Revenue Maximization in Transportation Networks

We study the joint optimization problem of pricing trips in a transportation network and serving the induced demands by routing a fleet of available service vehicles to maximize revenue. Our framework encompasses applications that include traditional transportation networks (e.g., airplanes, buses) and their more modern counterparts (e.g., ride-sharing systems). We describe a simple combinatorial model, in which each edge in the network is endowed with a curve that gives the demand for traveling between its endpoints at any given price. We are supplied with a number of vehicles and a time budget to serve the demands induced by the prices that we set, seeking to maximize revenue. We first focus on a (preliminary) special case of our model with unit distances and unit time horizon. We show that this version of the problem can be solved optimally in polynomial time. Switching to the general case of our model, we first present a two-stage approach that separately optimizes for prices and routes, achieving a logarithmic approximation to revenue in the process. Next, using the insights gathered in the first two results, we present a constant factor approximation algorithm that jointly optimizes for prices and routes for the supply vehicles. Finally, we discuss how our algorithms can handle capacitated vehicles, impatient demands, and selfish (wage-maximizing) drivers

Shared value economics: an axiomatic approach

The concept of shared value was introduced by Porter and Kramer as a new conception of capitalism. Shared value describes the strategy of organizations that simultaneously enhance their competitiveness and the social conditions of related stakeholders such as employees, suppliers and the natural environment. The idea has generated strong interest, but also some controversy due to a lack of a precise definition, measurement techniques and difficulties to connect theory to practice. We overcome these drawbacks by proposing an economic framework based on three key aspects: coalition formation, sustainability and consistency, meaning that conclusions can be tested by means of logical deductions and empirical applications. The presence of multiple agents to create shared value and the optimization of both social and economic criteria in decision making represent the core of our quantitative definition of shared value. We also show how economic models can be characterized as shared value models by means of logical deductions. Summarizing, our proposal builds on the foundations of shared value to improve its understanding and to facilitate the suggestion of economic hypotheses, hence accommodating the concept of shared value within modern economic theory.Comment: 22 pages, 4 figure

Az önvezető autók lehetséges hatásai az életmódra és a gazdaságra

Napjainkra, hosszú kísérletezések után, látható közelségbe kerültünk az önvezető autók megvalósulásához. Hatásuk azonban várhatóan nemcsak annyi, hogy ezentúl még könnyebb lesz a gépjárművek vezetése, hanem az élet szinte minden területén alapvető változásokat hoznak. A cikkben részben a vonatkozó szakirodalom feldolgozása, részben pedig azok továbbgondolása révén, megpróbálunk felvázolni néhány logikus következményt, hogy mi, hogyan és miért változhat meg a gazdaság és társadalom területén közép- és hosszú távon. Kísérletet teszünk annak felvázolására is, hogy Magyarország ezeket a várható tendenciákat hogyan tudja felhasználni a közepes jövedelmi csapdából való kitörésre

Autonomous, connected, electric shared vehicles (ACES) and public finance: An explorative analysis

This paper discusses the implications of autonomous-connected-electric-shared vehicles (ACES) for public finance, which have so far been widely ignored in the literature. In OECD countries, 5-12% of federal and up to 30% of local tax revenues are currently collected from fuel and vehicle taxation. The diffusion of ACES will significantly reduce these important sources of government revenues and affect transport-related government expenditures, unless additional policies are introduced to align the new technological context with the tax revenue requirements. We argue that the realization of socioeconomic benefits of ACES depends on the implementation of tailored public finance policies, which can take advantage of the increase in data availability from the further digitalization of transportation systems. In particular, the introduction of road tolls in line with "user Pays" and "polluter Pays" principles will become more feasible for policy. Moreover, innovation in taxation schemes to fit the changing technological circumstances may alter the relative importance of levels of governance in transport policy making, likely shifting power towards local, in particular urban, governmental levels. We finally argue that, given the risk of path-dependencies and lock-in to sub-optimal public finance regimes if policies are implemented late, further research and near-term policy actions taken during the diffusion process of ACES are required

A Machine Learning Recommender Model for Ride Sharing Based on Rider Characteristics and User Threshold Time

In the present age, human life is prospering incredibly due to the 4th Industrial Revolution or The Age of Digitization and Computing. The ubiquitous availability of the Internet and advanced computing systems have resulted in the rapid development of smart cities. From connected devices to live vehicle tracking, technology is taking the field of transportation to a new level. An essential part of the transportation domain in smart cities is Ride Sharing. It is an excellent solution to issues like pollution, traffic, and the rapid consumption of fuel. Even though Ride Sharing has several benefits, the current usage is significantly low due to limitations like social barriers and long rider waiting times. The thesis proposes a novel Ride Sharing model with two matching layers to eliminate most of the observed issues in the existing Ride Sharing applications like UberPool and LyftLine. The first matching layer matches riders based on specific human characteristics, and the second matching layer provides riders the option to restrict the waiting time by using personalized threshold time. At the end of trips, the system collects user feedback according to five characteristics. Then, at most, two main characteristics that are the most important to riders are determined based on the collected feedback. The registered characteristics and the two main determined characteristics are fed as the inputs to a Machine Learning classification module. For newly registering users, the module predicts the two main characteristics of riders, and that assists in matching with other riders having similar determined characteristics. The thesis includes subjecting the proposed model to an extensive simulation for measuring system efficiency. The model simulations have utilized the real-time New York City Cab traffic data with real-traffic conditions using Google Maps Application Programming Interface (API). Results indicate that the proposed Ride Sharing model is feasible, and efficient as the number of riders increases while maintaining the rider threshold time. The expected outcome of the thesis is to help service providers increase the usage of Ride Sharing, complete the pool for the maximum number of trips in minimal time and perform maximum rider matches based on similar characteristics, thus providing an energy-efficient and a social platform for Ride Sharing