Vehicles In Transportation System Research Articles

Memory-Event-Triggered Tracking Control for Intelligent Vehicle Transportation Systems: A Leader-Following Approach

Memory-Event-Triggered Tracking Control for Intelligent Vehicle Transportation Systems: A Leader-Following Approach

Decision making for selection of smart vehicle transportation system using VIKOR approach

Decision making for selection of smart vehicle transportation system using VIKOR approach

Simulating whole-body vibration for neonatal patients on a tire-coupled road simulator.

Exposure to excessive whole-body vibration is linked to health issues and may result in increased rates of mortality and morbidity in infants. Newborn infants requiring specialized treatment at neonatal intensive care units often require transportation by road ambulance to specialized care centers, exposing the infants to potentially harmful vibration and noise. A standardized Neonatal Patient Transport System (NPTS) has been deployed in Ontario, Canada, that provides life saving equipment to patients and safe operation for the clinical care staff. However, there is evidence that suggests patients may experience a higher amplitude of vibration at certain frequencies when compared with the vehicle vibration. In a multi-year collaborative project, we seek to create a standardized test procedure to evaluate the levels of vibration and the effectiveness of mitigation strategies. Previous studies have looked at laboratory vibration testing of a transport system or transport incubator and were limited to single degree of freedom excitation, neglecting the combined effects of rotational motion. This study considers laboratory testing of a full vehicle and patient transport system on an MTS Model 320 Tire-Coupled Road Simulator. The simulation of road profiles and discrete events on a tire-coupled road simulator allows for the evaluation of the vibration levels of the transport system and the exploration of mitigation strategies in a controlled setting. The tire-coupled simulator can excite six degrees-of-freedom motion of the transport system for vibration evaluation in three orthogonal directions including the contributions of the three rotational degrees of freedom. The vibration data measured on the transport system during the tire-coupled testing are compared to corresponding road test data to assess the accuracy of the vibration environment replication. Three runs of the same drive file were conducted during the laboratory testing, allowing the identification of anomalies and evaluation of the repeatability. The tire-coupled full vehicle testing revealed a high level of accuracy in re-creating the road sections and synthesized random profiles. The simulation of high amplitude discrete events, such as speed hump traverses, were highly repeatable, yet yielded less accurate results with respect to the peak amplitudes at the patient. The resulting accelerations collected at the input to the manikin (sensor located under the mattress) matched well between the real-world and road simulator. The sensors used during testing included series 3741B uni-axial and series 356A01 tri-axial accelerometers by PCB Piezotronics. These results indicate a tire-coupled road simulator can be used to accurately evaluate vibration levels and assess the benefits of future mitigation strategies in a controlled setting with a high level of repeatability.

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

SummaryIndustrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.

Development of a High-Precision and Lightweight Detector and Dataset for Construction-Related Vehicles

Effective vehicle detection plays a crucial role in various applications in cities, including traffic management, urban planning, vehicle transport, and surveillance systems. However, existing vehicle detection methods suffer from low recognition accuracy, high computational costs, and excessive parameters. To address these challenges, this paper proposed a high-precision and lightweight detector along with a new dataset for construction-related vehicles. The dataset comprises 8425 images across 13 different categories of vehicles. The detector was based on a modified version of the You Only Look Once (YOLOv4) algorithm. DenseNet was utilized as the backbone to optimize feature transmission and reuse, thereby improving detection accuracy and reducing computational costs. Additionally, the detector employed depth-wise separable convolutions to optimize the model structure, specifically focusing on the lightweight neck and head components. Furthermore, H-swish was used to enhance non-linear feature extraction. The experimental results demonstrated that the proposed detector achieves a mean average precision (mAP) of 96.95% on the provided dataset, signifying a 4.03% improvement over the original YOLOv4. The computational cost and parameter count of the detector were 26.09GFLops and 16.08 MB, respectively. The proposed detector not only achieves lower computational costs but also provides higher detection when compared to YOLOv4 and other state-of-the-art detectors.

Effect of Electric Vehicle Transportation and Carbon Capture System on Concept Ro-Ro ship Stability and EEDI

In terms of their service life, ships may operate for decades. Hence, it depicts the rapid development of machinery and equipment due to the substantial advancement of technology. Indeed, the ship’s systems must be updated to accommodate these new instruments. However, the importance of investigating the static-dynamic equilibrium and speed-power demand is a matter of concern as the ships are in motion on the water. There are currently limitations on carbon emissions from ships. To comply with these regulations, either the use of fuels that produce fewer carbon emissions or the use of after-treatment techniques to prevent the release of carbon into the atmosphere are employed. The difficulty of integrating any new system into an existing ship increases the scope of the renovation. This study compares the stability, speed-power, and EEDI values of today’s most popular electric vehicles while being transported on a concept Ro-Ro ship with and without a Carbon Capture System (CCS) ship. In the scenario where the ship transports both conventional and electric vehicles, the number of vehicles transported remains constant, but the effects of electric vehicles being heavier are illustrated. A ship with CCS and loaded with electric vehicles has 23.5% less maximum GZ than a regular ship with the traditional vehicles loaded condition by approximately 6% less at an angle of heeling. Also, the EEDI level is approximately one-twentieth of the conventional model, which is an advantage of CCS.

Tensor-Train-Based Multiuser Multivariate Multiorder Physical Markov Process Informed Multimodal Prediction for Industrial Trajectory Applications

By combining data driven approaches and physical laws for industrial trajectory prediction can improve the performance of industrial applications, such as path planning of robots, routing selection of vehicles in transportation system. In the era of industrial big data, the application of industrial trajectory prediction based on physical Markov process and tensor model has attracted much attention. To synchronously improve the prediction accuracy and computational efficiency, this paper proposes a tensor-train (TT) based multiuser multivariate multiorder (3M) physical Markov prediction approach for multimodal industrial trajectory pattern mining. First, we propose a TT-based unified product calculation rule with its scalable computation approach based on decomposed TT cores to speed up the execution efficiency. Then, a TT-based 3M (TT-3M) Markov transition approach is presented. Furthermore, we put forward a TT-based power method to calculate the stationary joint eigentensor (SJE) and an SJE-based multimodal prediction algorithm to mine concealed trajectory patterns. Experimental results based on real GPS trajectory dataset show that compared with the original tensor-based 3M approach, the TT-3M approach can improve the computational efficiency up to 3 times and reduce the memory space proportion to a minimum of 1‰ while ensuring basically consistent prediction accuracy.

State of Bicycle Modeling in SUMO

Microscopic traffic simulation tools provide ever-increasing value in the design and implementation of motor vehicle transport systems. Research and development of automated and intelligent technologies have highlighted the usefulness of simulation tools and development efforts have accelerated in recent years. However, the majority of traffic simulation software is developed with a focus on motor vehicle traffic and has limited capabilities in the simulation of bicycles and other micro-mobility modes. Bicycles, e-bikes and cargo bikes represent a non-negligible modal share in many urban areas and their impact on the operation, efficiency and safety of traffic systems must be considered in any comprehensive study. The Differentiation between different types of micro-mobility modes, including microcars, e-kick scooters, different types of bicycles and other personal mobility devices, has not yet attracted enough attention in the development of simulation software which creates difficulties in including these modes in simulation-based studies. On November 25th, 2022, members of the SUMO team at DLR organized a workshop to assess the state of bicycle simulation in SUMO, identify shortcomings and missing capabilities and prioritize the order in which bicycle traffic related features should be modified or implemented in the future. In this paper, different aspects of simulating bicycle traffic in SUMO are examined and an overview of the results of the workshop discussions is given. Some suggestions for the future development of SUMO emerging from this workshop, are presented as a conclusion.

Modeling and optimizing IoT-driven autonomous vehicle transportation systems using intelligent multimedia sensors

Modeling and optimizing IoT-driven autonomous vehicle transportation systems using intelligent multimedia sensors

Hybrid Framework for Safety Design of Human–Rail Vehicle Transportation System Using Stochastic Approach and Optimization

The human–rail vehicle transportation system safety design is complicated given that the complexity of the multilevel system with parameter uncertainties propagating from the vehicle structure (primary collision) to the interior human compartment (secondary collision). This study establishes a hybrid framework incorporating a stochastic approach and an integrated optimization strategy to improve train crashworthiness and reduce passenger crash injuries. The stochastic approach utilizes adaptive sparse polynomial chaos expansion models and variance-based sensitivity indices to evaluate the statistic characteristics of system responses and quantify the contribution ranking of uncertain parameters to response variations. The optimization strategy integrating the evolutionary algorithm and the multi-criteria decision making (MCDM) is proposed to solve the non-uniqueness of Pareto optimal solutions. In the optimization process, the modified DEMATEL–ANP method with interval type-2 fuzzy sets is developed to deal with vague linguistic judgments for the importance sequence of human injury responses. The q-rung orthopair trapezoidal fuzzy uncertain linguistic sets–TOPSIS method is established to address hesitant linguistic evaluations for the Pareto front and select the final optimal solution. Compared with the initial design, the driver Abbreviated Injury Scale (AIS) 3+ joint injury probability is reduced from 67.08% to 14.17% after optimization. Results prove that the proposed framework is a practical tool for improving the passive safety of railway industry.

Perceptions of Stress Among Pedestrians in Fully Automated Vehicle Transportation Systems

Fully automated vehicle (FAV) technology is recognized to offer a variety of benefits, such as increased safety, a drop in emissions, and better quality of life for people with disabilities or without a driving license. Despite the benefits, it has not been widely adopted. Demographics and user personalities play a large role in technology adoption, but nonusers’ involvement is often overlooked. Considering the current attention on FAV technology and its users, pedestrians (who do not use it) are understudied. In future, FAVs may be deployed once they have been proven safe and accepted by the public. This study aimed to provide insights into how pedestrians’ emotional perceptions might influence their adoption of FAVs. Within-subject research was conducted using virtual reality-based videos to assess pedestrians’ perceptions of stress. Videos showed identical first-person-view walking scenarios of various spatial contexts and activities in urban transportation networks comprising all-human-driven vehicles (HDVs) and futuristic all-FAVs. The results indicated that pedestrians’ perceptions of stress differed significantly by vehicle type, spatial context, and activity. Further, they differed significantly by gender and whether walking was perceived as safe. Walking in all-FAV transportation systems was perceived as more stressful than walking in all-HDV systems. Women tended to report higher levels of stress. Crossing roads and shared pathways were also associated with higher levels of perceived stress. Research related to pedestrians’ emotional perceptions could have important implications for future policymaking and urban planning.

Evaluation of Two Regional Transportation Systems Management and Operations Strategies for Improving the Response to Disabled Vehicles

Disabled vehicles are a common issue faced by agencies which can negatively affect traffic operations and safety. A wide range of response strategies exist, but determining which are best for a region can be challenging. After surveying 60 U.S. traffic incident management stakeholders, two commonly suggested improvements to disabled vehicle response strategies were: 1) expanding safety service patrols (SSPs) and 2) increasing towing capabilities. This paper develops a congestion-based methodology for quantifying the congestion reduction benefits of implementing these improved strategies for addressing disabled vehicles, specifically expanding SSP patrol hours and implementing instant dispatch tow (IDT) programs. Using 2019 incident data from three Florida, U.S., limited access roadways, the estimated congestion costs caused by disabled vehicles on the studied roadways were over $20 million. The estimated congestion savings for expanding Road Rangers (Florida’s SSP) ranged from $196,315 to $1,413,641 ($14,023 to $20,488 per incident). Implementing an IDT program had estimated congestion savings ranging from $398,353 to $3,910,299 ($9,264 to $76,485 per incident). IDT programs would address more disabled vehicle incidents than Road Ranger expansions, making the benefits substantially higher, especially on urban roadways. Based on the results, it is recommended that agencies prioritize IDT deployment on urban roadways with long incident durations for the most immediate benefits. An IDT program would also be able to address other types of incident, likely making the benefits greater than those estimated in this paper. Agencies nationwide can use this paper’s methodology and results to help identify effective regional disabled vehicle transportation systems management and operations strategies.

NegoSim: A Modular and Extendable Automated Negotiation Simulation Platform Considering EUBOA

In recent years, the research community has become increasingly interested in automated negotiation. It has been used in real-world systems such as autonomous vehicle transportation systems, smart grids, and e-commerce. Considering the broad range of applications, automated negotiation is of great use and interest to engineers, developers, and industry-focused researchers. Therefore, some researchers have developed platforms for automated negotiation. However, these systems do not provide complete control over the development of various protocols or analytic tools. This paper introduces NegoSim, a new negotiation simulator that introduces a new negotiation framework called EUBOA, though it also works on the well-known BOA framework. NegoSim is completely modular, with different APIs allowing users full control over developing and modifying automated negotiation agents, different assessments, protocols, and utility spaces, as well as the ability to create or modify different GUIs. NegoSim also includes predefined agents, protocols, utility space, and analytic tools, making it an ideal platform for researchers.

Measuring lane-changing trajectories by employing context-based modified dynamic time warping

The spatial lane-changing (LC) behavior should be analyzed for the vehicles in transportation systems in order to identify the patterns of vehicles’ movements using the similarities detected in their lane-changing trajectories. The trajectory of an LC vehicle is a function of its context. The present paper utilized spatial footprints and external/internal contexts to contextualize a measure applicable to the similarities found between LC trajectories. While only the external context of the previous investigation was constrained to the surrounding vehicles on the road, this study has investigated the idea of the contribution of solar radiation to the lane-changing trajectory patterns. The similarities found between multi-dimensional trajectories were determined by offering context-based modified dynamic time warping (CMDTW), and the CMDTW technique with the Next Generation Simulation (NGSIM) dataset was carefully analyzed. The weighting framework used for each dimension made it possible to quantify the similarities between lane-changing trajectories using the AHP technique. The obtained results showed that not only the lane-changing procedure depends on the conditions of the lane changer, but this procedure also depends on the solar radiation and the surrounding vehicles offered as the external contexts. Additionally, by including different dimensions, both internal and external contexts, the similarity results of LC trajectories turn into a more realistic phenomenon. The potential of the context-based modified dynamic time warping algorithm in the detection of a trajectory with the maximum similarity is also enhanced. Furthermore, in order to determine the LC trajectories, we used the Fuzzy C-means (FCM) clustering technique. We utilized Cohen’s kappa for the evaluation of the Fuzzy C-means results, and since the calculated Kappa score exceeds 0.8, the clustering algorithm has an excellent performance. The results obtained by comparing the suggested technique with commonly used similarity measurement techniques indicated that the accuracy of the CMDTW technique outperforms other techniques in the detection of the lane-changing trajectory patterns. The suggested CMDTW method has therefore been effective in the identification of the patterns of lane-changing trajectory.

Prioritization of unmanned aerial vehicles in transportation systems using the integrated stratified fuzzy rough decision-making approach with the hamacher operator

The Integration of Unmanned Aerial Vehicles (UAVs) into transportation systems has numerous benefits, ranging from the ability to record real-time data to having high mobility and broad vision. Because of the increasing levels of congestion in the cities, many of the transportation system tasks, such as traffic management and data collection, have become complicated to handle. The implementation of UAVs in transportation systems is a feasible solution to overcoming these problems. Therefore, UAVs have become a point of interest for many transportation system authorities. This study aims to introduce an integrated Fuzzy Level Based Weight Assessment (LBWA) in a stratification environment (SF-LBWA) and Fuzzy Rough Hamacher Combined Compromise Solution (CoCoSo) model to resolve the prioritization of Unmanned Aerial Vehicles in transportation system problems. The decision framework for such a problem is defined by establishing the relevant alternatives and criteria according to the specialized literature. Hence, a case study illustrates the formulation and solution of the problem. This study will indicate the most suitable alternative for the problem and provide guidance as authorities look for ways to integrate UAVs and transportation systems.

Automated Vehicle Path Following: A Non-Quadratic-Lyapunov-Function-Based Model Reference Adaptive Control Approach With C∞-Smooth Projection Modification

Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}^{\mathbf {1+\alpha }}$ </tex-math></inline-formula> tracking performance. Second, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{C}^{ \boldsymbol {\infty }}$ </tex-math></inline-formula> -differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.

Coordinated Planning of Electric Power and Natural Gas Distribution Systems With Refueling Stations for Alternative Fuel Vehicles in Transportation System

The alternative fuel vehicles (AFVs) which include electric vehicles (EVs) and natural gas vehicles (GVs) have gained much attention in recent years for delivering a clean and low-carbon urban mobility. The AFV development is promoting the coordination among electric power, natural gas, and transportation (ENT) networks. This paper proposes a coordinated planning method for investment on and operation of emerging urban energy infrastructures. The sites and sizes of refueling stations are optimized along with the expansion strategies for electric power distribution network (PDN) and natural gas distribution network (GDN). First, AFV refueling demands are analyzed according to the refueling station constraints and options, and transportation network (TN) constraints. Second, the coordinated planning model for PDN and GDN is formulated along with respective operation constraints in PDN, GDN, and TN. An efficient MILP solution method is developed for the proposed optimization problem. Finally, several case studies verify the effectiveness of the proposed model and its solution method. Compared with existing solutions, the proposed panning method offers a more accurate AFV refueling demand model with various refueling options. It is demonstrated that the coordinated planning can reduce investment and operation costs of urban energy infrastructures by exploring the pertinent interactions among constrained PDN, GDN, and TN.

Throughput evaluation of AGV transportation systems based on queueing modeling and AGV route decomposition

Automated guided vehicle (AGV) transportation systems are an important component of flexible warehouse systems, and research on the performance evaluation of AGV transportation systems contributes to the optimization and decision-making of flexible warehouse systems. Compared to simulation and experimental methods, analytic methods require less computational time, which is more applicable when integrated with optimization and decision-making methods. However, the previous analytical methods for performance evaluation neglected the velocity change and collision avoidance of AGVs, which decreased the evaluation accuracy. Therefore, herein, a throughput evaluation methodology based on AGV route decomposition is proposed for AGV transportation systems. In the proposed methodology, an AGV transportation system is modeled as an open queueing network model considering the velocity change of AGVs. The open queueing network model is decomposed into sub-models based on preplanned AGV routes, considering the collision avoidance among AGVs, to decrease the analysis complexity and computational time. Finally, the throughput of the AGV transportation system is obtained by the throughput sum of the sub-models corresponding to the unload stations. The numerical results indicate that the proposed methodology evaluates the throughput of an AGV transportation system with an accuracy of ≥94%. This study will facilitate the efficient throughput and decision-making of AGV transportation systems.

A neural network based multi-state scheduling algorithm for multi-AGV system in FMS

Due to its numerous advantages in terms of flexibility, maintainability and construction cost, the Flexible Manufacturing System (FMS) has received increasing attention over the last few years. The application of multi automated guided vehicle (AGV) transportation system promotes the flexibility of the FMS, which also imposes higher requirements for AGV scheduling algorithms. This paper develops a novel multi-state scheduling algorithm (MSSA) for AGV that makes a good trade-off between AGV utilization rate and total processing make-span in the FMS. Compared with classic idle-scheduling strategy, MSSA schedules more AGVs and tasks in each calculation, making its optimization target closer to the global optimization target. A neural network based travel time prediction is applied to improve the performance of the proposed algorithm on time accuracy. Five factors are used as neural network inputs to express the impact of vehicle state, distance travelled, AGV trajectory and multiple AGV collisions on travel time. Simulation experiments indicate the proposed algorithm has advantages in terms of total processing make-span, AGV load rate, AGV utilization and task execution time. The proposed algorithm has been applied in an actual air conditioner production line.

A hierarchical control scheme for multiple aerial vehicle transportation systems with uncertainties and state/input constraints

Multiple aerial vehicles have the potential to transport payload in any direction and with any orientation in Special Euclidean Group. In this article, the control problem of the multiple aerial vehicle transportation system with uncertainties and state/input constraints is considered. The multiple aerial vehicles connect with the load via spherical pairs that coincide with the center of mass of the aerial vehicles. A hierarchical control scheme of such challenging complex systems is proposed. The outer loop is designed by a tube-based model predictive control in order to deal with uncertainties, state constraints and input boundedness, while the inner loop is designed by robust control technique which forces the attitude tracking error of the aircraft into robust invariant set. The attitude tracking error of the inner loop induces a difference between the commanded and the actual equivalent wrench acting on the load. Such difference is treated as the equivalent disturbance of the outer loop to guarantee the fulfillment of the state and input constraints. The hierarchical control structure simplifies the design procedure, while still preserves the convergence and feasibility of the overall controlled system under uncertainties, which are proved strictly in the paper. Numerical simulations on a six-quadrotor transportation system are conducted to demonstrate the performance of the proposed control scheme. A real-world prototype including three quadrotors is developed. The hardware-in-the-loop simulation on the prototype supports the real-time feasibility of the proposed scheme.