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Data Fusion in Intelligent Traffic and Transportation Engineering Recent Advances and Challenges
Published in Hassen Fourati, Krzysztof Iniewski, Multisensor Data Fusion, 2016
Nour-Eddin El Faouzi, Lawrence A. Klein
One of these complementary data sources is probe vehicle data, also known as floating car data (FCD) and in its extended version as xFCD. With this technique, cars on the road shift from a passive attitude to an active one and act as moving sensors, continuously feeding information about traffic conditions to a traffic management center (TMC). More recently, cooperative systems research was performed where vehicles were connected via continuous wireless communication with the road infrastructure, exchanging data and information relevant to the specific road segment to increase overall road safety and enable cooperative traffic management [45]. Automatic vehicle identification (AVI) systems, based on different technologies, can be used as detection devices. These technologies include automatic vehicle tag identification, automatic license plate matching techniques, and GPS tracking and identification. With the advances in wireless communications and the spread of cellular phones, technical improvements in cellular positioning provide the opportunity to track cell phone equipped drivers as traffic probes. Many research studies have demonstrated the feasibility of using cell phones as traffic probes [46,47].
Road-Traffic Emissions
Published in Brian D. Fath, Sven E. Jørgensen, Megan Cole, Managing Air Quality and Energy Systems, 2020
Fabian Heidegger, Regine Gerike, Wolfram Schmidt, Udo Becker, Jens Borken-Kleefeld
Data sources for quantifying traffic factors can be traffic detectors, radar, video detection, Floating car data (FCD), short-time counting, traffic messages, and data from police and traffic models with origin–destination matrices and assignment processes.
Warning Apps for Road Safety: A Technological and Economical Perspective for Autonomous Driving – The Warning Task in the Transition from Human Driver to Automated Driving
Published in International Journal of Human–Computer Interaction, 2021
Johanna Trager, Lenka Kalová, Raphaela Pagany, Wolfgang Dorner
First, the ADAC apps were evaluated whether and if yes, how they can be applicable for all scenarios (see evaluation overview in Table 3). The ADAC services provide congestion warnings and an alarm system, e.g., for wrong-way drivers. Using real-time floating car data (FCD) in combination with official police reports, drivers can be warned against danger on the road and suggestions for alternative routes in case of traffic jams are made. Thus, the apps cannot only be applied for warning the driver (Scenario I), but can also be helpful in Scenario II and III. Driving assistance (II) or the autonomous vehicle (III) may reduce speed, for instance, to avoid high speeds in case of a traffic jam ahead. In Scenario II, the driver may still interact directly with the warning detection and with the driving system. In case the ADAC services send out a warning signal, the driver could instruct the system to navigate via an alternative route (information system-human communication). Not only the driver but also the driving assistance would interact with the warning system (information system-automated driving system communication). While the driving assistance reacts automatically, for example, with speed reduction, the driver still decides on his or her own an alternative route. In a higher level of autonomy (Scenario III), the information delivered from ADAC’s warning algorithm could take over the function of alternative route decisions for the driver (completely information system–automated driving system communication). HCI is neither obligatory with the warning information system, nor with the driving system.
The impact of probe sample bias on the accuracy of commercial floating car data speeds
Published in Transportation Planning and Technology, 2022
Megan M. Bruwer, Ian Walker, Simen J. Andersen
Floating car data (FCD), also referred to as probe data, are a means to calculate average speeds along vehicle trajectories from within the traffic stream (Bischoff et al. 2012; Altintasi, Tuydes-Yaman, and Tuncay 2019; Van Erp 2020). FCD are typically collected by GPS-enabled probe devices, including smartphones and navigation devices (Gwara and Andersen 2018), and are reported along any road where instrumentalised vehicles (probes) have travelled.
Smart Mobility in Smart Cities: Emerging challenges, recent advances and future directions
Published in Journal of Intelligent Transportation Systems, 2023
Soumia Goumiri, Saïd Yahiaoui, Soufiene Djahel
In-vehicle embedded devices: this class includes embedded devices in vehicles, such as FCD, RFID and FCO, used to measure and report some useful parameters for accurate traffic estimation.Floating Car Data (FCD): also known as floating cellular data, it is based on speed, localization, direction of movement and the pick up time of information collected from drivers’ phones to determine traffic conditions. As stated by Altintasi et al. (2018), despite FCD’s reliability issues, its low cost and high coverage make it an important traffic data source. FCD was used in Kong et al. (2018), to generate a realistic mobility dataset for vehicular social networks validation. This method is used to estimate and predict traffic conditions in the short-term The detection of crucial traffic patterns in urban areas was investigated in Altintasi et al. (2017) and an FCD based approach was proposed. Results have demonstrated that using only average movement speed outcome from FCD is sufficient to identify traffic patterns.Floating Car Observer (FCO): in this technology, vehicles are able to determine their positions and collect traffic data in both directions of traffic flow regarding time and space dimensions. FCO was introduced in Hoyer et al. (2006) to detect vehicles on a two-lane road using a public transport vehicle equipped with sensors. Recently, Bluetooth-based FCO has emerged as a new data source in which FCO is used for monitoring and the Bluetooth module for transmission. This technology is able to detect traffic participants such as cars, bicycles, buses, etc., equipped with a Bluetooth device. The German Aerospace Center (DLR) has developed an ITS approach called DYNAMIC Tcheumadjeu et al. (2017) in which Bluetooth-based FCO was used. Collected data is processed, fused and visualized to extract information such as trajectories of vehicles, origin/destination matrix, travel time, etc. Gurczik et al. in Gurczik (2015), used Bluetooth-based FCO to supervise traffic participants owning an activated Bluetooth.Radio-Frequency Identification (RFID): this technology consists of a chip and antenna and aims at identifying a vehicle by a unique ID. It can be used to count the number of vehicles entering or leaving a road segment. This information is useful to determine traffic conditions. The impact of days of the week, road layout and vehicle types on the increase in traffic during peak hours has been investigated in Wemegah et al. (2018). To study the influence of vehicle movements, Structural Equation Model (SEM) was used. Vehicle movements data is obtained using RFID devices. This method can assist decision makers to propose effective solutions that enhance travel time and reduce congestion.