A Geostatistical Approach to Traffic Flow Reconstruction from Sparse Floating-Car Data

2020 ◽  
pp. 441-447
Author(s):  
Eduardo del Arco ◽  
Mihaela I. Chidean ◽  
Inmaculada Mora-Jiménez ◽  
Samer H. Hamdar ◽  
Antonio J. Caamaño
Keyword(s):  
Traffic Flow ◽  
Floating Car Data ◽  
Flow Reconstruction ◽  
Geostatistical Approach

scholarly journals Floating Car Data Technology

Pomorstvo ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 22-32
Author(s):  
Damir Budimir ◽  
Niko Jelušić ◽  
Mile Perić
Keyword(s):  
Data Collection ◽  
Traffic Flow ◽  
Information Provision ◽  
Satellite System ◽  
Geographical Information ◽  
Sustainable Mobility ◽  
Professional Literature ◽  
Floating Car Data ◽  
Real Time Traffic ◽  
Probe Vehicle

The limiting conditions of traffic in cities, together with the complex and dynamic traffic flows, require an efficient and systematic management and information provision for the traffic participants, with the goal to achieve better utilisation of traffic resources and preserve sustainable mobility. In that context, it is important to identify the traffic flow location features, which requires data and information. This paper presents the application of mobile vehicles for the collection of real time traffic flow data. Such data have become an important source of traffic data, since they can be collected in a simple and cost-efficient way, enabling higher coverage than the conventional approaches, despite the reliability issues. The term referring to that type of data collection, commonly used in scientific and professional literature is FCD (Floating Car Data) and “Probe vehicle”. The efficiency presentation of applying this extensive data source for retrieving necessary parameters and information related to the achievement of sustainable mobility is the final objective of this paper. A description of modern technologies that serve as a basis for probe vehicle data collection has been provided: a geographical information system (GIS), global navigation satellite system (GNSS) and related wireless communication. Within the key technologies review, the development possibilities of data collection by mobile sensors have also been presented.

Analysis of Speed Differences in Data from Remote Traffic Microwave Sensors and Floating Car Data Systems on Expressways

2017 ◽  
Vol 2643 (1) ◽  
pp. 112-120
Author(s):  
Jingyi Wang ◽  
Lei Yu ◽  
Yong Gao ◽  
Jianbo Zhang ◽  
Guohua Song
Keyword(s):  
Traffic Flow ◽  
Cross Sections ◽  
Travel Speed ◽  
Flow Speed ◽  
Data Systems ◽  
Floating Car Data ◽  
Microwave Sensor ◽  
The Difference ◽  
Data Collections ◽  
The Relationship

The remote traffic microwave sensor (RTMS) and the floating car data (FCD) system are two important sources of traffic data; both provide key speed information. However, these two data processes gather data differently. The RTMS detects spot speeds at specific cross sections. The FCD system collects travel speed along a segment of a road link. Although the difference between time mean speed (TMS) and space mean speed (SMS) has been discussed for decades, the speed differences between RTMS and FCD have been underestimated in many engineering applications. This study examined the speed differences between the RTMS and FCD data on expressways in Beijing. First, the differences between the two data collections over 5 days were analyzed. The correlation between the difference and the value of the speeds was investigated. The relationships between TMS and SMS in existing studies were then compared with the relationship derived from the field data. It was found that the existing relationships between TMS and SMS were not valid for representing the relationship between the RTMS and FCD speeds. The flow–speed relationship from each data group was then investigated by using the Van Aerde traffic flow model; it was found that free-flow speed and speed at capacity determined on the basis of the RTMS data were significantly overestimated. It was inaccurate to apply the RTMS speed to the analysis of fundamental traffic flow diagrams. Finally, the repeatability and stability of the relationship between these two data groups were validated.

scholarly journals Estimation of Hourly Traffic Flows from Floating Car Data for Vehicle Emission Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yun Jiang ◽  
Guohua Song ◽  
Zeyu Zhang ◽  
Zhiqiang Zhai ◽  
Lei Yu
Keyword(s):  
Traffic Flow ◽  
Traffic Flows ◽  
Fundamental Diagram ◽  
Flow Estimation ◽  
Floating Car Data ◽  
Estimation Uncertainty ◽  
Spatial And Temporal Distributions ◽  
Significant Difference ◽  
Traffic Flow Estimation ◽  
Hysteresis Phenomena

In order to model air quality in heavy pollution days, a dynamic emission monitoring system is implemented in the Beijing road network, which requires the input of hourly traffic flows. Floating car data (FCD) is increasingly employed for flow estimation based on the fundamental diagrams to supplement data provided by stationary detectors. However, existing studies often used a typical fundamental diagram without considering the hysteresis phenomena and the uncertainty of traffic flow estimation. This study aims to develop a multiperiod fundamental diagram for the traffic flow estimation from FCD considering the hysteresis phenomena. The result shows that the proposed multiperiod fundamental diagram can improve the accuracy of flow estimation. The uncertainty of traffic flow estimation at both 10 minutes and 1 hour is also quantified, and the result indicates that the variation of the estimation uncertainty at 1 hour is lower than that at 10 minutes, with an average 7% reduction of the range of 95% confidence interval (CI). But there is no significant difference in magnitudes of the estimation uncertainty at 1 hour compared with that at 10 minutes. Moreover, the uncertainty for congested flows is lower than that for free flows. In the case study, the proposed model is employed to develop the spatial and temporal distributions of flows and emissions for the metropolitan area in Beijing.

scholarly journals Traffic and Energy Consumption Modelling of Electric Vehicles: Parameter Updating from Floating and Probe Vehicle Data

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 82
Author(s):  
Antonello Ignazio Croce ◽  
Giuseppe Musolino ◽  
Corrado Rindone ◽  
Antonino Vitetta
Keyword(s):  
Energy Consumption ◽  
Traffic Flow ◽  
Electric Vehicles ◽  
Internal Combustion Engines ◽  
Flow Models ◽  
Floating Car Data ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Regional Area ◽  
Traffic Flow Models

This paper focuses on the estimation of energy consumption of Electric Vehicles (EVs) by means of models derived from traffic flow theory and vehicle locomotion laws. In particular, it proposes a bi-level procedure with the aim to calibrate (or update) the whole parameters of traffic flow models and energy consumption laws by means of Floating Car Data (FCD) and probe vehicle data. The reported models may be part of a procedure for designing and planning transport and energy systems. This aim is to verify if, and in what amount, the existing parameters of the resistances/energy consumptions model calibrated in the literature for Internal Combustion Engines Vehicles (ICEVs) change for EVs, considering the above circular dependency between supply, demand, and supply–demand interaction. The final results concern updated parameters to be used for eco-driving and eco-routing applications for design and a planning transport system adopting a multidisciplinary approach. The focus of this manuscript is on the transport area. Experimental data concern vehicular data extracted from traffic (floating car data and probe vehicle data) and energy consumption data measured for equipped EVs performing trips inside a sub-regional area, located in the Città Metropolitana of Reggio Calabria (Italy). The results of the calibration process are encouraging, as they allow for updating parameters related to energy consumption and energy recovered in terms of EVs obtained from data observed in real conditions. The latter term is relevant in EVs, particularly on urban routes where drivers experience unstable traffic conditions.

WiP: Traffic Flow Reconstruction from Scattered Data

2018 ◽  
Author(s):  
Pierfrancesco Bellini ◽  
Stefano Bilotta ◽  
Paolo Nesi ◽  
Michela Paolucci ◽  
Mirco Soderi
Keyword(s):  
Traffic Flow ◽  
Scattered Data ◽  
Flow Reconstruction

The Analysis of Traffic Flow State on Foggy Expressway Based on Fuzzy Clustering

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Jianjun Wang ◽  
Chenfeng Xie ◽  
Zhenwen Chang ◽  
Jingjing Zhang
Keyword(s):  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Flow State
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