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1
Mendes, Lucas Mestres, Manel Rivera Bennàssar, and Joseph Y. J. Chow. "Comparison of Light Rail Streetcar Against Shared Autonomous Vehicle Fleet for Brooklyn–Queens Connector in New York City." Transportation Research Record: Journal of the Transportation Research Board 2650, no. 1 (January 2017): 142–51. http://dx.doi.org/10.3141/2650-17.
Повний текст джерелаАнотація:
Policy makers predict that autonomous vehicles will have significant market penetration in the next decade or so. In several simulation studies shared autonomous vehicle fleets have been shown to be effective public transit alternatives. This study compared the effectiveness of a shared autonomous vehicle fleet with an upcoming transit project proposed in New York City, the Brooklyn–Queens Connector light rail project. The study developed an event-based simulation model to compare the performance of the shared autonomous vehicle system with the light rail system under the same demand patterns, route alignment, and operating speeds. The simulation experiments revealed that a shared autonomous vehicle fleet of 500 vehicles of 12-person capacity (consistent with the EZ10 vehicle) would be needed to match the 39-vehicle light rail system if operated as a fixed-route system. However, as a demand-responsive system, a fleet of only 150 vehicles would lead to the same total travel time (22 min) as the 39-vehicle fleet light rail system. Furthermore, a fleet of 450 12-person vehicles in a demand-responsive operation would have the same average wait times while reducing total travel times by 36%. The implications for system resiliency, idle vehicle allocation, and vehicle modularity are discussed.
2
Gandomani, Roxana, Moataz Mohamed, Amir Amiri, and Saiedeh Razavi. "System Optimization of Shared Mobility in Suburban Contexts." Sustainability 14, no. 2 (January 13, 2022): 876. http://dx.doi.org/10.3390/su14020876.
Повний текст джерелаАнотація:
Shared mobility is a viable choice to improve the connectivity of lower-density neighbourhoods or suburbs that lack high-frequency public transportation services. In addition, its integration with new forms of powertrain and autonomous technologies can achieve more sustainable and efficient transportation. This study compares four shared-mobility technologies in suburban areas: the Internal Combustion Engine, Battery Electric, and two Autonomous Electric Vehicle scenarios, for various passenger capacities ranging from three to fifteen. The study aims to provide policymakers, transportation planners, and transit providers with insights into the potential costs and benefits as well as system configurations of shared mobility in a suburban context. A vehicle routing problem with time windows was applied using the J-Horizon software to optimize the costs of serving existing intra-community demand. The results indicate a similar fleet composition for Battery Electric and Autonomous Electric fleets. Furthermore, the resulting fleet for all four technologies is dominated by larger vehicle capacities. Due to the large share of driver cost in the total cost, the savings using a fleet of Autonomous Electric Vehicles are predicted to be 68% and 70%, respectively, compared to Internal Combustion and Battery Electric fleets.
3
Hyland, Michael F., and Hani S. Mahmassani. "Taxonomy of Shared Autonomous Vehicle Fleet Management Problems to Inform Future Transportation Mobility." Transportation Research Record: Journal of the Transportation Research Board 2653, no. 1 (January 2017): 26–34. http://dx.doi.org/10.3141/2653-04.
Повний текст джерелаАнотація:
This paper presents a taxonomy for classifying vehicle fleet management problems, across several dimensions, to inform future research on autonomous vehicle (AV) fleets. Modeling the AV fleet management problem will bring about new classes of vehicle routing, scheduling, and fleet management problems; nevertheless, the existing literature related to vehicle routing, scheduling, and fleet management is a valuable foundation for future research on the AV fleet management problem. This paper classifies the broadly defined AV fleet management problem by using existing taxonomic categories in the literature; adds additional, or more nuanced, dimensions to existing taxonomic categories; and presents new taxonomic categories to classify specific AV fleet management problems. The broadly defined AV fleet management problem can be classified as a dynamic multivehicle pickup and delivery problem with explicit or implicit time window constraints. Existing studies that fit into this class of fleet management problems are reviewed. New taxonomy categories presented in this paper include fleet size elasticity, reservation structure, accept–reject decision maker, reservation time frame, ridesharing, vehicle repositioning, underlying network structure, and network congestion. Two goals of the taxonomy presented in this study are to provide researchers with a valuable reference as they begin to model AV fleet management problems and to present novel AV fleet management problems to spur interest from researchers.
4
Hartmann, Martin, and Peter Vortisch. "A German Passenger Car and Heavy Vehicle Stock Model: Towards an Autonomous Vehicle Fleet." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 46 (June 17, 2018): 55–63. http://dx.doi.org/10.1177/0361198118782042.
Повний текст джерелаАнотація:
Automated vehicles are becoming a reality. Many pilot projects have already begun demonstrating the technological capabilities, as public authorities now allow the testing of automated vehicles in real traffic. To smooth the transition from a conventional to an automated fleet, effective fiscal and regulatory policies must be developed by governmental agencies. But at what rate will automated vehicles actually be adopted, and what automation technology will be available for use in new cars joining the national fleet? A national vehicle stock model can be used to answer these questions and to observe the aggregate impact of governmental policies on individual vehicle purchase decisions. In this paper, we present a passenger car and heavy vehicle stock cohort model that forecasts the diffusion of automation technology in Germany. The model uses national data on vehicle stock and vehicle utilization patterns on German freeways and predicts market shares of generic automation levels in predefined instances of a trend scenario. Results point toward market saturation of automated vehicles beyond 2050, with almost 90% of the passenger car fleet being classified as at least partially automatized by this date. The results also suggest that technology diffusion will be faster in the heavy vehicle fleet than in the passenger car fleet. This implies a positive correlation between emission-linked road user charges for heavy vehicles on the freeway network and the renewal rate of the heavy vehicle fleet. The forecast shares of automated vehicles can be used as an input for traffic flow simulations or as a basis for those infrastructure measures and traffic policies that are sensitive to the share of automated vehicles.
5
Moreno, Ana T., Andrzej Michalski, Carlos Llorca, and Rolf Moeckel. "Shared Autonomous Vehicles Effect on Vehicle-Km Traveled and Average Trip Duration." Journal of Advanced Transportation 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/8969353.
Повний текст джерелаАнотація:
Intermediate modes of transport, such as shared vehicles or ride sharing, are starting to increase their market share at the expense of traditional modes of car, public transport, and taxi. In the advent of autonomous vehicles, single occupancy shared vehicles are expected to substitute at least in part private conventional vehicle trips. The objective of this paper is to estimate the impact of shared autonomous vehicles on average trip duration and vehicle-km traveled in a large metropolitan area. A stated preference online survey was designed to gather data on the willingness to use shared autonomous vehicles. Then, commute trips and home-based other trips were generated microscopically for a synthetic population in the greater Munich metropolitan area. Individuals who traveled by auto were selected to switch from a conventional vehicle to a shared autonomous vehicle subject to their willingness to use them. The effect of shared autonomous vehicles on urban mobility was assessed through traffic simulations in MATSim with a varying autonomous taxi fleet size. The results indicated that the total traveled distance increased by up to 8% after autonomous fleets were introduced. Current travel demand can still be satisfied with an acceptable waiting time when 10 conventional vehicles are replaced with 4 shared autonomous vehicles.
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Soni, Aakash, and Huosheng Hu. "Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey." Robotics 7, no. 4 (November 1, 2018): 67. http://dx.doi.org/10.3390/robotics7040067.
Повний текст джерелаАнотація:
Autonomous/unmanned driving is the major state-of-the-art step that has a potential to fundamentally transform the mobility of individuals and goods. At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. This paper focuses on the next step in autonomous vehicle research, which is the collaboration between autonomous vehicles, mainly vehicle formation control or vehicle platooning. To gain a deeper understanding in this area, a large number of the existing published papers have been reviewed systemically. In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. Finally, both technical and implementation challenges for formation control are summarized.
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Reichsöllner, Emanuel, Andreas Freymann, Mirko Sonntag, and Ingo Trautwein. "SUMO4AV: An Environment to Simulate Scenarios for Shared Autonomous Vehicle Fleets with SUMO Based on OpenStreetMap Data." SUMO Conference Proceedings 3 (September 29, 2022): 83–94. http://dx.doi.org/10.52825/scp.v3i.113.
Повний текст джерелаАнотація:
In the past years the progress in the development of autonomous vehicles has increased tremendously. There are still technical, infrastructural and regulative obstacles which need to be overcome. However, there is a clear consent among experts that fully autonomous vehicles (level 5 of driving automation) will become reality in the coming years or at least in the coming decades. When fully autonomous vehicles are widely available for a fair trip price and when they can easily be utilized, a big shift from privately owned cars to carsharing will happen. On the one hand, this shift can bring a lot of chances for cities like the need of less parking space. But on the other hand, there is the risk of an increased traffic when walking or biking trips are substituted by trips with shared autonomous vehicle fleets. While the expected social, ecological and economical impact of widely used shared autonomous vehicle fleets is tremendous, there are hardly any scientific studies or data available for the effects on cities and municipalities. The research project KI4ROBOFLEET addressed this demand. A result of the project was SUMO4AV, a simulation environment for shared autonomous vehicle fleets, which we present in this paper. This simulation tool is based on SUMO, an open-source traffic simulation package. SUMO4AV can support city planners and carsharing companies to evaluate the chances and risks of running shared autonomous fleets in their local environment with their specific infrastructure. At its core it comprises the mapping of OpenStreetMap1 entities into SUMO objects as well as a Scenario Builder to create different operation scenarios for autonomous driving. Additionally, the simulation tool offers a recursive execution with different fleet sizes and optimization strategies evaluated by economic and ecologic parameters. As evaluation of the toolset a simulation of an ordinary scenario was performed. The workflow to simulate the scenario for shared autonomous vehicle fleets was successfully processed with the SUMO4AV environment.
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Chen, Hong Yun, Yan Qiang Li, Zi Hui Zhang, and Yong Wang. "Test Method for Decision Planning of Autonomous Vehicles Based on DQN Algorithm." E3S Web of Conferences 253 (2021): 03022. http://dx.doi.org/10.1051/e3sconf/202125303022.
Повний текст джерелаАнотація:
In February 2020, Beijing, China andCalifornia, USA respectively released road test reports of 2019 for autonomous vehicles. Beijing and California respectively represent the highest level of testing and application of autonomous vehicles in the two countries. This article will compare the test items, evaluation criteria and technical defects of each autonomous vehicle company in the road test reports of China and the United States, also analyze the existing problems, and propose an idea for the construction of a comprehensive test site for autonomous vehicles. This article aims to solve the prominently exposed problems in decision-making and planning in autonomous vehicles with DQN algorithm-base vehicle fleet, and to look forward to the future development trend of autonomous driving testing.
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Schlenther, Tilmann, Kai Martins-Turner, Joschka Felix Bischoff, and Kai Nagel. "Potential of Private Autonomous Vehicles for Parcel Delivery." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (September 10, 2020): 520–31. http://dx.doi.org/10.1177/0361198120949878.
Повний текст джерелаАнотація:
Using the same vehicles for both passenger and freight transport, to increase vehicle occupancy and decrease their number, is an idea that drives transport planners and is also being addressed by manufacturers. This paper proposes a methodology to simulate the behavior of such vehicles within an urban traffic system and evaluate their performance. The aim is to investigate the impacts of resignation from fleet ownership by a transport service company (TSC) operating on a city-wide scale. In the simulation, the service provider hires private autonomous cars for tour performance. Based on assumptions concerning the operation of such vehicles and TSCs, the software Multi-Agent Transport Simulation (MATSim) is extended to model vehicle and operator behavior. The proposed framework is applied to a case study of a parcel delivery service in Berlin serving a synthetic parcel demand. Results suggest that the vehicle miles traveled for freight purposes increase because of additional access and egress trips. Moreover, the number of vehicles en route is higher throughout the day. The lowering of driver costs can reduce the costs of the operator by approximately 74.5%. If the service provider additionally considers the resignation from fleet ownership, it might lower the operation cost by another 10%, not taking into account the costs of system transfer or risks like vehicle non-availability. From an economic perspective, the reduction of the overall number of vehicles in the system seems to be beneficial.
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Hogeveen, Peter, Maarten Steinbuch, Geert Verbong, and Auke Hoekstra. "Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach." Open Transportation Journal 15, no. 1 (May 17, 2021): 47–60. http://dx.doi.org/10.2174/1874447802115010047.
Повний текст джерелаАнотація:
Aims: Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society. Background: Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles. Objective: To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand. Methods: An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type. Results: The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles. Conclusion: Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.
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Scholz, Michael, Xu Zhang, and Jörg Franke. "Implementation of an Intralogistics Routing-Service Basing on Decentralized Workspace Digitization." Applied Mechanics and Materials 882 (July 2018): 90–95. http://dx.doi.org/10.4028/www.scientific.net/amm.882.90.
Повний текст джерелаАнотація:
The paper presents an intralogistics routing-service for autonomous and versatile transport vehicles. An infrastructural sensor digitize the workspace of the vehicle and is the basis for the vehicle-specific routing plan. Nowadays, a central computing unit allocates transportation task to a known number of automated guided vehicles, which are usually of the same type. Furthermore, this device generates a routing appropriate to the dimensions and the kinematic gauge of the vehicle fleet. The pathing for each specific vehicle is calculated and the result is send to the different entities. The approach of this paper bases on the digitization of the workspace with a ceiling camera, which divides the scenery into moving obstacles and an adaptive background picture. A central computing unit receives the background picture of several cameras and stitch them together to an overview of the entire workspace, e.g. a production hall. Furthermore, the approach includes the development of automated guided vehicles to versatile autonomous vehicles, were each entity is able to calculate the pathing on a given routing plan. A fleet of versatile autonomous vehicles consists of vehicles with task-specific dimensions and kinematic gauges. Therefore, each vehicle needs its own routing-plan. The solution is that each vehicles uses a vehicle parameter-server and register itself with these parameters at the routing unit. This unit is calculating a routing-plan for each specific vehicle dimension and gauge and providing it. When getting a new task, the vehicles uses this routing-plan to do the pathing. The routing-algorithm is implemented inside the service-layer of the versatile autonomous vehicle system. This approach lowers the amount of data, which is send between the service layer and the transportation entities by reducing the information of the workspace to the possible routes of each specific vehicle. Furthermore, the calculation time for routing and pathing is lowered, because each vehicle is calculating its task-specific path, but the route-map is calculated once for each vehicle-type by the routing-service.
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Turoń, Katarzyna, and Andrzej Kubik. "Economic Aspects of Driving Various Types of Vehicles in Intelligent Urban Transport Systems, Including Car-Sharing Services and Autonomous Vehicles." Applied Sciences 10, no. 16 (August 12, 2020): 5580. http://dx.doi.org/10.3390/app10165580.
Повний текст джерелаАнотація:
Nowadays, the concept of new mobility solutions like shared mobility systems is becoming more and more popular in current transport systems. The next technological step will be the idea of replacing traditional vehicles with autonomous ones. Because autonomous vehicles are a new concept in the automotive market, we dedicated this article to the idea of using autonomous vehicles as a part of car-sharing systems in intelligent, urban transport systems. The research herein is focused on the economic aspects of using autonomous vehicles in comparison to the classic car fleet available in car-sharing systems and to vehicles that belong to individual owners. We present our method for appropriate fleet selection based on the Delphi method and the calculations made through a scientific experiment performed based on Hartley’s plan. The results indicate the relation of travel parameters (including vehicle type) to the total cost of travel in urban transport systems. We also present the main terms related to autonomous vehicles. This article provides support for people who want to deepen knowledge about autonomous vehicles and new mobility solutions used in urban transport systems.
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Sheppard, Colin J. R., Gordon S. Bauer, Brian F. Gerke, Jeffery B. Greenblatt, Alan T. Jenn, and Anand R. Gopal. "Joint Optimization Scheme for the Planning and Operations of Shared Autonomous Electric Vehicle Fleets Serving Mobility on Demand." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (April 13, 2019): 579–97. http://dx.doi.org/10.1177/0361198119838270.
Повний текст джерелаАнотація:
As the transportation sector undergoes three major transformations—electrification, shared/on-demand mobility, and automation—there are new challenges to analyzing the impacts of these trends on both the transportation system and the power sector. Most models that analyze the requirements of fleets of shared autonomous electric vehicles (SAEVs) operate at the scale of an urban region, or smaller. A quadratically constrained, quadratic programming problem is formulated, designed to model the requirements of SAEVs at a national scale. The size of the SAEV fleet, the necessary charging infrastructure, the fleet charging schedule, and the dispatch required to serve demand for trips in a region are treated as decision variables. By minimizing both the amortized cost of the fleet and chargers as well as the operational costs of charging, it is possible to explore the coupled interactions between system design and operation. To apply the model at a national scale, key complications about fleet operations are simplified; but a detailed agent-based regional simulation model to parameterize those simplifications is leveraged. Preliminary results are presented, finding that all mobility in the United States (U.S.) currently served by 276 million personally owned vehicles could be served by 12.5 million SAEVs at a cost of $ 0.27/vehicle-mile or $ 0.18/passenger-mile. The energy requirements for this fleet would be 1142 GWh/day (8.5% of 2017 U.S. electricity demand) and the peak charging load 76.7 GW (11% of U.S. power peak). Several model sensitivities are explored, and it is found that sharing is a key factor in the analysis.
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Liang, Xiao (Joyce), S. Ilgin Guler, and Vikash V. Gayah. "Signal Timing Optimization with Connected Vehicle Technology: Platooning to Improve Computational Efficiency." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 18 (July 16, 2018): 81–92. http://dx.doi.org/10.1177/0361198118786842.
Повний текст джерелаАнотація:
This paper develops a real-time traffic signal optimization algorithm in the presence of connected and autonomous vehicles (CAVs). The proposed algorithm leverages information from connected vehicles (CVs) arriving at an intersection to identify naturally occurring platoons that consist of both CVs and non-CVs. Signal timings are then selected to optimize the sequence at which these platoons are allowed to discharge through the intersection to minimize total vehicle delay. Longitudinal trajectory guidance that explicitly accounts for vehicle acceleration and deceleration behavior is provided to the lead autonomous vehicle (AV) in any platoon to minimize the total number of stopping maneuvers performed by all vehicles. Simulation tests reveal that the proposed platoon-based algorithm provides superior computational savings (over 95%) compared with a previously developed algorithm that focuses on optimizing departure sequences of individual vehicles, with negligible changes in operational performance. The computational savings allow the platoon-based algorithm to accommodate intersections with four multi-lane approaches and left turns, whereas large computational costs limited the previous vehicle-based algorithm to only two single-lane approaches without conflicting left turns. Additional simulation tests of the platoon-based algorithm on these more realistic intersection configurations show that intersection performance increases as the penetration rate of CAVs in the vehicle fleet increases. However, the marginal benefits decrease rapidly after the fleet is composed of 40% CAVs.
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Hogeveen, Peter, Maarten Steinbuch, Geert Verbong, and Auke Hoekstra. "The Energy Consumption of Passenger Vehicles in a Transformed Mobility System with Autonomous, Shared and Fit-For-Purpose Electric Vehicles in the Netherlands." Open Transportation Journal 15, no. 1 (October 15, 2021): 201–9. http://dx.doi.org/10.2174/1874447802115010201.
Повний текст джерелаАнотація:
Aims: This article explores the tank-to-wheel energy consumption of passenger transport at full adoption of fit-for-purpose shared and autonomous electric vehicles. Background: The energy consumption of passenger transport is increasing every year. Electrification of vehicles reduces their energy consumption significantly but is not the only disruptive trend in mobility. Shared fleets and autonomous driving are also expected to have large impacts and lead to fleets with one-person fit-for-purpose vehicles. The energy consumption of passenger transport in such scenarios is rarely discussed and we have not yet seen attempts to quantify it. Objective: The objective of this study is to quantify the tank-to-wheel energy consumption of passenger transport when the vehicle fleet is comprised of shared autonomous and electric fit-for-purpose vehicles and where cheap and accessible mobility leads to significantly increased mobility demand. Methodology: The approach consists of four steps. First, describing the key characteristics of a future mobility system with fit-for-purpose shared autonomous electric vehicles. Second, estimating the vehicle miles traveled in such a scenario. Third, estimating the energy use of the fit-for-purpose vehicles. And last, multiplying the mileages and energy consumptions of the vehicles and scaling the results with the population of the Netherlands. Results: Our findings show that the daily tank-to-wheel energy consumption from Dutch passenger transport in full adoption scenarios of shared autonomous electric vehicles ranges from 700 Wh to 2200 Wh per capita. This implies a reduction of 90% to 70% compared to the current situation. Conclusion: Full adoption of shared autonomous electric vehicles could increase the vehicle-miles-travelled and thus energy use of passenger transport by 30% to 150%. Electrification of vehicles reduces energy consumption by 75%. Autonomous driving has the potential of reducing the energy consumption by up to 40% and implementing one-person fit-for-purpose vehicles by another 50% to 60%. For our case study of the Netherlands, this means that the current 600 TJ/day that is consumed by passenger vehicles will be reduced to about 50 to 150 TJ/day at full adoption of SAEVs.
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Zhou, Yefang, Hitomi Sato, and Toshiyuki Yamamoto. "Shared Low-Speed Autonomous Vehicle System for Suburban Residential Areas." Sustainability 13, no. 15 (August 3, 2021): 8638. http://dx.doi.org/10.3390/su13158638.
Повний текст джерелаАнотація:
In the context of global suburbanization and population aging, a low-speed, automated vehicle (LSAV) system provides essential mobility services in suburban residential areas. Although extensive studies on shared autonomous vehicle (SAV) services have been conducted, quantitative investigations on the operation of suburban LSAV systems are limited. Based on a demonstration pilot project of an autonomous vehicle called “Slocal Automated Driving”, we investigated the performance of an SAV system considering several scenarios in Kozoji Newtown, a suburban commuter town in Japan. The agent-based simulation results revealed that 40 LSAVs can satisfy the demands of 2263 daily trips with an average wait time of 15 min. However, in the case of a high-speed scenario, the same fleet size improved the level of service (LOS) by reducing the average wait time to two and a half minutes and halving the in-vehicle time. By contrast, the wait time in terms of the average and 95th percentile of the no-sharing ride scenario drastically deteriorated to an unacceptable level. Based on the fluctuations of hourly share rates, wait times, and the number of vacant vehicles, we determined that preparing for the potential fleet insufficiency periods from 7:00–13:00 and 15:00–18:00 can improve the LOS.
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Iacobucci, Riccardo, Raffaele Bruno, and Jan-Dirk Schmöcker. "An Integrated Optimisation-Simulation Framework for Scalable Smart Charging and Relocation of Shared Autonomous Electric Vehicles." Energies 14, no. 12 (June 18, 2021): 3633. http://dx.doi.org/10.3390/en14123633.
Повний текст джерелаАнотація:
Ride-hailing with autonomous electric vehicles and shared autonomous electric vehicle (SAEV) systems are expected to become widely used within this decade. These electrified vehicles can be key enablers of the shift to intermittent renewable energy by providing electricity storage to the grid and offering demand flexibility. In order to accomplish this goal, practical smart charging strategies for fleets of SAEVs must be developed. In this work, we present a scalable, flexible, and practical approach to optimise the operation of SAEVs including smart charging based on dynamic electricity prices. Our approach integrates independent optimisation modules with a simulation model to overcome the complexity and scalability limitations of previous works. We tested our solution on real transport and electricity data over four weeks using a publicly available dataset of taxi trips from New York City. Our approach can significantly lower charging costs and carbon emissions when compared to an uncoordinated charging strategy, and can lead to beneficial synergies for fleet operators, passengers, and the power grid.
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Kordelia, Cut Dona, and Armizoprades Armizoprades. "Autonomous Vehicle Radiation Based on Traffic Flow Analysis." Jurnal Teknik, Komputer, Agroteknologi Dan Sains 1, no. 1 (August 31, 2022): 77–84. http://dx.doi.org/10.56248/marostek.v1i1.21.
Повний текст джерелаАнотація:
The transportation sector is heading to a new futuristic era, the fully-autonomous vehicles. Radar, lidar, and Sonar are considered critical aspects of self-driving technology. However, these sensors come with inherent danger. Their exposure has a hazardous impact on humans and living things. A forecast made using the level of service classification shows that there will be excessive radiation exposure when these cars dominate the traffic. This prediction must be taken into account when designing a level-five autonomous fleet.
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Fagnant, Daniel J., Kara M. Kockelman, and Prateek Bansal. "Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market." Transportation Research Record: Journal of the Transportation Research Board 2563, no. 1 (January 2016): 98–106. http://dx.doi.org/10.3141/2536-12.
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Babicheva, Tatiana, Wilco Burghout, Ingmar Andreasson, and Nadege Faul. "Empty vehicle redistribution and fleet size in autonomous taxi systems." IET Intelligent Transport Systems 13, no. 4 (April 1, 2019): 677–82. http://dx.doi.org/10.1049/iet-its.2018.5260.
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Khoury, John, Kamar Amine, and Rima Abi Saad. "An Initial Investigation of the Effects of a Fully Automated Vehicle Fleet on Geometric Design." Journal of Advanced Transportation 2019 (May 26, 2019): 1–10. http://dx.doi.org/10.1155/2019/6126408.
Повний текст джерелаАнотація:
This paper investigates the potential changes in the geometric design elements in response to a fully autonomous vehicle fleet. When autonomous vehicles completely replace conventional vehicles, the human driver will no longer be a concern. Currently, and for safety reasons, the human driver plays an inherent role in designing highway elements, which depend on the driver’s perception-reaction time, driver’s eye height, and other driver related parameters. This study focuses on the geometric design elements that will directly be affected by the replacement of the human driver with fully autonomous vehicles. Stopping sight distance, decision sight distance, and length of sag and crest vertical curves are geometric design elements directly affected by the projected change. Revised values for these design elements are presented and their effects are quantified using a real-life scenario. An existing roadway designed using current AASHTO standards has been redesigned with the revised values. Compared with the existing design, the proposed design shows significant economic and environmental improvements, given the elimination of the human driver.
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König, Adrian, Sebastian Mayer, Lorenzo Nicoletti, Stephan Tumphart, and Markus Lienkamp. "The Impact of HVAC on the Development of Autonomous and Electric Vehicle Concepts." Energies 15, no. 2 (January 9, 2022): 441. http://dx.doi.org/10.3390/en15020441.
Повний текст джерелаАнотація:
Automation and electrification are changing vehicles and mobility. Whereas electrification is mainly changing the powertrain, automation enables the rethinking of the vehicle and its applications. The actual driving range is an important requirement for the design of automated and electric vehicles, especially if they are part of a fleet. To size the battery accordingly, not only the consumption of the powertrain has to be estimated, but also that of the auxiliary users. Heating Ventilation and Air Conditioning (HVAC) is one of the biggest auxiliary consumers. Thus, a variable HVAC model for vehicles with electric powertrain was developed to estimate the consumption depending on vehicle size and weather scenario. After integrating the model into a tool for autonomous and electric vehicle concept development, various vehicle concepts were simulated in different weather scenarios and driving cycles with the HVAC consumption considered for battery sizing. The results indicate that the battery must be resized significantly depending on the weather scenario to achieve the same driving ranges. Furthermore, the percentage of HVAC consumption is in some cases higher than that of the powertrain for urban driving cycles, due to lower average speeds. Thus, the HVAC and its energy demand should especially be considered in the development of autonomous and electric vehicles that are primarily used in cities.
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Boesch, Patrick M., Francesco Ciari, and Kay W. Axhausen. "Autonomous Vehicle Fleet Sizes Required to Serve Different Levels of Demand." Transportation Research Record: Journal of the Transportation Research Board 2542, no. 1 (January 2016): 111–19. http://dx.doi.org/10.3141/2542-13.
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Harik, El Houssein Chouaib. "Design and Implementation of an Autonomous Charging Station for Agricultural Electrical Vehicles." Applied Sciences 11, no. 13 (July 2, 2021): 6168. http://dx.doi.org/10.3390/app11136168.
Повний текст джерелаАнотація:
One of the goals in adopting more sustainable agricultural practices is to reduce green-house-gas emissions from current practices by replacing fossil-fuel-based heavy machinery with lighter, electrical ones. In a not-so-distant scenario where a single farmer owns a fleet of small electrical tractors/robots that can operate in an autonomous/semi-autonomous manner, this will bring along some logistic challenges. It will be highly impractical that the farmer follows each time a given vehicle moves to the charging point to manually charge it. We present in this paper the design and implementation of an autonomous charging station to be used for that purpose. The charging station is a combination of a holonomic mobile platform and a collaborative robotic arm. Vision-based navigation and detection are used in order to plug the power cable from the wall-plug to the vehicle and back to the wall-plug again when the vehicle has recharged its batteries or reached the required level to pursue its tasks in the field. A decision-tree-based scheme is used in order to define the necessary pick, navigate, and plug sequences to fulfill the charging task. Communication between the autonomous charging station and the vehicle is established in order to make the whole process completely autonomous without any manual intervention. We present in this paper the charging station, the docking mechanism, communication scheme, and the deployed algorithms to achieve the autonomous charging process for agricultural electrical vehicles. We also present real experiments performed using the developed platform on an electrical robot-tractor.
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Jang, Junwoo, Haggi Do, and Jinwhan Kim. "Mission Planning for Underwater Survey with Autonomous Marine Vehicles." Journal of Ocean Engineering and Technology 36, no. 1 (February 28, 2022): 41–49. http://dx.doi.org/10.26748/ksoe.2021.097.
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With the advancement of intelligent vehicles and unmanned systems, there is a growing interest in underwater surveys using autonomous marine vehicles (AMVs). This study presents an automated planning strategy for a long-term survey mission using a fleet of AMVs consisting of autonomous surface vehicles and autonomous underwater vehicles. Due to the complex nature of the mission, the actions of the vehicle must be of high-level abstraction, which means that the actions indicate not only motion of the vehicle but also symbols and semantics, such as those corresponding to deploy, charge, and survey. For automated planning, the planning domain definition language (PDDL) was employed to construct a mission planner for realizing a powerful and flexible planning system. Despite being able to handle abstract actions, such high-level planners have difficulty in efficiently optimizing numerical objectives such as obtaining the shortest route given multiple destinations. To alleviate this issue, a widely known technique in operations research was additionally employed, which limited the solution space so that the high-level planner could devise efficient plans. For a comprehensive evaluation of the proposed method, various PDDL-based planners with different parameter settings were implemented, and their performances were compared through simulation. The simulation result shows that the proposed method outperformed the baseline solutions by yielding plans that completed the missions more quickly, thereby demonstrating the efficacy of the proposed methodology.
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Chen, T. Donna, Kara M. Kockelman, and Josiah P. Hanna. "Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions." Transportation Research Part A: Policy and Practice 94 (December 2016): 243–54. http://dx.doi.org/10.1016/j.tra.2016.08.020.
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Silva, Dahlen, Dávid Földes, and Csaba Csiszár. "Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method." Sustainability 13, no. 6 (March 10, 2021): 3008. http://dx.doi.org/10.3390/su13063008.
Повний текст джерелаАнотація:
The use of autonomous vehicles (AVs) has the potential to transform users’ behaviour and urban space management. Quantitative and qualitative analyses of impacts require a scenario building method. We considered the fleet size, modal share, car ownership, parking preferences, and urban space repurposing during the elaboration of a novel method. Existing scenarios and results of a questionnaire survey have been used as sources. The method was applied to build scenarios in a case study in Budapest, Hungary. The results were used to calculate the impacts on urban space management, including environmental savings. The key findings are: scenarios with significant shared AV use show that parking demand may be minimised (almost 83%) and urban space repurposing has the highest potential; furthermore, AV use and sharing acceptability may decrease the fleet size and alter the type of shared mode to multiple occupancies. The developed scenario building method serves as a base for future studies. The produced scenarios allow the researchers to focus on the analysis of the impacts caused.
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Mocanu, Tudor. "What Types of Cars Will We Be Driving? Methods of Forecasting Car Travel Demand by Vehicle Type." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 49 (November 29, 2018): 125–34. http://dx.doi.org/10.1177/0361198118797457.
Повний текст джерелаАнотація:
New technologies are emerging in the private vehicle market. Conventional propulsion systems are set to be replaced by alternative, more environment-friendly ones (e.g., electric vehicles), and certain new features, like autonomous driving, will possibly change the way private cars are employed. To assess the impact of such technologies, one must estimate how often and for which trips these vehicle types will be used. Another issue is the exact localization of certain vehicle types on the network, to assess environmental effects and identify where specific roadside infrastructure (e.g., charging stations) will be required. This paper presents four approaches to forecasting car usage by vehicle type using a macroscopic travel demand model in combination with a vehicle fleet or technology diffusion model. Integrating the two types of models requires tools ranging from assumptions and extrapolation of empirical data to synthetic or incremental discrete choice models. The approaches are employed in a case study forecasting travel demand using privately owned autonomous vehicles (AVs) in Germany in 2030. Despite identical input data, the estimated proportion of vehicle miles traveled (VMT) using AVs varies between 11% and 23% of overall car VMT, depending on the approach chosen. The reasons for this variation in results are investigated and some recommendations are given. To avoid the difficulties of fitting a synthetic model to observed data and to increase the accuracy of the results, the recommendation is to formulate the vehicle type choice as an incremental model added to the travel demand model.
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Larson, Jonathan, Paul Isihara, Gabriel Flores, Edwin Townsend, Danilo R. Diedrichs, Christy Baars, Steven Kwon, et al. "A priori assessment of a smart-navigated unmanned aerial vehicle disaster cargo fleet." SIMULATION 96, no. 8 (June 7, 2020): 641–53. http://dx.doi.org/10.1177/0037549720921447.
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The United Nations Office for the Coordination of Humanitarian Affairs has asserted that risks in deployment of unmanned aerial vehicles (UAVs) within disaster response must be reduced by careful development of best-practice standards before implementing such systems. With recent humanitarian field tests of cargo UAVs as indication that implementation may soon become reality, a priori assessment of a smart-navigated (autonomous) UAV disaster cargo fleet via simulation modeling and analysis is vital to the best-practice development process. Logistical problems with ground transport of relief supplies in Puerto Rico after Hurricane Maria (2017) pose a compelling use scenario for UAV disaster cargo delivery. In this context, we introduce a General Purpose Assessment Model (GPAM) that can estimate the potential effectiveness of a cargo UAV fleet for any given response region. We evaluate this model using the following standards: (i) realistic specifications; (ii) stable output for various realistic specifications; and (iii) support of humanitarian goals. To this end, we discuss data from humanitarian cargo delivery field tests and feedback from practitioners, perform sensitivity analyses, and demonstrate the advantage of using humanitarian rather than geographic distance in making fleet delivery assignments. We conclude with several major challenges faced by those who wish to implement smart-navigated UAV cargo fleets in disaster response, and the need for further GPAM development. This paper proposes the GPAM as a useful simulation tool to encourage and guide steps toward humanitarian use of UAVs for cargo delivery. The model’s flexibility can allow organizations to quickly and effectively determine how best to respond to disasters.
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Chen, T. Donna, and Kara M. Kockelman. "Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes." Transportation Research Record: Journal of the Transportation Research Board 2572, no. 1 (January 2016): 37–46. http://dx.doi.org/10.3141/2572-05.
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Yan, Haonan, Kara M. Kockelman, and Krishna Murthy Gurumurthy. "Shared autonomous vehicle fleet performance: Impacts of trip densities and parking limitations." Transportation Research Part D: Transport and Environment 89 (December 2020): 102577. http://dx.doi.org/10.1016/j.trd.2020.102577.
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Farhan, J., and T. Donna Chen. "Impact of ridesharing on operational efficiency of shared autonomous electric vehicle fleet." Transportation Research Part C: Emerging Technologies 93 (August 2018): 310–21. http://dx.doi.org/10.1016/j.trc.2018.04.022.
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Obeidat, Mohammed Said. "Improving Roadway Navigation and Safety of Older Age Drivers." Review of European Studies 10, no. 2 (May 2, 2018): 150. http://dx.doi.org/10.5539/res.v10n2p150.
Повний текст джерелаАнотація:
Motor vehicles are the primary mode of transportation worldwide, but drivers must simultaneously utilize various skills and perform multiple tasks at the same time to safely operate the vehicle. As people age, however, physical changes can affect daily life, possibly contributing to declining driving skills. These changes affect vision, hearing, reaction time, and cognitive and motor ability, thus complicating driving. This paper discusses the importance of driving for people of age 65 and older, age-related physical changes, and methods of improving overhead guide sign visibility on roadways. Suggestions to improve navigation and safety on roadways, especially for older people, are presented, including the use of autonomous vehicles fleet.
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Alonso-Mora, Javier, Samitha Samaranayake, Alex Wallar, Emilio Frazzoli, and Daniela Rus. "On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment." Proceedings of the National Academy of Sciences 114, no. 3 (January 3, 2017): 462–67. http://dx.doi.org/10.1073/pnas.1611675114.
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Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.
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Kotobi, Khashayar, and Mina Sartipi. "A Novel Congestion Avoidance Algorithm for Autonomous Vehicles Assessed by Queue Modeling." International Journal of Interdisciplinary Telecommunications and Networking 11, no. 2 (April 2019): 1–11. http://dx.doi.org/10.4018/ijitn.2019040101.
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Autonomous vehicle (AV) fleet management is one of the major aspects of AV development that needs to be standardized before AV deployment. There has been no consensus on whether AV deployment in general will be beneficial or detrimental in terms of road congestion. There are similarities between packet transmission in computer networks and AV transportation in road networks. In this work, the authors argue that congestion avoidance algorithms used in computer networks can be applied for AV fleet management. Authors modify and evaluate a novel adaptation of additive increase and multiplicative decrease (AMID) congestion avoidance algorithm. The authors propose assigning different priorities to transportation tasks in order to facilitate sharing the limited resources in such as usage of the road network. This will be modeled and assessed using a queueing model based on AVs arrival distribution. This will result in a load balancing paradigm that can be used to share and manage limited resources. Then, by using numerical study authors merge congestion avoidance and load balancing to analyze the authors' scheme in term of road network throughput (number of cars in network for a given time) for AV fleet management. Their evaluation demonstrates the improvement in terms of road network throughput.
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Clements, Lewis M., and Kara M. Kockelman. "Economic Effects of Automated Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2606, no. 1 (January 2017): 106–14. http://dx.doi.org/10.3141/2606-14.
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Connected and fully automated or autonomous vehicles (CAVs) may soon dominate the automotive industry. Once CAVs are sufficiently reliable and affordable, they will penetrate markets and thereby generate economic ripple effects throughout industries. This paper synthesizes and expands on existing analyses of the economic effects of CAVs in the United States across 13 industries and the overall economy. CAVs will soon be central to the automotive industry, with software composing a greater share of vehicle value than previously. The number of vehicles purchased each year may fall because of vehicle sharing, but rising travel distances may increase vehicle sales. The opportunity for heavy-truck drivers to do other work or rest during long drives may lower freight costs and increase capacity. Personal transport may shift toward shared autonomous vehicle fleet use, reducing that of taxis, buses, and other forms of group travel. Fewer collisions and more law-abiding vehicles will lower demand for auto repair, traffic police, medical, insurance, and legal services. CAVs will also lead to new methods for managing travel demand and the repurposing of curbside and off-street parking and will generate major savings from productivity gains during hands-free travel and reduction of pain and suffering costs from crashes. If CAVs eventually capture a large share of the automotive market, they are estimated to have economic impacts of $1.2 trillion or $3,800 per American per year. This paper presents important considerations for CAVs’ overall effects and quantifies those impacts.
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Triebke, Henriette, Markus Kromer, and Peter Vortisch. "Pre-study and insights to a sequential MATSim-SUMO tool-coupling to deduce 24h driving profiles for SAEVs." SUMO Conference Proceedings 1 (June 28, 2022): 93–112. http://dx.doi.org/10.52825/scp.v1i.103.
Повний текст джерелаАнотація:
New mobility concepts such as shared, autonomous, electric vehicle (SAEV) fleets raise questions to the vehicles’ technical design. Compared to privately owned human driven cars, SAEVs are expected to exhibit different load profiles that entail the need for newly dimensioned powertrain and battery components. Since vehicle architecture is very sensitive to operating characteristics, detailed SAEV driving cycles are crucial for requirement engineering. As real world measurements reach their limit with new mobility concepts, this contribution seeks to evaluate three different traffic simulation approaches in their ability to model detailed SAEV driving profiles. (i) The mesoscopic traffic simulation framework MATSim is analyzed as it is predestined for large-scale fleet simulation and allows the tracking of individual vehicles. (ii) To improve driving dynamics, MATSim’s simplified velocity profiles are enhanced with real-world driving cycles. (iii) A sequential tool-coupling of MATSim with the microscopic traffic simulation tool SUMO is pursued. All three approaches are compared and evaluated by means of a comprehensive test case study. The simulation results are compared in terms of driving dynamics and energy related key performance indicators (KPI) and then benchmarked against real driving cycles. The sequential tool-coupling approach shows the greatest potential to generate reliable SAEV driving profiles.
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Du, Xinyu, Lichao Mai, and Hossein Sadjadi. "Fault Diagnostics and Prognostics for Vehicle Springs and Stablizer Bar." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1129.
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Vehicle springs and stabilizer bar are critical suspension components impacting vehicle riding and handling experience. Diagnostics and prognostics of springs and stabilizer bar can improve customer perceived quality, reduce repair cost and increase up-time for fleet vehicles. It’s even more important for autonomous vehicles, since there is no human driver to sense fault symptoms. Currently, there is no production solution to automatically diagnose and prognose spring and stabilizer bar failures, and most research work is suffered by various noise factors. In this work, a novel solution based on static ramp test is proposed to isolate and localize spring and stabilizer bar faults. With limited number of longitudinal and lateral acceleration measurements, the solution can quickly and effectively isolate faulty spring, disconnected stabilizer bar, loose bushing and loose end link. The validation results from a MY17 Bolt EV demonstrate the effectiveness and robustness of the proposed solution.
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Gurumurthy, Krishna Murthy, Kara M. Kockelman, and Michele D. Simoni. "Benefits and Costs of Ride-Sharing in Shared Automated Vehicles across Austin, Texas: Opportunities for Congestion Pricing." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 21, 2019): 548–56. http://dx.doi.org/10.1177/0361198119850785.
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A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.
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Conlon, James, and Jane Lin. "Greenhouse Gas Emission Impact of Autonomous Vehicle Introduction in an Urban Network." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 3, 2019): 142–52. http://dx.doi.org/10.1177/0361198119839970.
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This study attempts to quantify the change in carbon dioxide (CO2) emissions resulting from autonomous vehicle (AV) introduction in a congested urban network. An integrated traffic microsimulation and emission model is introduced, described, and used to estimate emissions for different AV penetration scenarios ranging from 0% to 100%. AVs show potential to reduce total CO2 emissions at a network scale, approaching 4% reduction at full autonomy, assuming the same fuel technology in AV as in today’s fleet. Furthermore, it is found that this reduction is sensitive to the penetration ratio of AVs, and is greatest at full AV penetration; AV penetration may generate greater total vehicular CO2 emissions than 0% AV penetration does, contrary to popular belief. This may be because of the heterogeneity in a mixed traffic environment of human-driven vehicles (HDVs) and AVs, as well as the complex interaction between HDVs and AVs that is not yet fully understood.
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Liu, Tao, Avishai (Avi) Ceder, and Andreas Rau. "Using Deficit Function to Determine the Minimum Fleet Size of an Autonomous Modular Public Transit System." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (September 10, 2020): 532–41. http://dx.doi.org/10.1177/0361198120945981.
Повний текст джерелаАнотація:
Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the concept of shared mobility, have great potential to transform existing public transit (PT) systems into far more user-oriented, system-optimal, smart, and sustainable new PT systems with increased service connectivity, synchronization, and better, more satisfactory user experiences. This work analyses such a new PT system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, that is, its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on a graphical method, adapted from the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The extended DF model was applied, as a case study, to a single line of an AMPT system, the dynamic autonomous road transit (DART) system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.
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Budd, Laurie, and Stuart Newstead. "Identifying Future Vehicle Safety Priority Areas in Australia for the Light Vehicle Fleet." Journal of Road Safety 32, no. 3 (August 1, 2021): 15–24. http://dx.doi.org/10.33492/jrs-d-21-00001.
Повний текст джерелаАнотація:
Formulating priorities for future road safety strategies requires supporting analysis to predict what the future crash population will look like and to assess how the countermeasures either already in place or planned will address the crash problems forecast. This analysis aimed to identify future priority action areas for light vehicle safety by identifying crash types that will not be fully addressed in the future by projected improvements in active and passive safety in the Australian light vehicle fleet. The future crash profile was modelled from 2017 to 2030 using crash data from 5 Australian jurisdictions overlayed with available evidence on vehicle safety feature fitment and effectiveness. The methodology can be applied to larger sets of safety technologies when sufficient evidence and supporting crash data become available. Three future vehicle safety priority areas were identified from the analysis: (i) fatal pedestrian crashes, (ii) single vehicle frontal crashes with objects, and (iii) front-to-front vehicle crashes both at intersections and midblocks, and front-to-side impacts at intersections including straight crossing path and right turn across path crash types. These crash types were projected to be the largest contributors to fatalities by 2030. Projections showed that remaining crash types in 2030 will be poorly addressed by current vehicle safety technologies such as autonomous emergency braking, lane departure warning and electronic stability control. Future vehicle safety policy priorities should address these crash types through the development of additional or enhanced vehicle safety technologies and where vehicle safety technology proves inadequate other countermeasures such as road infrastructure treatments and appropriate speed limit setting for high risk environments that address the key crash types remaining in the system.
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Loeb, Benjamin, and Kara M. Kockelman. "Fleet performance and cost evaluation of a shared autonomous electric vehicle (SAEV) fleet: A case study for Austin, Texas." Transportation Research Part A: Policy and Practice 121 (March 2019): 374–85. http://dx.doi.org/10.1016/j.tra.2019.01.025.
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Kathen, Micaela Jara Ten, Isabel Jurado Flores, and Daniel Gutiérrez Reina. "An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications." Electronics 10, no. 13 (July 4, 2021): 1605. http://dx.doi.org/10.3390/electronics10131605.
Повний текст джерелаАнотація:
Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.
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Richter, Maximilian A., Johannes Hess, Christoph Baur, and Raphael Stern. "Exploring the Financial Implications of Operating a Shared Autonomous Electric Vehicle Fleet in Zurich." Journal of Urban Mobility 1 (December 2021): 100001. http://dx.doi.org/10.1016/j.urbmob.2021.100001.
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Allahviranloo, Mahdieh, and Joseph Y. J. Chow. "A fractionally owned autonomous vehicle fleet sizing problem with time slot demand substitution effects." Transportation Research Part C: Emerging Technologies 98 (January 2019): 37–53. http://dx.doi.org/10.1016/j.trc.2018.11.006.
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Jurj, Sorin Liviu, Dominik Grundt, Tino Werner, Philipp Borchers, Karina Rothemann, and Eike Möhlmann. "Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning." Energies 14, no. 22 (November 12, 2021): 7572. http://dx.doi.org/10.3390/en14227572.
Повний текст джерелаАнотація:
This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution. In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs).
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Doecke, Sam, James Thompson, and Christopher Stokes. "How do we prevent and mitigate crashes? Evidence from Australian at-scene in-depth crash investigations." Journal of Road Safety 31, no. 2 (May 1, 2020): 35–43. http://dx.doi.org/10.33492/jrs-d-19-00254.
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The Centre for Automotive Safety Research conducts at-scene in-depth investigations of South Australian injury crashes that allow detailed analysis of the crash in order to determine the factors that contributed to the crash occurring, and the interventions that could prevent or mitigate them. This initial analysis of such a dataset (n=116) showed that the most common contributing factors are human errors, but the interventions to prevent or mitigate the crashes are most commonly infrastructure treatments or vehicle technologies that eliminate the human error and/or reduce the vehicle’s speed prior to impact in the event of a human error. It also found that most crashes can be prevented or mitigated. Key factors in meeting the goals of the safe system (zero deaths and serious injuries) were found to be: road infrastructure-based interventions at intersections (e.g. roundabouts); increased fleet penetration of the vehicle technologies Electronic Stability Control, Autonomous Emergency Braking, Emergency Braking Assist, Lane Keep Assist, Intelligent Speed Assist – Limiting; road interventions for errant vehicles that depart their lane or the road (e.g. median barriers); speed limit reductions; and a reduction in driving under the influence of alcohol and/or drugs.
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Wang, Haoxiang. "Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System." September 2021 3, no. 3 (October 11, 2021): 164–79. http://dx.doi.org/10.36548/jucct.2021.3.002.
Повний текст джерелаАнотація:
In recent times Automation is emerging every day and bloomed in every sector. Intelligent Transportation System (ITS) is one of the important branches of Automation. The major constrain in the transportation system is traffic congestion. This slurps the individual’s time and consequently pollutes the environment. A centralized management is required for optimizing the transportation system. The current traffic condition is predicted by evaluating the historical data and thereby it reduces the traffic congestion. The periodic update of traffic condition in each and every street of the city is obtained and the data is transferred to the autonomous vehicle. These data are obtained from the simulation results of transportation prediction tool SUMO. It is proved that our proposed work reduces the traffic congestion and maintains ease traffic flow and preserves the fleet management.
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Gurumurthy, Krishna Murthy, Joshua Auld, and Kara Kockelman. "A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service." Journal of Transport and Land Use 14, no. 1 (September 7, 2021): 933–48. http://dx.doi.org/10.5198/jtlu.2021.1926.
Повний текст джерелаАнотація:
With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.