An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented

Reconstructing the Traffic State by Fusion of Heterogeneous Data

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on stationary detector data as typically collected by traffic control centres, and may be augmented by floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: 1. compensation for gaps in data caused by detector failure; 2. separation of noise from dynamic traffic information; and 3. the fusion of floating car data with stationary detector data.Comment: For more information see http://www.mtreiber.de or http://www.akesting.d

Measurement of noise events in road traffic streams: initial results from a simulation study

A key question for road traffic noise management is whether prediction of human response to noise, including sleep quality, could be improved over the use of conventional energy equivalent, or percentile, measures, by accounting for noise events in road traffic streams. This paper reports initial results from a noise-events investigation into event-based indicators over an exhaustive set of traffic flow, traffic composition, and propagation distance, conditions in unshielded locations in proximity to roadways. We simulate the time-varying noise level histories at various distances from roadways using a dynamic micro-traffic model and a distribution of sound power levels of individual vehicles. We then develop a comprehensive set of noise event indicators, extrapolated from those suggested in the literature, and use them to count noise events in these simulated time histories. We report the noise-event algorithms that produce realistic, and reliable, counts of noise events for one-hour measurement periods, then reduce redundancy in the indicator set by suggesting a small number of representative event indicators. Later work will report the traffic composition and distance conditions under which noise event measures provide information uncorrelated with conventional road traffic noise indicators — and which thus may prove useful as supplementary indicators to energy-equivalent measures for road traffic noise

Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication

In this paper, a minimalist, completely distributed freeway traffic information system is introduced. It involves an autonomous, vehicle-based jam front detection, the information transmission via inter-vehicle communication, and the forecast of the spatial position of jam fronts by reconstructing the spatiotemporal traffic situation based on the transmitted information. The whole system is simulated with an integrated traffic simulator, that is based on a realistic microscopic traffic model for longitudinal movements and lane changes. The function of its communication module has been explicitly validated by comparing the simulation results with analytical calculations. By means of simulations, we show that the algorithms for a congestion-front recognition, message transmission, and processing predict reliably the existence and position of jam fronts for vehicle equipment rates as low as 3%. A reliable mode of operation already for small market penetrations is crucial for the successful introduction of inter-vehicle communication. The short-term prediction of jam fronts is not only useful for the driver, but is essential for enhancing road safety and road capacity by intelligent adaptive cruise control systems.Comment: Published in the Proceedings of the Annual Meeting of the Transportation Research Board 200