Assessing the Potential of “Mobility as a Service” in Passenger Maritime Transport

This article assesses the potential of Mobility as a Service in passenger maritime transport from the supply perspective by collecting and analyzing data provided by interviews to key experts in passenger transport from both industry and academia. “Mobility as a service” in passenger maritime transport (also in this article referred as “Maritime MaaS”) describes the integration of passenger maritime services with land mobility into a single mobility service delivered through a unique platform for planning, booking, ticketing, and payment. The scope of this article is to explore the potential interest of mobility service providers to develop a MaaS that has as a backbone coastal shipping at the Aegean Archipelagos, in Greece. The Maritime MaaS ecosystem with its key actors is identified, while the perceived challenges, opportunities, and benefits envisaged by the adaptation of this innovative concept from urban transport to the maritime sector are recorded. Computer-assisted interviews were performed at a panel of 17 experts representing different types of decision makers. Participants were selected according to their current industry position or their academic profile. A content analysis with the use of NVIVO was conducted, followed by a SWOT (strengths, weaknesses, opportunities, and threats) analysis based on the experts’ input, in order to assess the MaaS business environment. Results indicate that the maritime transport sector is relatively ready to adopt MaaS from a technological readiness perspective, while land transport seems to be in a lower level of technological readiness. PAYG (pay as you go) MaaS business model is preferred than a “MaaS package” model by most stakeholders. Finally, main challenges toward MaaS implementation are the discrepancies in reliability of service among different transport modes and the ferry fleet operational flexibility ceilings that are imposed by legal framework for ferry routings in Greece.

Design and Evaluation of Remote Driving Architecture on 4G and 5G Mobile Networks

Despite the progress in the development of automated vehicles in the last decade, reaching the level of reliability required at large-scale deployment at an economical price and combined with safety requirements is still a long road ahead. In certain use cases, such as automated shuttles and taxis, where there is no longer even a steering wheel and pedals required, remote driving could be implemented to bridge this gap; a remote operator can take control of the vehicle in situations where it is too difficult for an automated system to determine the next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing more flexible working conditions and less standstill time of the truck. An important aspect of remote driving is the connection between the remote station and the vehicle. With the current roll-out of 5G mobile technology in many countries throughout the world, the implementation of remote driving comes closer to large-scale deployment. 5G could be a potential game-changer in the deployment of this technology. In this work, we examine the remote driving application and network-level performance of remote driving on a recently deployed sub-6-GHz commercial 5G stand-alone (SA) mobile network. It evaluates the influence of the 5G architecture, such as mobile edge computing (MEC) integration, local breakout, and latency on the application performance of remote driving. We describe the design, development (based on Hardware-in-the-Loop simulations), and performance evaluation of a remote driving solution, tested on both 5G and 4G mobile SA networks using two different vehicles and two different remote stations. Two test cases have been defined to evaluate the application and network performance and are evaluated based on position accuracy, relative reaction times, and distance perception. Results show the performance of the network to be sufficient for remote driving applications at relatively low speeds (<40 km/h). Network latencies compared with 4G have dropped to half. A strong correlation between latency and remote driving performance is not clearly seen and requires further evaluation taking into account the influence of the user interface.

Robot-Based Last-Mile Deliveries With Pedestrian Zones

Since delivery robots share sidewalks with pedestrians, it may be beneficial to choose paths for them that avoid zones with high pedestrian density. In this paper, we investigate a robot-based last-mile delivery problem considering path flexibility given the presence of zones with varying pedestrian level of service (LOS). Pedestrian LOS is a measure of pedestrian flow density. We model this new problem with stochastic travel times and soft customer time windows. The model includes an objective that reflects customer service quality based on early and late arrivals. The heuristic solution approach uses the minimum travel time paths from different LOS zones (path flexibility). We demonstrate that the presence of pedestrian zones leads to alternative path choices in 30% of all cases. In addition, we find that extended time windows may help increase service quality in zones with high pedestrian density by up to 40%.

Performance Evaluation of Different ToS Using Heterogeneous Communication Interfaces in FANETs

The use of different types-of-services (ToS), such as voice, data, and video, has become increasingly present in the execution of applications involving networks composed of multiple UAVs. These applications usually require the UAVs to share different ToS in a dynamic and ad-hoc manner, such that they can support the execution of cooperative/collaborative tasks. The use of heterogeneous communication has showed gains in maintaining the connection among highly mobile nodes, while increasing the reliable transmission of data, as is necessary in MANETS, VANETs and, more recently, FANETs. The aim of this paper is to present a performance evaluation of a heterogeneous interface manager (IM), which applies a heuristic to choose the best among several single- and multi-band wireless communication interfaces, including IEEE 802.11n, IEEE 802.11p, IEEE 802.11ac, and IEEE 802.11ax. Simulated scenarios with three, five, and eight UAV nodes are developed by integrating NS-3 and Gazebo simulation tools. The IM performance is analyzed by applying different numbers of interfaces and comparing with interfaces applied homogeneously by defining two set of results, in terms of application and MAC and PHY metrics, respectively. Finally, we also evaluate the associated performance, considering voice, data, and video streaming ToS. The results indicate that the combination of different interfaces has a very powerful effect on maintaining or increasing the communication intensity.

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction

This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors and floating cars as well as travel time measurements are combined using different fusion techniques. A novel fusion approach is developed, which extends existing speed reconstruction methods to integrate low-resolution travel time data. Several state-of-the-art methods and the novel approach are evaluated on their performance in reconstructing traffic speeds and travel times using various combinations of sensor data. Algorithms and sensor setups are evaluated with real loop detector, floating car and Bluetooth data collected during severe congestion on German freeway A9. Two main aspects are examined: 1) which algorithm provides the most accurate result depending on the used data and 2) which type of sensor and which combination of sensors yields highest estimation accuracy. Results show that, overall, the novel approach applied to a combination of floating-car data and loop data provides the best speed and travel time accuracy. Furthermore, a fusion of sources improves the reconstruction quality in many, but not all cases. In particular, Bluetooth data only provide a benefit for reconstruction purposes if integrated subtly.

Understanding the Last Mile Transportation Concept Impacting Underserved Global Communities to Save Lives During COVID-19 Pandemic

The underserved population could be at risk during the times of crisis, unless there is strong involvement from government agencies such as local and state Health departments and federal Center for Disease Control (CDC). The COVID-19 pandemic was a crisis of different proportion, creating a different type of burden on government agencies. Vulnerable communities including the elderly populations and communities of color have been especially hard hit by this pandemic. This forced these agencies to change their strategies and supply chains to support all populations receiving therapeutics. The National Science Foundation [National Science Foundation (NSF) Award Abstract # 2028612] funded RAID Labs to help federal agencies with strategies. This paper is based on a NSF funded grant to work on investigating supply chain strategies that would minimize the impact on underserved populations during pandemic. This NSF funded study identified the phenomena of last mile importance. The last mile transportation concept was critical in saving lives during the pandemic for underserved populations. The supply chain model then maximizes social goods by sending drugs or vaccines to the communities that need it the most regardless of ability to pay. The outcome of this study helped us prioritize the communities that need the vaccines the most. This informs our supply chain model to shift resources to these areas showing the value in real time prioritization of the COVID-19 supply chain. This paper provides information can be used in our healthcare supply chain model to ensure timely delivery of vaccines and supplies to COVID-19 patients that are the most vulnerable and hence the overall impact of COVID-19 can be minimized. The use of electrical vehicles for last mile transportation can help in significantly fighting the climate change.

The Role of Emerging ICT in the Ports: Increasing Utilities According to Shared Decisions

Geographical location, infrastructures, and services are the main consolidated pillars of a port in terms of its capacity to compete and cooperate with other ports. In the last years, a new pillar was identified: emerging technologies. Ports’ issues were initially solved with individual ICT solutions adopted by each decision-maker, which generated efficiencies in the three main port flows: cargo, information, and financial. However, new benefits and challenges are connected with the introduction of shared emerging ICT among decision-makers inside ports. The crucial issue concerns the fact that several decision-makers could share a decision about a single-port operation. Therefore, the effectiveness and efficiency of ports depend on how the interactions between the decision-makers are solved. Port operations are associated with movements (cargo) and transactions (information and financial) in a synchronic graph, which allows highlighting the role of emerging technologies in the modification of port operation generalized cost, considering the different decision-makers. The focal point concerns the building of a theoretical model using the formal equations of Transport System Models (TSMs) for the estimation of the cost for a Unit of Load (UL), e.g., a container traveling along a path, composed of a sequence of port operations, inside a port with and without emerging technologies. The proposed theoretical model provides the possibility of estimating ex ante the reduction of cost (port time of UL) given by introducing new technologies and a Port Community System (PCS). Different scenarios, considering some cases, ranging from the absence of ICT to the presence of a PCS, are compared, considering the different situations from a non-congested port to a congested one. The main results of the study and its novelty concern, on the one hand, the extension of TSMs to port systems, highlighting the problem of a non-single decision-maker (two or more) in some port operations and, on the other hand, the possibility of reducing the generalized cost (e.g., time) in the same operations in which there are concurrent decision-makers, through the use of an advanced PCS. The reported numerical example confirms the theoretical results. The work can be useful for researchers for port planners (e.g., port authorities) because it permits evaluating the utility for introducing shared emerging technologies using advanced PCS in a unified view.

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.