An Ant-based Intelligent Design for Future Self-driving Commercial Car Service Strategy

The technology of self-driving cars will inevitably change the industry of taxis and ride-sharing cars that provide important commercial ground transportation services to travelers, tourists and local residents. There is no doubt that new techniques, business models and strategies will be needed to follow the use of self-driving cars. This paper focuses on a forward-looking research topic that route commercial, vacant self-driving vehicles so that the values to both businesses and passengers are improved. Importance of solutions to the new problem is discussed. We also propose a novel design which simulates behaviors of ants in nature to the vehicles. The goal of the system is to obtain an overall balance between the demands of using the services from the passengers and availability of the vehicles in all service areas. The system not only uses historical data to make decisions, it also responds promptly for demands appeared dynamically

Charging Recommender for Electric Taxis

Tato bakalářská práce se zabývá optimalizací strategie řidiče elektrického taxi ve smyslu plánování nabíjení, přibližování se klientům či čekání na klienty na potenciálně výhodných místech. Cílí především na doporučování místa a času nabíjení, ale také jednotlivých rozhodnutí řidiče se snahou maximalizovat jeho zisk. Nejprve jsem se zaměřil na obecný úvod do tématu elektrických vozidel, společně s průzkumem dostupných zdrojů zabývajících se vytvářením strategií pro pohyb nejen elektrických, ale i standardních (se spalovacími motory) taxi. Následně jsem navrhl dvě reprezentace prostředí (mřížková, K-means shlukování) a celý problém definoval pomocí frameworku MDP. Rovněž jsem představil algoritmus založený na dynamickém programování, který generuje navrhovanou sekvenci kroků, jichž by se měl řidič taxi držet. Důležitou částí řešení je rovněž odhad potřebných parametrů kupříkladu pravděpodobnosti vyzvednutí a vysazení zákazníka na konkrétních místech. Toto jsem provedl z rozsáhlého data setu historických cest taxíků. Závěrem jsem navržený algoritmus naimplementoval a experimentálně ukázal jeho výsledky v různých prostředích v porovnání se základním modelem chování řidiče elektrického taxi. Provedené experimenty ukázaly, že má metoda překonává zvolený základní model, jak v aspektu celkového přijmu řidiče, ve vzdálenosti nutné urazit k místu vyzvednutí pasažéra, ale také v efektivitě výběru nabíjecí stanice.This thesis deals with the problem of optimization of an electric taxi driver's strategy in terms of charging, passenger approaching or waiting in potentially favorable locations. It focuses primarily on recommending charging actions and taxi driver's decisions concerning the maximization of the driver's potential profit. Firstly I focused on a general introduction to the topic of electric vehicles together with a research of state of the art in the taxi movement strategy recommending field. I then proposed two concrete environment representations (Grid World, K-Means clustering) and defined the recommending problem as a Markov Decision Problem (MDP). I also presented an algorithm based on dynamic programming generating a policy for an electric taxi driver. An essential part of the presented solution method is an estimation of parameters such as pick-up and destination probability of passenger trips connected with particular locations on a planning map. It was done based on sizeable historical taxi trip data sets. Subsequently, I implemented the proposed algorithm based on a simple principle of dynamic programming. Finally, I experimentally showed the performance of my solution working in different environments compared with a base model of an electric taxi driver behavior. Experiments showed that my algorithm outperforms the base model in several fields, such as a total taxi driver's profit, distance to the next passenger, or charging station choice efficiency

Modeling and Evaluation of a Ridesharing Matching System from Multi-Stakeholders\u27 Perspective

With increasing travel demand and mobility service quality expectations, demand responsive innovative services continue to emerge. Ridesharing is an established, yet evolving, mobility option that can provide more customized, reliable shared service without any new investment in the transportation infrastructure. To maximize the benefits of ridesharing service, efficient matching and distribution of riders among available drivers can provide a reliable mobility option under most operating conditions. Service efficiency of ridesharing depends on the system performance (e.g., trip travel time, trip delay, trip distance, detour distance, and trip satisfaction) acceptable to diverse mobility stakeholders (e.g., riders, drivers, ridesharing operators, and transportation agencies). This research modeled the performance of a ridesharing service system considering four objectives: (i) minimization of system-wide passengers’ waiting time, (ii) minimization of system-wide vehicle miles travelled (VMT), (iii) minimization of system-wide detour distance, and (iv) maximization of system-wide drivers’ profit. Tradeoff evaluation of objectives revealed that system-wide VMT minimization objective performed best with least sacrifices on the other three objectives from their respective best performance level based on set of routes generated in this study. On the other hand, system-wide drivers’ profit maximization objective provided highest monetary incentives for drivers and riders in terms of maximizing profit and saving travel cost respectively. System-wide minimization of detour distance was found to be least flexible in providing shared rides. The findings of this research provide useful insights on ridesharing system modeling and performance evaluation, and can be used in developing and implementing ridesharing service considering multiple stakeholders’ concerns