Future Transportation Systems Research Articles

These aren’t the droids you’re looking for: artificial intelligence and safety in future transport systems

These aren’t the droids you’re looking for: artificial intelligence and safety in future transport systems

Research on autonomous driving technology in intelligent transportation systems

Abstract. The convergence of scientific and technological advancement with the acceleration of urbanization has resulted in a notable intensification of the issue of traffic congestion. In this context, the development of autonomous driving technology has emerged as a pivotal area of focus for future transportation systems. Therefore, this paper presents a review of the current status of automatic driving technology in intelligent transportation systems, based on methods such as literature, data analysis and case studies. Furthermore, it delves into the evolution, applications, challenges and solutions of automated driving technologies in intelligent transport systems, and presents forward-thinking insights into the future development of intelligent transportation. As autonomous driving technology continues to evolve, the integration of deep reinforcement learning and other advanced technologies will facilitate the advancement of intelligent vehicle human-machine collaborative decision-making technology. It is imperative that the construction technology of safety-critical scenarios be reinforced in order to enhance the interpretability of algorithmic models and the real-time nature of scenario generation. All parties need to collaborate to promote the innovation and improvement of autonomous driving technology, achieve widespread application and sustainable development, and create a better travelling environment.

Impact of continuously extending/retracting wall on Mack-mode evolution in hypersonic boundary layers

Structural morphing is regarded as a potential solution for future space transportation systems, necessitating aircraft capable of extensive altitude ranges, wide speed ranges, and multi-regime operations. The investigation of laminar-turbulent transition incorporating morphing effects aims to compensate for the lack of physical mechanisms in aircraft designs. This paper particularly focuses on the impact of a continuously extending or retracting wall in the chordwise direction on the evolution of inviscid Mack modes in a Mach 5.92 hypersonic boundary layer. Using the high-Reynolds-number asymptotic technique, a model is developed to quantify the morphing effect by an amplification factor, which includes both the scattering effect at the rigid-morphing junction and the successive modification of the Mack growth rate by the morphing velocity in the downstream region. Such a model enables us to conduct a comprehensive investigation across a wide parameter space defined by morphing speed and flow parameters. A critical frequency is identified near the most unstable second-mode frequency. For an extending wall, the oncoming Mack modes are suppressed for frequencies above this critical frequency and enhanced for frequencies below it. Conversely, the retracting-wall effect exhibits an inverse impact on the Mack modes. To validate the accuracy of the asymptotic predictions, the harmonic linearized Navier–Stokes calculations are performed, resulting in favorable agreements. This research sheds light on the complex interplay between wall morphing and the Mack-mode evolution, offering informative contributions to the understanding of the aerodynamic behaviors in hypersonic boundary layers.

Comparison of gap-based and flow-based control strategies using a new controlled stochastic cellular automaton model for traffic flow

ABSTRACT Autonomous vehicles are essential to future transportation systems, potentially reducing traffic congestion. This study examines the impact of different vehicle control strategies on traffic flow through simulations. We propose a novel stochastic cellular automaton model, the controlled stochastic optimal velocity (CSOV) model, which incorporates vehicle control effects. Within the CSOV model, two control strategies are implemented: gap-based control (GC), which adjusts vehicle velocity to balance the gaps between adjacent vehicles, and flow-based control (FC), which aims to maintain a consistent local flow between the front and rear vehicles. Results show that both control strategies improve traffic flow. However, under weaker control, the GC sometimes resulted in lower flow compared to no control. In contrast, the FC consistently enhanced flow across control strengths, yielding more robust outcomes. Furthermore, when both strategies achieved comparable flow rates, the FC provided a more stable velocity distribution under varying traffic densities than the GC.

Proceedings of the 2024 Australasian Road Safety Conference - Hobart, Tasmania, Australia

Proceedings of the 2024 annual Australasian Road Safety Conference held in Hobart, Tasmania, Australia. The proceedings provide the Extended Abstracts and Posters describing research, educational and policing program implementation and policy and management strategies related to all aspects of road safety and especially related to the conference theme of Target 2030: What's the pathway forward? Almost 600 delegates from 20 countries attended the face-to-face conference. Authors of accepted Extended Abstracts represented international and local institutions from all aspects of their respective communities including research centres, private companies, government agencies and community groups. These Extended Abstracts provide an indication of the important work being done in Australia, New Zealand and internationally as part of the United Nations, One UN Vision for Road Safety to reduce the number of crashes on the road by 50 percent by 2030. Almost 200 Extended Abstract manuscripts were accepted for long, short and poster sessions. Each Extended Abstract was peer-reviewed by two experts with relevant expertise. Authors were also provided the option of submitting a Full Paper. Those authors who chose to submit a Full Paper for consideration in the Journal of Road Safety (JRS) could do so based on the outcome of the peer review of their Extended Abstract. Submitted Full Papers undergo peer-review by three independent experts matched by their sector. The matching pairs academic authors iwth academic reviewers and practitioner authors with practitioner reivewers. Inclusion in the JRS follows acceptance by the peer-reviewers.The conference covered a comprehensive range of topics including speed, infrastructure and road design, cyclist safety, motorcyclist safety, pedestrian safety, road safety education, licensing, vehicle design, impairment due to distraction from mobile phones, alcohol, drugs as well as the impacts of climate change on future transport systems road safety, road safety issues for Indigenous people groups across New Zealand and Australia, integrating road safety into local Government, automated enforcement, gig economy road safety, older drivers, etc.

Real-Time Freeway Crash Detection Framework using Connected Vehicle Waypoint Data

Accurate and timely detection of traffic accidents is crucial for transportation agencies seeking swift responses and effective traffic management, particularly on freeways where crash severity and traffic flow disruptions are amplified. While several methods have been developed using cameras or infrastructure-mounted sensors, these approaches may face challenges in geographical scalability. To address this issue, a novel approach has been developed that leverages real-time connected vehicle trajectories at a market penetration rate of 4%–9%. The method involves extracting five key features from individual journeys that sense the speed and acceleration fluctuations. The results demonstrate promising outcomes, achieving an overall accuracy of 63.5% with 4.1% false detections and 33.4% non-detections. This accuracy surpasses the speed-alone model using the same data by 24%. Furthermore, higher traffic volumes lead to even greater accuracy, exceeding 80% at level of service D and 90% at level of service E. The developed algorithm also exhibits precision in crash detection, with a mean latency of only 2.5 min after the actual incidents. The entire processing time may additionally add up to 1 min. The robustness of the model is tested for a single day on one of the interstates, providing promising results. This study highlights the potential of connected vehicle data in enhancing crash prediction methods and underlines its value for the safety and efficiency of future transportation systems.

Cybersecurity in Electric and Flying Vehicles: Threats, Challenges, AI Solutions & Future Directions

Electric and Flying Vehicles (EnFVs) represent a transformative shift in transportation, promising enhanced efficiency and reduced environmental impact. However, their integration into interconnected digital ecosystems poses significant cybersecurity challenges, including cyber-physical threats, privacy vulnerabilities, and supply chain risks. This paper comprehensively explores these challenges and investigates artificial intelligence (AI)-driven solutions to bolster EnFV cybersecurity. The study begins with an overview of EnFV cybersecurity issues, emphasizing the increasing complexity of threats in digital transportation systems. Methodologically, the paper reviews existing literature to identify gaps and assesses recent advancements in AI for cybersecurity applications. Key methodologies include AI-powered intrusion detection, threat analysis leveraging machine learning algorithms, predictive maintenance strategies, and enhanced authentication protocols. Results underscore the effectiveness of AI technologies in mitigating EnFV cybersecurity risks, demonstrating improved threat detection and response capabilities. The study concludes by outlining future research directions, highlighting the need for continued innovation in AI, quantum computing resilience, blockchain applications, and ethical considerations. These findings contribute to a clearer understanding of EnFV cybersecurity dynamics and provide a roadmap for enhancing the security and reliability of future transportation systems.

Blockchain-based conditional privacy-preserving authentication scheme using PUF for vehicular ad hoc networks

Vehicular ad hoc networks (VANET) have been the key indispensable module of the future intelligent transportation system. Security and privacy are two essential attributes that protect the safe driving of vehicles. Over the last two decades, numerous conditional privacy-preserving authentication schemes have been presented for the VANET environment. However, existing schemes have various limitations, including security issues, high storage overhead, and frequent interactions. In order to bridge these difficulties, this work combines physically unclonable function and blockchain technology to construct a conditional privacy-preserving authentication scheme for the VANET environment. Specifically, we combine physical unclonable function and dynamic pseudonym techniques to generate unique pseudonym IDs dynamically and private keys using physical unclonable function to enhance privacy protection and resist physical attack. To reduce the number of communication rounds during the verification process, we deployed lightweight blockchain nodes to avoid direct communication between the receiver and the blockchain network. The proposed scheme demonstrates resilience against various potential attacks through comprehensive security analysis and proof. Furthermore, performance metrics indicate that our scheme outperforms similar schemes, making it suitable for resource-constrained VANET.

Smart Transportation Systems: IOT-enabled Traffic Management & Vehicle-to-infrastructure Communication

Recently, there has been a substantial increase in the number of cars on the road. Escalating traffic congestion is a widespread problem that folks face on a daily basis. The manual control of traffic-by-traffic police has shown to be ineffective. “Furthermore, the signal's set duration has failed to effectively address this issue, regardless of the level of traffic congestion. This research presents a communication system called Vehicle-to-Blockchain (V2B), which utilizes blockchain technology to enable transparent and decentralized communications. The aim is to optimize the incorporation of blockchain technology into V2X (Vehicle-to-Everything) and IoT (Internet of Things) systems, therefore enhancing the efficiency and effectiveness of future transportation systems. We present a range of advanced applications for blockchain technology, such as a car ownership system that utilizes the multi-token standard, a vehicle grading system, the integration of blockchain with the Internet of Things (IoT), and a decentralized ticket administration system for transportation services. The design places a high emphasis on essential elements such as the seamless integration of data, precise precision, and the establishment of secure connections. Furthermore, it includes a decentralized payment system and marketplace for transportation inside smart cities. Our main focus is on the technical execution of smart contracts in these particular scenarios, highlighting their importance in guaranteeing strong and dependable interactions. Our decentralized approach creates a transportation ecosystem that has the ability to adjust and fulfill the evolving requirements of intelligent urban areas. It is groundbreaking, durable, and capable

Automatic target detection in vehicle images based on YOLOv10 deep learning algorithm

In this paper, we explore the effect of its application in vehicle image detection based on the latest YOLOv10 model.YOLOv10, as the latest version in the YOLO series, inherits and optimises the advantages of the previous generations of models, aiming to provide higher detection accuracy and faster inference speed. In our experiments, we firstly divided the dataset reasonably and trained the YOLOv10 model with data from the training set. By analysing the Precision-Recall curve, we found that the area below the PR curve is larger, which indicates that the AUC value of the model is close to 1, thus reflecting the superiority of the model in classification performance. In addition, from the F1-confidence curve, the model is still able to maintain good classification results at higher confidence levels and maintains high F1 scores in most confidence intervals, which further validates the reliability of the model. Test results show that the trained YOLOv10 model is able to accurately recognise vehicles and effectively predict vehicle types. This enables the model to monitor the vehicle conditions on the road in real time and achieve accurate detection and classification of road traffic targets. Therefore, this paper demonstrates the powerful ability of YOLOv10 in the field of vehicle detection, which provides an important reference for the development of future intelligent transport systems. Overall, this study not only verifies the effectiveness of YOLOv10 in practical applications, but also lays the foundation for further improvement of target detection technology.

Future-Proofing Road Safety: Adapting ISO 26262 for Advanced V2X Integration

This study investigates the integration of ISO 26262 with Vehicle-to-Everything (V2X) communication technologies. Emphasize the necessity of expanding this safety standard to include vehicle-to-infrastructure integration and addressing additional safety measures to accommodate current vehicles’ complex and rapidly evolving networks. The study uses an extensive review approach, which considers existing safety pro-tocols and a qualitative literature synthesis to evaluate current standards and anticipate future requirements from them. It also looks at developments in V2X communication technology while identifying gaps within the present scope of ISO 26262 concerning autonomous and connected cars. The findings indicate a significant improvement in road safety plus efficiency in traffic management by integrating ISO 26262 with V2X communication. Further, the research points out areas such as security lapses, real-time data processing deficiencies, interoperability hitches, and scalability limits. These findings call for amendments to current safety standards so that they can take care of those problems. Failure to include V2X into ISO 26262 may com-promise reliability altogether, hence posing a grave danger to future intelligent transportation systems (ITS)’s dependability. The research concludes that integrating them will help lower risks brought about by the dynamic nature of vehicles’ environment, thereby facilitating the establishment of globally solid and safe systems for communicating between vehicles.

Market Penetration Rate Optimization for Mobility Benefits of Connected Vehicles: A Bayesian Optimization Approach

The advancements of connected vehicle (CV) technologies promise significant safety, mobility, and environmental benefits for future transportation systems. These benefits will largely rely on the market penetration rate (MPR) of CVs and connected infrastructure. However, higher MPR is not guaranteed to result in greater benefits in a transportation system in some cases even if we do not consider the deployment cost of CVs. Therefore, understanding the optimal CV MPR to achieve the best system benefits is informative and can provide some guidance for transportation agencies to use appropriate incentives or other policies to potentially affect the speed of CV adoption. Instead of using the traditional incremental method, this paper proposed a simulation-based approach combined with Bayesian optimization to determine the optimal CV MPR that achieves the highest performance benefits for a freeway segment. The proposed methodology is tested in the I-210 E (in California, U.S.) simulation freeway segment built and calibrated in Simulation of Urban Mobility software as a case study. The weighted sum of the average total travel time on the mainline and the average queue length of on-ramps is formulated as the objective function to optimize the CV MPR. Different weight combinations are tested as different scenarios. The optimization results of these scenarios show that, when the weight of total travel time is high, the optimal CV MPR tends to be high. On the contrary, when the weight of queue length increases, higher CV MPRs may not guarantee higher benefits for the traffic system. The globally optimal CV MPR can be as low as 3%. The case study also confirms the effectiveness of optimizing the CV MPR based on microsimulation and Bayesian optimization.

An Improved Longitudinal Driving Car-Following System Considering the Safe Time Domain Strategy.

Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model's adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems.

Deep learning in autonomous driving: Advantages, limitations, and innovative solutions

With the rapid development of autonomous driving technology, deep learning has become a core driver for innovation in testing autonomous driving scenarios. This review paper delves into the critical role of deep learning in autonomous driving technology. The paper will describe how deep learning is at the center of driving innovation. The paper thoroughly explores the application of deep learning in obstacle detection, scene classification and understanding, and image segmentation, emphasizing the significant benefits in perception and decision-making while pointing out the challenges and innovative solutions adopted. The innovative solutions section proposes multimodal fusion and joint learning, new methods for 3D semantic segmentation, etc., aiming to improve image segmentation's accuracy and generalization ability. Overall, deep learning has great potential in automated driving technology, and by innovating and solving challenges, it will advance the system and provide reliable, intelligent, and efficient solutions for future transportation systems.

FORECASTING FUTURE PUBLIC TRANSPORT MODE CHOICE BEHAVIOUR OF COMMUTERS IN BAHRAIN USING LOGIT AND CLASSIFICATION TREE MODELS: A COMPARATIVE STUDY

Global trade and social relationships are greatly facilitated by transportation. However, the majority of nations, including Bahrain, face substantial challenges with their transportation systems. For the development of technical solutions that can promote the progress of these transport systems, it is now crucial to have a complete understanding of travel demands and driver's characteristics. This paper aims to explore the influential factors concerning travel mode choice in Bahrain and utilize mode choice models to forecast the probable utilities of various future public transport modes. The study utilizes diverse, 3864 data records extracted from previous surveys as well as a recent one conducted within this research. Subsequently, using Minitab software, two types of mode choice models were built, namely the logit model and the classification tree model, focusing on modelling the future transportation system, considering potential public transport modes (Public Bus, Metro, and Tram). The analysis of the data identified trip cost as the top predictor, moreover, direct, and quick travel, accessibility, and convenience were also found to significantly influence the choice of travel mode in Bahrain. Additionally, the findings indicate that the metro is the preferred choice for future public transport, with a strong preference observed for a combination of metro and tram. The research also suggests, in terms of model performance, that when capturing more complex patterns, as in this study, the classification tree outperforms the multinomial Logit model. Overall, the research provides valuable insights into mode choice in Bahrain and highlights the important factors influencing commuting decisions. The results of this study can support the development of an efficient public transportation system that would satisfy the needs and preferences of commuters in Bahrain and ultimately lead to a sustainable and accessible transportation infrastructure in the country.

Optimal management of coupled shared autonomous electric vehicles and power grids: Potential of renewable energy integration

Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems.

Analysis of the Covid-19 pandemic on preferences of transport modes

The Covid-19 pandemic has affected the entire world and changed many aspects of daily life, including transportation behaviour and preferences. This study examines the situation of transportation behaviour and preferences before and after the Covid-19 pandemic process through a survey study. A total of 471 people participated in the survey, and 30 questions were posed to participants regarding transportation modes preference, daily usage habits, public transportation usage rate, income status, perceived risk level of transportation systems, preferences before and after the pandemic process, and more. The answers were classified, evaluated and statistically analysed. The results showed that users were significantly affected by the Covid-19 process and changed their transportation mode preferences. The use of buses in urban journeys decreased, and the use of private vehicles significantly increased after the Covid-19 process. Hygiene was the key factor in travel, followed by the vehicle occupancy rate, ventilation, fare and travel time factors. The perceived risk levels of public transportation systems were ranked as metrobus, minibus (paratransit), bus, metro, tram and ferry. The study suggests that future transportation system designs should consider user preferences, as well as changes in education and working conditions, and pandemic conditions.

Optimizing platoon safety through key node selection in pinning control strategy

The advent of Connected and Automated Vehicles (CAVs) and vehicular platooning has ignited high expectations for improving the safety performance of future transportation systems. Present control strategies for CAV platoons primarily concentrate on regulating each vehicle within the platoon. Although theoretically equipped to optimize a variety of performance metrics, this method is heavily dependent on communication and computational resources, which may lead to exorbitant costs. This study proposes a pinning control strategy that focuses on the selection of pivotal nodes, thereby facilitating the optimization of safety indexes for the entire platoon through the control of a select group of vehicles. The strategy encompasses two critical aspects: the identification of key control nodes and the application of node-based control. Initially, a feature matrix that includes both the intrinsic dynamics of the vehicles and the dynamics relative to other vehicles is developed. Spectral clustering is then utilized to identify these pivotal nodes. Subsequently, specialized control mechanisms for these nodes are devised, based on Proportional-Integral-Derivative (PID) controllers, with the objective of improving platoon safety. The efficacy of this strategy is corroborated through numerical simulations. To assess the safety performance, four categories of Surrogate Safety Measures (SSMs), which consider distance and acceleration, are applied. The findings confirm that the pinning control strategy significantly enhances performance across various SSMs, considerably reducing the risk of collisions during platoon operations. Moreover, it improves operational efficiency and platoon stability, establishing its value as an effective method for safety enhancement.

Well-to-wheel carbon footprint and cost analysis of gasoline, diesel, hydrogen ICE, hybrid and fully electric city buses

Carbon emissions in mobility, as one of the primary causes of global warming, are regulated by various organizations with different legislations. Different energy sources and power-train solutions are used to comply with these constraints. Hydrogen-fueled, fully electric, and hybrid vehicles are at the forefront of these technologies. Within the scope of this study, gasoline-, diesel-, and hydrogen-fueled internal combustion engines, and fully electric and serial hybrid 18-m city bus vehicles were modeled. Comprehensive analyses of emissions and costs, considering both the entire energy production and consumption cycles for various vehicle types were conducted. Models that are primarily based on fuel consumption data show that while fully electric vehicles currently have the lowest emissions, hydrogen-fueled vehicles can play a more prominent role soon with the increasing production of green hydrogen. For the first step of hydrogen implementation in the industry, H2ICE is a promising for both reduced carbon emissions in mobility and cost-effectiveness. If the forecasted hydrogen production and distribution scenario is adopted by industrial and energy organizations, it is evident that by the year 2030, more environmentally friendly operation can be achieved particularly in the context of public transportation. Embracing this approach is likely to lead to positive environmental sustainability impacts in future transportation systems.

Everyday mobility and citizenship: a living lab approach

ABSTRACT In the context of a living lab that aimed to reduce everyday car use, citizens in a newly established semi-urban residential area in Sweden were asked to travel less by car, try mobility and accessibility services, and to reason about travelling and their own role in a future sustainable transport system. By analysing the participants’ written reports about mobility practices and mobility citizenship, collected through a living lab app, the paper explores links between everyday mobility and mobility citizenship. The analysis, which is based on theories on citizenship and mobility and social practice theory, shows that the participants’ reports provided context-specific knowledge regarding everyday mobility and citizenship. The participants expressed both knowledge about and engagement in their local environment. This opens new ways to understand, explore and make use of mobility citizenship, in research and in practice.