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This article studies the introduction of the cargo truck to the sugarcane transportation system in six important sugar areas of the world: the United States (Louisiana, Florida and Hawaii), Australia, Cuba, Mexico, Brazil and the Philippines with the purpose of explaining the conditions economics that made it possible. Through this exercise, a direct relationship can be established between the increase in the productive capacity of the factories, the expansion of sugar cane cultivation, the growing demand for labor for the harvest of the plant, the mechanization of cutting and loading of grass and the takeoff of the American trucking industry, with the appearance of motor vehicles. Another relevant finding is that the motorization of transport did not extend in a homogeneous and synchronized manner to all points of the space studied. This caused the truck-tractor-railway triad to be consolidated (in order of importance) in the technologically developed areas of the global sugar geography; while in underdeveloped producing countries these means were combined with carts powered by blood traction. The contribution of the research lies in two fundamental aspects: the period of analysis (second half of the 20th century), which demands greater attention from sugar historiography, specifically in relation to technological changes-this has mainly specialized in what happened between the 18th and 19th centuries- and the approach to the object of study from a global and comparative perspective, which, although it does not exhaust all its possibilities, does provide elements for debate and the continuation of future investigations. The article was built with the specialized press with global coverage, national publications, reports on the sugar issue and the available bibliography. [ABSTRACT FROM AUTHOR]

A transportation system stress test is a set of one or more hypothetical scenarios designed to help determine if a transport system can continue to provide an acceptable level of service when subjected to one or more potentially disruptive events. Stress tests, if done well, enable policy makers, regulators, and network operators to assess the ability of transport systems to withstand and recover from potentially disruptive events, and modify the systems if necessary. Although work has been conducted on how to conduct high-level preliminary transportation stress tests, there has not yet been work conducted on how to conduct detailed quantitative simulation-based stress tests on transportation systems. This paper fills this gap by presenting both an approach to conduct simulation-based stress tests and example simulation-based stress tests for a transportation system subject to potentially disruptive hydrometeorological events. Three types of stress tests are conducted for the transportation system in the region of Chur, Switzerland, which is subject to heavy rainfalls that may result in floods and landslides. These include the stress caused by climate change leading to increasing the intensity of extreme rainfall events in the future, stress caused by uncertain future mobility behavior of people leading to excessive travel demand on the network, and stress caused by poor planning of the required resources for restoration interventions leading to having less resources for restoration than planned. It is argued that conducting such stress tests makes it clear to decision makers whether or not a transportation system is sufficiently resilient against various stressors, and if not, gives clear indications as to how it could be improved. [ABSTRACT FROM AUTHOR]

Transportation systems through the ages have seen drastic evolutions in terms of transportation methods, speed of transport, infrastructure, technology, connectivity, influence on the environment, and accessibility. The massive transformation seen in the transportation sector has been fueled by the Industrial Revolutions, which have continued expansion and progress into the fourth Industrial Revolution. However, the methodologies of data collection and processing used by the many drivers of this progress differ. In order to achieve a better understanding of the impact of these technologies, in this study, we methodically reviewed the literature on the subject of the data collection and processing mechanisms of 4IR technologies in the context of transport. Gaps in present practices are identified in the study, especially with regard to the integration and scalability of these technologies in transportation networks. In order to fully reap the rewards of 4IR technologies, it is also necessary to apply standardized methods for data gathering and processing. In this report, we offer insights into current obstacles and make recommendations for future research to solve these concerns through a comprehensive evaluation of the literature, with the goal of promoting the development of intelligent and sustainable transportation systems. [ABSTRACT FROM AUTHOR]

Motivated by a transit line planning problem in transportation systems, we investigate the following capacitated assignment problem under a budget constraint. Our model involves L bins and P items. Each bin l has a utilization cost c l and an n l -dimensional capacity vector. Each item p has an n l -dimensional binary weight vector r l p , where the 1s in r l p (if any) appear in consecutive positions, and its assignment to bin l yields a reward v l p. The objective is to maximize total rewards through an assignment that satisfies three constraints: (i) the total weights of assigned items do not violate any bin's capacity; (ii) each item is assigned to at most one open bin; and (iii) the overall utilization costs remain within a total budget B. We propose the first randomized rounding algorithm with a constant approximation ratio for this problem. We then apply our framework to the motivating transit line planning problem, presenting corresponding models and conducting numerical experiments using real-world data. Our results demonstrate significant improvements over previous approaches in addressing this critical transportation challenge. [ABSTRACT FROM AUTHOR]

As customers’ expectations continue to rise, advanced on-demand transport services face the challenge of meeting new requirements. This study addresses a specific transportation issue belonging to dial-a-ride problems, including constraints aimed at fulfilling customer needs. In order to provide more efficient on-demand transportation solutions, we propose a new hybrid evolutionary computation method. This method combines customized heuristics including two exchanged mutation operators, a crossover, and a tabu search. These optimization techniques have been empirically proven to support advanced designs and reduce operational costs, while significantly enhancing service quality. A comparative analysis with an evolutionary local search method from the literature has demonstrated the effectiveness of our approach across small-to-large-scale problems. The main results show that service providers can optimize their scheduling operations, reduce travel costs, and ensure a high level of service quality from the customer’s perspective.

This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.

In Turkey, social housing is generally built by the Mass Housing Administration on low-cost land far from the city center. This leads to a high dependency on public transportation services for users living in public housing areas. Public transportation is one of the public services that are effective in ensuring social equality. Integration of public transportation with social housing will ensure that users remain a part of society and participate in social life. In this study, Şilbe, Üçkuyu, Memursen, and Çölgüzeli mass housing areas in Diyarbakır are analyzed. It was investigated how the existing public transportation system provides transportation from these areas to Dağkapı, one of the historical and commercial centers of the city. Information on the routes of municipal buses was obtained from Diyarbakır Metropolitan Municipality and the shortest route from the selected areas to the destination was determined with the Moovit application. The routes were compared, visualized and evaluated. It was observed that there are problems in the integration of mass housing areas in Diyarbakır with the existing transportation system and suggestions for the future were presented. [ABSTRACT FROM AUTHOR]

The colonization of Mars poses unprecedented challenges in developing sustainable and efficient transportation systems to support inter-settlement connectivity and resource distribution. This study conducts a comprehensive evaluation of two proposed transportation systems for Martian colonies: a ground-based magnetically levitated (maglev) train and a low-orbital spaceplane. Through simulation models, we assess the energy consumption, operational and construction costs, and environmental impacts of each system. Monte Carlo simulations further provide insights into the cost variability and financial risk associated with each option over a decade. Our findings reveal that while the spaceplane system offers lower average costs and reduced financial risk, the maglev train boasts greater scalability and potential for integration with Martian infrastructural development. The maglev system, despite its higher initial cost, emerges as a strategic asset for long-term colony expansion and sustainability, highlighting the need for balanced investment in transportation technologies that align with the goals of Martian colonization. Further extending our exploration, this study introduces advanced analysis of alternative transportation technologies, including hyperloop systems, drones, and rovers, incorporating dynamic environmental modeling of Mars and reinforcement learning for autonomous navigation. In an effort to enhance the realism and complexity of our navigation simulation of Mars, we introduce several significant improvements. These enhancements focus on the inclusion of dynamic atmospheric conditions, the simulation of terrain-specific obstacles such as craters and rocks, and the introduction of a swarm intelligence approach for navigating multiple drones simultaneously. This analysis serves as a foundational framework for future research and strategic planning in Martian transportation infrastructure. [ABSTRACT FROM AUTHOR]

Road vehicles play an important role in the UK's energy systems and are a critical component in reducing the reliance on fossil fuels and mitigating emissions. A dynamic model of light-duty vehicle fleet, based on predator-prey concepts, is presented. This model is designed to be comprehensive but captures the important features of the competition between types of vehicles on the car market. It allows to predict the evolution of the hydrogen based vehicle's role in the UK's vehicle fleet. The model allows to forecast effects of policies, hence to inform policy makers. In particular, it is shown that the transition happens only if the hydrogen supply can absorb at least 350,000 new vehicles per year. In addition to this, the model is used to predict the demand for hydrogen for the passenger vehicle fleet for various scenarios. A key finding of the policy-oriented model is that a successful transition to a clean fleet before 2050 is unlikely without policies designed to fully support the supply chain development. It also shows that the amount of hydrogen required to support a full hydrogen based vehicle fleet is currently not economically viable; the needed infrastructure requires yearly investment larger than £2.5 billions. In order to mitigate these costs, the policy focus should shift from hydrogen based vehicles to hybrid vehicles and range extenders in the transport energy system. [ABSTRACT FROM AUTHOR]

The transportation system is one of the essential subjects for any country because it directly concerns the daily costs and social problems. Researchers have proposed different methods to develop transportation systems, each with its strengths and weaknesses. This paper has proposed a new bi-objective mathematical model based on three social equity theories, the first of which considers infrastructure development costs for a transportation system and includes operational, environmental, and pollution costs. The second objective focuses on enhancing social equity in investment distributions in the transportation system for a long time. Three theories, including Utilitarianism, Rawl's, and Sadr's, were modeled and used to assess social equity. The model was examined for an original case of data in Sanandaj city, and an improved version of the augmented ε-constraint method was developed to solve it. According to the general results, each social equity theory has its own merits. The results show that Rawls' theory, with minimum infrastructure costs, establishes the best social equity (70%) after 5 years of application; therefore, the Sanandaj city would be a sustainable city based on Rawls' theory which enhances region 1, and the three regions would be in balance after 5 years. Urban planners and transport network managers can use the developed model for equitable investment in an urban transportation system. [ABSTRACT FROM AUTHOR]

The implementation of blockchain technology in integrated IoT networks for constructing scalable Intelligent Transportation Systems (ITSs) in India has the potential to revolutionize the way we approach transportation. By leveraging the power of IoT and blockchain, we can create a highly secure, transparent, and efficient system that can transform the way we move people and goods. India, one of the world’s most populous countries, has a highly congested and inefficient transportation system that often leads to delays, accidents, and waste of time and resources. The integration of IoT and blockchain can help address these issues by enabling real-time monitoring, tracking, and optimization of traffic flows, thereby reducing congestion, improving safety, and increasing the overall efficiency of the transportation system. This paper explores the potential of blockchain technology in the context of integrated IoT networks for constructing scalable ITS systems in India. The methodology followed is to develop a proof-of-concept blockchain-based application for ITS, implement the blockchain solution into the existing ITS infrastructure, and ensure proper integration and compatibility with other systems. Conduct thorough research and maintenance to ensure the reliability and sustainability of such blockchain-based systems. This research discusses the various benefits and challenges of this approach and the various applications of this technology in the transportation sector, including the green sustainability concept. The results find various ways in which such implementations of blockchain and IoT-Machine Learning (IoT-ML) can revolutionize transportation systems.

This paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatial coverage) at regular and well-spread time intervals (temporal coverage). Although spatial coverage is a natural by-product of this approach, our main focus is to reach temporal coverage. To this goal, we model the interaction between participants (vehicles) as a non-cooperative game in which vehicles are the players, and the time to sample at a given PsI is the players’ strategy. Here, vehicles are rewarded for deviating from their pre-planned paths and visiting a set of PsIs. The rewarding formula is designed such that selfish vehicles trying to maximize their reward will collect high temporal coverage data. In particular, this paper analyses the effects of increasing the number of vehicle deviations on the utilities of both vehicles and the platform.

A federated learning-based intrusion detection system (FL-IDS) is introduced to enhance the security of vehicular networks in the context of IoT edge device implementations. The FL-IDS system protects data privacy by using local learning, in which devices share only model updates with an aggregation server. The server then generates an enhanced detection model. The FL-IDS system also incorporates a detection model (LR-IDS, PCC-CNN) based on machine learning (ML) and deep learning (DL) classifiers, namely logistic regression (LR) and convolution neural networks (CNN), to prevent attacks in transportation IoT environments. The proposed FL-IDS model uses embedded devices (such as Raspberry Pi for the client and Jetson Xavier for the server model). The real-time performance of the proposed IDS was evaluated using two different datasets, NSL-KDD and Car-Hacking. We deployed our IDS model on different architectures, testbed 1 (with 2 clients) and testbed 2 (with 4 clients). The model evaluation has been evaluated based on the accuracy, and loss parameters. The results show that the FL-IDS system outperforms traditional centralized learning with machine learning and deep learning approaches regarding accuracy (achieved overall 94% and 99%) and loss (achieved overall 0.28 and 0.009). These findings contribute to transportation IoT systems security by proposing a robust framework for enhancing the security and privacy of CAVs against cyber threats.

New air traffic control ideas are sought. Many studies point out the delegation of the responsibility for ensuring separation from air traffic controllers to the aircraft crews, but it should be assumed that the transition from centralized to decentralized air traffic control will occur in stages. It is, therefore, necessary to ensure effective communication between conflicting aircraft and to define the negotiation process between aircraft. The concept of the process of negotiation and communication between aircraft in conflict using a monotonic concession protocol is presented. The proposed solution was modeled using a Petri Net, which allowed us to analyze all the dependencies present in the system. The analysis allowed us to evaluate the method in the context of safety. The conducted research showed that, under the assumed conditions, the negotiation method allows obtaining the desired effect of negotiations while maintaining an adequate level of safety.

Intelligent Transportation Systems (ITS) have experienced significant growth over the past decade thanks to advances in control, communication, and information technology applied to vehicles, roads, and traffic control systems. Vehicle type classification plays a vital role in implementing ITS because of its ability to collect useful traffic information, enable future development of transport infrastructures, and increase human comfort. As a branch of machine learning, deep learning represents a frontier for artificial intelligence, which seeks to be closer to its primary goal. Deep learning is a powerful tool for classifying vehicle types because it can capture complex traffic data characteristics and learn from large amounts of data. This means that it can be used to accurately classify traffic data and generate valuable insights that can be used to improve traffic management. Researchers have successfully adopted these algorithms as a solution to propose optimal vehicle-type classification strategies. This paper highlights the role of deep learning algorithms in solving the vehicle type classification problem, reviewing the state-of-the-art approaches in this field. [ABSTRACT FROM AUTHOR]

Traffic congestion resulting from the escalation in the number of vehicles in cities, as a result of the increase in population and building density, requires the provision of appropriate infrastructure, especially transportation and logistics systems, that meet the necessary needs of the population, to meet the current multiple challenges, through the introduction of various means of transportation. This is done according to plans that complement each other, such as using environments that encourage pedestrians and cycling and their own lanes, light rail, metro, rapid buses, in addition to public transport buses and others, through the development of high-level road projects, such as annual and main roads and others, in a way Integrated with city planning, and adopting modern technology such as smart applications in transportation, which will facilitate access and participation in developing the city, and achieving transportation integration. Therefore, the research problem is that there is no clear vision of the developments in transportation systems that have increased over time, whether public transportation systems or private transportation, which should be made possible through sustainable planning and design standards in cities. The importance of research is highlighted in trying to diagnose the obstacles of current transportation systems and their contemporary developments, and trying to evaluate them and maximize their potential by applying international standards and approved studies in this field. [ABSTRACT FROM AUTHOR]

The use of autonomous vehicles (AVs) in transportation at signalized intersections is proposed to improve safety, efficiency, and sustainability. The discussion shall thus focus more on the impacts of different AV driving behaviors to some key transportation metrics solely at signalized intersections. Among the parameters considered are travel time, queue length, delay, and speed. This is clear from one such holistic study that Cautious AV behavior would reduce accidents, but on the other way around, it might increase traffic delays and lead to congestion at signalized junctions. Aggressive AVs will improve the flow of traffic but face problems in safety in this kind of set-up. Normal AV operation A balanced approach that offers intermediary levels of travel time and safety. The review further investigates the environmental effects of various driving patterns within signalized intersections, where it notes that there are noticeable differences in emissions and fuel consumed if the driving behavior is taken into consideration. It goes further into the implications for traffic management and control systems, noting challenges and opportunities while integrating AVs into existing infrastructure and spotlighting at signalized intersections. Paramount to the review are the considerations of safety, regulatory frameworks, and mitigating strategies relevant to AV behavior at signalized intersections. In so doing, this review seeks to inform future research and policy decisions with a nuanced understanding of how various AV driving behaviors affect signalized intersection performance, seeking to optimize AV benefits while mitigating possible risks at critical traffic junctures. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerator (TENG) devices with high robustness are promising in collecting powerful energy. In this study, highly elastic and pressure‐resistance sponge fabricated TENG capable of adapting to high strength impact in land and water transportation and scalable for any shape is demonstrated for harvesting wave energy and mechanical energy. The polydimethylsiloxane sponge prepared by sacrificial template method has interconnected network and large size ratio of cavity‐wall suitable for contact and separation. The operation modes of self‐contact and extra‐contact collaborating with MXene in electron transfer provide options for different operating conditions. The polydopamine‐MXene modification of the sponge enables higher output due to the combination of the electronegativity, excellent adhesion, and antioxidant ability. Sponges are used to collect mechanical energy and applied for TENG‐powered cathodic protection, making the 304 stainless steel (304 SS, Φ = 2 mm) electrode enter a thermodynamic stable state. What's more, the work also tries the universal strategies of program monitoring wave in the water tank and harvests the mechanical energy created by cars and passers‐by, which enrich the applications of sponge TENG. [ABSTRACT FROM AUTHOR]

One of the most key parts of a health system is the blood supply chain whose design is challenging due to the perishability of blood. In this research, an optimization model for multi-product blood supply chain network design is presented by considering blood deterioration. We consider a four-echelon blood supply chain that consists of blood donation centers, blood processing centers, blood products storage centers and hospitals as the user of the blood products. The locations of blood processing centers and blood products storage centers should be determined. Furthermore, considering different levels of technologies for blood processing, the suitable level for each opened center should be determined. In addition, different types of vehicle are also considered for blood transfer between different levels of the network. The objective is minimizing the total logistical costs including the costs of opening and running the blood processing centers and blood product storage centers and blood products transfer costs between different levels of the supply chain. Finally, we apply the given model to a real case study in Iranian blood supply chain, and sensitivity analysis is performed on some parameters. In the end, some managerial insights are given

The growing demand for electric vehicles (EVs) has necessitated the development of advanced maintenance systems to ensure their reliability, longevity, and cost-effectiveness. This paper presents an innovative approach for the predictive maintenance of EV components by integrating optical and quantum-enhanced artificial intelligence (AI) techniques. The proposed system employs fiber Bragg grating (FBG) sensors to capture high-resolution, real-time data on critical EV components such as the battery, electric motor, and power electronics. These sensors offer numerous advantages, including immunity to electromagnetic interference, high sensitivity, and multiplexing capabilities. To process the acquired data, we employ a quantum-enhanced machine learning algorithm, harnessing the power of quantum computing to handle large-scale data sets and improve prediction accuracy. Our AI model is trained to detect early signs of component degradation and predict potential failures, allowing for proactive maintenance and minimal downtime. The experimental results demonstrate the effectiveness of our approach in achieving accurate, timely predictions, thereby enhancing the overall performance and durability of electric vehicle components. This research paves the way for the development of advanced, efficient, and environmentally friendly transportation systems. [ABSTRACT FROM AUTHOR]

New air traffic control ideas are sought. Many studies point out the delegation of the responsibility for ensuring separation from air traffic controllers to the aircraft crews, but it should be assumed that the transition from centralized to decentralized air traffic control will occur in stages. It is, therefore, necessary to ensure effective communication between conflicting aircraft and to define the negotiation process between aircraft. The concept of the process of negotiation and communication between aircraft in conflict using a monotonic concession protocol is presented. The proposed solution was modeled using a Petri Net, which allowed us to analyze all the dependencies present in the system. The analysis allowed us to evaluate the method in the context of safety. The conducted research showed that, under the assumed conditions, the negotiation method allows obtaining the desired effect of negotiations while maintaining an adequate level of safety. [ABSTRACT FROM AUTHOR]

Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data for detecting events that influence traffic. The proposed technique uses various machine-learning methods to accurately identify events and location information. Therefore, it can resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Performance evaluations also demonstrated its validity and effectiveness. [ABSTRACT FROM AUTHOR]

The transportation industry has led efforts to fight climate change and reduce air pollution. Autonomous electric vehicles (A-EVs) that use artificial intelligence, next-generation batteries, etc., are predicted to replace conventional internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) in the coming years. In this study, we performed a life cycle assessment to analyze A-EVs and compare their impacts with those from EV and ICEV systems. The scope of the analysis consists of the manufacturing and use phases, and a functional unit of 150,000 miles·passenger was chosen for the assessment. Our results on the impacts from the manufacturing phase of the analyzed systems show that the A-EV systems have higher impacts than other transportation systems in the majority of the impacts categories analyzed (e.g., global warming potential, ozone depletion, human toxicity-cancer) and, on average, EV systems were found to be the slightly more environmentally friendly than ICEV systems. The high impacts in A-EV are due to additional components such as cameras, sonar, and radar. In comparing the impacts from the use phase, we also analyzed the impact of automation and found that the use phase impacts of A-EVs outperform EV and ICEV in many aspects, including global warming potential, acidification, and smog formation. To interpret the results better, we also investigated the impacts of electricity grids on the use phase impact of alternative transportation options for three representative countries with different combinations of renewable and conventional primary energy resources such as hydroelectric, nuclear, and coal. The results revealed that A-EVs used in regions that have hydropower-based electric mix become the most environmentally friendly transportation option than others. [ABSTRACT FROM AUTHOR]

The emerging field of the Internet of Vehicles (IoV) has garnered significant attention due to its potential to revolutionize transportation and mobility. IoV enables the development of innovative services and applications that can enhance the efficiency, safety, and sustainability of transportation systems. However, ensuring secure and reliable communication among different components of an IoV system poses a critical challenge. This study proposes a blockchain-based communication framework for secure and trustworthy IoV applications. The framework utilizes blockchain technology's decentralization and security features to create secure communication channels between IoV system components, including vehicles, infrastructure, and service providers. An identity management system is also integrated into the framework to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To assess the proposed framework's effectiveness, real-world IoV scenarios were used to conduct experiments, and the results demonstrate that the framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework provides a promising solution for addressing security and trust challenges in IoV communication systems. [ABSTRACT FROM AUTHOR]

Conventional transportation systems are facing many challenges related to reducing fuel consumption, noise, and pollutants to satisfy rising environmental and economic criteria. These requirements have prompted many researchers and manufacturers in the transportation sector to look for cleaner, more efficient, and more sustainable alternatives. Powertrains based on fuel cell systems could partially or completely replace their conventional counterparts used in all modes of transport, starting from small ones, such as scooters, to large mechanisms such as commercial airplanes. Since hydrogen fuel cells (HFCs) emit only water and heat as byproducts and have higher energy conversion efficiency in comparison with other conventional systems, it has become tempting for many scholars to explore their potential for resolving the environmental and economic concerns associated with the transportation sector. This paper thoroughly reviews the principles and applications of fuel cell systems for the main transportation schemes, including scooters, bicycles, motorcycles, cars, buses, trains, and aerial vehicles. The review showed that fuel cells would soon become the powertrain of choice for most modes of transportation. For commercial long-rage airplanes, however, employing fuel cells will be limited due to the replacement of the axillary power unit (APU) in the foreseeable future. Using fuel cells to propel such large airplanes would necessitate redesigning the airplane structure to accommodate the required hydrogen tanks, which could take a bit more time. [ABSTRACT FROM AUTHOR]

Transportation improvements affect technological and socio-economic development, and several scholars have researched various transportation problems. The current study aims to illustrate a thorough review of those transport problems, where the Analytic Hierarchic Process (AHP) is used for enumerating the related criteria and alternatives. A systematic review methodology, the PRISMA protocol, is applied in the review process. The contribution of this work is highlighted along with the extensions of the AHP improving decision-making support. To this end, current research demonstrates the relevant results of 58 papers published from 2003 to 2019. The results indicated that most researchers applied the conventional AHP method to deal with transportation issues, while the most critical issue was public transport, followed by logistics problems. On top of that, TOPSIS was integrated with AHP more than other MCDM methods when dealing with multi-criteria transportation problems. Moreover, the “Transportation Research Part A: Policy and Practice” journal achieved first place by publishing ten papers on the topic, and the highest number of articles was published in 2018. The results are discussed adequately, and in the conclusion, policy implications are presented.

As contemporary colleges and universities continue to undergo unprecedented growth, more and more students commuting to campus face parking and physical infrastructural connectivity problems. Using the case of a US college campus, this paper identifies areas in which physical infrastructural connectivity limits students’ ability to commute to campus, either due to gaps in the network or lack of awareness of the network. Graph theory application was used to gauge the overall connectivity of the sidewalk, bike lane, and bus route networks available to students at the university. Through GIS mapping, relationships between these networks were identified, as well as gaps in these networks. In addition, a survey of students’ commuting patterns was performed to identify how students travel to campus and their overall familiarity with the alternative transportation networks. Insights from this study could enhance planning for inter-modal connectivity and make student commuting safer, more accessible, more eco-friendly, and more convenient even as the student population continues to increase.

Considering the stochastic traffic networks one can follow an assignment procedure to estimate flows. However, the interdependent bi-modal assignment problems are solved just for deterministic status. To this gap, this paper extends a traffic assignment problem for an interdependent bi-modal network under Stochastic User Equilibrium (SUE) conditions. To solve this problem, a new algorithm is presented by combining a user equilibrium algorithm namely Streamline algorithm with a Logit model. In our algorithm, the interaction between private and public traffic flows is explicitly modeled and travel time for each mode is considered as a function of two-mode flows. Also, the origin-destination matrix was split between two modes based on the binomial Logit function. Some networks were considered to illustrate the performance and the accuracy of the proposed stochastic user equilibrium algorithm on the interdependent bi-modal networks. Numerical results showed that this algorithm provided reasonable solutions with high accuracy in a small computation time compared with the other user equilibrium (UE) algorithms.

Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction. [ABSTRACT FROM AUTHOR]

Histories of stilt-walking in the Landes de Gascogne reveal intertwinements between mobility politics and landscape modernization. Reading these stories through a lens of "landscape conquest" centers the ongoing presence of coloniality in modernity, while highlighting the ways in which modernization produces homogeneity. This process of making every place more like anywhere else is more-than-physical. Universal landscape features, including universal mobility cultures, reproduce universal ways of thinking and relating. This depauperation enables the continual conquest of natural, cultural, epistemological, and phenomenological diversity. Conversely, landscape diversification, including the diversification of mobility cultures, could support abundant futures. [ABSTRACT FROM AUTHOR]