Scipediacontent: Scipediacontent moved page Draft Content 820814762 to 238,295bd

2021-03-22T18:39:10Z

Scipediacontent moved page Draft Content 820814762 to 238,295bd

Scipediacontent: Created page with " == Abstract == The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck ind..."

2021-03-22T18:38:53Z

Created page with " == Abstract == The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck ind..."

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== Abstract ==

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_820814762-beopen1055-7822-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [https://www.mdpi.com/2079-9292/9/9/1412/pdf https://www.mdpi.com/2079-9292/9/9/1412/pdf] under the license https://creativecommons.org/licenses/by

* [https://www.mdpi.com/2079-9292/9/9/1412 https://www.mdpi.com/2079-9292/9/9/1412],
: [https://doaj.org/toc/2079-9292 https://doaj.org/toc/2079-9292] under the license cc-by

* [https://www.mdpi.com/2079-9292/9/9/1412/pdf https://www.mdpi.com/2079-9292/9/9/1412/pdf],
: [http://dx.doi.org/10.3390/electronics9091412 http://dx.doi.org/10.3390/electronics9091412]

* [https://www.mdpi.com/2079-9292/9/9/1412/pdf https://www.mdpi.com/2079-9292/9/9/1412/pdf],
: [https://www.mdpi.com/2079-9292/9/9/1412/htm https://www.mdpi.com/2079-9292/9/9/1412/htm],
: [https://academic.microsoft.com/#/detail/3082133527 https://academic.microsoft.com/#/detail/3082133527] under the license https://creativecommons.org/licenses/by/4.0/

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Scipediacontent: Scipediacontent moved page Draft Content 820814762 to 238,295bd

Scipediacontent: Created page with " == Abstract == The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck ind..."