Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Erkan, İlker; Haştemoğlu, Hasan Şehmuz

doi:10.1007/s40534-016-0101-y

Cited by 16 publications

(8 citation statements)

References 24 publications

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“…For the derivation of the ground-truth data set, LDs and microwave sensors are utilized; i.e., no actual empirical measurement of the traffic flow or travel time was performed. The results are supported by another study in Turkey, where a penetration rate of 5% was found [ 11 ]. Another field test was conducted by [ 12 ] on a freeway in Barcelona, Spain.…”

Section: Related Worksupporting

confidence: 80%

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Genser

Hautle

Makridis

et al. 2021

Sensors

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A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.

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Section: Related Worksupporting

confidence: 80%

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Genser

Hautle

Makridis

et al. 2021

Sensors

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“…A few examples of the use of mobile data for understanding activity chains and trajectories are presented in [3,4] while Bluetooth and Floating Car Data are used for the same purpose in [5,6], respectively.…”

Section: Probe Datamentioning

confidence: 99%

Multisource Data Framework for Road Traffic State Estimation

Grau

Mitsakis

Tzenos

et al. 2018

Journal of Advanced Transportation

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This paper presents a framework for data collection, filtering, and fusion, together with a set of operational tools to validate, analyze, utilize, and highlight the added value of probe data. Data is collected by both conventional (loops, radars, and cameras) and innovative (Floating Car Data, detectors of Bluetooth devices) technologies and refers to travel times and traffic flows on road networks. The city of Thessaloniki, Greece, serves as a case study for the implementation of the proposed framework. The methodology includes the estimation of traffic flow based on measured travel time along predefined routes and short-term forecasting of traffic volumes and their spatial expansion in the road network. The proposed processes and the framework itself have the potential of being implemented in urban road networks.

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“…Correct categorisation of the detected passing objects can be a challenging task as addressed in [175], [246], [242], [115], [182], [180]. For example, it is hard to distinguish a Bluetooth device, which belongs to a running person, from a slowly moving vehicle in a heavily congested area.…”

Section: Uncertainties In the Detection Of The Type Of Bluetooth Devicesmentioning

confidence: 99%

“…Bluetooth traffic monitoring devices record any detected Bluetooth devices, which can belong to a vehicle, printer, smartwatch, etc. Identifying and removing unrelated traffic data, vehicles which stop or park between two consecutive detections for a recognisable time, and obtaining an accurate travel-time (TT) are still challenges associated with the utilisation of BTMS for real-time traffic monitoring as addressed by [110], [175], [246], [242], [115], [182], [180]. In this chapter, I introduce an algorithm for Multi-Tenancy Detection in the collected Bluetooth data (MTDiBT).…”

Section: Introductionmentioning

confidence: 99%

“…estimating the Bluetooth detection rate, θ ) of BTMS through examining the traffic flow on an hourly basis for a closed segment of a road (i.e. part of the road without any intersection like a bridge or a tunnel), where mostly motor vehicles are allowed and flow cannot change at intersections [129], [115], [224]. Alternatively, the calibration can be done on routes where image processing can recognise the licence plates of vehicles to uniquely associate them with a detected MAC address collected by BTMS (e.g.…”

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confidence: 99%

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Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

Sichani¹

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<p>A comprehensive traffic monitoring system can assist authorities in identifying parts of a road transportation network that exhibit poor performance. In addition to monitoring, it is essential to develop a localized and efficient analytical transportation model that reflects various network scenarios and conditions. A comprehensive transportation model must consider various components such as vehicles and their different mechanical characteristics, human and their diverse behaviours, urban layouts and structures, and communication and transportation infrastructure and their limitations. Development of such a system requires a bringing together of ideas, tools, and techniques from multiple overlapping disciplines such as traffic and computer engineers, statistics, urban planning, and behavioural modelling. In addition to modelling of the urban traffic for a typical day, development of a large-scale emergency evacuation modelling is a critical task for an urban area as this assists traffic operation teams and local authorities to identify the limitations of traffic infrastructure during an evacuation process through examining various parameters such as evacuation time. In an evacuation, there may be severe and unpredictable damage to the infrastructure of a city such as the loss of power, telecommunications and transportation links. Traffic modelling of a large-scale evacuation is more challenging than modelling the traffic for a typical day as historical data is usually available for typical days, whereas each disaster and evacuation are typically one-off or rare events. Damage due to a disaster, combined with a sudden increase of demand due to the evacuation of people will likely result in increased pressure on the remaining, potentially fragmented, infrastructure. The lessons learnt from evacuation modelling can assist traffic operation teams and local authorities to provide safer and more efficient planning. The development of pervasive personal digital devices such as phones, watches, and headphones which can be interconnected with technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor traffic flows within their cities. Moreover, modern vehicles and navigation systems can interconnect to the personal devices of drivers and passengers primarily via Bluetooth technologies. By continuously monitoring such devices when they are discoverable and in range, traffic patterns can be estimated based on, not only the volume of detection, but also other characteristics of the devices that can be used to give more refined estimates of the real underlying traffic flows. This thesis examines Bluetooth traffic data collected from Bluetooth Traffic Monitoring Systems (BTMS) for modelling and monitoring the urban traffic. BTMS can monitor and track individual detected vehicles through a city. Installation, processing, data transmission, and maintenance of BTMS are easier, quicker and cheaper than existing standard monitoring systems such as CCTV cameras and inductive loops. Inductive loops are typically point-wise traffic monitoring systems that are installed in the roads and can measure the traffic flow. However, the use of BTMS devices presents several challenges: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This thesis enumerates and investigates these challenges through statistical modelling, various protocols for cleaning and data preparation, dynamic estimation of the detection rate, and simulation through the case study of the city of Wellington, New Zealand. The city of Wellington experienced damage from the 2016 Kaikoura earthquake (a magnitude 7.8 earthquake), which led to road closures and other infrastructure damage. As part of modelling, performance evaluation, and identifying impacted routes by the 2016 Kaikoura earthquake, this thesis analyses three weeks of BTMS data from the periods before and after the earthquake. Furthermore, this thesis proposes a multi-disciplinary dynamic traffic modelling (TFDA2M) framework and evaluates the performance of TFDA2M on various large-scale evacuation scenarios. These scenarios cover a wide range of real-world use cases which may occur during a disaster such as power failure, an abrupt increase in demand, and damage to the main transportation infrastructure. The findings of this thesis highlight an immediate need for preparations of a large-scale evacuation planning for Wellington to mitigate the consequences of a large-scale evacuation due to a future disaster. Moreover, TFDA2M can assist traffic operation managers and authorities in making smarter decisions (both quantitative and spatially) through the simulation process. Since TFDA2M has a flexible schema, it can be set to monitor, assess, and manage the traffic flow on a daily basis and disaster occasions.</p>

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Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Cited by 16 publications

References 24 publications

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

Multisource Data Framework for Road Traffic State Estimation

Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

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