Arterial roadway travel time distribution estimation and vehicle movement classification using a modified Gaussian Mixture Model

Yang, Qichi; Wu, Guoyuan; Boriboonsomsin, Kanok; Barth, Matthew

doi:10.1109/itsc.2013.6728310

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…Statistics model in [6] is used for urban road network travel time estimation using vehicle trajectories obtained from low-frequency GPS probes, where the vehicles typically cover multiple network links between reports. GMM proposed in [15] represents travel time distributions on arterial roads with signalised intersections. The proposed model is applicable to travel time data from both fixed and mobile sensors.…”

Section: Related Workmentioning

confidence: 99%

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Neighbouring link travel time inference method using artificial neural network

Passow

Paluszczyszyn

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods.

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Section: Related Workmentioning

confidence: 99%

“…Study of [15] shows that GMM is able to produce high accuracy rate of vehicle stop/non-stop movement classification. Therefore GMM can be utilised to detect outlier in sparse travel time data.…”

Section: A Outlier Travel Time Data Detectionmentioning

confidence: 99%

Neighbouring link travel time inference method using artificial neural network

Passow

Paluszczyszyn

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…GMM is robust to approximate any continuous probability density function (PDF) with arbitrary precision and is popular in various fields. Deping et al [40] used it to estimate the PDF of power flow in wind power generation; Yang et al [41] applied it to simulate the arterial roadway travel time distributions. It is composed of several single Gaussian models and its PDF is defined as…”

Section: Hypothetical Distributionsmentioning

confidence: 99%

Statistical inference and performance evaluation for failure assessment models of pipeline with external axial surface cracks

Guo

Niffenegger

Zhou

2021

International Journal of Pressure Vessels and Piping

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“…This difference makes analysis more challenging. Numerous studies have focused on the measurement and estimation of arterial travel times (8)(9)(10)(11)(12)(13)(14)(15)(16), filtering and correction of arterial travel time data sets (17)(18)(19)(20), analysis of arterial travel time reliability characteristics (21)(22)(23), and use of data to generate origin-destination information and route characteristics (24,25). These studies have improved the understanding of arterial travel times yet tend to focus on a single arterial route, or a group of surface streets in the same region.…”

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confidence: 99%

Performance Ranking of Arterial Corridors Using Travel Time and Travel Time Reliability Metrics

Day

Remias

et al. 2015

Transportation Research Record

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Performance measures are important for managing transportation systems and demonstrating accountability. Probe vehicle data has emerged as a means of gathering vast amounts of information about highway networks. This paper presents a scalable methodology for analyzing arterial travel times, taking into account both the central tendency of the travel time and its reliability. A pilot analysis is carried out for 28 arterials with a total of 341 signalized intersections across the state of Indiana. Starting from individual minute-by-minute speed records, the data are converted into travel times and aggregated into time series cohorts that correspond to typical traffic signal time-of-day periods, reflecting different time-of-day behavior characteristics of traffic control in arterials. The data is normalized with respect to the ideal travel time (based on the speed limits on each route) to account for individual route lengths and speeds. Data is compiled for all Wednesdays from January through July 2014 to investigate arterial characteristics. The data shows that a greater density of traffic signals on a route loosely corresponds to higher average travel times and less reliability. A composite index incorporating both the average values and reliability characteristics of travel time is developed, and used to rank the arterials according to their performance. • Data must be normalized because routes are of different length. DATA QUALITY ABSTRACT SURVEY OF ARTERIALS• Travel Rate method expresses travel time as amount of time per mile.• Normalization To Ideal Travel Time expresses travel time as percentage of the ideal travel time • This analysis uses speed limit travel time as surrogate for ideal travel time.• This analysis uses normalization to speed limit travel time in order to compare multiple roadways with different travel speed expectations.

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Arterial roadway travel time distribution estimation and vehicle movement classification using a modified Gaussian Mixture Model

Cited by 10 publications

References 6 publications

Neighbouring link travel time inference method using artificial neural network

Neighbouring link travel time inference method using artificial neural network

Statistical inference and performance evaluation for failure assessment models of pipeline with external axial surface cracks

Performance Ranking of Arterial Corridors Using Travel Time and Travel Time Reliability Metrics

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