Bluetooth-Measured Travel Times for Dynamic Re- Routing

From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are two of the key components of ATIS/ATMS and the quality of the results that they could provide depend not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology.

The tendency to use Bluetooth Technology (BT) for travel time estimation is increasing due to growing number of Bluetooth-enabled devices among road users, anonymity of BT detections, flexibility of deployment and maintenance of Bluetooth sensors etc. Although Bluetooth has been demonstrated as a promising technology, there remain problems which affect the accuracy of the estimation such as difficulty of distinguishing between multiple travel modes (e.g. motor vehicles, bicycles and pedestrians). This study aims to examine the feasibility of estimating mode-specific travel time using BT data under uncongested traffic conditions. In this context, three clustering methods Hierarchical, K-means and Two-Step are used as the core techniques for classification. The results show that the methods can successfully distinguish between motor vehicles and bicycles from BT detection events, resulting in accurate travel time estimation for motor vehicles.

Sustainability

With the current popularity of mobile devices with Bluetooth technology, numerous studies have developed methods to analyze the data from such devices to estimate a variety of traffic information, such as travel time, link speed, and origin–destination estimations. However, few studies have comprehensively determined the impact of the penetration rate on the estimated travel time derived from Bluetooth detectors. The objectives of this paper were threefold: (1) to develop a data-processing method to estimate the travel time based on Bluetooth transactional data; (2) to determine the impact of vehicle speeds on Bluetooth detection performance; and (3) to analyze how the Bluetooth penetration rate affected deviations in the estimated travel time. A 28 km toll section in Bangkok, Thailand, was chosen for the study. A number of Bluetooth detectors and microwave radar devices were installed to collect traffic data in October 2020. Five data-processing steps were developed to estimate the...

2020

Travel time plays a major role in handling the traffic rate. Bluetooth technology is one of the approaches this time observable. Traffic tracking, vehicle determination on a certain route, and travel time information can be obtaine dusing the bluetooth data gathered using this tool. The Bluetooth technology will be used to analyze certain features affecting travel time results. Highway travel time can be used as a new and efficient data collection tool through the bluetooth sensors which are widely used today. The central control software system consists of a comprehensive system for storing and organizing data at a central location, processing data in vehicles and displaying it to drivers. The central system architecture can be used to display congested road data to the driver, including scenarios, text messages and visuals, identified by traffic information message signs (VMS), which are also linked to the system on the particular highway via a data fusion process in line with dat...

2011

The travel time is an important measure for the quality of traffic. This paper discusses a few methods to measure or estimate the travel time in urban road networks. First of all, it is important to know that urban travel times display a large variation, so that the measurement of a single (average) travel time is not so meaningful. The travel time distribution is more relevant than the single value of the average. This distribution can be obtained from observations of travel times of individual vehicles, for instance by tracing probe vehicles with GPS. The distribution of travel time can also be measured by Bluetooth scanners that can recognize Bluetooth devices in a car. Travel time over a link can be estimated by comparing passing times at its beginning and end. Automated Number Plate Recognition cameras have a similar possibility to follow individual vehicles from point to point. In the study of travel time measured in urban areas, the quality of data from Bluetooth scanners appeared to be disappointing. The most important reason is that outliers cannot easily be eliminated. This is especially a problem in urban areas. Another reason is the uncertainty about the Bluetooth devices' carriers. It is difficult to identify whether the Bluetooth device belongs to a car or bus passenger, a cyclist or a pedestrian. Probe vehicles with GPS are very well appropriate for the measurements of travel times in urban areas.

Transportation Research Record: Journal of the Transportation Research Board, 2010

From the point of view of the information supplied by an ATIS to the motorists entering a freeway one of the most relevant ones is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS, the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are thus two key components of ATIS/ATMS and the quality of the results that they could provide depends not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology.

Transport and Telecommunication Journal, 2016

This paper describes a systematic calibration process of a Vissim model, based on data derived from BT detectors. It also provides instructions how to calibrate and validate a highway network model based upon a case study and establishes an example for practitioners that are interested in designing highway networks with micro simulation tools. Within this case study, a 94,5 % proper calibration to all segments was achieved First, an overview of the systematic calibration approach that will be followed is presented. A description of the given datasets follows. Finally, model’s systematic calibration and validation based on BT data from segments under free flow conditions is thoroughly explained. The delivered calibrated Vissim model acts as a test bed, which in combination with other analysis tools can be used for potential future exploitation regarding transportation related purposes.

Automatic Vehicle Identification Systems are being increasingly used as a new source of travel information. As in the last decades these systems relied on expensive new technologies, few of them were scattered along a networks making thus Travel-Time and Average Speed estimation their main objectives. However, as their price dropped, the opportunity of building dense AVI networks arose, as in Brisbane where more than 250 Bluetooth detectors are now installed. As a consequence this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed. Some of these problems stem from the structure of a network made out of isolated detectors itself while others are inherent of Bluetooth technology (overlapping detection area, missing detections,..). The aim of this paper is threefold : First, after having presented the level of details that can be reached with a network of isolated detectors we present how we modelled Brisbane's network, keeping only the information valuable for the retrieval of trip information. Second, we give an overview of the issues inherent to the Bluetooth technology and we propose a method for retrieving the itineraries of the individual Bluetooth vehicles. Last, through a comparison with Brisbane Transport Strategic Model results, we highlight the opportunities and the limits of Bluetooth detectors networks.

Journal of Transportation Technologies, 2020

Bluetooth technology emerged over twenty years ago and has continuously improved throughout the years to meet diverse and complex applications. Initially invented to replace the need for physical data cables, Bluetooth offers users a quick and easy way to share data files over a wireless network. Traffic engineers and transportation engineering researchers have utilized the potential opportunities that exist with Bluetooth and have implemented this technology into traffic monitoring techniques. To gain a better understanding of Bluetooth sensors and how they work, a comprehensive literature search was conducted. Twenty-five articles were studied regarding case studies of Bluetooth sensor implementation for travel time measurement. Besides reviewing the literature and previous case studies, three new case studies in the State of Delaware, USA, were also conducted and carefully analyzed. The benefits and drawbacks associated with Bluetooth technology for travel time measurements have been identified in this paper. The overall conclusion of the authors is Bluetooth alone and by itself is not a proper technology for travel time measurements. More studies need to be conducted on the accuracy and overall application, before one can confidently utilize the Bluetooth technology for travel time measurements.

Journal of Modern Transportation, 2016

Travel time estimation is an integral part of Intelligent Transportation Systems, and has been an important component in traffic management and operations for many years. Travel time, being spatial in nature, requires spatial sensors to measure it accurately. Bluetooth is emerging as a promising technology for the direct measurement of travel time, and is reported in a few studies from homogenous traffic conditions. At the same time, there have been no studies on the applicability of Bluetooth for travel time estimation in heterogeneous traffic seen in Istanbul and even that Turkey. Bluetooth data collected from a busy urban road in Istanbul city have been analyzed and the penetration rate was found to be about 5 %. Two wheelers and light motor vehicles have been detected using the Bluetooth sensor and the data have been extrapolated to estimate travel times of other classes of vehicles. The study developed linear relationships between speeds of different classes of vehicles through weighted linear regression methods and were used for the estimation of stream travel time. The results obtained were promising and show that Bluetooth is a cost-effective technology for estimation of travel time for heterogeneous traffic conditions.