A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook. Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas. Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention. Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users. Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data. The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users

How Can Autonomous and Connected Vehicles, Electromobility, BRT, Hyperloop, Shared Use Mobility and Mobility-As-A-Service Shape Transport Futures for the Context of Smart Cities?

A smarter transport system that caters for social, economic and environmental sustainability is arguably one of the most critical prerequisites for creating pathways to more livable urban futures. This paper aims to provide a state-of-the-art analysis of a selection of mobility initiatives that may dictate the future of urban transportation and make cities smarter. These are mechanisms either recently introduced with encouraging uptake so far and much greater potential to contribute in a shift to a better transport paradigm or still in an embryonic stage of their development and yet to be embraced as powerful mechanisms that could change travel behaviour norms. Autonomous and connected vehicles are set to revolutionise the urban landscape by allowing machines to take over driving that for over a century has been exclusively a human activity, while electrical vehicles are already helping decarbonising the transport sector. Bus rapid transit has been steadily reinventing and rebranding conventional bus services revitalising the use of the humblest form of public transport, while hyperloop is an entirely new, disruptive, and somewhat provocative, travel mode proposition based on the use of sealed tube systems through which pods could travel free of air resistance with speeds exceeding 1000 km/h. Shared use mobility mechanisms like car-sharing, ride-sharing, ride-sourcing and public bicycles can help establishing a culture for using mobility resources on an as-needed basis, while mobility-as-a-service will take this sharing culture a step further, offering tailored mobility and trip planning packages that could entirely replace the need for privately owned modes of transport

Data enabling digital ecosystem for sustainable shared electric mobility-as-a-service in smart cities-an innovative business model perspective

Increase in urbanization drives the need for municipalities to make mobility more efficient, both to address climate goals as well as creating a smart living environment for citizens, with less noise congestion, and pollution. As vehicles are being electrified, further advances will be needed to meet social, environmental, and economic sustainability targets, and a more efficient use of vehicles and public transport is central in this endeavor. Accordingly, Electric Mobility as a Service (eMaaS) has developed as a concept with the potential to increase sustainability mobility in cities and been designated as a phenomenon with potential to radically change how people move in the future. But presently there is the lack of a common business model that supports complex integration of all actors, digital technologies, and infrastructures involved in the eMaaS business ecosystem. This study aims to support the further development of eMaaS by providing a state of the art of eMaaS and further proposes a digital ecosystem as a business model for eMaaS sharing in smart cities. Accordingly, a systematic literature review was adopted grounded on secondary data from the literature to offers a new approach to urban mobility and demonstrate the suitability of the eMaaS concept in smart communities. The digital ecosystem is designed based on system design approach. Findings from this study provides a sustainable policy perspective, discusses the challenges and opportunities towards the development of eMaaS and its impact on electrification of vehicles. Overall, findings from this study considers the role of electric vehicles as part of the mobility sharing economy. Recommendations from this study provides designs and strategies for eMaaS, the interrelations between eMobility and other everyday practices, strategically highlighting the positive benefits of eMaaS and broader policies to limit private car usage in cities.publishedVersio

Divergent Paths: An Analysis of the Autonomous Future in McLean County

Autonomous vehicles (AVs) are expected to arrive on public roads in the mid-term future, but will vary in their uses and level of self-driving capabilities. On the heels of the rise of shared mobility services from transportation network companies like Uber and Lyft, the combination of these technologies has generated the anticipation of a diminishing need for private car ownership. The promises of when AVs will arrive has been somewhat tempered in recent years, allowing the public and stakeholders valuable time to more adequately plan for their arrival. A yet undetermined outcome is the influence these new technologies will have on traveler behavior, which impacts nearly every aspect of transportation planning. This report highlights two divergent paths that the autonomous future is likely to usher in: One scenario is marked by a new mobility ecosystem which enables people and things to move faster, cleaner, cheaper, and safer than today. The other possibility is that the autonomous future is marked by a decrease in overall safety, increased congestion, abandonment of public transport systems, lack of privacy, and transportation deserts. Which of these futures comes to fruition is dependent on various competing forces from public entities and the private sector. This discussion aims to provide a ten-thousand-foot view of the myriad of changes that self-driving vehicles are likely to generate. This report was written for multiple purposes, both for the formal needs of the McLean County Regional Planning Commission (MCRPC), as well as a brief introduction for Bloomington-Normal-McLean County stakeholders to start planning for the autonomous future. The author hopes it will be utilized as a resource for ongoing intergovernmental discussion of smart cities, intelligent transportation systems, and public technology currently being conducted by MCRPC and local governments. In addition, it will serve as a supplement to the 2045 Long Range Metropolitan Transportation Plan for the Bloomington-Normal urbanized area

Advanced Mechanism Design for Electric Vehicle Charging Scheduling in the Smart Infrastructure

Electric vehicle (EV) continues to grow rapidly due to low emission and high intelligence. This thesis considers a smart infrastructure (SI) as an EV-centered ecosystem, which is an integrated and connected multi-modal network involving interacting intelligent agents, such as EVs, charging facilities, electric power grids, distributed energy resources, etc. The system modeling paradigm is derived from distributed artificial intelligence and modelled as multi-agent systems (MAS), where the agents are self-interested and reacting strategically to maximize their own benefits. The integration, interaction, and coordination of EVs with SI components will raise various features and challenges on the transportation efficiency, power system stability, and user satisfaction, as well as opportunities provided by optimization, economics, and control theories, and other advanced technologies to engage more proactively and efficiently in allocating the limited charging resources and collaborative decision-making in a market environment. A core challenge in such an EV ecosystem is to trade-off the two objectives of the smart infrastructure, of system-wide efficiency and at the same time the social welfare and individual well-being against agents’ selfishness and collective behaviors. In light of this, scheduling EVs' charging activities is of great importance to ensure an efficient operation of the smart infrastructure and provide economical and satisfactory charging experiences to EV users under the support of two-way flow of information and energy of charging facilities. In this thesis, we develop an advanced mechanism design framework to optimize the charging resource allocation and automate the interaction process across the overall system. The key innovation is to design specific market-based mechanisms and interaction rules, integrated with concepts and principles of mechanism design, scheduling theory, optimization theory, and reinforcement learning, for charging scheduling and dynamic pricing problem in various market structures. Specifically, this research incorporates three synergistic areas: (1) Mathematical modelling for EV charging scheduling. We have developed various mixed-integer linear programs for single-charge with single station, single-charge with multiple stations, and multi-charge with multiple stations in urban or highway environments. (2) Market-based mechanism design. Based on the proposed mathematical models, we have developed particular market-based mechanisms from the resource provider’s prospective, including iterative bidding auction, incentive-compatible auction, and simultaneous multi-round auction. These proposed auctions contain bids, winner determination models, and bidding procedure, with which the designer can compute high quality schedules and preserve users’ privacy by progressively eliciting their preference information as necessary. (3) Reinforcement learning-based mechanism design. We also proposed a reinforcement mechanism design framework for dynamic pricing-based demand response, which determines the optimal charging prices over a sequence of time considering EV users’ private utility functions. The learning-based mechanism design has effectively improved the long-term revenue despite highly-uncertain requests and partially-known individual preferences of users. This Ph.D. dissertation presents a market prospective and unlocks economic opportunities for MAS optimization with applications to EV charging related problems; furthermore, applies AI techniques to facilitate the evolution from manual mechanism design to automated and data-driven mechanism design when gathering, distributing, storing, and mining data and state information in SI. The proposed advanced mechanism design framework will provide various collaboration opportunities with the research expertise of reinforcement learning with innovative collective intelligence and interaction rules in game theory and optimization tools, as well as offers research thrust to more complex interfaces in intelligent transportation system, smart grid, and smart city environments

Shaping future low-carbon energy and transportation systems: Digital technologies and applications

Digitalization and decarbonization are projected to be two major trends in the coming decades. As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern. This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles. This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Spatial-temporal domain charging optimization and charging scenario iteration for EV

Environmental problems have become increasingly serious around the world. With lower carbon emissions, Electric Vehicles (EVs) have been utilized on a large scale over the past few years. However, EVs are limited by battery capacity and require frequent charging. Currently, EVs suffer from long charging time and charging congestion. Therefore, EV charging optimization is vital to ensure drivers’ mobility. This study first presents a literature analysis of the current charging modes taxonomy to elucidate the advantages and disadvantages of different charging modes. In specific optimization, under plug-in charging mode, an Urgency First Charging (UFC) scheduling policy is proposed with collaborative optimization of the spatialtemporal domain. The UFC policy allows those EVs with charging urgency to get preempted charging services. As conventional plug-in charging mode is limited by the deployment of Charging Stations (CSs), this study further introduces and optimizes Vehicle-to-Vehicle (V2V) charging. This is aim to maximize the utilization of charging infrastructures and to balance the grid load. This proposed reservation-based V2V charging scheme optimizes pair matching of EVs based on minimized distance. Meanwhile, this V2V scheme allows more EVs get fully charged via minimized waiting time based parking lot allocation. Constrained by shortcomings (rigid location of CSs and slow charging power under V2V converters), a single charging mode can hardly meet a large number of parallel charging requests. Thus, this study further proposes a hybrid charging mode. This mode is to utilize the advantages of plug-in and V2V modes to alleviate the pressure on the grid. Finally, this study addresses the potential problems of EV charging with a view to further optimizing EV charging in subsequent studies