Browse Topic: Vehicle sharing services
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 and contents on basis of the platforms and products from other species. This paper establishes a cooperative game model composed of profit functions for all the stakeholders, considering both car sharing and private travel in the era of intelligent vehicles. A profit distribution strategy is proposed with Shapley value method, which ensures the efficiency and fairness. A quantitative analysis based on Chinese market data is conducted. The results indicate that the leaders and enablers will gain the most of the benefits of smart mobility, accounting for more than 80 percent. The cooperation improves the whole profits by around 60 percent, compared to the situation in which each species operates alone. Thus, the supporters and parasites who can benefit more from cooperation should assist in developing the market and reducing the operating costs of the whole business alliance.
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.
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 pedal stroke and is used to control the brake pressure. To confirm the real-world feasibility of this concept, the booster was installed in an actual vehicle. The evaluation of this vehicle confirmed that software-defined pedal feeling is feasible to implement in a real vehicle. Pedal feeling as good as that of a mass produced vehicle could be achieved, and the pedal feeling could be quickly and easily changed without the time and expense required to change brake hardware. Additionally, using this new booster, new types of pedal feeling that are not possible to achieve on a conventional vacuum booster vehicle could be easily implemented with software.
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 walkability, ecological awareness) by using a quantitative analysis with car-sharing users and non-users. This methodology has been implemented in the city of Enna (Italy), where university population constitute a high percentage of residents and a recent station-based car sharing service has been implemented. Besides the demographics characteristics, the students’ demand of mobility and acceptance of EVs have been investigated through a survey data analysis, considering several operational attributes and context-related variables in applying Likert scale. The results show that experience in using, EV vehicles leads to higher acceptance of this new technology. Furthermore, it emerged a correlation between gender distribution and operating and infrastructural characteristics of the service, like the presence of reserved parking with charging stations. This study lays the basis for more in-depth research for service design and reconversion through the introduction of shared EVs, improving their use both for home-school and home-leisure trips and discouraging the use of the private vehicle.
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 generation based on households. Census data provide the demographic information for each household (e.g., number of adults and kids, income and education, vehicle ownership etc.). The travel demands of each individual in the household are modeled as chains of trips with spatial and temporal details that match the travel patterns of the individual’s demographic profile. The trip chains also consider multi-person trips, accounting for group traveling of the individual with others within and outside of the households. Using Miami as a case study city, we demonstrate the feasibility and validity of the proposed approach. The proposed approach can be applied to any city using publicly available data as inputs.
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.
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.
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