It\u27s a Goodyear for Innovation

Innovation is nothing new to The Goodyear Tire and Rubber Company. The near future will bring another phase of evolution as the focus of tires sales will shift with the growing popularity of ride sharing, car sharing, and autonomous vehicles. For the scope of our project, it initially seemed obvious to use Goodyear’s good name as a selling point for a partnership with car sharing companies. We surveyed students at several colleges across Ohio that have car sharing fleets on campus and asked them about how they utilize the vehicles. After discovering through these interviews that ride sharing was more popular, we expanded our research rather than relying on our intuition about car sharing. Ride sharing became the focus of our project, and we explored how Goodyear’s name, quality, and resources can be leveraged to entice ride share companies as well as their users and drivers

A ride time-oriented scheduling algorithm for dial-a-ride problems

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theory, aims at finding minimal user ride time schedules in polynomial time. The algorithm can either return optimal feasible routes or it can return incorrect infeasibility declarations, on which feasibility can be recovered through a specifically-designed heuristic. The algorithm is furthermore supplemented by a battery management algorithm that can be used to determine charging decisions for electric and autonomous vehicle fleets. Timing solutions from the proposed scheduling algorithm are obtained on millions of routes extracted from DARP and e-ADARP benchmark instances. They are compared to those obtained from a linear program, as well as to popular scheduling procedures from the DARP literature. Results show that the proposed procedure outperforms state-of-the-art scheduling algorithms, both in terms of compute-efficiency and solution quality.Comment: 12 pages, 1 figur

Demand estimation and chance-constrained fleet management for ride hailing

In autonomous Mobility on Demand (MOD) systems, customers request rides from a fleet of shared vehicles that can be automatically positioned in response to customer demand. Recent approaches to MOD systems have focused on environments where customers can only request rides through an app or by waiting at a station. This paper develops MOD fleet management approaches for ride hailing, where customers may instead request rides simply by hailing a passing vehicle, an approach of particular importance for campus MOD systems. The challenge for ride hailing is that customer demand is not explicitly provided as it would be with an app, but rather customers are only served if a vehicle happens to be located at the arrival location. This work focuses on maximizing the number of served hailing customers in an MOD system by learning and utilizing customer demand. A Bayesian framework is used to define a novel customer demand model which incorporates observed pedestrian traffic to estimate customer arrival locations with a quantification of uncertainty. An exploration planner is proposed which routes MOD vehicles in order to reduce arrival rate uncertainty. A robust ride hailing fleet management planner is proposed which routes vehicles under the presence of uncertainty using a chance-constrained formulation. Simulation of a real-world MOD system on MIT's campus demonstrates the effectiveness of the planners. The customer demand model and exploration planner are demonstrated to reduce estimation error over time and the ride hailing planner is shown to improve the fraction of served customers in the system by 73% over a baseline exploration approach.Ford-MIT AllianceFord Motor Compan

Carpooling Liability?: Applying Tort Law Principles to the Joint Emergence of Self-Driving Automobiles and Transportation Network Companies

Self-driving automobiles have emerged as the future of vehicular travel, but this innovation is not developing in isolation. Simultaneously, the popularity of transportation network companies functioning as ride-hailing and ride-sharing services have altered traditional conceptions of personal transportation. Technology companies, conventional automakers, and start-up businesses each play significant roles in fundamentally transforming transportation methods. These transformations raise numerous liability questions. Specifically, the emergence of self-driving vehicles and transportation network companies create uncertainty for the application of tort law’s negligence standard. This Note addresses technological innovations in vehicular transportation and their accompanying legislative and regulatory developments. Then, this Note discusses the implications for vicarious liability for vehicle owners, duties of care for vehicle operators, and corresponding insurance regimes. This Note also considers theoretical justifications for tort concepts including enterprise liability. Accounting for the inevitable uncertainty in applying tort law to new invention, this Note proposes a strict and vicarious liability regime with corresponding no-fault automobile insurance

Full Potential of Future Robotaxis Achievable with Trip-Based Subsidies and Fees Applied to the For-Hire Vehicles of Today

As described by Grush and Niles in their textbook, The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles, there are two distinct market states for the future of automobility as vehicles become increasingly automated. The first, Market-1, is comprised of all vehicles that are manufactured and sold to private owners and used as household vehicles. This private consumer fleet will—through automated driver assistance systems (ADAS)—be increasingly capable of hands-off operation, even self-driving in certain environments such as limited-access expressways. The second category, Market-2, represents all the vehicles made expressly for the service market, i.e., roboshuttles and robotaxis, meant to be eventually driverless in prepared, defined areas and streets. Ford, GM, Lyft, Uber, Waymo, and dozens of other companies assert that they are preparing vehicles for Market-2. The main thesis in this perspective is that a productive, efficient system of on-demand Market-2 mobility can evolve from incentive-based governance—here termed “harmonization management.” This approach strikes a contrast with rigid regulation of a style seen with big city taxicabs and based on using constrained service classifications or per-vehicle medallion approaches. This essay recommends that transportation authorities set up systems of robust pricing signals—incentives and fees—delivered through a universal, mandatory system providing efficient, equitable distribution of these signals

Shared Autonomous Vehicles Effect on Vehicle-Km Traveled and Average Trip Duration

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. Document type: Articl

Autonomous Cars, Electric and Hybrid Cars, and Ridesharing: Perceptions vs. Reality

Autonomous Cars, Electric and Hybrid Cars, and Ridesharing are all important new technologies in today\u27s society that can have potentially large impacts on the environment in the future. This study was conducted to determine the differences in perceptions of Gettysburg College students regarding Autonomous Cars, Electric and Hybrid Cars, and Ridesharing and the reality of these topics in the real world. This paper also compares the perceptions of Environmental Studies majors/minors to the perceptions of other majors at Gettysburg College. The primary research was conducted by analyzing questions that were a part of a survey consisting of 16 questions which was administered to Gettysburg College students via Facebook class group pages and the Environmental Studies majors email alias. The study group consisted of 110 students with 31 of them being Environmental Studies majors/minors and 79 of them being non-Environmental Studies majors/minors. It was determined that there were no statistically significant differences between the Environmental Studies majors/minors and students that are other majors/minors at Gettysburg College. From our survey, we found that there is a distinct gap in knowledge on the current and future impacts on the environment from Autonomous Cars, Electric and Hybrid Cars, and Ridesharing. The questions that ask which power method produces more greenhouse gas emissions as well as the questions about the miles per gallon of participants’ personal vehicles were the most accurately answered. Overall, Gettysburg College students regardless of major or minor were found to have mostly inaccurate perceptions on the topics of Autonomous Cars, Electric and Hybrid Cars, and Ridesharing

What impressions do users have after a ride in an automated shuttle? An interview study

In the future, automated shuttles may provide on-demand transport and serve as feeders to public transport systems. However, automated shuttles will only become widely used if they are accepted by the public. This paper presents results of an interview study with 30 users of an automated shuttle on the EUREF (Europäisches Energieforum) campus in Berlin-Schöneberg to obtain in-depth understanding of the acceptance of automated shuttles as feeders to public transport systems. From the interviews, we identified 340 quotes, which were classified into six categories: (1) expectations about the capabilities of the automated shuttle (10% of quotes), (2) evaluation of the shuttle performance (10%), (3) service quality (34%), (4) risk and benefit perception (15%), (5) travel purpose (25%), and (6) trust (6%). The quotes indicated that respondents had idealized expectations about the technological capabilities of the automated shuttle, which may have been fostered by the media. Respondents were positive about the idea of using automated shuttles as feeders to public transport systems but did not believe that the shuttle will allow them to engage in cognitively demanding activities such as working. Furthermore, 20% of respondents indicated to prefer supervision of shuttles via an external control room or steward on board over unsupervised automation. In conclusion, even though the current automated shuttle did not live up to the respondents’ expectations, respondents still perceived automated shuttles as a viable option for feeders to public transport systems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and PlanningHuman-Robot InteractionIntelligent VehiclesTransport and Plannin

Regulating TNCs: Should Uber and Lyft Set Their Own Rules?

We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) a minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings pushes the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard economic theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floors curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles