Highway Travel Time Estimation With Data Fusion

This chapter starts analyzing the importance of travel time information in mobility management. After that, it presents a qualitative description of direct and indirect methods for travel time estimation and defines data fusion concepts and their relationship with travel time forecasting. The effects of travel time information dissemination strategies on drivers and a discussion on some issues regarding the value of travel time information as a traveler oriented reliability measure are also presented. Finally, some overall conclusions and issues for further research are outlined.

In this Chapter, travel time definitions are analytically presented. Also, a trajectory reconstruction algorithm necessary in order to navigate between different travel time definitions is proposed. The concepts presented in this chapter are aimed to create a conceptual framework useful in comparing travel times obtained from different methodologies. This should be considered as baseline knowledge when going through the whole book.

The accuracy of real-time travel time information disseminated on metropolitan freeways is one of the key issues in the development of advanced traveler information systems. Although very accurate estimations could be obtained if suitable and intensive monitoring systems were available, travel time estimations must usually rely on data obtained from the preexisting surveillance equipment installed on freeways: loop detectors. Travel time estimation from loop measurements has attracted extensive research in the last decade, resulting in numerous methodologies. Among these, the ones that rely on spot speed measurements at detector sites in order to obtain the travel time estimation on the target stretch are the most intuitive. The key issue concerning these methods is the spatial generalization of point measurements over a freeway link. Multiple approaches can be found in the literature, ranging from the simplest, and mostly implemented in practice, constant speed approach, to recent and more complex mathematical interpolations. The present chapter shows that all speed interpolation methods that omit traffic dynamics and queue evolution do not contribute to better travel time estimations. All methods are inaccurate in congested and transition conditions, and the claimed relative benefits using various speed interpolation methods result from context specific experiments. Therefore, these methods should be used carefully, and not taken as perfect. Lacking a better approach, it is recommended to avoid overcomplicated mathematical interpolations and focus the efforts on intelligent smoothing of the noisy loop detector data, reducing the fluctuations of short time interval aggregations while maintaining the immediacy of the measurements.

The accuracy of travel time information disseminated in real-time on metropolitan freeways is one of the key issues in the development of advanced traveler information systems. It is generally considered that travel time estimations based on spot speed measurements at loop detectors are not accurate enough to support this real-time information need. This has brought traffic agencies to the view that for real-time information systems to be effective they would need to have much more accurate ways of measuring travel times. Many fancy technologies to directly measure vehicular travel times are being proposed. This chapter shows that, in the real-time context, the precision of the system is not related solely to the accuracy of the measurement. Immediacy and forecasting capabilities play a role. Therefore, focusing only on the accuracy of the travel time measurement is a myopic approach, which can lead to counterintuitive results. Specifically, it is claimed that using travel times estimated with the traditional spot speed Midpoint Algorithm, the performance of the real-time information system is better than by using much more accurate directly measured travel times. Guidelines for an adequate configuration of the common parameters of the system are provided. In addition, real-time context enhancements for travel time estimation methods based on punctual speed measurements are proposed. These are addressed by taking into account an easy and practical implementation. They have been proven to work well in an empirical application on a Spanish Freeway.

Travel time for a road trip is a drivers’ most appreciated traffic information. Measuring travel times on a real time basis is also a perfect indicator of the level of service in a road link, and therefore is a useful measurement for traffic managers in order to improve traffic operations on the network. In conclusion, accurate travel time measurement is one of the key factors in traffic management systems. This chapter presents a new approach for measuring travel times on closed toll highways using the existing surveillance infrastructure. In a closed toll system, where toll plazas are located on the on/off ramps and each vehicle is charged a particular fee depending on its origin and destination, the data used for toll collection can also be valuable for measuring mainline travel times on the highway. The proposed method allows estimating mainline travel times on single sections of highway (defined as a section between two neighboring ramps) using itineraries covering different origin–destinations. The method provides trip time estimations without investing in any kind of infrastructure or technology. This overcomes some of the limitations of other methods, like the information delay and the excess in the travel time estimation due to the accumulation of exit times (i.e. the time required to travel along the exit link plus the time required to pay the fee at the toll gate). The results obtained in a pilot test on the AP-7 toll highway, near Barcelona in Spain, show that the developed methodology is sound.

The development of new traffic monitoring systems and the increasing interest of road operators and researchers in obtaining reliable travel time measurements, motivated by society’s demands, have led to the development of multiple travel time data sources and estimation algorithms. This situation provides a perfect context for the implementation of data fusion methodologies to obtain the maximum accuracy from the combination of the available data. This chapter presents a new and simple approach for the short term prediction of highway travel times, which represent an accurate estimation of the expected travel time for a driver commencing on a particular route. The algorithm is based on the fusion of different types of data that come from different sources (inductive loop detectors and toll tickets) and from different calculation algorithms. Although the data fusion algorithm presented herein is applied to these particular sources of data, it could easily be generalized to other equivalent types of data. The objective of the proposed data fusion process is to obtain a fused value more reliable and accurate than any of the individual estimations. The methodology overcomes some of the limitations of travel time estimation algorithms based on unique data sources, as the limited spatial coverage of the algorithms based on spot measurement or the information delay of direct travel time itinerary measurements when disseminating the information to the drivers in real time. The results obtained in the application of the methodology on the AP-7 highway, near Barcelona in Spain, are found to be reasonable and accurate.

After the generalized deployment of advanced traveler information systems, there exists an increasing concern about their profitability. The costs of such systems are clear, but the quantification of the benefits still generates debate. This chapter analyzes the value of highway travel time information systems. This is achieved by using notions of expected utility theory to develop a departure time selection and route choice model. The model assumes that every driver has a level of accepted lateness for his trip and some perceived knowledge of the travel times on the route. Only these two inputs support his decisions. The decision making process does not require the consideration of a complex cost function and does not involve any optimization. The results of the model are used to compute the unreliability costs of the trip (i.e. scheduling costs and stress) and to obtain the benefits of real-time information systems. Results show that travel time information only has a significant value when there is an important scheduled activity at the destination (e.g. morning commute trips), in case of total uncertainty about the conditions of the trip (e.g. sporadic trips), or when more than one route is possible. Systems with very high accuracy do not produce better results. The chapter also highlights the difference between the actual value that information provides to the drivers and the value they perceive, which is much smaller. For instance, massive dissemination of travel time information contributes to the reduction of day-to-day travel time variance. This favors all drivers, even those without information, although they do not realize it. This misperception suggests limited willingness to pay for travel time information.