Browse Topic: Vehicle sharing services

Items (19)
Shared autonomous vehicles systems (SAVS) are regarded as a promising mode of carsharing service with the potential for realization in the near future. However, the uncertainty in user demand complicates the system optimization decisions for SAVS, potentially interfering with the achievement of desired performance or objectives, and may even render decisions derived from deterministic solutions infeasible. Therefore, considering the uncertainty in demand, this study proposes a two-stage robust optimization approach to jointly optimize the fleet sizing and relocation strategies in a one-way SAVS. We use the budget polyhedral uncertainty set to describe the volatility, uncertainty, and correlation characteristics of user demand, and construct a two-stage robust optimization model to identify a compromise between the level of robustness and the economic viability of the solution. In the first stage, tactical decisions are made to determine autonomous vehicle (AV) fleet sizing and the
Li, KangjiaoCao, YichiZhou, BojianWang, ShuaiqiYu, Yaofeng
Shared mobility will become an important part in the future smart transportation and contribute to sustainable development. However, recently a large number of pioneers in this market have failed in making profits, and have to declare bankrupt or give up this promising business. One main cause is that it is difficult to find a method to allocate the profits to all the partners reasonably. In other words, there is still no effective business model in smart mobility. This study discusses cooperation among all stakeholders, including four species of participants, in smart mobility business alliance based on the theory of community ecology. The leaders are the enterprises who offer business platforms for the other players. The enablers include OEMs, hardware and software suppliers who contribute to smart mobility with intelligent vehicle products and technologies. The supporters can provide infrastructure and market channels. And the parasites are able to create added value with services
Kuang, Xu
Car-share trajectory is the big data of time and space that contains the travel behavior of residents. It is of great significance for station planning to dig out residents’ travel hotspots from the Car-share track data. This paper uses a clustering algorithm based on grid density. The algorithm first divides the trajectory space into grid cells and sets the density threshold of the grid cells; then maps the trajectory points to the grid cells and extracts hot grid cells based on the density threshold; By merging reachable hotspot grid units, hotspot areas of cities are discovered. This paper analyzes the demand for residents’ travel in the hotspot area, and uses the random forest model to predict the demand, which can make a reference for the car-share company to launch cars and provide convenience for users to travel.
Wu, ZhenBi, JunSai, Qiuyue
A new type of electric brake booster, which can control brake pedal feeling completely with software, has been developed to explore how a brake system can be used to differentiate and personalize vehicles. In the future, vehicles may share an increasing amount of hardware and rely more heavily on software to differentiate between models. Car sharing, vehicle subscriptions, and other new business models may create a new emphasis on the personalization of vehicles that may be achieved most cost effectively by using software. This new brake booster controls the brake pedal force and brake pressure independently based on the brake pedal stroke so that the pedal feeling is completely defined by software. The booster uses two electric motors and one master cylinder. One electric motor controls the pedal force and provides an assist force that amplifies the force that the driver applies to the brake pedal. The second electric motor moves the master cylinder piston independently of the brake
Kakizoe, KentaBull, Marshall
Sharing mobility has led to a reduction of car ownership with consequent decrease in impacts from a multiple economic, social and environmental perspective. One way of promoting sustainable mobility is to establish the use of electric vehicles (EVs), but insufficient knowledge and high uncertainty towards EV technology can represent a barrier to the acceptance of these new forms of mobility. Under-thirty are recognized as a prospective customer group for car sharing services, very receptive to technological innovation. Based on this premise, the study proposed a double-structured methodological framework to investigate university student user profile defining the heterogeneous preferences regarding a mix of attributes of the service design and to assess the impact of car-sharing experience on acceptance of EVs. Preferences for specific service attributes have been explored (e.g. rate, different power systems) and possible predictors have been tested (e.g. car ownership, neighborhood
Campisi, TizianaIgnaccolo, MatteoTesoriere, GiovanniInturri, GiuseppeTorrisi, Vincenza
The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs. To address this data gap, we propose a new approach of travel demand
Wen, RuoxiJiang, ZhenLiang, ChenTelenko, CassandraWang, BoFu, YanCai, Hua
This Recommended Practice provides a taxonomy and definitions for terms related to shared mobility and enabling technologies. Included are functional definitions for shared modes (e.g., carsharing, bikesharing, ridesourcing, etc.). Public transit services and other incumbent services—such as car rentals, shuttles, taxis, paratransit, ridesharing (carpooling/vanpooling), and pedicabs—are also included in the ecosystem of shared mobility services. This Recommended Practice also provides a taxonomy of related terms and definitions (e.g., station-based roundtrip, free-floating one-way, etc.). This Recommended Practice does not provide specifications or otherwise impose requirements on shared mobility.
Shared and Digital Mobility Committee
ABSTRACT The concept of Autonomous Vehicles ultimately generating an “order of magnitude” potential increase in the duty or usage cycle of a vehicle needs to be addressed in terms of impact on the reliability domain. Voice of the customer data indicates current passenger vehicle usage cycles are typically very low, 5% or less. Meaning, out of a 24 hour day, perhaps the average vehicle is actually driven only 70 minutes or less. Therefore, approximately 95% of the day, the vehicles lay dormant in an unused state. Within the context of future fully mature Autonomous Vehicle environment involving structured car sharing, the daily vehicle usage rate could grow to 95% or more.
Wasiloff, James
This paper proposes a low-cost but indirect method for occupancy detection and occupant counting purpose in current and future automotive systems. It can serve as either a way to determine the number of occupants riding inside a car or a way to complement the other devices in determining the occupancy. The proposed method is useful for various mobility applications including car rental, fleet management, taxi, car sharing, occupancy in autonomous vehicles, etc. It utilizes existing on-board motion sensor measurements, such as those used in the vehicle stability control function, together with door open and closed status. The vehicle’s motion signature in response to an occupant’s boarding and alighting is first extracted from the motion sensors that measure the responses of the vehicle body. Then the weights of the occupants are estimated by fitting the vehicle responses with a transient vehicle dynamics model. This two stage approach is further used to determine how many occupants are
Luo, DaweiLu, JianboGuo, Gang
Nowadays, the automotive industry is experiencing the advent of unprecedented applications with connected devices, such as identifying safe users for insurance companies or assessing vehicle health. To enable such applications, driving behavior data are collected from vehicles and provided to third parties (e.g., insurance firms, car sharing businesses, healthcare providers). In the new wave of IoT (Internet of Things), driving statistics and users’ data generated from wearable devices can be exploited to better assess driving behaviors and construct driver models. We propose a framework for securely collecting data from multiple sources (e.g., vehicles and brought-in devices) and integrating them in the cloud to enable next-generation services with guaranteed user privacy protection. To achieve this goal, we design fine-grained privacy-aware data collection and upload policies that balance between enforcing privacy requirements and optimizing resource consumption (e.g., processing
Li, HuaxinMa, DiMedjahed, BrahimWang, QianyiKim, Yu SeungMitra, Pramita
Items per page:
1 – 19 of 19