Von Fahrzeugen des Öffentlichen Verkehrs sind deren Positionen bekannt, wenn sie informationstechnisch in ein Betriebsleitsystem eingebunden sind. Über die auf dem Streckenband zwischen Meldepunkten zurückgelegte Wegstecke und die jeweils dafür benötigte Zeit kann auf die mittlere Geschwindigkeit geschlossen werden. Aus dieser wiederum kann eine Verkehrslageaussage abgeleitet werden. In wie weit diese für den Gesamtverkehrsstrom gültig, belastbar und richtlinienkonform ist, welche Randbedingungen für eine Auswertung einzuhalten sind, welche Verfahren sich zur Aufbereitung anbieten und welche Nutzungsszenarien unterstützt werden, wird aufgezeigt.

The term physical accessibility has widely been used by geographers, economists and urban planners and basically reflects the relative ease of access to/from several urban/rural services by considering various travelling costs. Numerous accessibility measures, ranging from simple to sophisticated, can be found in the GIS based accessibility modeling literature. However, whether simple or sophisticated, one of the fundamental shortcomings of the current GIS-based accessibility measures is that they are generally calculated from a fixed catchment area boundary based on constant traveling costs such as Euclidian (bird-flight) distance costs or transportation network-based average speed costs (e.g. 50 km/h for main streets and 30 km/h for local streets, etc.). Although such deterministic approaches are widely used in GIS-based accessibility modeling literature, they are not realistic, especially due to highly variable speeds in road segments and uncertainty in the accuracy and reliability of the accessibility measures. Therefore, this dissertation provides a new stochastic methodology for GIS-based accessibility modeling process by using GPS-based floating car data and Monte Carlo Simulation (MCS) that could handle variations in traveling costs and consider all possible catchment area boundaries, instead of one average or maximum fixed catchment area boundary. The main contribution of the research is that / the proposed physical accessibility modeling could handle uncertainties in transportation costs, create significant improvement on accuracy and reliability of accessibility measures in terms of catchment area boundaries and support decision makers who are supposed to deal with accessibility, location/allocation and service/catchment area related issues. The proposed stochastic methodology is implemented to a case study on medical emergency service accessibility, in Eskisehir, Turkey and the results of the deterministic and stochastic accessibility models are compared. The main focus of the case study is not to evaluate a specific accessibility condition in a detailed manner but to provide a methodological discussion and comparison between the deterministic and stochastic accessibility modeling process. With the implementation to a case study, it is shown that / the results of the proposed methodology are more realistic than the conventional deterministic approaches.

Geographic space is the large-scale and continuous space that encircles the earth and in which human activities occur. The study of geographic space has drawn attention in many different fields and has been applied in a variety of studies, including those on cognition, urban planning and navigation systems. A scaling property indicates that small objects are far more numerous than large ones, i.e., the size of objects is extremely diverse. The concept of scaling resembles a fractal in geometric terms and a power law distribution from the perspective of statistical physics, but it is different from both in terms of application. Combining the concepts of geographic space and scaling, this thesis proposes the concept of the scaling of geographic space, which refers to the phenomenon that small geographic objects or representations are far more numerous than large ones. From the perspectives of statistics and mathematics, the scaling of geographic space can be characterized by the fact that the sizes of geographic objects follow heavy-tailed distributions, i.e., the special non-linear relationships between variables and their probability. In this thesis, the heavy-tailed distributions refer to the power law, lognormal, exponential, power law with an exponential cutoff and stretched exponential. The first three are the basic distributions, and the last two are their degenerate versions. If the measurements of the geographic objects follow a heavy-tailed distribution, then their mean value can divide them into two groups: large ones (a low percentage) whose values lie above the mean value and small ones (a high percentage) whose values lie below. This regularity is termed as the head/tail division rule. That is, a two-tier hierarchical structure can be obtained naturally. The scaling property of geographic space and the head/tail division rule are verified at city and country levels from the perspectives of axial lines and blocks, respectively. In the study of geographic space, the most important concept is geographic representation, which represents or partitions a large-scale geographic space into numerous small pieces, e.g., vector and raster data in conventional spatial analysis. In a different context, each geographic representation possesses different geographic implications and a rich partial knowledge of space. The emergence of geographic information science (GIScience) and volunteered geographic information (VGI) greatly enable the generation of new types of geographic representations. In addition to the old axial lines, this thesis generated several types of representations of geographic space: (a) blocks that were decomposed from road segments, each of which forms a minimum cycle such as city and field blocks (b) natural streets that were generated from street center lines using the Gestalt principle of good continuity; (c) new axial lines that were defined as the least number of individual straight line segments mutually intersected along natural streets; (d) the fewest-turn map direction (route) that possesses the hierarchical structure and indicates the scaling of geographic space; (e) spatio-temporal clusters of the stop points in the trajectories of large-scale floating car data. Based on the generated geographic representations, this thesis further applies the scaling property and the head/tail division rule to these representations for urban studies. First, all of the above geographic representations demonstrate the scaling property, which indicates the scaling of geographic space. Furthermore, the head/tail division rule performs well in obtaining the hierarchical structures of geographic objects. In a sense, the scaling property reveals the hierarchical structures of geographic objects. According to the above analysis and findings, several urban studies are performed as follows: (1) generate new axial lines based on natural streets for a better understanding of urban morphologies; (2) compute the fewest-turn and shortest map direction; (3) identify urban sprawl patches based on the statistics of blocks and natural cities; (4) categorize spatio-temporal clusters of long stop points into hotspots and traffic jams; and (5) perform an across-country comparison of hierarchical spatial structures. The overall contribution of this thesis is first to propose the principle of scaling of geographic space as well as the head/tail division rule, which provide a new and quantitative perspective to efficiently reduce the high degree of complexity and effectively solve the issues in urban studies. Several successful applications prove that the scaling of geographic space and the head/tail division rule are inspiring and can in fact be applied as a universal law, in particular, to urban studies and other fields. The data sets that were generated via an intensive geo-computation process are as large as hundreds of gigabytes and will be of great value to further data mining studies. / <p>QC 20120301</p> / Hägerstrand project entitled “GIS-based mobility information for sustainable urban planning and design”

Die Kenntnis von Straßensperrungen ist wesentliche Randbedingung bei der privaten Disposition von Fahrten als auch der Entscheidungsfindung von Baulastträgern zu Verkehrslenkungsmaßnahmen. Zur Nutzbarmachung von Sperrinformationen bietet sich zuerst die Etablierung geeigneter Schnittstellen zum administrativen Prozess an. Dass dieser Weg noch nicht breitenwirksam Umsetzung gefunden hat, liegt oft im Aufwand bei der Anpassung komplexer Verwaltungsabläufe und -systeme begründet. Um trotzdem mit einer großen räumlichen Abdeckung und hoher Aktualität Sperrinformationen zu erschließen, wurde ein Ansatz entwickelt, welcher auf der Seite der verkehrlichen Wirkungen von Sperranordnungen ansetzt. Grundlage bilden die Daten von GNSS-basierten Floating Car Systemen. Zur Sperrungsermittlung wird die Befahrungshäufigkeit für Straßenabschnitte ausgewertet. Werden auf einem Abschnitt keine Fahrzeuge mehr erfasst, so wird von einer Sperrung ausgegangen. Dass die so gewonnene Sperraussage mit hoher Wahrscheinlichkeit mit der Realität übereinstimmt, sind geeignete Parametrierungen der Auswertung zu finden, welche die durch unterschiedliche Verkehrsnachfrage bedingte Befahrungshäufigkeiten der Straßenabschnitte berücksichtigt. Umgesetzt und getestet wurde der Ansatz im Kontext des Dresdner operativen Straßenverkehrsmanagementsystems VAMOS mit seiner Taxi-Floating Car-Komponente. Es konnte aufgezeigt werden, dass in dem für Verkehrsmanagementmaßnahmen relevanten Vorrangnetz mit einer Gesamtlänge von 540 Kilometern für 8 Prozent der Straßenabschnitte die Wirkung von Sperrungen in weniger als 6 Stunden, bei 59 % unter 24 Stunden und bei 79 % in weniger als 72 Stunden registriert werden können. Operative Reaktionen z. B. Warnhinweise auf Informationstafeln oder die Anpassung der kollektiven Zielführung an das spezifische Verkehrslagebild, erscheinen hier möglich. Natürlich können diese Informationen auch der Anreicherung etablierter Informationsflüsse, wie die Versorgung der Landesmeldestellen für den Verkehrswarndienst oder Routing-Dienste privater Service Provider, dienen.

More than a million people die every year due to road traffic accidents globally where one in five serious or fatal accidents occurs during severe weather conditions. Sweden is in the lead of Vision Zero, with the aim of zero casualties due to road traffic, and every year new countries and organisations accept the challenge of saving lives. Early studies have shown that one way of decreasing casualties is to implement floating car data (FCD), which is data obtained from connected road vehicles. One example of such an implementation is to gather knowledge about the current state of the road network enabling targeted winter road maintenance. By implementing FCD for winter road maintenance, besides creating safer roads, savings will be made for the environment by reducing the use of fossil fuels and salt. Since the fleet of road vehicles is going towards being fully autonomous, the volumes of FCD will increase rapidly generating new possibilities for FCD usage. Recent research regarding FCD has mainly focused on traffic flow, speed and route optimisation, together with general methods for FCD mining creating intelligent transport systems. Studies have also been made to cover the gap between road weather information systems (RWIS) monitoring the road condition and thereby improving road weather forecasts. But there is a need for research regarding the implementation of FCD at a level of action, closer to the road users improving winter road maintenance. Presented in this thesis are results gathered in the project Digital Winter, a project initiated by the Swedish Traffic Administration, where FCD regarding tire-to-road friction has been procured for all public roads in Sweden. Results show promising numbers regarding coverage and reliability for implementation of FCD for winter road maintenance follow-up, managing that assigned levels of road friction are achieved. Examples are given for different areas in Sweden where harsh weather conditions are detected and statistics show coverage of FCD both at a daily and hourly level. Multiple suppliers of FCD have been participating in Digital Winter and the measurements presented, show a correlation between suppliers of FCD and methods that today are approved for winter road maintenance follow-up in Sweden. But also that the friction measured using FCD is closer to the true road friction experienced by road users.

Floating Car Data (FCD) besitzen eine sehr breite Palette an Anwendungsmöglichkeiten, die aber teilweise noch keine massenhafte Verbreitung gefunden haben, auch wenn das Innovationspotenzial als sehr hoch eingeschätzt wird. Dies begründet sich in erster Linie durch die meist relativ großen Erfassungsintervalle bei der derzeitigen FCD-Erfassung. In Dresden ist ein Taxi-FCD-System in Betrieb, welches sich durch eine sehr hohe Detektionsdichte auszeichnet. Die Fahrzeugpositionen werden mindestens alle 5 Sekunden aufgezeichnet. Damit bestehen ausgezeichnete Möglichkeiten, mögliche Mehrwerte zu prüfen und Prototypen zu etablieren. Getestet wurde u. a. die Generierung von Straßennetzabbildern. Im Dauerbetrieb befindet sich die FCD-basierte Verkehrslageermittlung.

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

Von Fahrzeugen des Öffentlichen Verkehrs sind deren Positionen bekannt, wenn sie informationstechnisch in ein Betriebsleitsystem eingebunden sind. Über die auf dem Streckenband zwischen Meldepunkten zurückgelegte Wegstecke und die jeweils dafür benötigte Zeit kann auf die mittlere Geschwindigkeit geschlossen werden. Aus dieser wiederum kann eine Verkehrslageaussage abgeleitet werden. In wie weit diese für den Gesamtverkehrsstrom gültig, belastbar und richtlinienkonform ist, welche Randbedingungen für eine Auswertung einzuhalten sind, welche Verfahren sich zur Aufbereitung anbieten und welche Nutzungsszenarien unterstützt werden, wird aufgezeigt.