Availability-based dynamic pricing on a round-trip carsharing service: an explorative analysis using agent-based simulation

Carsharing companies aim to customize their service to increase fleet usage and revenues with different pricing schemes and offer types. Dynamic pricing policies can be designed to adjust and balance temporally and spatially cars availability but may pose some question on customers’ fairness. In this paper, we propose an explorative analysis of how an availability-based dynamic pricing scheme impacts the demand and the supply performance. The policy is simulated in MATSim and compared to a fixed pricing policy scheme. This simulation consists of analyzing the behavior of a synthetic population of car-sharing members for Berlin and the surrounding region in which is applied an availability-based dynamic pricing in which price depends on vehicle availability in booking stations. Results show that when the dynamic pricing is applied there is a light decrease in the number of bookings and people with low value of time tend to abandon the carsharing mode in favor of other modes of transportation

Experimental analysis of eGLOSA and eGLODTA transit control strategies

Battery powered electric buses have higher energy efficiency, lower emissions and noise when compared to buses with internal combustion engines. However, due to battery charging requirements, their large-scale integration into public transport operations is more complex. This study proposes a novel concept supporting said integration via new control strategies, dubbed e-GLOSA and e-GLODTA. These strategies extend the existing Green Light Optimal Speed and Dwell Time Systems (GLOSA/GLODTA) to account for the specific needs of electric buses. That is, they include the goals of minimizing the energy consumption between charging stations, and maximizing available charging time. At the same time, interference with schedule requirements is minimized. The formulated heuristics are tested on a Bus Rapid Transit (BRT) corridor case study, where different scenarios—such as placement of charging stations and bus regularity—are studied to assess under which conditions each action (maintain speed, accelerate or dwell for a longer time at a stop) is beneficial. Results show that eGLOSA contributes to schedule adherence while eGLODTA allows satisfying charging time constraints

Dynamic Pricing Strategies in the Carsharing Business, Profit Maximization and Equity Considerations

In the last two decades the development of mobile technology and the ease of access to an internet connection helped the consolidation of the sharing economy paradigm. This new way of purchasing goods and services differs from old traditional business models since it enables a shared use of resources in order to save money and generate profit. As an important player in the sharing economy, sharing mobility continues, nowadays, to shape urban mobility with the introduction of different modes of shared transport such as carsharing, bike sharing, ride sourcing, and other collective mobility services. Different stakeholders participate in the creation and exploitation of these new mobility services: governmental agencies, customers, and private companies. Each of them has a specific purpose that can affect and stir the benefit of sharing platforms. Focusing on the carsharing service, on the business side and on the user side, profit and customer satisfaction are usually the main goals even if, at times, both difficult to pursue together. Competition on today's landscape leaves little room for both established and less established businesses. Opportunities to increase corporate profit become scarcer and more refined systems to better manage carsharing operations are needed to guarantee commercial viability. Evaluating business models for carsharing is no trivial task. Several methods are used for assessing the quality of changes in some operations or to evaluate possible approaches. Combinatorial and stochastic optimization are used to answer decision-making problems in the case of deterministic or uncertain problems. The shortcoming of these approaches is that they are limited at solving problems related to fleet management or service planning as it is more difficult to have an overview in which multiple properties (e.g., demography, territorial distribution, specificity of the fleet, ...) of both supply and demand are considered. This happens because car sharing is a highly complex service that has many interdependent factors. Given this complexity, a more favorable approach to estimate the demand for the service - together with all its peculiarities - and to help operators in the decision-making process, is the simulation one. This criterion allows the interaction of multiple factors which, through functional relationships between the decision-making parameters of the supplier, can introduce indicators to evaluate the quality of the solutions that cannot be easily derived analytically. This dissertation focuses on a simulation-based approach that aims to create a decision support system for carsharing business. This decision support system aims to use demographic and land use data as input, once the provider's needs are known, and to return solutions regarding the optimization of the carsharing service. The development of this thesis is conceived from the point of view of the service provider, even if considerations regarding the equity of the various strategies proposed therein for the service customers constitute an integral and fundamental part of the construction of this system of support for decisions. In this manuscript, we discuss the introduction of different dynamic pricing strategies that aim to increase the profit of the carsharing service, along with other indicators such as the number of bookings and utilization time of vehicles. By developing different price models, the introduction of dynamic prices based on the quantity of vehicles present in the station at the time of booking is evaluated and the output of the implementation of a dynamic price based on the time of the day is examined. In the first part of this thesis, we discuss how it is possible to evaluate the quality of a carsharing service from the point of view of its members, focusing on how different strategies generate or can reduce inequalities due to different wages or purchasing powers. Furthermore, using data collected by a car-sharing company operating in Germany and the United Kingdom, Oply, we implement these same strategies in a scenario calibrated with real data. Finally, we propose a methodology for calibrating carsharing scenarios in an agent-based environment.Moreover, we use these scenarios to demonstrate how it is possible, once there is complete knowledge of the demand and the status of the offer, to attribute a certain price to a single booking that maximizes the profit of the service. The overall results show that the introduction of dynamic pricing strategies does not always benefit all segments of the population and that the goals of a carsharing company are not always compatible with those of its members. Furthermore, they show how it is possible to increase the profit of a carsharing company accordingly to its position on the market, whether it has a total knowledge of the territory or not, whether it is an established company or not yet fully established. As we will also see in the final chapter of this thesis, the product of this work does not consist only in a practical contribution aimed mostly at carsharing companies, but also in a scientific counterpart that outlines new research directions

Dynamic Pricing on Round-Trip Carsharing Services: Travel Behavior and Equity Impact Analysis through an Agent-Based Simulation

Carsharing companies can customize their service by adopting different pricing schemes and offers with the goal of increasing service attractivity, fleet usage and profits. Dynamic pricing strategies can be designed to adjust and balance cars’ availability temporally and spatially; they may pose some questions regarding customer equity, since such measures could impact their activities and mode choice. In this paper, we develop an experimental design for conducting an explorative analysis of how availability-based and time-based dynamic pricing schemes impact demand and supply performances. The strategy is simulated in the open-source agent-based software MATSim and compared to a fixed pricing policy scheme. Two spatial distributions of the value of time for the population of Berlin are applied (radially and coaxially) to systematically analyze agents’ behavior response to these pricing policies. Results confirm that when dynamic pricing is applied people with low value of time tend to abandon the carsharing mode in favor of other modes of transportation. Overall, the strategy applied in this study appears to be unfair, since it hinders low income groups from using the carsharing service