A behavioral choice model of the use of car-sharing and ride-sourcing services

There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014-2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand servicesStatistic

Carsharing systems demand estimation and defined operations: a literature review

Efforts have been made in the last few decades to provide new urban transport alternatives. One of these is carsharing, which involves a fleet of vehicles scattered around a city for the use of a group of members. At first, part of the research effort was put into setting up real life experiments with vehicle fleets and observing the performance of major private operators. In the meantime, with the growth of this alternative and the need to better plan its deployment, researchers started to create more advanced methods to study carsharing systems’ planning issues. In this paper, we review those methods, identifying gaps and suggesting how to bridge them in the future. Based on that review we concluded that carsharing demand is difficult to model due to the fact that the availability of vehicles is intrinsically dependent on the number of trips and vice versa. Moreover, despite the existence of carsharing simulation models that offer very detailed mobility representations, no model is able to characterise accurately the supply side, thus hindering the cost-benefit assessment that is fundamental to justify investment in this transport alternative, in particular those that are being endorsed by the European Union. More complex, however, is the operation of the emerging one-way carsharing systems, where a vehicle may be dropped off at any station, which adds uncertainty as to the location where vehicles can be picked up. Several optimisation approaches have been proposed to mitigate this problem but they are always limited in scope and leave other aspects out for model simplification purposes. Some simulation models have also been developed to study the performance of this type of carsharing system, but they have not included ways of balancing the vehicle stocks

A Decision Support System for the Optimization of Electric Car Sharing Stations

Electric car sharing is a mobility alternative addressing the world’s growing need for sustainability and allowing to reduce pollution, traffic congestion, and shortage of parking in cities. The positioning and sizing of car sharing stations are critical success factors for reaching many potential users. This represents a multi-dimensional challenge that requires decision makers to address the conflicting goals of fulfilling demands and maximizing profit. To provide decision support in anticipating optimal locations and to further achieve profitability, an optimization model in accordance to design science research principles is developed. The integration of the model into a decision support system (DSS) enables easy operability by providing a graphical user interface that helps the user import, edit, export, and visualize data. Solutions are illustrated, discussed, and evaluated using San Francisco as an application example. Results demonstrate the applicability of the DSS and indicate that profitable operation of electric car sharing is possible

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

Migrating towards Using Electric Vehicles in Fleets – Proposed Methods for Demand Estimation and Fleet Design

Carsharing and electric vehicles have emerged as sustainable transportation alternatives to mitigate transportation, environmental, and social issues in cities. This dissertation combines three correlated topics: carsharing feasibility, electric vehicle carsharing fleet optimization, and efficient fleet management. First, the potential demand for electric vehicle carsharing in Beijing is estimated using data from a survey conducted the summer of 2013 in Beijing. This utilizes statistical analysis method, binary logit regression. Secondly, a model was developed to estimate carsharing mode split by the function of utilization and appropriate carsharing fleet size was simulated under three different fleet types: an EV fleet with level 2 chargers, an EV fleet with level 3 chargers, and a gasoline vehicle fleet. This study also performs an economic analysis to determine the payback period for recovering the initial EV charging infrastructure costs. Finally, this study develops a fleet size and composition optimization model with cost constraints for the University of Tennessee, Knoxville motor pool fleet. This will help the fleet manage efficiently with minimum total costs and greater demand satisfaction. This dissertation can help guide future sustainable transportation planning and policy

A review of the socio-demographic characteristics affecting the demand for different car-sharing operational schemes

In this paper, socio-demographic factors influencing the demand for different car-sharing forms are examined. An in-depth review of such factors is provided based on the type of shared car service, geographic area, and specific travel demand aspect being considered. Conclusions highlight the differences between car sharing operational schemes. The number of males, young individuals, and people with above-average income among free-floating members is higher than in other car-sharing services. Also, although round-trip car-sharing users appear to be less educated than other car-sharing services users, car-sharing members may follow a more efficient and sustainable lifestyle than the one-way shared car system members. Besides, some suggestions are recommended for future studies. A research gap has been identified regarding the direction of causation between vehicle ownership levels and car-sharing demand. Most studies have worked on the impact of vehicle ownership on car-sharing or simply noted a correlation between the two. However, clarifying any reverse effect would help in better assessing the sustainability of car-sharing services. This overview can guide policymakers, planners, and other stakeholders to enhance the car-sharing program's effectiveness and opt for the best kind of service according to their goals

ENHANCING MUNICIPAL ANALYTICS CAPABILITIES TO ENABLE SUSTAINABLE URBAN TRANSPORTATION

Intermodal mobility, the IT-enabled, seamless transition between different modes of transportation to reach one’s destination, is a promising approach towards reducing the environmental footprint of urban mobility. We introduce a prototype, a geospatial data analytics system, that allows decision-makers at the municipal level to better understand how different means of transportation interact and interfere with each other within their city. Through a demonstration case, we particularly focus on the relationship between public transportation and private sector carsharing services in the city of Berlin. We outline the methods employed by the prototype to investigate the spatial and temporal dimensions of carsharing usage and how they relate to public transport offers. Our results suggest that carsharing complements public transport in some ways – e.g. by linking parts of the city with an insufficient public transport connection but also low demand – while potentially cannibalizing customers from public transport in the city center due to the increased comfort. We conclude by discussing how stakeholders can transform these insights into actionable advice