Chair of Traffic Engineering and Control

Electric vehicles are increasingly becoming the vehicle of choice in today’s environmentally conscious society, and the heart of an electric vehicle is its battery. Today, lithium-ion batteries are mainly used to power electric vehicles for its increased energy storage density and longevity. However, in order to estimate battery life, long and costly battery testing is required. Therefore, there is a need to investigate efficient ways that could reduce the amount of testing required by reusing existing knowledge of aging patterns from different kinds of battery chemistry. This work aims to answer two research questions. The first addresses the challenge of battery cell testing data that contain battery cells that do not reach the End-Of-Life (EOL) threshold by the time the testing has been completed. For this challenge, we propose to implement survival analysis that is able to handle incomplete data or what is referred to as censored data. The second addresses how to reuse a model t...

Crowdsourcing has been recently a popular solution to overcome the high cost of acquiring labeled datasets. However, the reliability of crowdsourced labels remains a challenge. Many approaches rely on domain experts who are scarce and expensive. In this work, we propose to use Twitter to acquire labels and to juxtapose them with crowdsourced ones. This allows us to measure annotator reliability. Since annotator expertise may vary, depending on content, we propose a new topic-based reliability measurement approach. We compare our model with Kappa Weighted Voting and Majority Voting as baseline methods, and show that our approach performs well and is robust when up to 30% of the annotators is not reliable.

Im Rahmen dieser Arbeit wird auf die verkehrstechnische Anwendung von Blue-tooth-Technologie auf Autobahnen eingegangen. Mittels neben der Fahrbahn in-stallierter Antennen können Bluetooth-Geräte in vorbeifahrenden Fahrzeugen er-fasst und anhand ihrer weltweit eindeutigen Kennung wieder erkannt werden. Auf Grundlage dieser Detektionen, angewandt an mehreren Messquerschnitten im zu betrachtenden Verkehrsnetz, lassen sich repräsentative Reisezeiten auf den aus-gewählten Routen ermitteln.
Eine der Zielsetzungen hierbei besteht darin, die vorliegenden Realdaten der Ver-suchsstrecken in Nordbayern für die anschließende Auswertung aufzubereiten. Hierzu werden die detektierten Reisezeiten auf ihre Plausibilität überprüft, um bei-spielsweise Fahrzeuge heraus zu filtern, die kurzzeitig die Autobahn an einer Rast-stätte oder Anschlussstelle verlassen haben und damit die Ergebnisse ver-fälschen würden.
Ein besonderes Augenmerk liegt auch auf der automatisierten Erkennung von Ver-kehrsstörungen auf Basis der ermittelten durchschnittlichen Geschwindigkeiten, die sich aus dem streckenbezogenen Kehrwert der Reisezeiten berechnen. Hierzu wird ein Verfahren vorgestellt, welches zwischen zwei Bluetooth-Empfängern Änderun-gen in der mittleren sowie auch der erreichbaren Maximalgeschwindigkeit identifi-ziert und damit Behinderungen im Verkehrsablauf erkennen kann.
Um die entwickelten Methoden an den vorliegenden Realdaten der Autobahndirek-tion Nordbayern zu testen, wurde prototypisch eine Java-Applikation entwickelt, die Bluetooth-Datensätze von beliebigen Teststrecken einlesen und verarbeiten kann. Zusätzlich erfolgt im Anschluss eine umfassende Auswertung der Ergebnisse in Di-agrammen sowie Tabellen.

This article describes an approach to use travel times derived from Bluetooth detector data for dynamic net control in a freeway system for dynamic rerouting. The developed algorithms detect speed drops among sequenced vehicles and were implemented and tested in Northern Bavaria (Germany). Ongoing research aims at developing a fast, reliable and cost-efficient method for incident detection using several Bluetooth receivers for vehicle re-identification. This article describes the methodological approach, focusing on the current test site around Nuremberg. Data from evaluation tests shows promising results and encourages the use of the relatively cheap data source Bluetooth for traffic control approaches.

This paper shows the potential of using in-vehicle sensors to determine free parking places in cities. It will also be shown how the crowdsourcing of this data can be used to enrich the existing information about available parking places. Furthermore this concept is compared to the state of the art and its future potential is analysed. Additionally the results of a user survey are discussed, which forms the basis for the characteristics of the presented crowdsourcing concept. The urban mobility based on private transport more and more faces the problem of limited space for public parking places especially in dense cities all over the world. In the individual perspective, this leads to frustration of the drivers due to time-consuming search for a parking place. On the other hand this also affects the overall traffic within urban areas and studies show that between 20 and 40 percent of the overall traffic in cities is generated by searching for parking places [2,3,5,8,10]. Nevertheless, except for private or public owned parking garages, the information on available parking places in most cities is still quite poor. A solution might be the installation of stationary sensors in the on-street parking infrastructure to determine real-time occupancies. Preliminary steps towards such a solution are already taken in San Francisco [12] since 2010. But due to the high costs for installing and maintaining around 12,000 of these on-street sensors this solution might not be feasible for the majority of cities worldwide. Therefore this paper investigates the possibility to use already available in-vehicle sensors to detect free on-street parking places when passing by. To achieve a solution which also focuses on the user's constraints while searching for a parking place, a survey was conducted to get more detailed information about the drivers' typical behaviour and needs. The results show that most drivers are not willing to invest more than five minutes in searching for a parking place and this is the reason that quite often they prefer to drive on a detour where the probability could be significantly higher. It is of vital importance for them that the proposed method will not only contribute in saving time but also in detecting and suggesting parking places near enough to their destination. The survey revealed that the potential users request a really reliable application that will ensure the validity of the detected parking place. This means that they need to be aware of the length of the space (and thus if their car fits in it) as well as its exact location and type (short/long term, parking place for residents/disabled people). In conclusion through the conducted survey the participants gave their preference in terms of the kind of information they want to

This article analyzes the use of Bluetooth-based travel times, for Automatic Incident Detection (AID) purposes. Automatic incident messages were derived for simulated data through the use of an AID algorithm, which was developed by Technical University of Munich (TUM). A Vissim model of a 15 kilometre section of A9 motorway in Germany was set up, where different scenarios of traffic situation, incidents and detector layout were introduced and travel times were generated, processed and then run through the TUM algorithm. The performance measures Detection Rate (DR), False Alarm Rate (FAR) and Mean Time To Detect (MTTD) were used for the analysis of the incident messages' quality of the simulated data and compared for every incident scenario. Local data were also generated in the Vissim model and used by VK Diff algorithm for incident detection. A comparison of the quality of the incident messages of both TUM and VK Diff algorithm was conducted.

In this work, we present a routing approach avoiding relatively dangerous areas within a city. The information of how dangerous some urban areas are is derived using volunteered geographic information (VGI), governmental open data for detecting properties and functionalities of the urban infrastructure and historical crime data from police departments for detecting crime hot spots. Therefore, we present the basics of crime mapping and analysis with GIS, the practical use of VGI for routing and describe our contribution within the field of routing solutions. Afterwards, we explore our test data in detail. For the practical use, we simplify all the urban infrastructure information and propose a safety index, which represents the relative safety in the investigation area. Additionally, historical crime hot spots are detected and used as routing obstacles. The arcs in the road network are weighted by our safety index and the historical crime hot spots are introduced as obstacle polygons. We test our safetyaware routing design on Los Angeles (LA) and assume its use during night times. In this regard, from two relatively far away situated origin and destination points, we calculate the least dangerous path and compare it with the calculated shortest path. Vehicle drivers without knowledge about the dangerous areas in the city may use the least dangerous path, which is based on our calculated safety index. Finally, we discuss the effectiveness of our method and consider further extensions using freely available geodata.

Personalized navigation and way-finding are prominent research areas of location-based service (LBSs). This includes innovative concepts for car navigation. Within this paper, we investigate the idea of providing drivers a routing suggestion which avoids ‘complicated crossings’ in urban areas. Inexperienced drivers include persons who have a driver’s license but, for whatever reason, feel uncomfortable to drive in a city environment. Situations where the inexperienced driver has to depend on a navigation device and reach a destination in an unfamiliar territory may be difficult. Preferences of inexperienced drivers are investigated. ‘Fears’ include driving into ‘complicated crossings’. Therefore, the definition and spatial characteristics of ‘complicated crossings’ are investigated. We use OpenStreetMap as a road dataset for the routing network. Based on the topological characteristics of the dataset, measured by the number of nodes, we identify crossings that are ‘complicated’. The user can choose to compute an alternative route that avoids these complicated crossings. This methodology is one step in building a full ‘inexperienced drivers’ routing system, which includes additional preferences from the user group, for example, as avoiding left turns where no traffic light is present.

Floating Car Data (FCD) are rich data sources collected from GPS-equipped vehicles for analyzing and modelling traffic information and human mobility. Due to the large amount of these movement data, we need visual analysis methods that enable users to inspect traffic situations. This paper investigates two visualization techniques for FCD: (1) three-dimensional representations of both average speed and average density derived from Floating Car Data (FCD) based on road segments and (2) two-dimensional maps representing qualitative traffic congestion inferred from the average speed and density. We propose a selection circle visualization method showing average speeds and average densities within a selected area. This information is represented in a 3-D extrusion projected onto the two-dimensional street network map. A test FCD set from Shanghai is used to evaluate the visualization technique. We can compare the visualization results at different time windows and detect spatio-temporal changes of the traffic situation in Shanghai. This helps to understand the general movement patterns

People in growing urban areas are more and more influenced by emissions coming from numerous vehicles and factories. In this paper we inspect the concentration of particulate matter (PM2.5) visually over time. This information stems from a data set of air quality measurements from 36 static sensors in Beijing over one year (from 8.02.2013 till 8.02.2014). One possibility for creating an overview for 36 positions with varying PM2.5 measurements in time is the use of interpolation techniques. In our approach, we generate surfaces of PM2.5 concentration using inverse distance weighting (IDW). The resulting surfaces represent interpolated PM2.5 values, based on averaged PM2.5 information (e.g. average of one day). We create simple interactive visualizations using points as surface representations. Each surface point within the 3D visual analysis display exhibits its PM2.5 value by differing coloration and z-value (height component). The interactivity consists of using selection circles for stacked 3D displays of interpolated PM2.5 surfaces for different times (time series). The aim of this visual information analysis is the possible detection of periodical hotspots of high PM2.5 concentrations, which might be useful for people with respiratory diseases. For the detection of dynamic PM2.5 hotspot variations, we introduce thresholds for querying only the highest PM2.5 values of the surfaces. Afterwards, these points are aggregated into convex hulls (polygons), with the idea of comparing the size and shape of the PM2.5 hotspots in each created surface. The change of position and size of these polygons over time may be an indicator for air quality changes within an urban environment. Considering the above, this may be a starting point for the conception of a person-alized routing solution for pedestrians or vehicle drivers with respiratory diseases, who want to avoid these hotspots of high PM2.5 concentrations.

A person suffering from a chronic respiratory disease has to take current air quality into account for each errand that takes the person outside their home . These individuals are interested in estimating ahead of time how high the exposure to e.g., particulate matter would be when leaving home and taking a specific route. In order to make a good decision, a selection of routes with minimal exposure to air pollutants should be offered, e.g. as a location-based service. This type of problem calls for mapping a changing continuous field onto a network in order to enable optimization for routing.

From a personalized computing standpoint, current in-car routing systems are somewhat primitive. Usually routing options are dependent on the fastest (usually default) or the shortest route from a start point to an end point. Start and end points are matched to an address or another point of interest (POI) via geocoding functions.

Floating Car Data (FCD) is GNSS-tracked vehicle movement, includes often large data size and is difficult to handle, especially in terms of visualization. Recently, FCD is often the base for interactive traffic maps for navigation and traffic forecasting. Handling FCD includes problems of large computational efforts, especially in case of connecting tracked vehicle positions to digitized road networks and subsequent traffic state derivations. Established interactive traffic maps show one possible visual representation for FCD. We propose a user-adapted map for the visual analysis of massive vehicle movement data. In our visual analysis approach we distinguish between a global and a local view on the data. Global views show the distribution of user-defined selection areas, in the way of focus maps. Local views show user-defined polygons with 2-D and 3-D traffic parameter visualizations and additional diagrams. Each area selection is timestamped with the time of its creation by the user. After defining a number of area selections it is possible to calculate weekday-dependent travel times based on historical taxi FCD. There are 3 different types of defined connections in global views. This has the aim to provide personalization for specific commuters by delivering only traffic and travel time information for and between user-selected areas. In a case study we inspect traffic parameters based on taxi FCD from Shanghai observed within 15 days in 2007. We introduce test selection areas, calculate their average traffic parameters and compare them with recent (2015) and typical traffic states coming from the Google traffic layer.

Analyzing large amounts of complex movement data requires appropriate visual and analytical methods. This paper proposes a 2-D star-icon based visualization technique for the visual exploration of multivariate movement events in a space-time cube. To test the proposed method, we derive multivariate events from massive real-world floating car data and visually explore spatio-temporal patterns. The experimental results show that our proposed methods are helpful in identifying interesting locations or functional areas, and assist the understanding of dynamic patterns.

Extended Abstract Tracked movement from numerous observed objects includes often large data size and is difficult to handle, especially in terms of visualization. In the following we describe the possibility of getting more insight into massive movement data. Our inspected data set consists of more than 7000 observed taxis in Shanghai and is referred to as Floating Car Data (FCD). With this term numerous approaches appeared in the last decades facing the problem of how to connect digitized road network with tracked positions of moving objects. The aim is often to improve the modelling of traffic in road networks. Since FCD sets of taxis have often large size not only problems of reasonable processing are appearing but as well advanced ideas of geovisualization. Established interactive traffic maps show one possible solution for the visual inspection. Other approaches use advanced techniques for the detection of interesting patterns, which may be connected with appearing events (e.g. congestion).

Floating Car Data (FCD) of taxis provides useful traffic information. Its handling needs effective analysis steps, including Map Matching (MM) onto road segments. Another challenge is to compute realistic FCD-based travel times in complex urban road networks with different elevation levels. We propose a method for inferring travel time variations in intersections of these networks. Based on intersecting recorded taxi trajectories in Euclidean space, we assign average travel time differences. Additionally, the same technique allows distinguishing between elevated and non-elevated road intersections in complex urban environment. In the end we test and evaluate the method with taxi FCD from Shanghai.

Daily mobility patterns in highly populated urban environments rely on a well-functioning effective road network. Nevertheless, traffic bottlenecks are typical for urban environments with periodic traffic congestion. In this paper, we focus on the investigation of how traffic congestion is related with complicated crossings. First, we select an approach for the classification of the complexity of road partitions and the derivation of complicated crossings based on geodata from Open-StreetMap (OSM). Second, we calculate traffic congestions using Floating Taxi Data (FTD) from Shanghai in 2007. Then, we develop a matching technique to link the congestion and complicated crossings, and subsequently define the concept of traffic bottlenecks represented by polygons. The bottlenecks indicate locations where the transportation infrastructure is complex and traffic congestion appears periodically. Finally, we select suitable cartographic representations of traffic bottlenecks in potential thematic vehicle traffic maps.

Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.