Lars Habel

Efficient Floating Car Data Transmission via LTE for Travel Time Estimation of Vehicles

The travel time estimation of vehicles is a major challenge in the area of dynamic traffic prognosis. Our approach is to increase the number of considered sensor objects in the road network. For this purpose Floating Car Data (FCD) including travel time information of vehicles is transmitted to a server via Long Term Evolution (LTE). In this paper, the benefit of FCD on the accuracy of travel time estimation, depending on the FCD penetration rate is analyzed by an enhanced Nagel-Schreckenberg cellular automaton model. Furthermore, the negative impact of the FCD transmission on the air interface of the cellular communication system is evaluated for various penetration rates and different transmission strategies, including a channel sensitive transmission. Therefore, a close to reality parameterized Markovian model is used. The results show that a penetration rate of a few percent is sufficient for a realistic travel time estimation. The respective influence on the LTE network is tolerable, especially for channel sensitive transmission.

Asymmetric Lane Change Rules for a Microscopic Highway Traffic Model

For simulating multi-lane highway traffic with cellular automata (CA) traffic models, realistic lane change rules are required. In many countries, legal regulations distinguish between driving lanes and overtaking lanes. Therefore, asymmetric lane change rules are needed. In this contribution, the CA traffic model by Lee et al (Phys. Rev. Lett. 92(23) (2004) 238702) is extended with those rules. The presented ruleset is then studied in simulations of two-lane and three-lane highways.

Interaction between Machine-Type Communication and H2H LTE Traffic in Vehicular Environments

Extended Floating Car Data (xFCD) is the fundamental for many modern traffic forecast systems. In this paper, an xFCD source model with different transmission rules for making the data efficiently available at the forecast server by means of Long Term Evolution (LTE) enabled Machine-Type Communication (MTC) is proposed. Thereby, the delay requirements of the sensor data and the channel conditions of the communication link are taken into account. The results show that the new source model leads to a decreased utilization of the LTE system and therefore to a low service degradation of other human cellular users. Furthermore, the inserted delay of the data transmission is tolerable for the considered application.

Radio-Based Traffic Flow Detection and Vehicle Classification for Future Smart Cities

Intelligent Transportation Systems (ITSs) providing vehicle-related statistical data are one of the key components for future smart cities. In this context, knowledge about the current traffic flow is used for travel time reduction and proactive jam avoidance by intelligent traffic control mechanisms. In addition, the monitoring and classification of vehicles can be used in the field of smart parking systems. The required data is measured using networks with a wide range of sensors. Nevertheless, in the context of smart cities no existing solution for traffic flow detection and vehicle classification is able to guarantee high classification accuracy, low deployment and maintenance costs, low power consumption and a weather-independent operation while respecting privacy. In this paper, we propose a radiobased approach for traffic flow detection and vehicle classification using signal attenuation measurements and machine learning algorithms. The results of comprehensive measurements in the field prove its high classification success rate of about 99%.

Mechanisms of jamming in the Nagel-Schreckenberg model for traffic flow

We study the Nagel-Schreckenberg cellular automata model for traffic flow by both simulations and analytical techniques. To better understand the nature of the jamming transition, we analyze the fraction of stopped cars P(v=0) as a function of the mean car density. We present a simple argument that yields an estimate for the free density where jamming occurs, and show satisfying agreement with simulation results. We demonstrate that the fraction of jammed cars P(v∈{0,1}) can be decomposed into the three factors (jamming rate, jam lifetime, and jam size) for which we derive, from random walk arguments, exponents that control their scaling close to the critical density.

Traffic Simulations with Empirical Data: How to Replace Missing Traffic Flows?

For the real-time microscopic simulation of traffic on a real-world road network, a continuous input stream of empirical data from different locations is usually needed to achieve good results. Traffic flows for example are needed to properly simulate the influence of slip roads and motorway exits. However, quality and reliability of empirical traffic data is sometimes a problem for example because of damaged detectors, transmission errors or simply lane diversions at road works. In this contribution, we attempt to close those data gaps of missing traffic flows with processed historical traffic data. Therefore, we compare a temporal approach based on exponential smoothing with a data-driven approach based on Poisson Dependency Networks.

Analytical method for the precise and fast prediction of railway running times

The precise and fast prediction of running times is extremely important in railway operations and management. This paper is about the analytical calculation of railway running times. It contains a new method for the computation of the acceleration process, leading to a fast and compact simulation framework. The approach is compared to a common algorithm, the velocity micro-step Euler-method (VMSEM). Additionally, the accuracy of the new acceleration calculation method is also compared to empirical acceleration behavior of a standard commuter train in Germany.

The importance of antipersistence for traffic jams

Universal characteristics of road networks and traffic patterns can help to forecast and control traffic congestion. The antipersistence of traffic flow time series has been found for many data sets, but its relevance for congestion has been overseen. Based on empirical data from motorways in Germany, we study how antipersistence of traffic flow time-series impacts the duration of traffic congestion on a wide range of time scales. We find a large number of short-lasting traffic jams, which implies a large risk for rear-end collisions.

Improving Vehicular Traffic Simulations Using Real-Time Information on Environmental Conditions

This contribution illustrates the benefit of incorporating real-time environmental data into highway traffic information systems and describes the modelling and integration of weather conditions into a complex microscopic traffic simulation. Using stationary measured weather data as an example, the achieved results show the potential extended Floating Car Data (xFCD) - submitted to the traffic information system by cellular communication - can have for traffic simulation. Therefore, an estimation about the expected communication network load is given, when xFCD equipped vehicles have become prevalent.