Existing Transportation Systems Research Articles

Long-Term Performance of Natural Stone Cobbles for Paving Raised Junctions: Findings from over a Decade of Use

Raised junctions (RJs) are chosen more and more frequently by town planners and road designers in traffic calming (TC) projects. This choice is supported by analyses of the existing transport systems in question. Where a few raised junctions have been designed for the project, use of different paving options may be worth consideration. This article describes a situation where a few RJs were placed on a short street section, all of which were provided with a cobblestone or cobblestone pavement (SBP). In order to verify the commercial viability of this option, we examined the traffic conditions, assessed the severity of condition of the pavements under analysis, and estimated the vehicle accelerations (ACs) and decelerations (DEs) in two chosen study areas, further referred to as the first and second study area. Two noise surveys were also carried out for the purposes of this research: the first one after a few years of operation and the second one after another ten years. Considering the problems of sustainable road construction, many environmental factors were taken into account in the studies. Based on the experimental results, we could assess the effectiveness of using RJs as the only TC measure in 30 km/h zones and check whether the severity of pavement condition depended on the AC and DE values and if SBP can be recommended to mitigate environmental impacts of street traffic. Finally, two pavement structures are proposed to choose from, depending on the local transport system conditions and streetscape characteristics. In addition, the authors recommend the use of solar-power elements at RJs to enhance their visibility and increase the traffic slowing effect.

Can Urban Air Mobility become reality? Opportunities and challenges of UAM as innovative mode of transport and DLR contribution to ongoing research

AbstractUrban Air Mobility (UAM) is a new air transportation system for passengers and cargo in urban environments, enabled by new technologies and integrated into multimodal transportation systems. The vision of UAM comprises the mass use in urban and suburban environments, complementing existing transportation systems and contributing to the decarbonization of the transport sector. Initial attempts to create a market for urban air transportation in the last century failed due to lack of profitability and community acceptance. Technological advances in numerous fields over the past few decades have led to a renewed interest in urban air transportation. UAM is expected to benefit users and to also have a positive impact on the economy by creating new markets and employment opportunities for manufacturing and operation of UAM vehicles and the construction of related ground infrastructure. However, there are also concerns about noise, safety and security, privacy and environmental impacts. Therefore, the UAM system needs to be designed carefully to become safe, affordable, accessible, environmentally friendly, economically viable and thus sustainable. This paper provides an overview of selected key research topics related to UAM and how the German Aerospace Center (DLR) contributed to this research in the project "HorizonUAM - Urban Air Mobility Research at the German Aerospace Center (DLR)". Selected research results on the topics of market potential and public acceptance, vehicle design (including battery degradation, onboard systems, cabin design, cabin simulation), infrastructure, operations (including U-space, safe autonomy, navigation, communication, cost modeling) and overall system modeling are briefly presented.

SVD-based adaptive fuzzy for generalized transportation

This research stems from the urgent need to address the complex challenges faced in multimodal transportation within the cargo sector, particularly concerning the reduction of transportation costs and CO2 emissions. Existing transportation systems often struggle to efficiently balance economic and environmental concerns, necessitating innovative solutions. In this paper, we have introduced a novel mathematical model that integrates Singular Value Decomposition (SVD), Genetic Algorithms (GA), and the Adaptive Neural Fuzzy Inference System (ANFIS). The synergy of GA and the ANFIS forms a dynamic learning system that continually refines solutions by adapting to historical decisions and aligning with supply-demand dynamics. This integrated approach aims to optimize cost allocation and resource utilization while simultaneously minimizing CO2 emissions. Deploying the ANFIS+GA+SVD model in multimodal scenarios yields impressive results, with a minimal average percentage error (MAPE) of 0.28% for transportation costs and a remarkable coefficient of determination R2 of 0.992 for the Truck + Ship scenario. Similarly, a MAPE of 0.11% for CO2 emissions, supported by an impressive R2 of 0.989, underscores the model's accuracy. Observations of people's transportation choices in response to emission price changes reveal a significant decrease in truck usage, plummeting from 84% to below 50%. Alternative transportation methods, especially in the 3S-3 M-3 C scenario, show a substantial surge, with railways leading at a remarkable 30% increase in utilization. The combined quantity and cost comparison, utilizing the ANFIS+GA+SVD model, emphasizes the suitability of the Truck + Ship scenario for Iraq's transportation network, reinforcing the case for sustainable and efficient multimodal transportation systems.

Sustainable Societal Transformation: Shaping Renewable Energy Technologies in Transport

Traditional transportation systems are increasingly scrutinized for their environmental footprint, driving the need for sustainable alternatives. Recent developments in renewable energy technologies have paved the way for Electric Vehicles, Hydrogen Fuel Cells, Biofuels, Solar-Powered Vehicles, and Hybrid Systems, all of which offer environmentally-friendly solutions. However, the successful integration of these technologies into existing transport systems presents challenges such as infrastructure development, policymaking, public perception, and societal acceptance. Sustainable transportation encompasses more than technology and economics. It addresses societal inclusivity, environmental conservation, and long-term viability. Social equality ensures equitable access to transport, economic considerations highlight short-term costs and long-term benefits, and health and well-being are affected by sustainable transportation patterns. Community engagement, though challenging, is essential to craft locally tailored solutions and foster a collective sense of ownership. These facets form the foundation for a greener, healthier, and more inclusive transport system. The global shift towards renewable transport technologies is multifaceted, integrating political, economic, social, technological, environmental, and legal dimensions. Using the PESTEL method, the complex transition towards renewable transport technologies can be understood, especially in contexts like Ukraine. Given its unique socio-political landscape, Ukraine's move towards sustainable transport is influenced by multiple factors, including international climate agreements, economic restructuring, public perception, and technological advancements. Ukraine's transport sector, heavily reliant on fossil fuels, has the potential to transform using its abundant renewable resources. Tapping into these resources can offer numerous benefits, though technological and infrastructural challenges persist. Strategic investments and embracing renewable technologies are key for Ukraine to build a sustainable transport and societal framework.

Methodology for Designing a System of Public Passenger Transport in a Functional Region

Transport, as an object of research and design, is directly influenced by other functional components of the territory, it is caused by them, but at the same time it determines them in terms of limitations and development. A comprehensive approach to the design of transport systems involves a wide range of analytical and synthetic activities. Transport and territory are interrelated, while transport is one of the main factors defining the functionality of territorial structures. The development of transport systems brings greater availability of work, services, and leisure activities. The transport solution in the territory must be directed towards the adequate satisfaction of the transport needs, which are characterized by the intensity of the transport relations in the territory, depending on the time and quality of the relocation. The article presents a methodology for the design of regional public passenger transport lines, in accordance with the principles of sustainable mobility, supporting the competitiveness of public passenger transport and the efficient use of resources. The proposed methodology presupposes an already completed and processed analytical phase of the transport planning process and includes the impact of public passenger transport on the external environment. The methodology is applicable under the conditions of designing a completely new transport service system, as well as in the process of reorganizing existing transport systems, including rail and bus transport.

Applying Utility Criteria to Select the Design Variant of the Transport System in Underground Mine Workings

This article presents a new, in-house developed method of selecting a variant of the transport system in the underground of a mine, using multi-variant decision support, taking into account the specificity of an underground mining plant. The implementation of the method should facilitate the selection of the most optimal transport system, ensuring continuity and the lowest operating costs. Seven functional criteria are proposed herein, which may be of a stimulant or destimulant nature. Each criterion was assigned a specific scoring weight reflecting the level of significance, with the sum of the weights being 100. The highest scores for the variants in the individual criteria go to those characterized by the following traits: the shortest transport time, the highest compatibility with the transport system already existing in the mine, transport routes with the greatest coverage communication, allow workers to be transported to the front of the excavation as quickly as possible, are most compatible with the existing transport systems in terms of the reinforcement and removal of longwalls, have a drive with the lowest operational hazard, have the least negative impact on the atmosphere of workings (exhaust gas emissions), and those that will ensure the best functioning of transport in emergency situations involving risk or uncertainty. For each criterion, a scoring formula based on specific parameters is provided. The method was used to select the optimal variant of the transport system in one of the mines, where four long walls were cut and four long galleries were drilled. Out of ten variants, the variant that should ensure the highest degree of reliable transport operation and continuity of operation has been determined using seven usability criteria.

Transfer Learning Based Traffic Light Detection and Recognition Using CNN Inception-V3 Model

Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on a transfer learning-based model that uses the Inception-V3 network model to significantly reduce the amount of training data and computing costs. The proposed model was trained and tested in the laboratory for the intelligent and safe automobiles (LISA) traffic light dataset, which has been augmented by several pre-processing methods. Then, using different convolution and pooling techniques, the retrieved layer-wise features were compared and analyzed. Lastly, great reliability and repeatability are seen based on statistical analysis when the transfer learning-based model is frequently retrained utilizing precise tuning parameters. The results demonstrate that a transfer learning-based model is capable of high-level recognition performance in the proposed model, with an accuracy rate of 98.6%.

Fostering an Age-Friendly Sustainable Transport System: A Psychological Perspective

The aging population is rapidly growing across the world, with the number of people aged 65 or older projected to reach 1.6 billion by 2050. As such, it is essential to consider how to develop sustainable transport systems that are age-friendly. This perspective paper investigates how to foster an age-friendly transport system with a particular focus on public and sustainable transport options for the elderly. Existing transport systems are evaluated to determine their adherence to an effective age-friendly transport system in terms of three main requirements, namely affordability, accessibility and safety. Then, a psychological perspective is introduced by considering the psycho-physical needs and preferences of the elderly as well as individual factors affecting them. Four areas are considered: (a) independence and autonomy, (b) comfort and convenience, (c) social inclusion and ageism, (d) physical health and well-being, especially injuries due to mobility and fear connected with them. Finally, a proposal is made about psychological training programs directed to both elderly users and transport workers to overcome concerns for an age-friendly transportation system. Such programs could effectively support the age-friendly use of public transport systems simply by increasing the age-friendly skills of both users as well as transport workers.

A large-scale last-mile consolidation model for e-commerce home delivery

E-commerce's rapid growth, combined with customer demand for fast shipping, has significantly escalated last-mile transportation, particularly home deliveries. This surge calls for novel strategies to optimize vehicle utilization while maintaining timely delivery. In response, many online retailers are incentivizing customers to embrace delayed home deliveries by offering economic incentives. Nonetheless, current transportation systems fall short in accommodating orders with extended delivery windows, primarily adhering to the First-In-First-Out rule and distance minimization via the Vehicle Routing Problem (VRP). In this paper, we introduce a new consolidation-based delivery methodology that addresses these challenges. Our approach accounts for orders with daily-based time windows and also anticipates future demand realizations (i.e., expected incoming orders). We characterize this consolidation problem using a Mixed Integer Linear Programming model and propose a custom metaheuristic approach that can tackle the problem on a large-scale setting, introducing a significant novelty approach in last-mile delivery research. We applied our methodology to one of Mexico's largest retailers and compared its performance against the company's existing transportation systems. Our approach substantially improves vehicle utilization and yields considerable reductions in distance traveled, time, and overall transportation costs, achieving cost savings of up to 52%. These savings represent tangible benefits, enabling potential revenue enhancements for businesses and cost-effective, timely deliveries for consumers. Additionally, the increased vehicle utilization implies fewer vehicles are needed for the same volume of deliveries, thereby enhancing operational efficiency. This innovative approach, therefore, presents a practical and highly effective solution for managing large-scale last-mile delivery scenarios.

Stereotypes and the public acceptability of shared micromobility

Understanding the public's views and acceptability of shared micromobility (SMM) is crucial for uncovering barriers to incorporating SMM into sustainable urban mobility. This research studies stereotypes of SMM and their impacts on SMM's public acceptability. We focus on shared e-scooters and shared bikes/e-bikes in England, the Netherlands, and Sweden. We conceptualise stereotypes of SMM, developing a scale for their measurement. Factor analysis reveals that stereotypes of SMM are structured around two cognitive dimensions, namely, warmth and competence, which aligns with the stereotype content model. Warmth captures individuals' stereotyped SMM user images regarding how the users interact with other road users; competence reflects individuals' stereotyped SMM's capabilities to improve existing transport systems. Overall, stereotypes of SMM, characterised by low levels of warmth and high levels of competence, are ambivalent. Yet, they are also variable. Analysis of covariance unveils variations in SMM stereotypes across countries, sociodemographics, and travel patterns. Swedish residents, older adults, individuals from households without children, SMM users, and car-oriented individuals hold lower levels of warmth (for both types of SMM) and competence (for shared e-scooters) than their counterparts. Finally, the public acceptability of shared e-scooters is neutral, whilst that of shared bikes/e-bikes is mildly positive. Multivariate analyses show that stereotypes' warmth and competence are positively associated with individuals' acceptability of SMM, cumulatively accounting for over 50% of the variation in acceptability. Our findings help inform policies and planning on SMM. The developed scale holds the potential for evaluating stereotypes of SMM in particular and emerging transport services in general.

Investigating the impacts of the COVID-19 pandemic on Eco-driving behavior

The outburst of the COVID-19 pandemic has significantly disrupted the operation of the existing transportation systems, decreasing the total number of trips, as well as the habits of the majority of the travelers. In this paper, the impact of the pandemic on driving behavior and, specifically, on ecological driving is investigated. First, a clustering approach is adopted to identify different driving profiles in a dataset of naturalistic driving data. It was found that there are three discrete profiles, each with its respective characteristics: aggressive, eco and typical. Furthermore, statistical analysis and comparison of the distribution of the trips in the three profiles were conducted in four different time periods: before the lockdowns, during the first lockdown, between the two lockdowns and during the second lockdown. Interestingly, the results indicate that during the above period, the drivers gradually moved to smoother and, thus, safer and more ecological driving behavior.

Перспективное развитие новых услуг сервиса в городском транспортном потоке

Purpose: One of the possible solutions to the problem of congestion of the road network in conditions of a high level of motorization of cities is considered. Methods: The concept of the service “Mobility as a service” as one of the progressive methods of urban traffic management is described. The technologies that allow this service to work, while increasing the efficiency of the use of vehicles, are considered. Results: The analysis of carsharing, kicksharing and bikesharing services as a “first and last mile” transport system was carried out. Practical significance: The solution to this problem can be the integration of all existing transport systems into a single system that can have a greater impact on the efficiency of mobility of urban users.

The Role of Internet of Things on Electric Vehicle Charging Infrastructure and Consumer Experience

The drive for net-zero emission and global decarbonization spurred the need for a worldwide transition towards cleaner energy options. The fossil-fuel-dominated global transportation system is a target for these initiatives, accounting for 37% of recent carbon emissions. This has accelerated the adoption of electric vehicles (EVs) into the global market to cut down carbon emissions and improve efficiency in the transportation sector. In the face of this growth, limitations in EV charging infrastructure still loom large amongst EV consumers. Resolving this bottleneck requires systematic approaches to ensure seamless operation and integration into the existing transport systems. This study examines the critical role of IoT in addressing the challenges of EV public charging through reviewing the literature to understand the inter-relation and highlighting its attendant impact on consumer experience. Findings show that while IoT serves as a strong tool to foster public interest through favorable public policy, its novel and innovative nature faces developmental challenges based on existing government policies that could hinder the interest of potential investors. Therefore, governments should consider evaluating existing policies and practices to ascertain their suitability for IoT adoption in EVs, ensuring that they do not constitute unintentional barriers.

Bilateral Adaptation of Longitudinal Control of Automated Vehicles and Human Drivers

Automated vehicles have great potential to transform our existing transportation systems by improving driving safety, comfort, congestion, and emissions. Despite the tremendous efforts that have been spent on the development of various automated driving technologies, user acceptance of automated driving technologies is still low, which is largely caused by the gaps between automated driving controllers and human preferences. Recent research efforts have been focusing on adapting automated driving behaviors to human demonstrations. However, most existing methods assume that human demonstration is perfect and only focus on mimicking human driving behaviors. In reality, the human demonstration will not be ideal and will include some over-aggressive or over-conservative actions that compromise the safety or efficiency of the trained automated driving controller. In this paper, an Inverse Model Predictive Control (IMPC) based bilateral adaptation method for automated vehicles and human drivers is proposed. The method can adapt automated longitudinal driving behaviors to human preferences based on human interventions during automated driving. Meanwhile, it can also reject improper interventions and send warnings to the human driver such that he/she can realize the irrationality in his/her behaviors. Eventually, the automated driving controller will adapt to the human driver’s preferences and the human driver will get rid of his/her bad driving habits. Human-in-the-loop experiments were conducted using a driving simulator to demonstrate the effectiveness of the proposed approach.

Smart Mobility in Urban Areas: A Bibliometric Review and Research Agenda

Transportation systems globally face challenges related to congestion, decreased quality of life, limited accessibility, increased harmful emissions and costs, growing use of private cars and in some cases lack of intra and intermodal integration. Smart Mobility is believed to be a solution to some of these challenges by providing comprehensive and intelligent mobility services, decreasing transportation costs, promoting safety, and combating pollution and traffic congestion. Despite this potential, there is still uncertainty surrounding what smart mobility is and whether it is moving toward improving the quality of life and making cities more sustainable. To address this gap, this paper conducts a bibliometric review of 3223 Web of Science Core Collection-indexed documents to provide a comprehensive understanding of smart mobility research. The findings reveal a lack of multi-disciplinary approaches in previous studies with a strong emphasis on technological aspects and limited social or economic considerations in current research. The review identifies four distinct periods of smart mobility research, with recent interest sparked by advancements in big data, deep learning, artificial intelligence, and real-time technologies in transport systems. However, there is a dearth of research on smart mobility in developing countries, where urban populations are rapidly increasing. Thus, the review proposes a research agenda to address the current gaps in knowledge. Furthermore, the review provides an updated and integrated definition of smart mobility as the use of advanced technologies, such as the Internet of Things (IoT), big data analytics, and artificial intelligence, to improve transportation efficiency, mobility for all, and sustainability while safeguarding the quality of life. The primary challenge for smart mobility is the co-evolution with existing transport systems, making further research on integration with these systems and real-time technologies essential for advancing smart mobility research. The paper’s main contribution is an integrated conceptualisation of smart mobility research and novel research topics that build on this unified base.

A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles

Autonomous vehicle (AV) technology is likely to bring unprecedented change to existing transport systems due to its potential to enhance safety and efficiency. Nevertheless, before entering a fully AV era, human-driven vehicles (HVs) and AVs are expected to coexist for a long time, during which HVs and AVs could behave differently in route choice. On the one hand, compared with HVs, AVs can acquire more comprehensive traffic information and also process the information more swiftly. On the other hand, HV users may habitually maintain a satisfactory path but not necessarily choose the optimal one to the same destination to save cognitive during daily trips. AV users, however, may not show such behavior on account of the advantages of AVs. To capture these features in the coexistent period, understand their effects on the evolution of daily traffic flow, and provide managerial insights, this study presents a deterministic discrete time day-to-day dynamic assignment model for the mixed HV and AV traffic flows. In which both AV users and HV users choose their path according to their perception, AV users have better perceptual ability than HV users, and bounded behavioral inertia is first introduced to depict the travel behavior that HV users habitually maintain a satisfactory path during daily trips. The existence and uniqueness of the fixed-point and the convergence conditions of the proposed dynamic model are demonstrated and given. Theoretical results are verified in a simple two-path network, the Nguyen-Dupuis network, and the Sioux Falls network, respectively. The interactions between AVs and HVs are explored in the three numerical examples. Our research indicates that the introduction of AVs can reduce the overall travel time of a transport system and increase road capacity. Bounded behavioral inertia of HV users can maintain more HV flows on a better route, brings a more balanced distribution of travel flow on different paths, and facilitate the stability of a whole transport system.

Mode Choice Preferences of Female University Students and Impacts of Ride-Sharing Thereon

The equality of gender is accounted to be an indispensable element when it comes to sustainable transportation infrastructure planning in an inclusive society. Special attention in this regard must be required to reach the growing transportation demands of the urban population- especially in a developing country like Bangladesh. In this inclusive society, females constitute a major portion of the students as well as the workforce and contribute significantly to the economy. Although females have to rely on the different modes available in the existing transportation system for their day-to-day travel, they have different preferences among the alternative modes in terms of accessibility and safety. To assess the travel behavior of female students, this study explores the elements those influence travel choices (travel cost, travel time, security, and comfort) and evaluates the effect of ride-sharing facilities on their choice of mode. Data were collected from 360 female university students through an online questionnaire. The research focuses on two divisions of female students: staying at the residence hall (residents) and not staying at the residence hall (non-residents). Descriptive and inferential statistical methods were utilized to explain female travel behaviors and determine the importance of ridesharing services in their mode selection (rickshaw, bus, autorickshaw, bicycle, private car, and ridesharing). The study reveals that female students (both resident and non-resident) prefer rickshaws over other modes and the inception of ride-sharing services had a consequential effect on their choice of modes and travel behavior. This analysis will deliver beneficial information to relevant authorities and policymakers in evaluating existing transportation systems and will indicate the required mitigations that can be made to ensure social equity. Journal of Engineering Science 13(2), 2022, 43-49

Impact of Urban Transport on City Development in the Context of Sustainable Development

The article examines the impact of urban transport systems on the development of urban areas in the context of compliance with the goals of sustainable development (SDG 11). The problem of developing ecological transport both in Kazakhstan and in other countries is relevant due to the constant expansion of cities and the unavailability of existing transport systems to provide all citizens with access to quality living conditions. The authors studied the world experience and, on its basis, developed a concept for the introduction of ecological transport in Kazakhstan.

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.

Safety and impact assessment for seamless interactions through human-machine interfaces: indicators and practical considerations

Autonomous Vehicles are expected to change the existing transportation systems radically and at the intermediate SAE automation levels before highly automated vehicles (i.e., SAE levels 2 and 3), the driving task will still require human interactions with the vehicle. To that end, Human-Machine Interfaces (HMIs) are expected to play a key role in the cooperation between users and vehicles. The current study proposes a methodology for assessing new types of HMIs for automated vehicles. The study aims to cover this gap by proposing a method for assessing novel types of HMIs for automated vehicles. Also, directives related to indicators for assessing safety and impact, and practical considerations are given. The outcomes can contribute to guiding further research in this direction and additionally can be transferred and expanded to other automated technologies and systems.