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Lecturers: Jan Fabian Ehmke, Steffen Elting, Yllka Velaj Course Description: The Data Science course is offered at University of Vienna in the first semester of the Business Analytics and Data Science masters’ programs, and in the third semester of the Digital Humanities masters’ program. This class consists of different parts: 1. Part I: How to approach data science projects, 2. Part II: Showcase examples for data science projects, 3. Tutorial: Introduction to the data science tool KNIME, 4. Project Work: Students work in an interdisciplinary group on a data science project.

The abstract is available here: https://uscholar.univie.ac.at/o:1588378

This report documents the talks and group work of Dagstuhl Seminar 16091 “Computational Challenges in Cooperative Intelligent Urban Transport”. This interdisciplinary seminar brought researchers together from many fields including computer science, transportation, operations research, mathematics, machine learning and artificial intelligence. The seminar included two formats of talks: several minute research statements and longer overview talks. The talks given are documented here with abstracts. Furthermore, this seminar consisted of significant amounts of group work that is also documented with short abstracts detailing group discussions and planned outcomes.

Throughout the last decade, the advent of novel mobility services such as ride-hailing, car-sharing, and ride-sharing has shaped urban mobility. While these types of services offer flexible on-demand transportation for customers, they may also increase the load on the, already strained, road infrastructure and exacerbate traffic congestion problems. One potential way to remedy this problem is the increased usage of dynamic ride-sharing services. In this type of service, multiple customer trips are combined into share a vehicle simultaneously. This leads to more efficient vehicle utilization, reduced prices for customers, and less traffic congestion at the cost of slight delays compared to direct transportation in ride-hailing services. In this thesis, we consider the planning and operation of such dynamic ride-sharing services. We present a wider look at the planning context of dynamic ride-sharing and discuss planning problems on the strategical, tactical, and operational level. Subsequently, our focus is on two operational planning problems: dynamic vehicle routing, and idle vehicle repositioning. Regarding vehicle routing, we introduce the vehicle routing problem for dynamic ridesharing and present a solution procedure. Our algorithmic approach consists of two phases: a fast insertion heuristic, and a local search improvement phase. The former handles incoming trip requests and quickly assigns them to suitable vehicles while the latter is responsible for continuously improving the current routing plan. This way, we enable fast response times for customers while simultaneously effectively utilizing available computational resources. Concerning the idle vehicle repositioning problem, we propose a mathematical model that takes repositioning decisions and adequately reflects available vehicle resources as well as a forecast of the upcoming trip request demand. This model is embedded into a real-time planning algorithm that regularly re-optimizes the movement of idle vehicles. Through an adaptive parameter calculation process, our algorithm dynamically adapts to changes in the current system state. To evaluate our algorithms, we present a modular simulation-based evaluation framework. We envision that this framework may also be used by other researchers and developers. In this thesis, we perform computational evaluations on a variety of scenarios based on real-world data from Chengdu, New York City, and Hamburg. The computational results show that we are able to produce high-quality solutions in real-time, enabling the usage in high-demand settings. In addition, our algorithms perform robustly in a variety of settings and are quickly adapted to new application settings, such as the deployment in a new city.

With the increasing interest in creating Smart Cities, traffic speed prediction has attracted more attention in contemporary transportation research. Neural networks have been utilized in many studies to address this problem; yet, they have mainly focused on the short-term prediction while longer forecast horizons are needed for more reliable mobility and route planning. In this work we tackle the medium-term prediction as well as the short-term. We employ feedforward neural networks that combine time series forecasting techniques where the predicted speed values are fed into the network. We train our networks and select the hyper-parameters to minimize the mean absolute error. To test the performance of our method, we consider two multi-segment routes in Istanbul. The speed data are collected from floating cars for every minute over a 5-month horizon. Our computational results showed that accurate predictions can be achieved in medium-term horizon.

Bus rapid transit systems in developing and newly industrialized countries are often operated at the limits of passenger capacity. In particular, demand during morning and afternoon peaks is hardly or even not covered with available line plans. In order to develop demand-driven line plans, we use two mathematical models in the form of integer programming problem formulations. While the actual demand data is specified with origin-destination pairs, the arc-based model considers the demand over the arcs derived from the origin-destination demand. In order to test the accuracy of the models in terms of demand satisfaction, we simulate the optimal solutions and compare number of transfers and travel times. We also question the effect of a selfish route choice behavior which in theory results in a Braess-like paradox by increasing the number of transfers when system capacity is increased with additional lines.

Current order picking technologies are characterized by different degrees of automation. Irrespective of the automation level, order picking remains a labor-intensive process. Hence, the decisions on the deployment of installed technologies and on labor utilization are interdependent. We develop two planning approaches for an integrative decision support. In comparison to the simultaneous approach the sequential one induces coordination deficits, but less computational effort. In order to inquire into the behavior of both approaches, we conduct a numerical study using sampled data of a pharmaceutical wholesaler.

We study an integrated production–inventory system that manufactures new items of a certain product and receives some of the used items back. These can be either remanufactured on the same production line or disposed of. Used items awaiting remanufacturing need to be held in stock. New and as-good-as-new items are kept in stock, from which the product demand is satisfied. Controlling such a system involves decisions with regard to disposal of used items, succession of manufacturing and remanufacturing operations, and the choice of respective lot sizes. Recent works have referred to settings assuming variation in quality of returned items and a limited number of remanufacturing cycles that an item can undergo. We seek to determine optimal lot sizing for such a system and derive sufficient conditions for an optimal policy to forego remanufacturing.