Simulation as a Tool in Traffic Control System Evaluation

Gerlough, D L

doi:10.1007/978-1-4684-1722-7_5

Cited by 3 publications

(4 citation statements)

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“…continuous-time dynamical system. Whether classical physics-guided traditional theories [56], or human intuitive perception of traffic flow transitions [7,8] have confirmed that comparing with conventional spatial-temporal models that directly fusing spatial information and temporal information [27-30, 62, 63], our proposed continuous-time transition of traffic flow can better reflect the nature of the road network as a complex physical system. Meanwhile, such a continuous-time nature can also more accurately approximate the real traffic flow at arbitrary timestamps in a more natural mean.…”

Section: Performance Comparison Of Tsrfsupporting

confidence: 52%

“…Intuitively, in the context of traffic flow on road network, the above idea can be interpreted as a straightforward phenomenon that the ceaseless traffic flow transition in the road network will trigger the evolution of traffic flow over time. The intuition is exactly meets the conventional physicalguided traffic flow theories [7,8,56].…”

Section: An Overall Solutionmentioning

confidence: 59%

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Temporal super-resolution traffic flow forecasting via continuous-time network dynamics

et al. 2023

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Traffic flow forecasting is a critical task for Intelligent Transportation Systems. However, the existed forecasting can only be conducted at certain timestamps, because the data, is discretely collected at these timestamps. In contrast, traffic flow evolves in real-time via a continuous manner in real world. Therefore, an ideal forecasting paradigm should be performed at arbitrary timestamps instead of only at these certain timestamps. Considering the forecasting timestamps will no longer be restricted by these timestamps, we call such paradigm as temporal super-resolution forecasting. In this paper, we incorporate the idea of neural ordinary differential equations (Neural ODEs) to handle the problem, modeling the change rate of traffic flow on the Article Title urban road. Therefore, due to the continuous nature of ordinary differential equations, the traffic flow at arbitrary timestamps can be forecasted by performing definite integral for the change rate. The urban road is usually regarded as a network, and the change rate of which can be described by continuous-time network dynamics, we parameterize the network dynamics of the traffic flow to quantify the change rate. On these foundations, we propose Spatial-Temporal Continuous Dynamics Network (STCDN) to complete the temporal super-resolution forecasting task. Extensive experiments on public traffic flow datasets illustrate that our model can achieve high accuracy on temporal super-resolution forecasting, while ensuring its performance on conventional experimental settings at these certain timestamps.

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Section: Performance Comparison Of Tsrfsupporting

confidence: 52%

Section: An Overall Solutionmentioning

confidence: 59%

Temporal super-resolution traffic flow forecasting via continuous-time network dynamics

et al. 2023

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“…The mean posted speed limits are higher for roads with more businesses; however, when compared to Figure 7, it can be seen that the average driving speed on these roads is lower than the posted speed limit. A basic relationship between traffic volumes, traffic density, and average speed from traffic engineering (Gerlough & Huber, 1975) states that average speeds on roadways of a given capacity decrease as the traffic density increases. This relationship is reflected in Figure 7, providing that higher business concentrations draw more traffic onto adjacent roads resulting in greater traffic density.…”

Section: Figure 2 Illustration Of Segment-based Data Integrationmentioning

confidence: 99%

Built Environment and Driving Outcomes

Wang

Kostyniuk

Barnes

2014

International Journal of Applied Geospatial Research

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This study demonstrates a segment-based approach to integrate GIS and GPS data to address questions about the connections between the built environment and travel behaviors. Methods and challenges of GPS/GIS integration are discussed, and an application integrating GPS naturalistic driving data from Southeast Michigan together with GIS data from several sources is demonstrated. The integrated dataset is used to explore connections between the built environment and driving behavior, specifically between business concentration, driving speed, vehicle stops and rear-end crashes. Driving speed, an important determinant of driver behavior linked to traffic safety, is found to be inversely related to business concentration, a pattern that does not vary by time of day. Rear-end crashes are found to increase with vehicle stops which increase with business concentration. This demonstration showed that fusing GPS and GPS data provides spatial intelligence which can be used to address planning, traffic safety, and transportation related issues.

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“…Indeed, the variability in a time series can correspond to the volatility of an underlying process (Brillinger, 2008). Even periodic variations on short timescales, e.g., due to the variation in supply at signalized intersections, introduce noise on a 5-15min level (Gerlough & Huber, 1976;Koen & Lombard, 1993;Thomas et al, 2010). An understanding of the underlying processes that cause random changes in aggregated time series helps one to separate noise from systematic differences in the conditions and to assess the quality of a model or prediction scheme a priori.…”

Section: Modeling Traffic Variationsmentioning

confidence: 99%

Variations in urban traffic

Eikenbroek¹

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Simulation as a Tool in Traffic Control System Evaluation

Cited by 3 publications

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Temporal super-resolution traffic flow forecasting via continuous-time network dynamics

Temporal super-resolution traffic flow forecasting via continuous-time network dynamics

Built Environment and Driving Outcomes

Variations in urban traffic

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