Multi-perspective embedding for non-metric time series classification

The interest in time series analysis is rapidly increasing, providing new challenges for machine learning. Over many decades, Dynamic Time Warping (DTW) is referred to as the de facto standard distance measure for time series and the tool of choice when analyzing such data. Nevertheless, DTW has two major drawbacks: (a) it is non-metric and therefore hard to handle by standard machine learning techniques, and (b) it is not well suited for multi-dimensional time series. For this purpose, we propose a multi-perspective embedding of the time series into a complex-valued vector space and the evaluation by a model that is able to handle complex-valued data. The approach is evaluated on various multi-dimensional time series data and with different classifier techniques

Capturing the Usage of the German Car Fleet for a One Year Period to Evaluate the Suitability of Battery Electric Vehicles – A Model based Approach

AbstractThe low driving range of battery electric vehicles (BEV) is often considered as relevant reason for the low BEV sales. In order to verify this assumption, the usage of conventional cars in Germany needs to be analyzed. These analyses may help to make more reliable and realistic statements to what extent German cars could be replaced by BEVs without restrictions for their users. Most travel surveys do only consider a single day or a short period of time in the analysis. Longer time periods should be taken into consideration when analyzing the travel data since the daily car usage is not identical every day. Since there are no representative and detailed car usage surveys over longer periods available a hybrid car usage model was developed to close that gap. This model is mainly based on three mobility surveys: the German Mobility Panel (MOP), the car mileage and fuel consumption survey, and the long distance travel survey INVERMO. We show that 13% of the modeled German private car fleet never exceeds 100km per day during a full year and could be replaced by BEVs without any usage restrictions for their car owners. Another 16% of the modeled private car fleet is driven more than 100km on 1-4 days during a full year and can be substituted with slight adjustments. These cars are often second cars of a household and used less intensively (6,600km/year resp. 7600km/year) than cars not suited for BEV substitution (14,800km/year). Households that could replace their cars tend to have a lower disposable income. The crux of the matter, however, is that substitution of conventional cars is often not feasible since the mobility budget of BEV suited households tends to be too low or does not make economic sense due to the low annual mileage

Microscopic Demand Modeling of Urban and Regional Commercial Transport

Commercial transport is an intrinsic part of the evaluation of traffic volumes. However, it is often limited to freight transport, and while this is a significant element, it disregards the share of trips contributed by plumbers, electricians, care services, and the like. These businesses add a significant part to the commercial traffic volume, especially in urban areas. The reasons, commercial passenger transport lacks behind are wide-ranging, one of the leading causes being difficulties in gathering sufficient data. In this paper, we present a microscopic approach to model commercial travel demand, including but not limited to freight traffic, based on data from a national survey and open data. We differentiate between vehicles of businesses that have a fixed daily schedule, with only small variations of their trip purposes and vehicles of businesses that can predict their daily schedules only to a certain degree. The latter have varying trip purposes and decide on a short-term base if and what sort of trip is to be pursued. Vehicles with fixed daily schedules include plumbers, electricians, care services, and delivery trucks. Due to our database, we produced a model for these vehicles exemplary for delivery by determining the number of trips for a day and assigning destinations to those trips afterward. We also take the number of private trips into account, laying the foundation of being able to incorporate the commercial transport model into a passenger transport model. We show that our model can overcome the lack of regional data. Based on generic data, the application of our approach shows promising results for the urban and regional commercial travel demand of a model region. By basing our model on generic data, we introduced an opportunity to model commercial travel demand not only in one model region but also for other urban areas in Germany and possibly in various areas in Europe, assuming that structural data is similar

Scalable embedding of multiple perspectives for indefinite life-science data analysis

Life science data analysis frequently encounters particular challenges that cannot be solved with classical techniques from data analytics or machine learning domains. The complex inherent structure of the data and especially the encoding in non-standard ways, e.g., as genome- or protein-sequences, graph structure or histograms, often limit the development of appropriate classification models. To address these limitations, the application of domain-specific expert similarity measures has gained a lot of attention in the past. However, the use of such expert measures suffers from two major drawbacks: (a) there is not one outstanding similarity measure that guarantees success in all application scenarios, and (b) such similarity functions often lead to indefinite data that cannot be processed by classical machine learning methods. In order to tackle both of these limitations, this paper presents a method to embed indefinite life science data with various similarity measures at the same time into a complex-valued vector space. We test our approach on various life science data sets and evaluate the performance against other competitive methods to show its efficiency

Modeling intermodal travel behavior in an agent-based travel demand model

The topic of intermodal passenger mobility has become more important during the last 20 years. As mobility options increase in number and flexibility, it gets more and more attractive to combine multiple modes on single trips. In addition, intermodal travel behavior is expected to contribute to less car dependent mobility and transport sector’s reduction of greenhouse gas emissions. Creating and improving the conditions for such a behavior requires planning with knowledge about influencing factors and highest resistances. Empirical evidence and behavioral models can support decisions on measures improving intermodal travel supply. This work presents an agent-based model approach containing intermodal travel behavior with regard to its most important decisions. It enables the combination of a multitude of modes and can be extended to even more modes. By combining many decisions and influences it is comprehensible and adaptable to different surveys and circumstances. We show that results are realistic and impacts are valid to be able to forecast effects of potential measures

The effects of spatial characteristics on car ownership and its impacts on agent-based travel demand models

The objective of this study is to investigate the role of spatial characteristics on car ownership and availability respectively in agent-based travel demand models and its affection on the model’s results. Based on Open Data we generate an automated workflow to evaluate spatial characteristics such as land use, points of interest, private vehicle network, and public transport quality. We estimate two multinomial logit models for car ownership: one considering socio-demographic characteristics only, and one considering both, socio-demographic and spatial characteristics. The models’ results are spatially evaluated and compared with statistical data. Moreover, we analyze the sensitivity of public transport quality measures on car ownership. The application of both car ownership models in the agent-based travel demand model mobiTopp to the city of Hamburg, Germany shows that integrating spatial characteristics significantly improves the model’s goodness of fit as well as its overall prediction power. Moreover, the application demonstrates that a detailed consideration of spatial characteristics in car ownership models contributes to a more realistic spatial distribution of cars. Furthermore, the study shows that i.e., public transport quality measures in car ownership models are relevant to reflect secondary mode choice effects (i.e., different mode choice sets due to change in car stock) in travel demand models

Determining service provider and transport system related effects of ridesourcing services by simulation within the travel demand model mobiTopp

Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services

Integrating Autonomous Busses as Door-to-Door and First-/Last-Mile Service into Public Transport: Findings from a Stated Choice Experiment

Autonomous busses and on-demand (OD) services have the potential to improve the public transport system. However, research on potential traffic impacts is still ongoing, mainly because of a lack of existing use cases of autonomous driving as part of public transport. The availability of revealed preference data for mode choice decisions is thus very limited. Therefore, we conducted a stated choice experiment to assess mode choice preferences with regard to use cases as the main mode of transport and as the solution for the first and last mile. We also distinguished between OD and schedule-based (sched.) services. The target population of the survey is the population of Baden-Württemberg, a state in southwestern Germany. The responses of 1,434 people were analyzed using a nested logit approach. On this basis, we established exemplary utility functions and descriptively derived recommendations for efficient forms of deploying autonomous busses in addition to already existing well-developed public transport systems. It was found that, under the given conditions, public transport pass owners without a car in their household would be the most interested in using autonomous busses. Car owners without a smartphone see less benefit. It was also shown that the recruiting method of the respondents is crucial. Those reached via social media were significantly more positive than those contacted via an online panel. Further evaluations show that autonomous busses are rated similarly to existing public transport and consequently have particularly high potential on medium distances, especially if their deployment leads to shorter access routes

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

We present a methodology to extract points of interest (POIs) data from OpenStreetMap (OSM) for application in travel demand models. We use custom taglists to identify and assign POI elements to typical activities used in travel demand models. We then compare the extracted OSM data with official sources and point out that the OSM data quality depends on the type of POI and that it generally matches the quality of official sources. It can therefore be used in travel demand models. However, we recommend that plausibility checks should be done to ensure a certain quality. Further, we present a methodology for calculating attractiveness measures for typical activities from single POIs and national trip generation guidelines. We show that the quality of these calculated measures is good enough for them to be used in travel demand models. Using our approach, therefore, allows the quick, automated, and flexible generation of attractiveness measures for travel demand models