data2supplymodel 0.0.3

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.

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:

pip install data2supply

If you meet installation issues, please refer to the user guide for solutions.

Simple examples:

Calibrate traffic flow models

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)

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

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')