Abstract

A two lane, non-periodic highway with on-ramps and off-ramps is simulated using a simple cellular automata model. The main experimental features of highway traffic are reproduced, indicating the usefulness of this approach for the study of traffic and it is shown the need of ramps to produce congested traffic. A fractal dimension for the variables used for traffic analysis, that is car density, car speed and flow has been measured with a Box Counting algorithm. Finally, it is suggested how to perform real-time traffic forecasts. Document type: Part of book or chapter of book

Abstract

This study uses regression analysis to evaluate long-run traffic management system performance. Three important traffic management systems in the Twin Cities metro area - Ramp Metering, Variable Message Signs (VMS), and Freeway Service Patrol (the Highway Helper Program) were evaluated with multiple regression models to predict link speed and incident rate. We find that ramp meters increase freeway link speed and reduce incident rate. Freeway Service Patrols increase link speed when incidents are present. The results for variable message signs are ambiguous. Document type: Part of book or chapter of book

Abstract

In view of the steadily growing car traffic and the limited capacity of our street networks, we are facing a situation where methods for better traffic management are becoming more and more important. Studies [92] show that an individual "blind" choice of routes leads to travel times that are between 6% and 19% longer than necessary. On the other hand, telematics and sensory devices are providing or will shortly provide detailed information about the actual traffic flows, thus making available the necessary data to employ better means of traffic management. Document type: Part of book or chapter of book

Abstract

Within the European research project SimpleFleet, a telematics system for fleet management business intelligence was developed, delivering live or near-realtime statistics about individual vehicles or the entire fleet. This supports the fleet manager in ad-hoc decisions and in medium and long term improvements. The fleet vehicles (e.g. taxis, delivery vans) are equipped with an On-Board-Unit (OBU) for GPS tracking and data collection from other sensors, e.g., an engine on/off detector. These raw data are sent to a host system and database, being enhanced to Floating Car Data (FCD) using map-matching and routing. Beside other route-related information, consulted fleet operators expressed their interest especially in fuel consumption calculation. For this purpose the German EWS procedure was chosen, providing 2nd degree polynomial functions of the driven speed. Final results for the test in the Athens region are presented, including the comparison of two calculation approaches with different aggregation levels of driven speeds. Document type: Part of book or chapter of book

Abstract

Traffic regulation techniques for the optimization of car traffic flows in congested urban networks was considered. The approach used for the description of traffic flows is of fluiddynamic type. The main advantages of this approach, with respect to existing ones, can be summarized as follows. The fluid-dynamic models are completely evolutive, thus they are able to describe the traffic situation of a network at every instant of time. This overcomes the difficulties encountered by many static models. An accurate description of queues formation and evolution on the network is possible. The theory permits the development of efficient numerical schemes also for very large networks. This is possible since traffic at junctions is modelled in a simple and computationally convenient way (resorting to a linear programming problem). The performance analysis of the networks was made through the use of different cost functionals, measuring car average velocity weighted or not weighted with the number of cars moving on each road, the average travelling time, velocity variation, kinetic energy, etc. Exact analytical results were given for simple junctions of 1 ×2 and 2 × 1 type, and then used in order to simulate more complex urban networks. Moreover the problem of emergency vehicles transit has been treated. The problem has been faced choosing a route for emergency vehicles (not dedicated, i.e. not limited only to emergency needs) and redistributing traffic flows at junctions on the basis of the current traffic load in such way that emergency vehicles could travel at the maximum allowed speed along the assigned roads (and without blocking the traffic on other roads). All the optimization results have been obtained using a decentralized approach, i.e. an approach which sets local optimal parameters for each junction of the network. In future we aim to extend the optimization results to more general junctions and to explore global optimization techniques. In addition, the definition and optimization of functionals which take into account the emission and propagation of pollutants produced by cars might provide powerful technological tools to rationalize the design and use of public and private transportation resources, and to reduce unpleasant effects of urban traffic on the environment. Document type: Part of book or chapter of book

Abstract

Traffic congestion is a widespread epidemic that continually wreaks havoc in urban areas. Traffic jams, car wrecks, construction delays, and other causes of congestion, can turn even the biggest highways into a parking lot. Several congestion mitigation strategies are being studied, many focusing on micro-simulation of traffic to determine how modifying road structures will affect the flow of traffic and the networking perspective of vehicle-to-vehicle communication. Vehicle routing on a network of roads and intersections can be modeled as a distributed constraint optimization problem and solved using a range of centralized to decentralized techniques. In this paper, we present a constraint optimization model of a traffic routing problem. We produce congestion data using a sinusoidal wave pattern and vary its amplitude (saturation) and frequency (vehicle waves through a given intersection). Through empirical evaluation, we show how a centralized and decentralized solution each react to unknown congestion information that occurs after the initial route planning period. Document type: Part of book or chapter of book

Abstract

This paper describes a study which gives insight into the size of improvement that is possible with individual in-car routing advice based on the actual traffic situation derived from floating car data (FCD). It also gives an idea about the required penetration rate of floating car data needed to achieve a certain degree of improvement. The study uses real loop detector data from the region of Amsterdam collected for over a year, a route generating algorithm for in-car routing advice, and emulated floating car data to generate the routing advice. The case with in-car routing advice has been compared to the base case, where drivers base their routing decisions on average knowledge of travel times in the network. The improvement in total delay using the in-vehicle system is dependent on penetration rate and accuracy of the floating car data and varies from 2.0% to 3.4% for 10% penetration rate. This leads to yearly savings of about 15 million euros if delay is monetarised using standard prices for value of time (VOT). © 2017 Gerdien A. Klunder et al. Document type: Article

Abstract

Part 5: Industrial Management and Other Applications; International audience; This paper describes methods of traffic monitoring based on on-line retrieval of big data both from cars equipped with GPS devices and stationary sensor systems. Various visualization methods and styles of presentation are discussed with focus on linear structure of gathered traffic data along observed routes. Visualized data is available via interactive web interface which uses modern vector graphic standard and enables presentation of as much information as possible with common, well-known traffic symbolism. Document type: Part of book or chapter of book

Abstract

In this paper, we explore spatio-temporal clusters using massive floating car data from a complex network perspective. We analyzed over 85 million taxicab GPS points (floating car data) collected in Wuhan, Hubei, China. Low-speed and stop points were selected to generate spatio-temporal clusters, which indicated the typical stop-and-go movement pattern in real-world traffic congestion. We found that the sizes of spatio-temporal clusters exhibited a power law distribution. This implies the presence of a scaling property i.e., they can be naturally divided into a strong hierarchical structure: long time-duration ones (a low percentage) whose values lie above the mean value and short ones (a high percentage) whose values lie below. The spatio-temporal clusters at different levels represented the degree of traffic congestions, for example the higher the level, the worse the traffic congestions. Moreover, the distribution of traffic congestions varied spatio-temporally and demonstrated a multinuclear structure in urban road networks, which suggested there is a correlation to the corresponding internal mobile regularities of an urban system. Document type: Article