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Mobile robots with independent wheel control face challenges in steering precision, motion stability and robustness across various wheel and steering system types. This paper aims to propose a coordinated torque distribution control approach that compensates for tracking deviations using the longitudinal moment generated by active steering.

Building upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment. An adaptive robust control scheme is developed to achieve accurate and stable generation of the desired total moment value. Furthermore, quadratic programming is used for torque allocation, optimizing maneuverability and tracking precision by considering the robot’s dynamic model, tire load rate and maximum motor torque output.

Comparative evaluations with autonomous steering Ackermann speed control and the average torque method validate the superior performance of the proposed control strategy, demonstrating improved tracking accuracy and robot stability under diverse driving conditions.

When designing adaptive algorithms, using models with higher degrees of freedom can enhance accuracy. Furthermore, incorporating additional objective functions in moment distribution can be explored to enhance adaptability, particularly in extreme environments.

By combining this method with the path-tracking algorithm, the robot’s structural path-tracking capabilities and ability to navigate a variety of difficult terrains can be optimized and improved.

The ongoing adverse effects of climate change have led scientific think tanks to aim towards achieving net-zero greenhouse gas (GHG) emissions targets with affordable and clean energy (Sustainable Development Goal 7). One of the significant contributors to the escalating emissions rate is the use of conventional vehicles. The uptake of electric vehicles (EVs) is a promising solution for a cleaner economy. However, increased penetration poses various challenges to the power system. There is a need to explore alternatives, such as hydrogen fuel cell vehicles (HFCVs), to use the advantages of both electric and conventional vehicles and bridge the gap between them. However, the transition to hydrogen-based transport requires intensive study of its key benefits and issues, the actions that need to be taken to achieve a changeover concerning light and heavy vehicles and whether such kind of transformation is likely or even possible. This chapter highlights the brief history and mechanics of HFCVs. It further analyses the various benefits and challenges which the technology poses. Additionally, it addresses critical questions regarding the feasibility of the shift towards hydrogen fuel to satisfy the world's rapidly growing energy needs and meet net-zero targets based on real-life applications. This chapter will be a valuable resource for further research, development and education efforts in HCFVs to assist in the rapidly growing transportation needs for automobiles and other vehicles.

Bangladesh is one of the leading countries that has been facing serious air pollution issues, with an exponentially higher death rate attributed to it than other environmental pollution. This study aims to identify the sources and dynamics of particulate matter (PM) pollution across different micro-environments in Rajshahi City.

PMs’ concentration data were collected from 60 sampling stations, located across the six micro-environments of the study area, throughout the year using “HT 9600 Particle Counter.” To assess the level of pollution, the air quality index (AQI) was calculated, and different methods, including observation, group discussion, interview and questionnaire survey, were used to identify the pollution sources.

Both PM_2.5 and PM₁₀ exhibit varied concentrations in different micro-environments, and the area covered by different AQI classes differs considerably throughout the year. The monthly average concentration of PM_2.5 and PM₁₀ was highest in January, 200 and 400 µg/m³ and was lowest in September, 46 and 99 µg/m³, respectively. Among the total 1,440 observations, 853 observations (59.24%) exceeded the national standard. Based on the pollution level, different months and micro-environments in the city have been ranked in descending order as January > December > February > March > April > November > October > May > June > July > August > September and traffic > commercial > industrial > residential > green cover > riverine environment.

Although numerous research has been conducted on air pollution in Bangladesh, the authors are certain that no attempt has been made to address the issue from a multi- micro-environmental perspective. This makes the methodology and findings truly unique and significant in the context of air pollution research in Bangladesh.

Gunung Mulu National Park (GMNP) is one of the two protected areas with UNESCO World Heritage Site (WHS) status in Malaysia. Until now, this area can only be accessed by tourists via air and water transportation only. Recently, the government has proposed the construction of a road connecting GMNP to several areas, including Miri City through a high-impact infrastructure project. However, this project might instigate the potential benefits and challenges in terms of tourism development and community well-being. As custodians of the park, their support for this initiative needs to be dismantled so that the management of the national park can still be implemented holistically and does not jeopardise the current UNESCO status of WHS. WHS is UNESCO-designated area of cultural, historical, scientific, or other significant value, legally safeguarded by international agreements, and preserved for the benefit of future generations due to its exceptional value to humanity. Thus, this study aims to examine challenges and socioeconomic impacts of the proposed road among key informants' perspectives at Long Terawan Village, Long Iman Village, Batu Bungan Village and GMNP Headquarters. The study was conducted via in-depth interviews with 10 community members residing in the settlement, including a tribal chief, boatman, lodging operator, park guide and farmer, all of whom are professionals and representatives of the local community. They were designated as key informants on account of their extensive engagement and development within the community. Through a thematic analysis, their perspectives on the proposed roads to be built in the area were elucidated. The study offers pragmatic understanding of socioeconomic impact assessment, which could inform strategic decision-making by incorporating information regarding potential benefits and challenges of the proposed road construction to an isolated protected area.

This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and the analysis will be done based on the metrics and datasets.

In this study, different papers were analyzed about the issues of obstacle detection (OD) and sign detection. This survey reviewed the information from different journals, along with their advantages and disadvantages and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of autonomous vehicles and is obliged to accurately identify various TS.

The review of different approaches based on deep learning (DL), machine learning (ML) and other hybrid models that are utilized in the modern era. Datasets in the review are described clearly, and cited references are detailed in the tabulation. For dataset and model analysis, the information search process utilized datasets, performance measures and achievements based on reviewed papers in this survey.

Various techniques, search procedures, used databases and achievement metrics are surveyed and characterized below for traffic signal detection and obstacle avoidance.

The purpose of this paper is to introduce a three-echelon multimodal transportation problem applied to a humanitarian logistic case study that occurred in Mexico.

This study develops a methodology combining a transshipment problem and an adaptation of the multidepot heterogeneous fleet vehicle routing problem to construct a mathematical model that incorporates the use of land-based vehicles and drones. The model was applied to the case study of the Earthquake on September 19, 2017, in Mexico, using the Gurobi optimization solver.

The results ratified the relevance of the study, showing an inverse relationship between transportation costs and delivery time; on the flip side, the model performed in a shorter CPU time with medium and small instances than with large instances.

While the size of the instances limits the use of the model for big-scale problems, this approach manages to provide a good representation of a transportation network during a natural disaster using drones in the last-mile deliveries.

The present study contributes to a model that combines a vehicle routing problem with transshipment, multiple depots and a heterogeneous fleet including land-based vehicles and drones. There are multiple models present in the literature for these types of problems that incorporate the use of these transportation modes; however, to the best of the authors’ knowledge, there are still no proposals similar to this study.

This paper's purpose is twofold. First, based on a case study, it aims to comprehend the consequences of COVID-19 on the demand and supply shocks of the freight transportation system in Mexico. Second, it seeks to provide an integrated perspective of four transportation modes, which would help prepare public policies for future global pandemics.

Analyzing the impact of the COVID-19 pandemic on the freight transportation system, which affects national and global economies, is essential to drawing valuable insights for the future. To facilitate international comparative analysis, conducting case studies at a country level was deemed necessary. As a result, a case study was conducted in Mexico using an integrated approach involving four transportation modes.

To manage disruptions in freight flow during uncertain conditions, a comprehensive perspective on the four modes of transportation and data-driven decision-making is crucial. Under this context, three initiatives can be identified: 1) establishing a National Center for Intelligence in Logistics to improve data-driven governance; 2) appointing the “Integrated Transportation Corridor Management Manager” (ITCMM) function to coordinate multiple authorities with different acting in critical freight transport corridors, and 3) creation of a digital tool based on millions of GPS data to monitor freight flows, allowing for collective intelligence among logistics actors.

This research's limitations are related to using non-standardized databases to gather information on four transportation modes. However, this limitation is also an interesting discovery. Mexico is becoming a strategic logistics hub between North America and Latin America, especially under the “Nearshoring” trend. Unfortunately, the lack of an integrated public policy in logistics and transportation reduces Mexico's capacity to deal with disruptions and its economic competitiveness.

This research has identified practices that could be crucial in improving public policies to optimize shipping routes and reduce wait times while minimizing disruptions caused by unforeseen events. A concrete example is the digital platform called “eraclitux,” a computer tool similar to an Enterprise Resource Planning (ERP) system companies use. This tool can enable a “Control Tower” that monitors freight flow in transportation corridors under the supervision of “Integrated Transportation Corridor Management Managers.” The tool can make reactive and predictive decisions that help to enhance the logistics value provided by transportation infrastructure.

The importance of a well-coordinated and integrated public policy for freight transportation was identified to ensure better performance during disruptions. Delays in the flow of goods can significantly impact the supply of essential items such as food and medicine, ultimately affecting the population's quality of life.

Numerous studies have been conducted to determine the extent of vulnerability and the impact of COVID-19 on freight transportation. However, most of these studies assume a developed market context or a single-mode transportation approach, which only applies to some situations. To gain a comprehensive understanding of how pandemics-induced demand and supply shocks affected freight transportation in developing countries such as Mexico, this paper offers insights from a four-transportation mode perspective. Mexico is facing a challenging Nearshoring trend in manufacturing, making it a significant logistics node between North and South America.

Electrification is a promising solution for decarbonising the road freight transport system, but it is challenging to understand its impact on the system. The purpose of this research is to provide a system-level understanding of how electrification impacts the road freight transport system. The goal is to develop a model that illustrates the system and its dynamics, emphasising the importance of understanding these dynamics in order to comprehend the effects of electrification.

The main methodological contribution of the study is the combination of the multi-layer model with system dynamics methodology. A mixed methods approach is used, including group model building, impact analysis, and literature analysis.

The study presents a conceptual multi-layer dynamic model, illustrating the complex causal relationships between variables in the different layers and how electrification impacts the system. It distinguishes between direct and induced impacts, along with potential policy interventions. Moreover, two causal loop diagrams (CLDs) provide practical insights: one explores factors influencing electric truck attractiveness, and the other illustrates the trade-off between battery size and fast charging infrastructure for electric trucks.

The study provides stakeholders, particularly policymakers, with a system-level understanding of the different impacts of electrification and their ripple effects. This understanding is crucial for making strategic decisions and steering the transition towards a sustainable road freight transport system.

Through the “Going Digital Initiative,” the Ghanaian government has introduced policies that aim at improving the information and communication technology (ICT) infrastructure of the country. These ICT policies have benefited numerous sectors of the Ghanaian economy. In logistics management, ICT has impacted drone medical delivery in the healthcare and maritime sectors. However, the importance of ICT is not realized in the motorcycle goods transport (MGT) industry, regardless of its popularity and high economic dependency. Second, all research on motorcycles is focused on diverse social concerns, and no study has attempted to analyze ICT implementation for MGT operations. This is a significant gap in logistics management. Hence, the study aimed to investigate the impact of ICT on Ghana's MGT industry empirically.

The study adopts a two-phase data collection approach to collect the data. The authors use partial least square structural equation modeling to analyze the study's measurement and structural assessment model.

ICT positively impacts MGT and the drivers considered. The drivers positively influence MGT. The study further analyzes novel results on the relationships between the drivers and their mediating roles in enhancing MGT performance.

The study's originality is the extension of ICT adoption and usage in MGT. The lack of literature on the importance of ICT for MGT services makes this study the primary source of literature, and the relationships investigated are unique as the research area is unexplored.

The purpose of this paper is to study the aerodynamic characteristics of Ahmed body in longitudinal and lateral platoons under crosswind by computational fluid dynamics simulation. It helps to improve the aerodynamic characteristics of vehicles by providing theoretical basis and engineering direction for the development and progress of intelligent transportation.

A two-car platoon model is used to compare with the experiment to prove the accuracy of the simulation method. The simplified Ahmed body model and the Reynolds Averaged N-S equation method are used to study the aerodynamic characteristics of vehicles at different distances under cross-winds.

When the longitudinal distance x/L = 0.25, the drag coefficients of the middle and trailing cars at β = 30° are improved by about 272% and 160% compared with β = 10°. The side force coefficients of the middle and trailing cars are increased by 50% and 62%. When the lateral distance y/W = 0.25, the side force coefficients of left and middle cars at β = 30° are reduced by 38% and 37.5% compared with β = 10°. However, the side force coefficient of the right car are increased by about 84.3%.

Most of the researches focus on the overtaking process, and there are few researches on the neat lateral platoon. The innovation of this paper is that in addition to studying the aerodynamic characteristics of longitudinal driving, the aerodynamic characteristics of neat lateral driving are also studied, and crosswind conditions are added. The authors hope to contribute to the development of intelligent transportation.