Future Transportation Research Articles

Construction of Transportation Economics Course Incorporating Carbon Reduction Concept

The impact of carbon emissions on the environment is becoming increasingly severe. The transportation industry is an important pillar of the national economy and also generates a large amount of carbon emissions. Therefore, it is necessary to provide education on energy conservation and emission reduction to future transportation practitioners in higher education. This article takes "Transportation Economics" as an example to analyze the new approach of curriculum construction based on the concept of carbon reduction in the core courses of transportation majors, and introduces the innovative teaching methods and content of the "Transportation Economics" course based on the concept of carbon reduction.

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

A review of the future transport labour market: An EU approach

Mobility and transportation are a fundamental cornerstone of societies. They cannot be performed without a specific and varied workforce that keep all modes functioning. However, the transport sector currently faces many challenges including volatile high energy prices, labour and skills shortages, harsh working conditions, an ageing work force, and many labour relations’ issues. There are also deep and widespread market changes to confront in relation to automation and digitalisation, the move towards greener, cleaner technologies and sustainability. The risk of unemployment for certain jobs is evident. Transport workers and transport undertakings are thus confronted with the very challenging landscape to decarbonise the sector but also to address social sustainability and remain competitive in a fast-changing environment. This study makes use of a systematic review to analyse the main challenges that the land transport sector faces. This paper informs policy makers and relevant stakeholders on the current trends; highlights challenges and opportunities for transport workers and provides recommendations to help achieve that will result in a digital and fair, ethical, just, and green transition not only for the transport sector but also for the society as a whole.

The Influence of Safety Compliance on Safety Climate with Safety Culture as a Mediator among Grab Drivers in South Jakarta

This study aims to analyze the influence of safety compliance, safety culture and safety climate among Grab drivers in South Jakarta. By gaining an in-depth understanding of these factors, it is expected that more effective safety strategies and programs can be developed to ensure the safety of both drivers and passengers, as well as to enhance security in future online transportation operations. The research employs a quantitative methodology with descriptive and inferential approaches. Data collection was conducted through questionnaires distributed to 98 Grab drivers in the South Jakarta area. The data were analyzed using SmartPLS 3.0 software with the Partial Least Square (PLS) method. The results of the study indicate a significant influence of safety compliance on safety culture, as well as on safety climate. These findings confirm that good safety compliance contributes to the formation of a strong safety culture and overall enhancement of the safety climate. Additionally, it was found that safety culture has a strong positive impact on safety climate.

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.

Visual guidance technology of flying cars based on multilevel markers and depth

Split-type flying car will play an important role in the future transportation. This paper adopts a guidance method that couples visual information and depth information, and improves the docking accuracy through the mutual cooperation of the drone and the vehicle. Firstly, a multilevel docking marker is designed to achieve adaptive target matching within different distances during the docking process. The marker has strong robustness and can adapt to complex scenes such as occlusion, strong light, and large angle tilting, providing the redundant corner points required for machine vision detection pose information accurately. Secondly, a three-dimensional pose estimation algorithm is proposed, which can introduce depth information to correct the homography matrix. The algorithm combines the advantages of strong robustness to multilevel marker detection and high accuracy of depth information, and can output millimeter-level precision pose information in different environments, different inclination angles, and different occlusions. Finally, a flying car model experiment was carried out, and the results showed that the guidance technology can obtain millimeter-level precise pose information during the entire process of long distance-near distance-completion of docking, thus realizing precise docking.

Implementing Model Predictive Control (MPC) in Steer-by-Wire Systems for Future Automated Vehicles

Abstract The need for future green sustainable transportation represents a motivation for the researchers in order to create a better society. In this paper a proposed Steer-by-Wire system for future automated vehicles is analysed by implementing model predictive control (MPC) controller that would optimise the desired behaviour of the vehicle. By using MATLAB/Simulink, a bicycle vehicle model is simulated in standardised test manoeuvres according to standard ISO:7401. These results are used as referent data for the desired trajectory of the vehicle and as input data for the MPC controller. The idea of using these results for the controller is generated by the desire of creating a shared information intelligent systems. Shared information and control in the future intelligent systems would be crucial for creating the desired sustainable transport. By traveling, the vehicle would collect data of the desired measured variables and those data would be send to every other vehicle. Therefore, the next vehicle would possess the previous data and would try to improve the vehicle response and safety. Every optimised trajectory would be shared with the rest of the vehicles and this cycle would result in further optimization and in creation of a safer, more comfortable and more sustainable transport. The optimisation is done by the MPC controller which is responsible for defining the steering wheel angle and therefore creating the Steer-by-Wire system that is entirely defined by the MPC controller without driver commands. By comparing the trajectory of the automated vehicle and the desired trajectory gained from the previous simulations, the desired steering wheel angle is defined. This results in creation of a Steer-by-Wire system that improves the vehicle dynamics, safety and dynamic response of the vehicle. The mathematical modelling, results of the simulations and advantages of using the MPC controller for the Steer-by-Wire system in automated vehicle are presented.

Design and test of a contactless damper for HTS maglev systems via electromagnetic shunt dampers

High-temperature superconducting (HTS) maglev system is promising to become the future high-speed transport due to its numerous advantages. However, the low-damping dynamic characteristics of superconductors make the system vulnerable to external disturbances, which present a significant challenge to its implementation. To enhance the vibration attenuation effect of the HTS maglev system, a non-contact damper that employs electromagnetic shunt damping (EMSD) and negative resistance is incorporated into the HTS maglev system. This study elucidates the principles of EMSD and negative resistance, and establishes the governing equations to describe the behavior of the HTS maglev model equipped with EMSD. Subsequently, the EMSD coupling coefficients are analysed via the finite element method (FEM) under varying conditions. Finally, a dedicated vibration test rig is designed and fabricated to validate the effectiveness of the proposed damper. The results demonstrate that the proposed damper, in combination with negative resistance, is capable of effectively suppressing vibration in HTS maglev systems. The maximum acceleration of the test model can be reduced by 86% compared with the original system without the damper. This work may provide valuable guidance for future practical implementations.

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.

Research on battery heat generation characteristics and thermal management system applied to a typical eVTOL

Electric Vertical Takeoff and Landing (eVTOL) aircraft, as a highly promising future transportation mode, offers new possibilities for solving traffic congestion. Its unique flight mode and high power requirements present significant challenges for battery thermal management system design. Compared to electric vehicles, eVTOLs have totally different usage scenarios and operating conditions, leading to the fact that the thermal management system of conventional electric vehicles does not apply to eVTOLs. This paper proposes a battery thermal management design method for eVTOLs. The energy and power requirements for a short-distance eVTOL flight are determined through theoretical calculations. Additionally, experimental studies explore the thermal characteristics of a 61.5Ah lithium-ion battery under flight discharge conditions. Moreover, a battery thermal management system, which combines flat heat pipes and ram air, is analyzed through numerical simulations. Furthermore, this study investigates the effects of various air temperatures, air inlet mass flow rates, and fin spacing on the performance of the thermal management system. Finally, under the flight discharge cycle of a 61.5 Ah lithium-ion battery, with the environment temperature of 20 °C, the temperature can be controlled within 38.46 °C. The temperature difference can be cooled within 3.85 °C by the passive cooling system. Even at an extreme environment temperature of 40 °C, the battery temperature can still meet the standard. Meanwhile, comparing from multiple dimensions, this solution has a better overall performance than the traditional liquid cooling solution, which provides a foundation for designing a battery thermal management system for eVTOLs.

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.

Integrated Transportation Case: Planning of Future Transport Infrastructure/Connectivity In Dar es Salaam

Urbanization poses significant challenges to transportation systems in rapidly growing cities like Dar es Salaam. This paper examines the potential of Bus Rapid Transit (BRT) and Intelligent Transport Systems (ITS) to address transportation issues in the Ilala municipality. Despite ongoing infrastructure projects, the city lacks a comprehensive master plan to support its projected growth. The research underscores the importance of integrating infrastructure improvements and ITS to optimize the BRT system in Ilala. The study begins with an analysis of the current transportation landscape, highlighting deficiencies in road networks and public transportation that lead to severe congestion and unreliable commutes. Key stakeholders are identified, and the necessity of their involvement in transportation planning is emphasized. The research questions focus on enhancing stakeholder engagement, integrating ITS, and improving road infrastructure design. A mixed-methods approach is employed, including literature reviews, case studies of successful BRT implementations worldwide, and primary data collection through interviews, surveys, and focus groups with various stakeholders. This comprehensive approach provides actionable insights for enhancing urban mobility in Ilala. Future recommendations include upgrading the BRT system to a Level 3 configuration, incorporating elevated BRT sections to separate bus lanes from other traffic, and optimizing the design of lanes, stations, and vehicles to improve efficiency. Additionally, the paper discusses the potential benefits of a well-implemented ITS, providing real-time information to commuters and enhancing overall transportation experience. By addressing these challenges, the research aims to improve the quality of life for Dar es Salaam residents, stimulate economic growth, and contribute to a more sustainable urban environment. The findings and recommendations offer a strategic framework for future transportation planning in Ilala and beyond

O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

Driving Towards Sustainability: A Comprehensive Review of Electric Vehicles

This research summarizes research on electric vehicles (EVs) and sustainable transit solutions, highlighting advancements in battery technologies, motor efficiency, and vehicle-to-grid (V2G) integration. It explores the conversion of a Rover Mini into the electric E-MINI and discusses the potential of proton exchange membrane (PEM) fuel cells and hybrid electric vehicle (HEV) architectures. The study emphasizes the importance of effective power management and infrastructure upgrades to support EV adoption. Despite inherent complexities, innovative designs offer promising solutions for future transportation sustainability. advanced strategies for optimizing electric powertrains, transit networks, and fast charging systems to enhance sustainability in electromobility. Additionally, analysis is done on how quick charging affects Li-ion battery deterioration, revealing insights into mitigating effects and identifying cost-effective solutions for various battery technologies. Overall, this comprehensive investigation contributes crucial insights for advancing sustainable electric mobility.

Comparison of transfer trajectory to NRHO and operation plan for logistics resupply mission to gateway

NASA and other space agencies have been planning the construction of a space station in lunar orbit called the Gateway. They have also begun studies for its resupply missions using an HTV-X. There are several ways to transition to NRHO, a candidate orbit. In-Direct Transfer (IDT) has a short transition period of seven days, but the required ΔV is inefficient at more than 400 m/s. The Weak Stability Boundary (WSB) transfer requires a small ΔV, less than 100 m/s, but the transition period is more than 100 days. This paper proposes the Perilune Rendezvous Method (PRM), recently discovered in 2022. Its transition period is two weeks longer than IDT, but the period is much shorter than WSB. The required ΔV is 40 m/s lower than IDT but still larger than WSB. The PRM has an intermediate ΔV and a transition period between IDT and WSB. Since fuel consumption and transition duration requirements depend on the mission, the PRM expands the scope of future cargo transportation. To adopt the PRM in a mission, an efficient trajectory correction plan was found based on a Monte Carlo analysis that considered navigation and control errors. It confirmed that the PRM could rendezvous with Gateway with higher accuracy than IDT.

Методология анализа функционирования и прогнозирования развития региональной транспортной сети (на примере Европейской и Приуральской Арктики)

The article is devoted to the development of theoretical and methodological approaches to the fundamentals of analyzing the functioning and forecasting of the current and future transport network of the European and Cisural Arctic in modern economic conditions, taking into account: the location of promising sources of freight flows in the territory under consideration, data on the carrying and capacity of the network and its development prospects. Considering the incompleteness of time series of statistical indicators characterizing the operation of the transport network, the study, along with classical methods of forecasting traffic volumes, pays significant attention to scenarios for its development. The paper describes the transport network of the European and Cisural Arctic of the Russian Federation, defines strategic planning, and shows the importance of analyzing the functioning of the transport network of the region. Forecasts of traffic volumes have been obtained, their assessment and scenarios for the development of the transport network have been compiled using a combined forecasting system. A methodology has been developed for analyzing the functioning and forecasting the development of the transport network of the European and Cisural Arctic.

A Method for Air Route Network Planning of Urban Air Mobility

Urban air mobility is an effective solution to address the current issue of ground traffic congestion in future cities. However, as the user scale continues to expand, the current civil aviation flight scheduling and control methods are becoming inadequate to meet the high-volume flight guarantee demands of future urban air transportation. In order to effectively handle and resolve potential issues in this field in the future, this paper proposes a method for planning urban air mobility route networks. The planning process is divided into two stages: construction and optimization. Methods for constructing urban air mobility route networks based on flight routes and global optimization methods based on node movement are proposed in each stage. In the construction stage, a complete construction process is designed to generate routes based on existing flight routes, in line with the trend of urban air transportation development. In the optimization stage, inspired by the ant colony algorithm, node transfer rules and information transfer rules are incorporated to design a global optimization process and algorithm for route networks. Experimental results demonstrate the effectiveness and advancement of the proposed planning method.

Refined mode classification and performance analysis method of dual-mode scramjet vehicles for flight trajectory optimization

Hypersonic air-breathing vehicles, utilizing ramjet and scramjet propulsion systems, are emerging as a promising option for future hypersonic transportation due to high specific impulse. The performance of hypersonic aircraft is significantly influenced by their flight trajectory, as combustion efficiency and aerodynamic forces vary markedly with altitude. Although numerous studies have focused on optimizing these trajectories, they have not adequately considered the potential for operational failures in the propulsion system, such as unstart and blowout. This study introduces a trajectory optimization approach for hypersonic aircraft that proactively addresses and mitigates these operational failures. This is achieved by establishing an operational classification process for a dual-mode scramjet engine and proposing a failure mode avoidance strategy using a Gaussian process classifier. Optimized ascent trajectories are achieved through the development of a two-stage robust sequential convex programming approach. The results of trajectory optimization indicate that the failure mode avoidance strategy proposed is crucial in obtaining minimal time trajectories, and that the optimal trajectories include drop motion in the initial stage flight in order to increase combustion efficiency.