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.