Johannes Asamer

Novel road classifications for large scale traffic networks

Establishing a highly sophisticated large-scale Traffic Information System (TIS) requires the creation and deployment of link travel time prediction models for large road networks. Due to the dimension of typical road networks and low coverage with Floating Cars (FC), data sets that can be used for prediction contain a large number of missing observations. Additionally, specifying prediction models for each link separately is impossible due to restrictions of both computational as well as modeling resources. This paper aims to improve the scalability of link travel time predictions by combining information from roads with similar characteristics. The Functional Road Class (FRC) is a widely accepted indicator for road similarity mainly based on static information from infrastructure planning. The coherence between the clustering introduced by the FRC and road dynamics measured by Floating Car Data (FCD) in the city of Vienna is discussed and analyzed. Clustering approaches that are based on indices characterizing speed measurement distributions are proposed as alternatives to the FRC system. It is demonstrated by way of examples that the new clustering is much more appropriate to provide predictions of link travel times.

Saturation Flow Under Adverse Weather Conditions

In the modeling of signalized intersections, one parameter is of crucial importance: the saturation flow rate. This value defines the number of vehicles that pass an intersection within 1 h of effective green time per lane. In this study, changes in the saturation flow were investigated under adverse weather conditions, such as precipitation or snow that covered the road surface. Data were obtained from video recordings, and a timestamp was recorded for each vehicle as its rear axle crossed the intersection. Subsequently, all observations were aggregated to a longer time interval. These measurements were then used to train a model by minimizing the squared error between model output and observation. The advantages of the model were the incorporation of various vehicle classes and the consideration of driving behavior at the beginning and the end of the green phase (start and end lags). These parameters were investigated under various weather conditions and showed that the saturation flow rate was significantly influenced by snow on the road surface. To improve traffic models, it is thus important to consider the dependence of the saturation flow rate on the weather. To adjust the saturation flow, adjustments in certain other parameters influenced by prevailing weather conditions were investigated in a microscopic traffic simulation.

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.

Prediction of Velocities on Motorways by k-Means Clustering

In this paper a method for the prediction of vehicle velocities is described that can be used for any point of time in the future. The approach is based on a two step clustering which uses toll transaction data of the training of the model. The results are different clusters for each road segment, containing the velocity value and its probability of occurrences. Furthermore the results of the method have been compared to a statistical method as well as to a neural network.

The elevation factor: Digital elevation model quality and sampling impacts on electric vehicle energy estimation errors

Energy used to overcome elevation is a significant factor in estimating energy consumption of moving objects and (electric) vehicles in particular. A common source of elevation data for electric vehicle energy estimations are digital elevation models (DEMs). These DEMs are available from multiple providers and with varying quality as free or paid data. This paper presents an evaluation of the impacts of DEM quality and methods used to sample DEM values for elevation profiles on energy estimations for electric vehicle routes. The evaluation is carried out for two different study areas: an urban mostly flat area, and a rural alpine area. An overview of the error obtained with different DEMs and sampling methods in these two areas is provided. These results can serve as a reference for estimating the magnitude of the energy estimation error in case high resolution elevation data is not available in a study area.

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.

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.

Detection of faulty flow measurements based on queueing models and measured delays at intersections

Results of a case study for two signalized intersections in the Viennese intraurban road network have shown that detectors delivering artificially introduced faulty flow measurements can be detected. The method compares the theoretical delay distribution based on a deterministic queuing model to the empirical delay distribution of measured flow and speed values. A prerequisite is that the measurements at the intersection cover both under- and over-saturated traffic situations. The described method can detect several types of errors, namely overcount, undercount and Gaussian noise errors. The sensitivity of the method depends on the intensity of the error, i.e. relative error and standard deviation. As the method has achieved good results for artificially introduced errors, similar errors in real data will also be detected, since the investigated error types are typical for urban flow measurements.