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This chapter describes the Transportation 5.0 paradigm, providing ideas for the transformation of the transportation industry from Industry 4.0 to Industry 5.0. Transportation 5.0 is based on parallel intelligence (PI) as the theoretical foundation, with artistic societies-computational experiences-parallel execution (ACP) as the basic method, and cyber-physical-social transportation systems (CPSTS) as the framework, enabling the transportation system to smoothly transition to an ethical, responsible, and sustainable intelligent transportation paradigm. Firstly, the CPSTS framework was proposed, and the theories followed and goals pursued by Transportation 5.0 were explored. The social nature of intelligent transportation systems was explained. Furthermore, key supporting technologies for Transportation 5.0, including a series of enabling technologies for parallel transportation robots, were provided. Then, the application of Transportation 5.0 in the fields of transportation was demonstrated. At the same time, the challenges and potential research directions of Transportation 5.0 were explored.
Shenzhen Zhongwe Electronics Co., Ltd, Shenzhen, China
Shizi Ma
Coventry University, Coventry, England
Sheng Wang*
Harbin Institute of Technology (Weihai), Weihai, China
*Address all correspondence to: 2200510129@stu.hit.edu.cn
1. Introduction
The proposal of Transportation 5.0 can be traced back to 2017 or even earlier [1], and it is worth noting that it is still 3 years since the European Commission proposed “Industrial 5.0” in September 2020. However, with the continuous development of the Transportation 5.0 paradigm, we have found that the Transportation 5.0 paradigm and the Industrial 5.0 paradigm are similar in many aspects [2]. Therefore, we can further develop Transportation 5.0 as the transportation paradigm of the Industrial 5.0 era. Therefore, based on Dr. Wang’s research, this chapter further redefines Transportation 5.0 from four perspectives: its objectives, theoretical basis, basic methods, and framework, in order to make it more in line with the development requirements of Industry 5.0.
Dr. Wang was the first to introduce CPSS into the Transportation 5.0 paradigm and pointed out that the transportation system is using methods such as ACP and PI theory to present a sustainable and social-centric trend [3]. Meanwhile, in the context of Industry 5.0, travel, as a major application scenario in transportation systems, also has a sustainable trend [4]. Furthermore, it is undeniable that communication technology, as the fundamental technology applied to Transportation 5.0, should also be given special attention to how to make communication in the Transportation 5.0 paradigm more sustainable [5, 6]. Furthermore, a comparison of existing studies on Transportation 5.0 is presented in Table 1.
Comparison of research on Transportation 5.0 (Y: yes, N: no).
Transportation systems can be divided into four main categories: road vehicles, railways, aviation, and ocean transportation. It is worth noting that in the United States, highway transportation uses approximately 84% of all transportation energy. Therefore, due to space limitations, this chapter focuses on the Transportation 5.0 framework based on unmanned vehicles and drones, which does not deny the irreplaceable role of railway and ocean transportation in transportation systems. The current transportation system consists of many subsystems, which may lead to the collapse of the entire system if all subsystems are embedded into the system for operation, or if there is disharmony between subsystems. For example, early research on vehicle route planning (VRP) focused on the efficiency and economy of transportation systems [7]. However, environmentalists are more concerned about the sustainability of transportation systems [8] and the final routes provided by the two systems can be said to run counter to each other.
The transportation system is a social and technological system composed of transportation infrastructure (highways, railways, stations, parking lots, etc.), vehicles, and humans. Therefore, it is necessary to introduce technical ethics into Transportation 5.0. In addition, the uncertainty and complexity of the transportation system caused by human activities and social operations also need to be considered in the Transportation 5.0 paradigm. Industry 5.0 puts forward requirements for the elasticity, sustainability, and people-oriented nature of industrial systems [9]. The Cyber-Physical System CPS framework, as the core technology of Industry 4.0, focuses on how to use technological means to deeply integrate physical space and cyberspace, while neglecting the people-oriented nature of technology. Prior to the proposal of Industry 5.0, relevant researchers had conducted a preliminary exploration of the people-oriented approach.
In order to meet the requirement of “people-oriented”, the concept of “Cognitive” was introduced based on the CPS framework, and the cognitive cyber-physical system (C-CPS) was proposed, aiming to make the framework meet the requirements of the Human Rights Council [10, 11]. In addition, another framework is human cyber-physical systems (HCPS) or cyber-physical human systems (CPHS), which attempts to introduce the concept of “Human” into CPS and establish a people-oriented CPS framework [12, 13]. Another similar framework is Cyber-physical-social systems (CPSS), which is also proposed for human-machine symbiotic systems, aiming to study the impact of human and social factors on large-scale complex systems [14]. Although the above three frameworks have a certain degree of application in different fields, in existing research, the main application fields of C-CPS and CPHS still remain in industrial fields such as factories, production lines, and manufacturing [10, 11, 12, 13], rather than in the transportation field where human beings are highly involved. The “social system” in CPSS better reflects the impact of the entire human society on the system.
Therefore, in order to reasonably present the impact of the entire human society on the transportation system, this chapter selects CPSS as the framework and proposes cyber-physical-social transportation systems (CPSTS) based on CPSS, providing an overall overview of Transportation 5.0. Summarize and provide eight aspects that the transportation system should focus on under the Transportation 5.0 paradigm, namely efficiency, reliability, sustainability, service, social equity, economic benefits, and fulfilling ethics and responsibilities. Based on the theory of parallel intelligence, a transportation system based on unmanned vehicles and multimodal transportation modes was designed, which further demonstrates how CPSTS operates using advanced sensing, communication, and automation equipment. Furthermore, the main application scenarios of CPSTS are presented, based on the transportation scenarios of connected automated vehicle (CAV) and unmanned aerial vehicle (UAV). In addition, some other technologies and systems related to the Transportation 5.0 paradigm have been described.
According to the smart park logistics system and power grid inspection system established based on CPSS [9, 15], this chapter establishes a Transportation 5.0 paradigm based on parallel intelligence (PI) theory, ACP as the basic method, and CPSTS as the framework. Figure 1 shows the overall overview of the Industry 5.0 paradigm. As previously mentioned, CPSS, as a variant of CPS, aims to introduce “social system” into CPS. PI theory is a theory about systems science that mainly studies and describes the interaction and collaborative behavior of various components in complex systems.
Figure 1.
Overview of Transportation 5.0.
The definition of PI theory is as follows:
Definition 1: Each subsystem in a complex large-scale system is interdependent and can interact with each other in real time. When the system is running, each subsystem runs simultaneously and does not follow a linear time series.
The core concept of PI theory is parallelism, that is, parallel operation and parallel interaction. Parallel operation refers to the ability of various parts of a system to operate and process information simultaneously, without the need to follow a strict sequence. Parallel interaction refers to the interaction and interaction between various components in a system, forming a common whole that can significantly improve the efficiency and stability of the system.
The ACP method is a method used to study complex large-scale systems. This method combines parallel computing techniques in computer simulation and PI theory to simulate and analyze large-scale complex systems through parallel execution and computational experiments. The ACP method first constructs an artificial system that maps the entire physical social world to the cyber world. Furthermore, a series of computational experiments were designed and executed to simulate and analyze the behavior and dynamic changes of large-scale complex systems. Finally, through advanced internet technology, real-time transmission of experimental results from the artificial world to the real world for parallel operation.
2.1 Cyber-physical-social transportation system framework
Figure 2 shows the framework of CPSTS. CPSTS consists of three systems: social system, physical system, and artificial system. The software transportation robot (shown in Figure 2 as a software vehicle) serves as the carrier of CPSTS and is responsible for performing artificial system design-related experiments and transmitting the experimental results in real-time to the real world. To ensure the availability of experimental results, it is necessary to ensure the accuracy and reliability of the relevant scene models in the scene library. This requires real-time, comprehensive, and accurate modeling of the entire transportation system. The data in the database includes traffic flow, average speed, vehicle type, crowd density, and other traffic data.
Figure 2.
Framework of cyber-physical-social transportation systems.
2.2 Evaluation indicators of CPSTS
The eight areas that the transportation system should focus on under the Transportation 5.0 paradigm are efficiency, reliability, sustainability, service, social equity, economic benefits, and fulfilling ethics and responsibilities [16, 17].
2.2.1 Efficiency and reliability of CPSTS
The CPSTS will use advanced sensing devices to transmit relevant data in real-time to artificial systems, monitoring traffic flow, road conditions, and traffic signals. Therefore, the efficiency of CPSTS under the Transportation 5.0 paradigm can be optimized by utilizing perception technology, real-time data, and intelligent algorithms for traffic management. In addition, CPSTS can provide personalized traffic navigation and route optimization functions for humans, improve the efficiency and convenience of public transportation, and reduce accidents and malfunctions. CPSTS has the ability to operate stably in different situations and provide accurate and reliable traffic information and services, with robust reliability. Therefore, CPSTS needs to have strong data collection and processing capabilities, redundant backup capabilities for multi-source data, real-time monitoring, and fault detection capabilities. It is worth pointing out that the disaster recovery speed and emergency response speed of the transportation system are also indicators for evaluating the reliability of CPSTS.
2.2.2 Service level and sustainability of CPSTS
CPSTS aims to provide users with higher service quality and satisfaction, thereby improving the service level of the system. More specifically, CPSTS can provide more timely services, both in logistics and transportation, while ensuring the safety of passengers, goods, and vehicles. On this basis, CPSTS can provide a comfortable and convenient passenger experience, while ensuring service accessibility and coverage. The sustainability of the transportation system can be reflected in several directions such as vehicle development, infrastructure construction, and route planning. CPSTS uses energy-efficient technologies and facilities to reduce energy consumption. For example, measures such as introducing new energy vehicles, improving the efficiency of vehicle power systems, and improving traffic signal control systems can reduce vehicle exhaust pollution and energy consumption. In addition, CPSTS can reduce traffic demand and congestion by optimizing the layout of transportation roads and urban design.
2.2.3 Social equity and economic benefits of CPSTS
CPSTS can ensure that all members of society have equal access to transportation services and opportunities, without unfair impacts due to differences in personal background, income level, or geographical location. CPSTS can ensure that transportation services are easily accessible to all populations, have wide coverage within the city, and have reasonable and equal costs. Differentiated fare policies, it will not cause excessive pressure on economically disadvantaged populations. At the same time, the design of CPSTS should take into account the needs of various groups of people, including the elderly, disabled people, and other special groups. For example, setting up accessible facilities, and providing convenient user interfaces and information transmission methods to ensure that they have equal access to transportation services. Furthermore, through technological means, the ineffective driving distance during transportation can be reduced, thereby reducing transportation costs for enterprises and ensuring their economic benefits.
2.2.4 The moral and social responsibilities that CPSTS needs to fulfill
CPSTS will better fulfill relevant ethical and social responsibilities, including (1) data privacy and protection responsibilities. System operators should take necessary security measures to protect user data from illegal acquisition, abuse, or leakage, and comply with relevant data privacy laws and regulations. (2) Fairness and equal responsibility, CPSTS should treat all users fairly during its operation, without discrimination against any specific group, and ensure the fair distribution of transportation resources and services. (3) Safety and risk management responsibilities, CPSTS should take necessary measures to prevent traffic accidents, and promptly handle and report accident events to ensure the safety and reliability of the system.
Because the supporting theory of CPSTS is the PI theory, parallel transportation robots are considered carriers of the Traffic 5.0 paradigm. As previously mentioned, this chapter focuses on CPSTS based on unmanned vehicles and drones. Therefore, parallel robots can be specifically divided into two categories: parallel autonomous vehicles and parallel UAVs. Eq. (1) shows the components of parallel transportation robots.
Parallel Transportation Robot=∑Physical Transportation Robot+∑Software Transportation Robot+∑Database of ScenariosE1
Parallel robots are a combination of physical robots, software robots, artificial experimental systems, the Internet of Things, and databases. Parallel transportation robots have added virtual control and parallel execution compared to ordinary machine robots.
3.2 Vehicle road collaboration technology
With the development of technologies such as unmanned aerial vehicles (UAVs) and intelligent connected vehicles (CAVs). It can be foreseen that in the future, clean transportation vehicles such as CAVs and UAVs will be widely used in transportation systems. Therefore, Figure 3 shows the cutting-edge technologies used in CPSTS based on CAVs and UAVs. CPSTS and its subsystems use a large number of sensors, so networked sensor technology is important in 10 minutes. Networked sensor technology provides guarantees for data collection and transmission throughout the entire smart city. In this section, we will briefly introduce airborne sensors such as LiDAR, millimeter wave radar, ultrasonic radar, and cameras. On this basis, we introduced several technologies used in parallel transportation systems, including road side unit (RSU) and cellular vehicle-to-everything (C-V2X) communication.
Figure 3.
Cutting-edge technologies used in CPSTS.
Airborne sensors: LiDAR obtains target status information in the environment, including speed, distance, and other information, by transmitting and receiving laser beam signals; millimeter wave radar determines the distance between vehicles and targets by sending and receiving millimeter wave signals; ultrasonic radar is mainly used to identify obstacles within a short distance range; cameras are mainly used for pedestrian detection, signal lights, traffic sign recognition, and other work. It should be pointed out that ultrasonic radar is susceptible to physical conditions such as vehicle speed; millimeter microwave radar is more suitable for extreme weather conditions (such as rain, snow, and dust), but it is not easy to penetrate obstacles.
Road side unit: as the carrier of DSRC technology, RUS provides a hardware foundation for communication between roads and vehicles. During the working process, RSU obtains relevant information about various traffic participants (including all vehicles and pedestrians) and traffic signs through sensors such as radar and cameras and then broadcasts information to all vehicles within its coverage through the vehicle’s On-board unit (OBU). It mainly includes five types of messages: Road Side Information (RSI), Basic Safety Message (BSM), Signal phase timing message (SPAT), Road Safety Message (RSM), and Map Message (MAP). It should be pointed out that in scenes with many obstacles, DSRC’s communication performance may decrease due to the limitations of line-of-sight transmission technology.
C-V2X communication: as shown in Figure 1, C-V2X communication is mainly divided into four categories: vehicle-to-pedestrian (V2P), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle to network (V2N). At present, many V2X modules support the IEEE 802.11p standard DSRC and the 3GPP Release 14/15/16 standard C-V2X communication methods. During system operation, CAV and UAV receive signals sent by the cloud platform through 4G/5G networks and use software CAV/UAV to download algorithms on the built-in computer of the physical CAV/UAV to control themselves. In addition, a 5G pan low-altitude private network can be established through 5G base stations to solve the problems of airspace signal coverage and private transmission, thus achieving UAV network connectivity. Through the above technologies, CPSTS has the ability to connect and process data.
CAV/UAV intelligent perception: CAV intelligent perception refers to the perception of its own vehicle information, other vehicle information, and environmental information around the vehicle through various sensors, including cameras, LiDAR, gyroscopes, etc.; the intelligent perception of UAVs is also based on various sensors such as LiDAR, in order to obtain UAV’s own state information, other UAV’s state information, and surrounding environmental information. After the onboard sensors obtain this information, they transmit it to the parallel transportation system through the base station.
CAV/UAV automatic positioning: automatic positioning refers to the post-processing of automatic perception results, in which the UAV or CAV determines its accurate position relative to the external environment. In complex urban driving scenarios, the geometric shape of the road requires that the accuracy error of CAV positioning should not exceed 0.10 m and 0.17 deg. The current CAV/UAV positioning methods used include the Global Navigation Satellite System (GNSS), point cloud map positioning (also known as visual SLAM positioning), radar and camera fusion positioning, and vehicle networking positioning.
CAV/UAV automatic obstacle avoidance: the main purpose of CAV/UAV automatic obstacle avoidance is to reduce the incidence of accidents during driving, thereby avoiding losses to human society. It can be divided into the following two categories. The first type is obstacle avoidance between CAV/UAV and static obstacles such as buildings; The second type is CAV/UAV for obstacle avoidance against other dynamic obstacles such as pedestrians and birds.
CAV traffic control: CAV traffic control is based on the information of all traffic participants in complex scenes (such as intersections), and plans and controls all vehicles in the scene through a manager. When a vehicle approaches an intersection, it sends a vehicle status signal to the manager, who plans the driving route and speed for all vehicles within the same time period at the intersection based on the urgency of the task being performed by the vehicle (such as an ambulance).
CAV autonomous decision-making: during the CAV driving process, there are many route options between two points. We have established a traction model and a road damage model for CAVs to calculate the energy consumption required for each CAV to travel on each road section and the damage caused to the road surface by each CAV when passing through each road section. CAV will make independent decisions based on the following indicators: energy consumption of CAV; CAV damage to the road surface; real-time congestion situation of road sections; and the number of pedestrians on the road section.
CAV trajectory planning: based on the global path of CAV, in parallel transportation systems, we also need to consider local path planning of CAV to provide the complete driving trajectory of CAV. The local path planning of CAVs mainly includes three parts: CAV autonomous obstacle avoidance, CAV traffic control, and CAV autonomous decision-making.
UAV trajectory planning: the main steps include mobilizing the software UAV physical model in the database, mobilizing the algorithms in the algorithm library, and planning the flight trajectory of the drone based on multi-objective functions to minimize energy consumption during operation.
3.3 Energy conservation and emission reduction technology
In order to further improve the sustainability of parallel distribution systems, we consider CPSTS consisting of various types of vehicles, including electric trucks, hybrid trucks, and fuel trucks. Due to the need for charging during the use of electric trucks and hybrid electric trucks, this provides opportunities for the use of renewable energy technologies and further reduces the harm of CPTLS to the environment. We adopt a solar power generation model and install solar panels in the warehouse (also known as the distribution center) to power the vehicles and UAVs in CPSTS. The specific schematic diagram is shown in Figure 4.
Figure 4.
CPSTS power source schematic diagram.
During the operation of the system, CPSTS determines the number of customers it serves based on their emergency situation and the storage of solar panels installed in the warehouse. If the electricity demand of existing customers’ vehicles exceeds the amount of electricity stored in the solar panels, external power sources are connected to charge the CAV and UAV. The top of the CAV is also equipped with solar panels, which can provide some electricity to the UAV. When the UAV is not in service, it can supply electricity, which to some extent extends the working hours of the UAV.
3.4 Intelligent vehicle design based on digital twins
At present, most sustainable solutions are based on vehicle control or vehicle path selection, neglecting the impact on the overall transportation environment. However, as connected autonomous electric vehicles are the main driving force of modern transportation systems, automakers are also reassessing the sustainability of their value chains. Specifically, in terms of intelligent vehicle design, reference [18] aims to accurately predict the production capacity of the production line by establishing a digital twin system for the automotive production line. This work proposes a CPS system based on Digital Twin (DT), which establishes a digital twin production line by analyzing the operating process of the current car body production line. Reference [19] established a digital twin system for body control in automotive manufacturing enterprises, aiming to improve the development efficiency of CPS for automotive manufacturing. Based on the above research, this chapter establishes a vehicle design framework based on PI theory and parallel testing, as shown in Figure 5. On the other hand, the energy conservation and emission reduction of intelligent vehicles in the design stage can also be recovered from the vertical vibration process during vehicle movement, and corresponding vertical vibration energy recovery systems can be designed. In addition, hydrogen fuel cells and electric road systems also deserve further attention.
Figure 5.
A vehicle design framework based on PI theory and parallel testing.
3.5 Simulation techniques for environmental and social impacts
The simulation and measurement technology of environmental and social impacts, as an important technology in parallel distribution planning systems, distinguishes CPS from CPSS and is the core of CPSTS. This technology is used in CPSTS to evaluate the impact of the system on the environment, its harm to human society, and the degree of wear and tear of the system on infrastructure. In addition, in the autonomous driving subsystem, this technology is also used for vehicle-pedestrian autonomous obstacle avoidance. In CPSTS, we calculate the environmental impact of the entire system during operation by calculating the energy consumption and greenhouse gas emissions of all CAVs and UAVs in the entire system. Among them, greenhouse gas emissions include direct emissions from autonomous vehicles during driving. In addition, the electricity generated by solar energy in CPSTS may not be sufficient to power the entire system, so greenhouse gas emissions also include the greenhouse gas emissions generated by the electricity obtained from external sources (such as greenhouse gas emissions from thermal power generation). Furthermore, we evaluate the impact of the overall system on society through the accident risk of Occupational Safety and Health, CAV, and UAV, as well as the threat to pedestrians posed by the system.
Social behavior modeling is a dynamic model of pedestrian behavior based on “social forces” and elements such as pedestrian intentions, actions, and attributes. This model is of great significance in predicting pedestrian trajectories during vehicle-pedestrian obstacle avoidance. Among them, “social force” refers to the interaction between pedestrians, pedestrians, and obstacles, that is, the impact on pedestrians in the process of interacting with the environment. When predicting pedestrian trajectories based on social behavior models, methods such as neural networks, adversarial theory, and reinforcement learning are often used.
The application of CPS in logistics systems has been described, and relevant researchers have combined CPS technology with various aspects of the logistics system, aiming to monitor the entire logistics system and assist relevant personnel in decision-making [20]. The difference is that in the future, the demand for multimodal transportation in smart cities will continue to grow, so Transportation 5.0 will definitely adopt multimodal transportation modes. Therefore, this chapter demonstrates the superiority of multimodal transportation solutions in terms of logistics delivery time efficiency compared to traditional VRP problems. This chapter selects a single-vehicle delivery problem with 21 consumers (also known as customers) for case analysis. Considering that one of the main application scenarios of CPSTS is for the last-mile delivery to cities, the maximum speed of vehicles and the speed limit imposed by urban areas are taken into account, and the driving speed of vehicles is set at 40 km/h; The flight speed of the UAV is set at 80% of the maximum flight speed of the Horsefly drone, which is 16 m/s. The specific solution results are shown in Figure 6.
Figure 6.
Comparison between multimodal transportation systems and traditional VRP.
In Figure 6(a) and (b) are the results of using swarm intelligence algorithms to solve traditional VRP problems and joint delivery problems, respectively. The simulation experiment results show that the CAV and UAV joint delivery scheme designed in the chapter saves delivery time compared to the traditional VRP delivery mode when the delivery customer points are the same. Figure 6(c) shows the results of three-dimensional trajectory planning for delivery drones based on the I-GSO algorithm.
This chapter describes the paradigm of Transportation 5.0. Firstly, it describes the basic framework of CPSTS, aiming to further highlight the people-oriented goal of Transportation 5.0, thereby enabling the transportation system to smoothly transition to an ethical, responsible, and sustainable intelligent transportation paradigm. In addition, eight aspects of the transportation system under the Transportation 5.0 paradigm, including efficiency, reliability, sustainability, service, social equity, economic benefits, and fulfilling ethics and responsibilities, are presented. In addition, key enabling technologies for Transportation 5.0 have been provided, including parallel robot technology, vehicle road collaboration technology, energy conservation and emission reduction technology, intelligent vehicle design based on digital twins, and simulation and measurement technologies for environmental and social impacts. Finally, the application of Transportation 5.0 in the logistics field was demonstrated. Compared with existing work, this article provides a more comprehensive introduction to the Transportation 5.0 paradigm. However, there are still some shortcomings. In the future research process, the focus will be on studying multimodal transportation systems that combine air transportation, ocean transportation, rail transportation, and road transportation, in order to provide ideas for the development of Transportation 5.0.
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Written By
Zhitao Ma, Shizi Ma and Sheng Wang
Submitted: 06 October 2023Reviewed: 11 October 2023Published: 13 November 2023
Open access peer-reviewed chapter - ONLINE FIRST
