| Type of data: Check all that apply. Use "Other" to specify other types so that we can include them in further updates. |
number
series
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| Variable labels of dataset (the names of the variables) |
vehicle speed|time|date|longitude|latitude|road width|road length|road slope gradient |
| Outline of data |
Being one of the biggest metropolises in China with over 20 million people, Beijing provides a variety of traffic patterns under different pressure. These patterns may also be valuable for the study of traffic optimization problem in many other cities. This dataset is aiming at describing the transportation properties of traffic flows in the central and near-suburban areas of Beijing, including the rash hours and common hours on weekdays, weekends and public holidays. |
| Simulation process |
By analyzing the traffic flow data, the bottlenecks of road network can be found, optimal parameters for road properties can be calculated by macroscopic fundamental graph. The performance of this optimization can be simulated by density wave model or car-following model with the original flux of traffic in this dataset. |
| Expected outcome of the process (obtained knowledge, analysis results, output of tools) |
The list of bottleneck roads (geographical coordinates), the optimal parameters of the bottleneck roads (road width, road slope gradient, max speed limitation, etc.), the congestion time (stuck in relative low speed) on the original roads, the congestion time on the optimized roads, etc. |
| Anticipation for analyses/simulations other than the typical ones provided above |
Patterns of traffic that may be optimized with the same method may be found after the process of analyses and simulations. These methods may also be effective to road networks in other cities with the same pattern of traffic. |
Last Update：December,21,2021 Created：December,21,2021
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February,13,2022
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