Martin Reinthaler

Estimation of road capacity and free flow speed for urban roads under adverse weather conditions

This work aims to estimate changes in traffic characteristics of urban roads in dependence of adverse weather conditions like rain and snow. Investigated traffic characteristics are capacity and free flow speed which are elementary for describing the performance of traffic networks and setting up macroscopic traffic models. The methods are based on aggregated flow and speed measurements from local sensors. Results show a significant reduction of road capacity and free flow speed in dependence of intensity and type of precipitation.

Robust road link speed estimates for sparse or missing probe vehicle data

Probe vehicles equipped with GPS can be used to permanently collect traffic speed information for an entire road network, and the statistical mean value of link speeds collected over time is often used as an estimator for mid-term predictions. For road links with sparse probe vehicle data, the estimated mean may be too inaccurate due to the low sample size, and speeds for road links with missing probe vehicle data must be imputed from other data. This paper proposes to apply a Gaussian-mixture based technique to increase the robustness of speed estimates. Typical shapes of the diurnal speed curve are learnt from historical data of all links in the road network. The model is able to provide robust estimates of mean speed curves based on only a few available observations and drastically reduces the amount of data needed to store them by 95%. Experimental results on a comprehensive set of 857527 day speed curves show that the predictions are superior to traditional approaches based on aggregated or disaggregated historical mean values.

Utilizing mobility data to facilitate the introduction of E-Taxis in Vienna: Feasibility study of a decision support system for the introduction of battery electric vehicles as Taxis

Introducing E-Taxi fleets in urban areas poses a number of economic, organizational and technical challenges related to the nature of Battery Electric Vehicles (BEV). This paper discusses these challenges and demonstrates how existing mobility data can aid the underlying decision process to overcome them. We present an integrated approach developed for the introduction of an E-Taxi system in the city of Vienna, where mobility data based on taxi floating car data (FCD) was used as decision support.

Requirements on tomorrow's road infrastructure for highly automated driving

This paper presents the results of a study on the requirements on road infrastructure regarding increased use of highly automated vehicles. Based on the outcome of a literature review and a web questionnaire, factors that most influence the performance of automated driving systems are given. Requirements for future road design and planning are recommended to ensure a safe and efficient operation.

A data-driven approach for travel time prediction on motorway sections

In the presented case study, travel times for passenger cars (PC) and heavy goods vehicles (HGV) were predicted with a data-driven, hybrid approach, using historical traffic data of the entire high-ranking Austrian road network. In case flow data were available, travel time was predicted with a Kernel predictor searching for similar speed-density patterns. In case of missing flow data, travel time was predicted with deviations from typical historical speed time series. The performed steps in pre-processing traffic data, the hybrid prediction method as well as the results for selected road sections are described and analysed.

Lane-Sensitive Positioning and Navigation for Innovative ITS Services

The goals of the project NAV-CAR are both to enable lane sensitive navigation for cars on highways and to increase robustness for high precision positioning in specific environments such as urban canyons and alpine regions where satellite based navigation systems may fail. The challenge of the project is the technical realization with a reasonable ratio of accuracy vs. costs, which is met by using sensor fusion technologies and stepwise integration of car specific data with GPS, which was implemented via an on-board unit (OBU) with CAN-bus interface. The approach was validated by test drives on urban (Vienna, A 23) as well as alpine highways (Brenner, A11/A12). Precise lane-specific trajectory reference data were derived from test drives with a special surveillance truck RoadStar. To estimate the potential impact of Galileo services as compared to existing GPS a simulation with data input from test drives in an alpine region was performed. The generation and inclusion of enhanced maps as a further option was evaluated.