intermodal transport and the road transport on the corridor of which the origin is close to the port. Hence, maritime intermodal transport may compete with road transport in the medium long corridor provided that the land and the maritime distances are relatively equal and the origin and the destination are close to ports.

In Indian cities, pedestrian fatalities and injuries have emerged as significant concerns. However, obtaining consistent and reliable crash information poses a significant challenge, particularly in mid-sized Indian cities. In this framework, this study aims to identify and quantify the critical factors influencing pedestrian perceived safety and satisfaction levels in a mid-sized Indian city with respect to diverse land use patterns. A dataset comprising perceptions of 2112 pedestrians regarding 'safety' and 'satisfaction level' has been collected and analyzed across six major intersections characterized by three distinct land use patterns—religious places, commercial areas, and educational hubs—in the central business district area of Patiala city, Punjab, India. With the help of ordered logit models, it has been concluded that the predominant land use pattern, the presence of a pedestrian signal, carriageway width, presence of a curve section at an intersection, vehicular speed, average value of time-to-collision (TTC) at the junction, pedestrian's gender and educational background, and trip purpose significantly affect pedestrians' perceived safety and satisfaction levels. The model outcomes are further constructively utilized to frame suitable policy interventions and recommend remedial measures to enhance pedestrian safety in Indian cities and comparable cities in other low- and middle-income countries (LMICs).

Rapid growth in the global electric vehicle (EV) market has sparked extensive research, with charging time remaining a critical concern. This study presents a descriptive analysis of research publications on EV charging time from 2017 to April 2024, highlighting trends, characteristics, and global perspectives and identifying research gaps. Scopus, an extensively utilized and frequently cited bibliographic database among the global research community, served as the primary data source for this investigation. Based on the Scopus database, the analysis reveals a growing interest in optimizing charging times, with notable peaks and troughs in publication trends. Interdisciplinary collaboration is evident, with engineering, computer science, and energy research leading the field. Key thematic clusters focus on charging infrastructure, battery optimization, and integration with renewable energy sources. Research gaps and emerging areas include fast-charging technology, battery management systems, and grid integration. A future research roadmap suggests investigating fundamental charging mechanisms, developing intelligent charging systems, exploring socio-economic implications, and fostering international collaborations. While progress has been made, further research is needed to address challenges and drive innovation in EV charging technology for sustainable transportation solutions.

Pedestrians are one of the essential parts of the transportation system. In order to encourage walking and reduce the use of personal vehicles, pedestrian’s facilities need to be provided in the campus since they are facing many problems. Pedestrian’s level of service (LOS) is the common approach to estimate the quality of pedestrian facilities. The highway capacity manual (HCM) defines six Pedestrians (LOS) namely LOS A, B, C, D, E and F, where A shows high Levels of comfort and capacity, while F represents a poor level of comfort and capacity. Reconnaissance and Field survey measurement was done to collect and study the pedestrians and sidewalks characteristic by using video camera and Measuring tape. Walking speed also was studied to find the speed at which pedestrians appear in term of (15th, 50th, 85th, 98th) percentile speed. The study showed that LOS for study areas ranges between (B) to (E) also it was observed that pedestrians used the roadway in moving which indicate the inefficiency of sidewalk capacity. As the accident in front of university gates increased during crossing, the study suggested (4) alternatives facilities for pedestrians crossing which are: stairways, escalator, ramps and underpasses.

This study seeks to determine the prospects for airline passenger mobility acting as freelance couriers. Analyzing four variables: passenger mobility, internet utilization, crowdshipping, and social crowdcourier environment from 200 aircraft passenger data surveyed and processed using SEM PLS. The results obtained show that passenger mobility has a significant effect on internet utilization by 0.942, crowdshipping by 0.527, and the social crowdcourier environment by 0.369. Internet use has a significant effect on crowdshipping by 0.441, social crowdcourier environment by 0.211. Crowdshipping has a significant effect on the social crowdcourier environment of 0.416. The research succeeded in revealing the impact and implications of passenger mobility and the use of the internet in crowdshipping on the influence of the social crowdcourier environment as well as offering new business opportunities, namely that airplane passengers can also become freelance couriers.

Container vessel accidents risk maritime safety and the environment, and understanding their causes and consequences is vital to developing effective preventive measures. This study analyzes the distribution of latent factors and active events related to container vessel accidents by applying the Human Factors Analysis and Classification System (HFACS) derived NASAFACS framework. The study employs a varied dataset comprising different types of container vessel accidents that occurred worldwide from 2010 to 2021. Findings suggest that latent factors, i.e., 'Preconditions,' are the predominant causative agents behind container vessel accidents, followed by 'Acts,' which involve active events leading to them. Damage to vessels is usually the most common outcome, and container loss and environmental pollution are sizeable. Collision incidents frequently involve both latent factors and active errors, while fire incidents typically are solely driven by latent ones; other accident types, including heavy weather damage, grounding, and allision incidents, show evidence of both latent and active factors; heavy weather damage incidents tend to exhibit higher incidences of environmental pollution than other accident types. This research offers unique insight into container vessel accidents, underlining the need for enhanced securing practices, accurate cargo declaration, and stricter cargo stowage compliance to improve safety and reduce pollution.

This study aims to assess the probability of unsafe operations on horizontal curves resulting from speed variation, employing both statistical analysis and machine learning (ML) techniques. The statistical analysis was conducted using Minitab software to assess the probability of non-compliance through the Monte-Carlo simulation method. Additionally, the research applied three ML classification models—a novel optimized version of the Random Forest (RF) classifier, Naive Bayes (NB), and Extreme Gradient Boosting (XGBoost). Nine curves with radii ranging from 700m to 2000m were selected from two rural roads in Egypt for the study. The evaluation of non-compliance probability on each curve involved contrasting the supply (design speed, a fixed value) with the demand (actual speed, characterized by actual speed distributions). Findings revealed that using the 85th percentile speed in the analysis, the probability of non-compliance during off-peak hours exceeded 50% for all curves except two, where it reached 100%. This indicates that approximately 100% of vehicles engage in unsafe operations during off-peak hours on these specific curves. Accuracy results of the ML classifiers showed that the proposed RF classifier performed exceptionally well with a perfect score of 1.0, followed by XGB and NB classifiers for all curves. A comparative analysis between the results of statistical analysis and ML in estimating curve safety suggests that ML outperforms statistical analysis, demonstrating its potential as a more reliable tool for assessing road safety on horizontal curves.