Research output: Thesis › Dissertation (TU Delft) › Scientific

**Relative Flow Data: New Opportunities for Traffic State Estimation.** / van Erp, Paul.

Research output: Thesis › Dissertation (TU Delft) › Scientific

van Erp, P 2020, 'Relative Flow Data: New Opportunities for Traffic State Estimation', Doctor of Philosophy, Delft University of Technology, Delft. https://doi.org/10.4233/uuid:4fa16188-7431-4a6e-8097-5bde9cabc466

van Erp, P. (2020). *Relative Flow Data: New Opportunities for Traffic State Estimation*. Delft: Trail. https://doi.org/10.4233/uuid:4fa16188-7431-4a6e-8097-5bde9cabc466

van Erp P. Relative Flow Data: New Opportunities for Traffic State Estimation. Delft: Trail, 2020. 214 p. (TRAIL Thesis Series; 1). https://doi.org/10.4233/uuid:4fa16188-7431-4a6e-8097-5bde9cabc466

@phdthesis{4fa1618874314a6e80975bde9cabc466,

title = "Relative Flow Data: New Opportunities for Traffic State Estimation",

abstract = "Traffic state information is crucial for different applications, e.g., in design and operation of road traffic networks, and in navigation services. Traffic sensing data, e.g., loop-detector data, may directly provide the desired information. Alternatively, the traffic state information may be estimated with data that only provides partial and noisy information. To apply this process, i.e., traffic state estimation, we have to make choices related to which data are collected and how these are processed. The macroscopic traffic state can be described using the variables flow, density and mean speed, where flow is equal to the product of density and mean speed. Edie’s generalized definitions of traffic flow define these three variables for spatial-temporal areas. Alternatively, traffic flow can be described using the three dimensions space, time and cumulative flow. The cumulative flow is defined as the cumulative number of vehicles that have passed a position at a specific time, which means that it is a discrete variable. However, the discrete function can be smoothed over space and time. In this case, the macroscopic variables flow and (negative) density can be determined for points in space-time by taking the derivatives to time and space of the smoothed cumulative flow function. In this thesis, a distinction is made between microscopic and macroscopic traffic sensing data. Examples of microscopic traffic sensing data are probe individual speed data and spacing data. Macroscopic data can describe Edie’s generalized definitions of traffic flow for spatial-temporal areas, e.g., probe mean speed data or aggregated double loop-detector data. Alternatively, macroscopic sensing data can describe the change in cumulative flow between points in space-time, e.g., detector count data or relative flow data. The scientific gaps addressed in this thesis are subdivided in four parts that relate to each other. First, we evaluate the errors that are induced when estimating the mean speed for spatial-temporal areas based on error-free data. This provides insight in the errors that arise due to incomplete information and incorrect assumptions when estimating the mean speed. Second, the option to use probe data to mitigate the cumulative count error problem is considered. This problem occurs when estimating the cumulative flow curves based on (stationary) detector data. For this purpose, both probe mean speed and probe trajectory data are used. The probe mean speed data relates to the first part as they describe the mean speed for spatial-temporal areas. If relative flow observations are added to the probe trajectory data, relative flow data from moving observers that are part of the traffic flow are obtained. In the third part, these relative flow data are used to estimate the traffic state. In this part, different combinations of observers are used, which includes stationary observers, moving observers that are part of the traffic flow and moving observers that travel in opposing direction. To estimate the traffic state with relative flow data, streaming-data-driven and model-driven estimation approaches are considered. In a model-driven estimation approach historical data are used to expose traffic flow models. Therefore, we address the possibility to use historical relative flow data to expose these model. The fourth and final part relates the option that road authorities collect personal traffic sensing data (e.g., probe trajectory and/or relative flow data) directly from road-users. In other parts of this thesis, we designed methodologies to use these data, which may be valuable for road authorities. Therefore, it is interesting to investigate how road authorities can gain access to these personal data.",

keywords = "Relative flow data, Traffic state estimation",

author = "{van Erp}, Paul",

note = "TRAIL Thesis Series T2020/1",

year = "2020",

doi = "10.4233/uuid:4fa16188-7431-4a6e-8097-5bde9cabc466",

language = "English",

isbn = "978-90-5584-260-5",

series = "TRAIL Thesis Series",

publisher = "Trail",

number = "1",

school = "Delft University of Technology",

}

TY - THES

T1 - Relative Flow Data: New Opportunities for Traffic State Estimation

AU - van Erp, Paul

N1 - TRAIL Thesis Series T2020/1

PY - 2020

Y1 - 2020

N2 - Traffic state information is crucial for different applications, e.g., in design and operation of road traffic networks, and in navigation services. Traffic sensing data, e.g., loop-detector data, may directly provide the desired information. Alternatively, the traffic state information may be estimated with data that only provides partial and noisy information. To apply this process, i.e., traffic state estimation, we have to make choices related to which data are collected and how these are processed. The macroscopic traffic state can be described using the variables flow, density and mean speed, where flow is equal to the product of density and mean speed. Edie’s generalized definitions of traffic flow define these three variables for spatial-temporal areas. Alternatively, traffic flow can be described using the three dimensions space, time and cumulative flow. The cumulative flow is defined as the cumulative number of vehicles that have passed a position at a specific time, which means that it is a discrete variable. However, the discrete function can be smoothed over space and time. In this case, the macroscopic variables flow and (negative) density can be determined for points in space-time by taking the derivatives to time and space of the smoothed cumulative flow function. In this thesis, a distinction is made between microscopic and macroscopic traffic sensing data. Examples of microscopic traffic sensing data are probe individual speed data and spacing data. Macroscopic data can describe Edie’s generalized definitions of traffic flow for spatial-temporal areas, e.g., probe mean speed data or aggregated double loop-detector data. Alternatively, macroscopic sensing data can describe the change in cumulative flow between points in space-time, e.g., detector count data or relative flow data. The scientific gaps addressed in this thesis are subdivided in four parts that relate to each other. First, we evaluate the errors that are induced when estimating the mean speed for spatial-temporal areas based on error-free data. This provides insight in the errors that arise due to incomplete information and incorrect assumptions when estimating the mean speed. Second, the option to use probe data to mitigate the cumulative count error problem is considered. This problem occurs when estimating the cumulative flow curves based on (stationary) detector data. For this purpose, both probe mean speed and probe trajectory data are used. The probe mean speed data relates to the first part as they describe the mean speed for spatial-temporal areas. If relative flow observations are added to the probe trajectory data, relative flow data from moving observers that are part of the traffic flow are obtained. In the third part, these relative flow data are used to estimate the traffic state. In this part, different combinations of observers are used, which includes stationary observers, moving observers that are part of the traffic flow and moving observers that travel in opposing direction. To estimate the traffic state with relative flow data, streaming-data-driven and model-driven estimation approaches are considered. In a model-driven estimation approach historical data are used to expose traffic flow models. Therefore, we address the possibility to use historical relative flow data to expose these model. The fourth and final part relates the option that road authorities collect personal traffic sensing data (e.g., probe trajectory and/or relative flow data) directly from road-users. In other parts of this thesis, we designed methodologies to use these data, which may be valuable for road authorities. Therefore, it is interesting to investigate how road authorities can gain access to these personal data.

AB - Traffic state information is crucial for different applications, e.g., in design and operation of road traffic networks, and in navigation services. Traffic sensing data, e.g., loop-detector data, may directly provide the desired information. Alternatively, the traffic state information may be estimated with data that only provides partial and noisy information. To apply this process, i.e., traffic state estimation, we have to make choices related to which data are collected and how these are processed. The macroscopic traffic state can be described using the variables flow, density and mean speed, where flow is equal to the product of density and mean speed. Edie’s generalized definitions of traffic flow define these three variables for spatial-temporal areas. Alternatively, traffic flow can be described using the three dimensions space, time and cumulative flow. The cumulative flow is defined as the cumulative number of vehicles that have passed a position at a specific time, which means that it is a discrete variable. However, the discrete function can be smoothed over space and time. In this case, the macroscopic variables flow and (negative) density can be determined for points in space-time by taking the derivatives to time and space of the smoothed cumulative flow function. In this thesis, a distinction is made between microscopic and macroscopic traffic sensing data. Examples of microscopic traffic sensing data are probe individual speed data and spacing data. Macroscopic data can describe Edie’s generalized definitions of traffic flow for spatial-temporal areas, e.g., probe mean speed data or aggregated double loop-detector data. Alternatively, macroscopic sensing data can describe the change in cumulative flow between points in space-time, e.g., detector count data or relative flow data. The scientific gaps addressed in this thesis are subdivided in four parts that relate to each other. First, we evaluate the errors that are induced when estimating the mean speed for spatial-temporal areas based on error-free data. This provides insight in the errors that arise due to incomplete information and incorrect assumptions when estimating the mean speed. Second, the option to use probe data to mitigate the cumulative count error problem is considered. This problem occurs when estimating the cumulative flow curves based on (stationary) detector data. For this purpose, both probe mean speed and probe trajectory data are used. The probe mean speed data relates to the first part as they describe the mean speed for spatial-temporal areas. If relative flow observations are added to the probe trajectory data, relative flow data from moving observers that are part of the traffic flow are obtained. In the third part, these relative flow data are used to estimate the traffic state. In this part, different combinations of observers are used, which includes stationary observers, moving observers that are part of the traffic flow and moving observers that travel in opposing direction. To estimate the traffic state with relative flow data, streaming-data-driven and model-driven estimation approaches are considered. In a model-driven estimation approach historical data are used to expose traffic flow models. Therefore, we address the possibility to use historical relative flow data to expose these model. The fourth and final part relates the option that road authorities collect personal traffic sensing data (e.g., probe trajectory and/or relative flow data) directly from road-users. In other parts of this thesis, we designed methodologies to use these data, which may be valuable for road authorities. Therefore, it is interesting to investigate how road authorities can gain access to these personal data.

KW - Relative flow data

KW - Traffic state estimation

U2 - 10.4233/uuid:4fa16188-7431-4a6e-8097-5bde9cabc466

DO - 10.4233/uuid:4fa16188-7431-4a6e-8097-5bde9cabc466

M3 - Dissertation (TU Delft)

SN - 978-90-5584-260-5

T3 - TRAIL Thesis Series

PB - Trail

CY - Delft

ER -

ID: 67899228