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data2supplymodel-0.0.3
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
The data2supply is a data-driven calibration package for traffic flow model calibration, Bureau of Public Roads (BPR) function calibration, and the queueing characterization for transportation planners, engineers, and researchers.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | data2supplymodel-0.0.3 |
| نام | data2supplymodel |
| نسخه کتابخانه | 0.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Xin Wu, Xuesong Zhou |
| ایمیل نویسنده | xinwu3@asu.edu, xzhou74@asu.edu |
| آدرس صفحه اصلی | https://github.com/Grieverwzn/data2supplymodel |
| آدرس اینترنتی | https://pypi.org/project/data2supplymodel/ |
| مجوز | GPLb3+ |
In the travel demand model, the performance of traffic systems is evaluated via traffic assignment for assessing the impacts of transportation improvement projects. The fundamentally important volume-delay functions (VDFs) have been used as the building blocks to account for the effects of traffic flow on roadway segments’ capacities.
The data2supply is a data-driven calibration package for traffic flow model calibration, Bureau of Public Roads (BPR) function calibration, and the queueing characterization for transportation planners, engineers, and researchers.
The development of data2supplymodel is motivated by the following perspectives.
**1. Support the implementation of traffic assignment model **
The development of the package is motivated by the evaluation of the current traffic assignment model and implement a refined or modified BPR function. The calibration will be conducted under different area types (AT) and facility types (FT).
**2. Data-driven calibration and validation tool for integrated traffic analysis**
The development goal of data2supply aims to provide an integrated open-source package for data processing workflow, parameter estimation in the traffic stream model (i.e., ultimate capacity, critical density, free-flow speed, and the speed at capacity as well as the validation of the traffic assignment results.
**3. Adopting open network standard of GMNS**
The General Modeling Network Specification (GMNS) defines a common human and machine-readable format for sharing routable road network files. It is designed to be used in multi-modal static and dynamic transportation planning and operations models. Further details can be found in https://zephyrtransport.org/projects/2-network-standard-and-tools/
**4. New VDF calibration method**
This package also attempts to provide a theoretically consistent and practically effective framework for a data-driven VDF calibration process. By defining the queueing demand in the D/C ratio in the BPR function, the proposed Queue-based method (QBM) provides a new method for the BPR calibration and bridges the gap between the different temporal resolution of the demand-supply relation.
**Input files:**
link_performance.csv
Example:
| link_id | lanes | length | from_node_id | to_node_id | FT | AT | time_period | speed | date | volume | geometry |
| ------- | ----- | ------ | ------------ | ---------- | ---- | ---- | ----------- | ----------- | -------- | ------ | ------------------------------------ |
| 1040 | 1 | 1.3 | 511 | 548 | 0 | 1 | 1400_1415 | 72 | 1/1/2016 | 186 | LINESTRING ( -112.0846681 33.461167) |
| 1317 | 3 | 1.3 | 511 | 512 | 1 | 1 | 1400_1415 | 62.33333333 | 1/1/2016 | 686 | LINESTRING ( -112.0846681 33.461168) |
| 1040 | 1 | 1.3 | 511 | 548 | 0 | 1 | 1415_1430 | 71.66666667 | 1/1/2016 | 197 | LINESTRING ( -112.0846681 33.461169) |
| **Field Name** | **Description** | **Sample Value** |
| -------------- | --------------------------------------------- | ---------------- |
| link_id | Link identification number of a road segment | 10024AB |
| lanes | Number of lanes of a link | 2 |
| length | Length of the link (units: miles or km) | 0.22148 |
| from_node_id | Upstream node of the link | 12391 |
| to_node_id | Downstream node of the link | 27808 |
| FT | Facility type | 6 |
| AT | Area type | 1 |
| time_period | Timestamp of an observation | 000_0015 |
| volume | Observed link count | 50 |
| speed | Observed link speed | 24 |
| speed_limit | Speed limit of the link | 35 |
| date | Date of the data | 1/1/2018 |
**output files:**
speed_density fitting curve
speed_volume fitting curve
volume_density fitting curve
training set for each AT and FT
hourly VDF fitting curve for each AT, FT and time period
period VDF fitting curve for each AT, FT and time period
daily based calibration result
summary.csv
**Installation:**
```python
pip install data2supply
```
If you meet installation issues, please refer to the user guide for solutions.
**Simple examples:**
Calibrate traffic flow models
```python
import data2supplymodel as ds
# insert the assignment period for the link performance
period_list=['1400_1800']
ds.joinDemandPeriod(period_list,performance_file_name='link_performance.csv')
# calibrate traffic flow models (when facility type = 1 and area type =1 )
ds.calibrateFundamentalDiagram(ft_list=[1],at_list=[1],link_performance_file='link_performance.csv')
# calibrate traffic flow models (for each combination of facility types and area types )
ds.calibrateFundamentalDiagram()
```
Calibrate volume-delay functions (VDFs)
```Python
import data2supplymodel as ds
# calibrate traffic flow models (for each combination of facility types and area types )
ds.calibrateVdfCurve(ft_list=[1],at_list=[1],link_performance_file='link_performance.csv')
# calibrate VDF (or BPR) functions (for each combination of facility types and area types )
ds.calibrateVdfCurve()
```
**Integrate data2supplymodel with path4gmns**
```python
import data2supplymodel as ds
period_list=['1400_1800']
ds.joinDemandPeriod(period_list,performance_file_name='link_performance_1.csv')
ds.calibrateFundamentalDiagram(ft_list=[1],at_list=[1],link_performance_file='link_performance_1.csv')
ds.calibrateVdfCurve(ft_list=[1],at_list=[1],link_performance_file='link_performance_1.csv')
ds.joinAllVdfFieldToLink(linkfilename ='link.csv', dictfilename ='updated_vdf_table.csv')
import path4gmns as pg
# no need to call read_network() like the python module
# as network and demand loading will be handled within DTALite
# path-based UE
mode = 1
assignment_num = 10
column_update_num = 10
pg.perform_network_assignment_DTALite(mode, assignment_num, column_update_num)
# no need to call output_columns() and output_link_performance()
# since outputs will be processed within DTALite
print('\npath finding results can be found in agent.csv')
```
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| - | numpy |
| - | scipy |
| - | sklearn |
| - | matplotlib |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6.0 | Python |
نحوه نصب
نصب پکیج whl data2supplymodel-0.0.3:
pip install data2supplymodel-0.0.3.whl
نصب پکیج tar.gz data2supplymodel-0.0.3:
pip install data2supplymodel-0.0.3.tar.gz