Accurate travel time information is essential to the effective management of traffic conditions. Traditionally, floating car data have been used as the primary source of ground truth for measuring the quality of real-time travel time provided by traffic surveillance systems. This paper introduces Bluetooth sensors as a new and effective means of data collection of freeway ground truth travel time. The concept of vehicle identification using Bluetooth signatures for travel time estimation along a section of freeway is explained. Issues related to error analysis, filtering of raw matched data, and accuracy of the resulting ground truth compared with floating car are discussed. Data from loop detectors on several freeway segments are used to approximate and report the average sampling rate of Bluetooth sensors. Results show that the new technology is a promising method for collecting high-quality travel time data that can be used as ground truth for evaluating other sources of travel time and other intelligent transportation system applications.
Crowdsourced GPS probe data has been gaining popularity in recent years as a source for realtime traffic information. Efforts have been made to evaluate the quality of such data from different perspectives. A quality indicator of any traffic data source is latency that describes the punctuality of data, which is critical for real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring the probe data latency, with respect to a selected reference source. Although Bluetooth re-identification data is used as the reference source, the methodology can be applied to any other ground-truth data source of choice (i.e. Automatic License Plate Readers, Electronic Toll Tag). The core of the methodology is a maximum pattern matching algorithm that works with three different fitness objectives. To test the methodology, sample field reference data were collected on multiple freeways segments for a two-week period using portable Bluetooth sensors as ground-truth. Equivalent GPS probe data was obtained from a private vendor, and its latency was evaluated. Latency at different times of the day, the impact of road segmentation scheme on latency, and sensitivity of the latency to both speed slowdown, and recovery from slowdown episodes are also discussed.Keywords: Latency, GPS-probe data, Bluetooth
INTRODUCTIONAccurate and timely data is a vital component of any Intelligent Transportation System. In recent years, proliferation of location-aware internet connected devices has enabled private sector to use crowd sourcing technics for providing network wide real-time travel time and speed data for traffic management applications. This has resulted in traffic data services that report speed and travel time in real-time. This data in turn is used by private industry for traveler information and routing, and increasingly by public entities as a replacement for field data collection and to expand observability of roadway conditions network wide. The I-95 Corridor Coalition's Vehicle Probe Project has successfully integrated third party data of this nature, commonly referred to as probe data, for a number of public agency applications. Initial concerns about accuracy were addressed by a comprehensive validation program that compared probe industry reported speeds and travel times with those from a sensor-based reference source. Real-time applications are also sensitive to the latency, that is the time delay between actual field conditions, such as a major slowdown, and when it is reflected in the traffic data stream. Appropriate method to benchmark latency is currently lacking, and is the focus of this paper.Consumer electronics are finding an ever-increasing role in our everyday lives. A majority of these devices are also equipped with a point-to-point networking protocol commonly referred to as Bluetooth. Bluetooth enabled devices can communicate with other Bluetooth enabled devices anywhere from one meter to about 100 meters. This variability in the communications cap...
Reliable travel time prediction enables both road users and system controllers to be well informed about future conditions on roadways so that pretrip plans and traffic control strategies can be made to reduce travel time and relieve traffic congestion. The objective of this research was to use traffic and weather data from multiple data sources to develop an integrated model that could predict travel times under various weather conditions, especially severe weather conditions. Prediction models are compared, and their performance in case studies is investigated.
In an emergency medical service (EMS) system, the depot location and fleet assignment greatly affect the average response time, which is the main criterion for measuring system performance. Whereas the EMS depot location problem is a strategic problem, the fleet assignment problem is a tactical one. As such, the EMS depot location and fleet assignment problems are usually solved separately under some simplified assumptions. However, there is a potential for savings in both the average response times and the capital and operating costs if these problems can be solved simultaneously. A simulation model for EMS vehicle dispatching was developed. This model is calibrated with real-world data, and it is incorporated in a genetic algorithm to help solve the EMS depot location and fleet assignment problems simultaneously. Emergency types, their response priorities, and whether or not they require dispatching of multiple units are taken into consideration in the model. The average response time and the capital and operating costs are used as criteria for evaluation.
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