The Evolution of Travel Time Information Systems

In traffic engineering, as in so many other disciplines, any good analysis requires data. Regardless of whether the most powerful software is available, it will not produce good results if it does not receive the necessary inputs. It is generally accepted that the more data available, the better results can be achieved. Omitting data-driven techniques, this is true only if the data is adequate and, of course, more or less accurate. In this sense, the equipment that collects the data also plays a fundamental role, since it will determine what data can be collected and in what amount. This chapter provides a simple but very useful classification of the most commonly used sensors and explains the data they can collect. It also gives a brief and simplified introduction to the reconstruction of traffic conditions from these data using the most common techniques. Both aspects will be discussed in more detail throughout this book.

As with speed and many other traffic engineering parameters, there is no single definition of travel time: there are individual travel times, average travel times referenced to the moment when vehicles start their journey, or to the moment they arrive to destination, past travel times, predicted travel times, etc. Knowing the differences among these definitions is essential, for example, to determine which one to estimate and disseminate in each case according to the objective sought. Also, because in order to calculate each of them, it is necessary to have certain data (i.e. certain equipment) and to apply particular estimation methodologies. This chapter explains the most important definitions behind the general concept of travel time and the most common procedures to obtain each of them. As a preview: a good travel time information system is able to provide accurate real-time predicted travel times. The likelihood of such estimates being robust increases if data fusion methodologies are applied.

In traffic engineering, a lot of valuable information is obtained after appropriate processing of data collected by certain sensors. However good the data may be, the information extracted can be completely wrong if the processing is inadequate. One of the most common simplifications in the field, which, for example, affects some travel time estimation methodologies, is the use of temporal average speeds as equivalent to spatial averages. This chapter explains the causes of this bad practice, which is linked to the most traditional (and most extended) road equipment and procedures. To correct this trend, a highly applicable solution in the form of an algorithm is proposed. Although the results of the algorithm are not fully robust, they are favorable in a wide variety of cases, with the added bonus that no additional investment would be required.

Today, technology allows highly accurate direct travel time measurements. These can be attained by identifying vehicles at several locations on the freeway or by directly tracking vehicles’ trajectories. The penetration rate of these technologies is higher than ever before and continuously growing so that the traditional problem of data significance (i.e. not having enough measurements during a short updating period) is being attenuated. This fact has encouraged traffic administrations and private companies to deploy real-time information systems based on these data. However, even in an ideal scenario, direct measurements of travel times are representative of near past traffic conditions for vehicles entering the target stretch, while the objective of real-time information systems is to transmit information about traffic conditions in the near future. This chapter aims to fuse the information provided by input–output diagrams obtained from loop detectors with direct measurements of travel times obtained from automatic vehicle identification (AVI) or tracking technologies. This fusion allows exploiting the accuracy of the direct measurements to correct the count drift in loop detectors. Then, corrected input–output curves can be used to obtain reliable short-term predictions of travel time from vehicles’ accumulation. The proposed data fusion method has been applied to a test site in the AP7 freeway near Barcelona using real and simulated data. Results show that the method is able to provide predicted travel times that anticipate changes in traffic conditions much faster than the simple dissemination of measured travel times, implying lower average and maximum errors of the real-time information systems. The benefits of using the method grow with the severity of congestion and in low surveillance environments, which represent the scenarios where the travel time information is more precious and more difficult to obtain.

Vehicle automation, together with the development of communications, is already leading to the emergence of new forms of mobility and allows aspiring to new paradigms that are increasingly efficient, safe, sustainable, and inclusive. Obviously, these changes must also reach traffic management for these positive effects to become a reality. In this regard, major challenges such as the processing of huge amounts of data in real time are already the subject of research around the world. In some cases, it will be enough to improve the accuracy of existing management strategies based on these data and on fusion methodologies, AI, etc. However, it will also be necessary to open the mind and explore new ideas, new forms of management to which only cooperative mobility gives meaning. All these topics could give rise to a book, or many. This chapter is intended only to serve as a transition to the following ones and to highlight some of the characteristics of such cooperative scenarios, as well as some expected mobility impacts.

Traffic systems evolved rapidly, becoming soon a specific case of a complex dynamic system, what raised the need for controlling them in order to achieve an efficient performance. One of the main factors of complexity of traffic systems is a consequence of the variable human traveling behavior in time and space. Therefore, traffic control, in the way it had been conceived and implemented, appeared as a restrictive approach just considering one of the control aspects: the time the vehicles are flowing through the network. This raised the need to move a step forward. Thus, traffic management could be seen as an extension of traffic control that simultaneously controls time and space, and is aimed at adjusting the demand and the capacity to avoid mismatching. This chapter summarily reviews the main concepts and approaches in the development of traffic management systems (TMSs) both in terms of managing the supply as well as managing (or influencing) the demand. In this context, travel times become one of the key factors to induce changes in drivers’ behavior in terms of making decisions on departure times and route choices. To better achieve such objectives, it would be desirable that TMS have predictive capabilities. The main approaches addressed here support the predictive capabilities of dynamic traffic models, one of whose main components is an estimation of the dynamic mobility patterns in terms of origin to destination (OD) matrices. This chapter summarizes the architecture of such approaches.

The estimation of the network traffic state, its likely short-term evolution and the prediction of the expected travel times in a network are key steps of traffic management and information systems, especially in urban areas and in real-time applications. To perform such functions, most systems have at their core engine specific dynamic traffic models whose main input is a dynamic OD-matrix describing the time dependency of travel patterns in urban scenarios. This chapter provides an overview of the main concepts supporting these dynamic traffic models and their practical implementations in some software platforms, as well as an outline on the main approaches for the estimation of dynamic OD-matrices. Additionally, this chapter provides a basic discussion on one of the main emerging trends: strategies aimed at using the unprecedented amount of new traffic data made available by “new” mobile technologies.

This chapter summarizes very succinctly the main contributions included in this book and suggests some challenges for new research that, among others and from the traffic management point of view, would gradually contribute to achieving the optimal mobility we look for.