Future Transportation Systems Research Articles

Rapidly declining costs of truck batteries and fuel cells enable large-scale road freight electrification

Low-carbon road freight transport is pivotal in mitigating global warming. Nonetheless, electrifying heavy-duty vehicles poses a tremendous challenge due to high technical requirements and cost competitiveness. Data on future truck costs are scarce and uncertain, complicating assessments of the future role of zero-emission truck (ZET) technologies. Here we derive most likely cost developments for price setting ZET components by meta forecasting from more than 200 original sources. We find that costs are primed to decline much faster than expected, with significant differences between scientific and near-market estimates. Specifically, battery system costs could drop by 64% to 75% and fall below €150 kWh−1 by no later than 2035, whereas fuel cell system costs may exhibit even higher cost reductions but are unlikely to reach €100 kW−1 before the early 2040s. This fast cost decline supports an optimistic view on the ZET market diffusion and has substantial implications for future energy and transport systems.

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

Sensitivity analysis for knowledge discovery in scramjet intake design optimization using deep-learning flowfield prediction

Scramjet engines are a promising propulsion technology for future space transportation systems, and its design exploration represents a significant task toward development of high-performance scramjets. While sensitivity analysis is expected to facilitate acquisition of physical insights for effective scramjet design, the substantial computational cost incurred due to numerical simulations required for statistical analysis poses a challenge to the application, limiting the capability of this analysis. The present study has conducted sensitivity analysis using deep-learning-based flowfield prediction that can significantly reduce the computational cost for numerical simulations providing flowfield data. Global sensitivity analyses have been employed to investigate influential design variables on the flowfield and performance. These have allowed for identifying the design variables that dominantly influence or determine the performance parameters. Local sensitivity analyses have been performed to elucidate the design rationales and the characteristic flow structures for high-performance intake designs. The sensitivity analysis methods in conjunction with flowfield prediction have enabled generation of rich insights that would otherwise be difficult to acquire without this approach, demonstrating the capabilities of the proposed methodology.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

Ramping-Up Electro-Fuel Production

Future transport systems will rely on new electrified drives utilizing batteries and hydrogen-powered fuel cells or combustion engines with sustainable fuels. These systems must complement each other and should not be viewed as competing. Properties such as efficiency, range, as well as transport and storage properties will determine their use cases. This article looks at the usability of liquid electro-fuels in freight transport and analyzes the production capacities that will be necessary through 2050 in Germany. Different scenarios with varying market shares of electro-fuels are considered. A scenario with a focus on fuel cells foresees a quantity of 220 PJ of electro-fuels, i.e., 5.1 million tons, which reduces 80% of carbon dioxide emissions in LDV and HDV transport. A further scenario achieves carbon-neutrality and leads to a demand for nearly 17 million tons of e-fuel, corresponding to 640 PJ. Considering a final production rate of 5.1 million tons of electro-fuels per year leads to maximum investment costs of around EUR 350 million/year in 2036 during the ramp-up phase. The total investment costs for synthesis plants amount to EUR 4.02 billion. A carbon-neutrality scenario requires more than a factor 3 for investment for the production facilities of electro-fuels alone.

Exploring VANET Security within the VEINS Framework: A Review

Vehicular Ad Hoc Systems (VANETs) are urgent components of future shrewd transportation frameworks, encouraging communication between vehicles and framework to move forward security, effectiveness, and comfort through vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) intuitive. In any case, the broad selection of VANETs postures critical security challenges, counting potential malevolent assaults and security breaches, which debilitate the unwavering quality and reliability of vehicular communication systems. In reaction to these challenges, the VEINS Vehicles in Organize Recreation system has risen as an unmistakable apparatus for re enacting VANETs and assessing their security instruments. This systematic literature audit points to supplying a comprehensive investigation of investigative endeavours centring on the security angles of VANETs inside the VEINS framework. By synthesizing discoveries from existing considerations, this audit recognizes key security dangers, vulnerabilities, assault models, and countermeasures significant to VANETs re enacted utilizing VEINS. It sheds light on the current state of VANET security inside re enacted situations, highlighting crevices in information and giving experiences into future investigation directions. The discoveries of this audit contribute to a more profound understanding of VANET security challenges and offer profitable direction for analysts, professionals, and policymakers included in planning, creating, and sending secure VANET frameworks. Eventually, the point is to upgrade the security pose of VANETs and guarantee the astuteness and dependability of vehicular communication in future transportation systems.

Mobile Collaborative Learning Over Opportunistic Internet of Vehicles

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions.

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing-a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems.

A Comparative Analysis of Traditional Roadways vs. Electric Vehicle (EV) Roads: Environmental, Infrastructure, And Technological Perspectives

As the global transportation sector seeks to address pressing environmental challenges and transition towards sustainable mobility solutions, the comparative analysis between Traditional roadways and Electric Vehicle (EV) roads emerges as a critical area of investigation. This paper presents a comprehensive comparative analysis of traditional road infrastructure and EV road infrastructure from environmental, infrastructure, and technological perspectives. From an environmental standpoint, the analysis evaluates the life cycle environmental impacts, including greenhouse gas emissions, air and noise pollution, and resource consumption associated with traditional roadways and EV roads. It examines the potential for EV roads to mitigate environmental impacts through reduced fossil fuel dependency, lower emissions, and integration with renewable energy sources. Infrastructure comparisons encompass the design, construction, maintenance, and operation of traditional roadways versus EV roads. Factors such as pavement materials, structural integrity, durability, and lifecycle costs are scrutinized to assess the performance and resilience of each infrastructure type. Additionally, the analysis explores the implications for urban planning, land use, and transportation networks in accommodating EV road infrastructure. Technological perspectives delve into the advancements and innovations driving both traditional roadways and EV roads. It examines the integration of smart technologies, intelligent traffic management systems, and renewable energy solutions in enhancing the functionality, efficiency, and sustainability of road infrastructure. Furthermore, the analysis explores the implications of EV road technologies, such as dynamic wireless charging systems and vehicle-to-grid communication, on future transportation systems. Through a systematic comparison of traditional roadways and EV roads, this study provides insights into the environmental, infrastructure, and technological implications of each infrastructure type. By elucidating the benefits, challenges, and opportunities associated with EV road adoption, policymakers, urban planners, and transportation stakeholders can make informed decisions to advance sustainable transportation solutions and mitigate environmental impacts in the transition to electric mobility.

DETECTION AND RECOGNITION OF TRAFFIC SIGNS FROM DATA COLLECTED BY THE MOBILE MAPPING SYSTEM

Abstract. Autonomous vehicles and high-resolution maps are key elements of future transport systems. Detection and recognition of traffic signs is an important element for the safe driving of autonomous vehicles and the development of high-resolution maps. In this study, it is aimed to accurately detect and identify traffic signs based on the data collected by the mobile mapping system in order to ensure the safe movement of autonomous vehicles in traffic. A low-cost method is proposed with the ResNet-50 model for an autonomous vehicle to automatically detect and recognise traffic signs while moving on the road. As a result of the model training, 0.99 accuracy and 0.016 loss were obtained. The success of the method was first observed on images randomly selected from the dataset. Then, a real-time test was performed on a low-cost webcam. The tests showed that the handled method detects and identifies the traffic sign quickly and accurately.

Integrating deep reinforcement learning and social-behavioral cues: A new human-centric cyber-physical approach in automated vehicle decision-making

This paper proposes a novel human-centric approach to enhance decision-making for autonomous vehicles in complex urban driving situations by integrating Deep Q-Network (DQN) reinforcement learning and social value orientation. In the proposed method, a deep neural network (DNN) is employed to approximate the optimal Q-values for various states and actions in the space of possible actions and reachable states. In order to improve the optimization convergence, an Adam optimization is proposed by combining the advantages of adaptive learning rates and momentum methods. The proposed framework also incorporates a collision avoidance component that allows vehicles to navigate safely through pedestrian crossings. The proposed method is validated through simulation experiments, which show that the proposed approach outperforms traditional decision-making and RL methods in terms of safety and efficiency. Finally, the results demonstrate that integrating social value orientation and DQN-RL can lead to more human-like and socially compliant decision-making frameworks for automated vehicles. This research contributes to developing a new human-centric cyber-physical approach for automated vehicle decision-making and has significant implications for designing future intelligent transportation systems.

Energy use and CO<sub>2</sub> emissions of the Moroccan transport sector

In this paper, optimized models based on two different machine learning (ML) methods were developed to forecast the transport energy consumption (TEC) and carbon dioxide (CO<sub>2</sub>) emissions in Morocco by 2030. More precisely, artificial neural networks (ANN) and support vector regression (SVR) were used for modelling non-linear TEC and CO<sub>2</sub> emissions data. This study uses data from 1990 to 2020 and employs various independent parameters, including population, gross domestic product, urbanization rate, evolution of the number of vehicles, and the number of electric vehicle introductions. Four statistical metrics are derived to assess the effectiveness of the ML algorithms used. The forecasts for 2030 were based on six scenarios, including three scenarios for the growth of gross domestic product (GDP) and two scenarios for the evolution of electric cars’ introduction into Moroccan vehicle fleet. The ANN model outputs showed that a decrease in TEC and CO<sub>2</sub> emissions is expected until 2030. However, the SVR model predicts outputs values close to those in 2020. The study's results also indicate that: i) TEC and transport CO<sub>2</sub> emissions are positively impacted by economic growth in Morocco and ii) electric vehicles will be essential components enabling substantial reductions in overall CO<sub>2</sub> emissions in future transport systems.

Wireless Energy Transfer with Three-Phase Magnetic Field System: Experimental Results

In this paper a three-phase magnetic field system is applied to the wireless power transfer system. The research is directed not only to the distribution of the magnetic field but to optimize the energy transfer efficiency, and to reduce the electromagnetic field influence to the surroundings. The development of the future intelligent transportation system depends on the electric mobility, namely, the individual or the public electric vehicles. It is crucial to achieve progress in the batteries and the battery charging, especially through a wireless power transfer technology. The study of the magnetic field is important in this technology. The energy transfer efficiency depends of the alignment, the size of the coils, the spatial orientation of the magnetic field, the detachment and the tilt between the windings.

An experimental study for the decision-making support of shared transport options in Western Australia

The road transport systems have a direct impact on both passenger and freight movements, leading towards negative environmental effects plus economical and societal costs. It has reached an alarming level. Afford to purchase more than one vehicle by an individual who can pay for it may solve the transportation problem for them; however, not everyone has a personal vehicle for movement. Moreover, providing public transport facilities to everyone on a demand basis or on time basis is not possible for several reasons. Further, personal or commercial vehicles are not being used all the time or not carrying passengers in full though it can afford with the number of seats available. One possible solution is to share these vehicles for minimising the number of vehicles and to ensure the effective use of vehicles. Thus, the shared transport systems, such as ride-sharing, car-sharing, car-pooling, or bike-sharing are the promising choice to facilitate good accessibilities to the city or urban living population, especially in a densely populated area. In this paper, we have worked with such shared transport options by collecting data through an online survey and analysed data in various ways. We have also designed and developed a decision support system for this data to offer the city planners in Western Australia to make informed decisions about the future transportation systems for their people. Finally, we have predicted on the use of rail transport, for example, how a train station can be used to facilitate the shared transport options for better accessibility.

Stochastic Modelling and Integration of Electric Vehicles in the Distribution System Using the Jaya Algorithm

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore, EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work.

The impact of revolutionary aircraft designs on global aviation emissions

The discussion about the environmental impact caused by aviation has gained greater prominence due to the increased demand for this sector and, consequently, the increase in the number of flights. Environmental concerns have stimulated the development of novel approaches to reduce pollutants and CO2 emissions. This study aims to assess the impact of disruptive concepts on commercial aircraft by reducing CO2 emissions by 50% by 2050. In this regard the fleet system dynamics model is used to assess the effects of technological progress on future air transport systems. It accounts for the manufacturer’s production capabilities and current projections and forecasts on the needs and evolution of global air transport, as well as their expected entry into service. The main factors reported were production capacity, year of entry of the technology/concept, and the transport capacity and range of aircraft. The sensitivity study on the production capacity of new aircraft/concepts showed that with a 15% increase, emissions can be reduced between 1 and 2.6%, depending on the case and scenario. On the other hand, increasing the aircraft production capacity could lead to a problem of overcapacity.

Driving into the future: a cross-cutting analysis of distributed artificial intelligence, CCAM and the platform economy

The future of driving is autonomous. It requires a comprehensive stack of embedded software components, enabled by open-source and proprietary platforms at different abstraction layers, and then operating within a larger ecosystem. Autonomous driving demands connectivity, cooperation and automation to form the cornerstone of autonomous mobility solutions. Platform economy principles have revolutionized the way we produce, deliver and consume products and services worldwide. More and more businesses in the field of mobility and transport appear to implement transaction, innovation, and integration platforms as core enablers for Mobility-as-a-Service and transport applications. Artificial intelligence approaches, especially those dealing with distributed systems, enable new mobility solutions, such as autonomous driving. This paper contributes to understanding the intertwining role between distributed artificial intelligence, autonomous mobility and the resulting platform ecosystem. A systematic literature review is applied, in order to identify the intersection between those aspects. Furthermore, the research project BeIntelli is considered as a hands-on application of our findings. Taking into account our analysis and the aforementioned research project, we pose a blueprint architecture for autonomous mobility. This architecture is the subject of further research. Our conclusions facilitate the development and implementation of future urban transportation systems and resulting mobility ecosystems in practice.

From Transportation Equity to Accessibility Capability: A New Framework to Guide Transportation Planning for Older People

Despite increasing attention to theories in transportation equity, few focus on specific social groups, such as older people. Scholars and practitioners need to better understand the implications that residential location, access to transportation, and technology resources have for older people's quality of life. Drawing on literature from urban planning, gerontology, and public health, I develop a framework, accessibility capability, that will support the planning of future transportation systems and neighborhoods to accommodate older people's travel needs. Scholars and practitioners can benefit from this critical synthesis of literature as they consider how to build more age-friendly cities and communities.

Steady-Speed Traffic Capacity Analysis for Autonomous and Human-Driven Vehicles

As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human drivers. The experiments involved driving at multiple constant speeds to evaluate the efficacy of ACC in maintaining safe following distances. Our findings indicate that ACC systems consistently converge on optimal following distances, demonstrating their ability to regulate spacing between vehicles effectively. However, a notable downside emerged in terms of their adverse impact on road capacities, where the results indicate a mitigation in capacity percentages of 7.6%, 9.3%, and 15.6% for the three types of ACC-equipped vehicles compared to human drivers. This study sheds light on the intricate interplay between ACC systems and human driving behaviors, emphasizing the need to consider both factors when envisioning the future of autonomous vehicles. While ACC systems provide a standardized and reliable approach to following distances, the shorter distances observed in human-driven scenarios suggest a potential trade-off between safety and traffic capacity. These insights contribute to a comprehensive understanding of the dynamics involved in autonomous driving, facilitating informed decision making for the integration of autonomous vehicles into future transportation systems.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven – 40% 1st generation AVs– 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.