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manta

Microsimulation Analysis for Network Traffic Assignment. MANTA employs a highly parallelized GPU implementation that is fast enough to run simulations on large-scale demand and networks within a few minutes - metropolitan and regional scale with hundreds of thousands of nodes and edges and millions of trips. We test our platform to simulate the entire Bay Area metropolitan region over the course of the morning using half-second time steps. The runtime for the nine-county Bay Area simulation is just over four minutes, not including routing and initialization. This computational performance significantly improves state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management.

Initial checks

sudo apt update
sudo apt install qtchooser
sudo apt-get install qt5-default
sudo apt-get install libglew-dev
sudo apt-get install build-essential
sudo apt-get install libfontconfig1
sudo apt-get install mesa-common-dev
sudo apt-get install wget
sudo apt-get install pciutils
sudo apt install git

Dependencies

  • Boost 1.59
wget http://sourceforge.net/projects/boost/files/boost/1.59.0/boost_1_59_0.tar.gz
sudo tar xf boost_1_59_0.tar.gz -C /usr/local
  • CUDA (used versions: 9.0 in Ubuntu) (If you are using Google Cloud Platform, please follow these instructions)
  • g++ (used versions: 6.4.0 in Ubuntu)
  • Qt5 (used versions: 5.9.5 in Ubuntu)
  • qmake (used versions: 3.1 in Ubuntu)
  • Python (used versions: 3.6.5 in Ubuntu)
  • pytest (used versions: 6.1.1 in Ubuntu)
  • pytest-cov (used versions: 2.10.1 in Ubuntu)
  • pytest-remotedata (used versions: 0.3.2 in Ubuntu)
  • psutil (used versions: 5.7.2 in Ubuntu)
  • xlwt (used versions: 1.3.0 in Ubuntu)

Installation & Compilation

Manual installation

Once the necessary dependencies are installed, you can use the following lines to make sure the correct versions of each one are used:

export PATH=/usr/local/cuda-9.0/bin:$PATH
export LIBRARY_PATH=/usr/local/cuda-9.0/lib64:$LIBRARY_PATH 
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64:$LD_LIBRARY_PATH 
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/{YOUR_USERNAME}/pandana/src

You can also add the export lines at the end of your user's ~/.bashrc to avoid re-entering them in each session.

Clone the repo in your home directory with:

git clone [email protected]:udst/manta.git ~/manta && cd ~/manta

Clone the Pandana repository to your home directory stay on the main branch, since MANTA now uses a fast contraction hierarchies framework for shortest path routing. Previously implemented shortest path frameworks include Johnson's all pairs shortest path and a parallelized Dijkstra's priority queue.

Create Makefile and compile with:

sudo qmake LivingCity/LivingCity.pro

Importantly, because MANTA uses a shared library from Pandana, a Pandana makefile must be created (to create a shared object file) and the MANTA makefile must be modified.

Pandana Makefile:

  1. Create Makefile in pandana/src/ containing the following:

CC = gcc  # C compiler
CXX = g++
CPPFLAGS = -DLINUX -DMAC -std=c++0x -c -fPIC -g -O3 -Wall -pedantic -fopenmp  # C flags
LDFLAGS = -shared   # linking flags
RM = rm -f   # rm command
TARGET_LIB = libchrouting.so  # target lib

SRCS =  accessibility.cpp graphalg.cpp contraction_hierarchies/src/libch.cpp

OBJS = $(SRCS:.cpp=.o)

.PHONY: all
all: ${TARGET_LIB}

$(TARGET_LIB): $(OBJS)
        $(CXX) ${LDFLAGS} -o $@ $^

.PHONY: clean
clean:
        -${RM} ${TARGET_LIB} ${OBJS}
  1. Run make.

MANTA Makefile:

  1. Add -I/home/{YOUR_USERNAME}/pandana/src to INCPATH.
  2. Add -L/home/{YOUR_USERNAME}/pandana/src -lchrouting to LIBS.
  3. Run sudo make -j.

Run with Docker

  1. Make sure that you have Docker, its NVidia container toolkit and the necessary permissions:
sudo apt install docker.io
sudo groupadd docker
sudo usermod -aG docker {YOUR_USERNAME}
sudo apt-get install -y nvidia-container-toolkit
  1. You can either pull and run our built image
docker pull gcr.io/blissful-jet-303616/manta:latest
docker run -it --rm --gpus all -v "$PWD":/manta -w /manta gcr.io/blissful-jet-303616/manta:latest  bash

Or build it yourself

docker build -t manta:latest .
docker run -it --rm --gpus all -v "$PWD":/manta -w /manta manta:latest bash
  1. Once inside the container, compile and run
qmake LivingCity/LivingCity.pro
make
cd LivingCity
./LivingCity

Data

Before running everything, you need the appropriate data:

  1. Network
  2. Demand

The networks currently reside in manta/LivingCity/berkeley_2018, and the default directory is the full SF Bay Area network in new_full_network/. This contains the nodes.csv and edges.csv files to create the network.

The demand is not in new_full_network/, but needs to reside there in order to run it. Please contact Pavan Yedavalli to procure real or sample demands.

Running

If you wish to edit the microsimulation configuration, modify manta/LivingCity/command_line_options.ini, which contains the following:

GUI=false
USE_CPU=false
NETWORK_PATH=berkeley_2018/new_full_network/
USE_JOHNSON_ROUTING=false
USE_SP_ROUTING=true
USE_PREV_PATHS=true
LIMIT_NUM_PEOPLE=256000
ADD_RANDOM_PEOPLE=false
NUM_PASSES=1
TIME_STEP=0.5
START_HR=5
END_HR=12

Here, you can modify the:

  1. GUI - deprecated. Do not touch.
  2. USE_CPU - deprecated. Do not touch.
  3. NETWORK_PATH - specific path to the network files. Default is berkeley_2018/new_full_network/.
  4. USE_JOHNSON_ROUTING - uses Johnson's all pairs shortest path routing. This should always be set to false.
  5. USE_SP_ROUTING - uses new SP routing framework. This should always be set to true.
  6. USE_PREV_PATHS - uses paths already produced and saved to file. Set to false if running for the first time. Set to true if the simulation was already run and it was saved to file.
  7. LIMIT_NUM_PEOPLE - deprecated. Do not touch.
  8. ADD_RANDOM_PEOPLE - deprecated. Do not touch.
  9. NUM_PASSES - the number of times the simulation is run. Set to 1.
  10. TIME_STEP - timestep. Default is .5 seconds.
  11. START_HR - start hour of the simulation. Default is 5am.
  12. END_HR - end hour of the simulation. Default is 12pm.

Run with:

cd LivingCity
./LivingCity

Development

Should you wish to make any changes, please create a new branch. In addition, once the original Makefile is created, you can simply run sudo make -j from the manta/ directory to compile any new changes.

If necessary, you can checkout a different existing branch from main (edge_speeds_over_time, for instance):

git checkout edge_speeds_over_time

Debugging

For debugging we recommend cuda-memcheck ./LivingCity for out-of-bounds memory bugs in the CUDA section and cuda-gdb for more advanced features such as breakpoints.

In order to use cuda-gdb, manta/Makefile must be modified by adding the flag -G to enable debugging and changing -O3 to -O to avoid optimizations that restrict the use of the debugger.

For example, to enable debugging at LivingCity/traffic/b18CUDA_trafficSimulator.cu, its compilation at the line manta/Makefile:1756:

/usr/local/cuda-9.0/bin/nvcc -m64 -O3 -arch=sm_50 -c --compiler-options -f
no-strict-aliasing -use_fast_math --ptxas-options=-v -Xcompiler -fopenmp -I/u
sr/include/opencv2/ -I/opt/local/include/ -I/usr/local/boost_1_59_0/ -I/home/
{YOUR_USERNAME}/manta/LivingCity/glew/include/ -I/usr/local/cuda-9.0/include  -L/opt/l
ocal/lib -lopencv_imgcodecs -lopencv_core -lopencv_imgproc -lcudart -lcuda -g -lgomp
LivingCity/traffic/b18CUDA_trafficSimulator.cu -o
${OBJECTS_DIR}b18CUDA_trafficSimulator_cuda.o

must be modified to:

/usr/local/cuda-9.0/bin/nvcc -m64 -O -arch=sm_50 -c --compiler-options -f
no-strict-aliasing -use_fast_math --ptxas-options=-v -Xcompiler -fopenmp -I/u
sr/include/opencv2/ -I/opt/local/include/ -I/usr/local/boost_1_59_0/ -I/home/
{YOUR_USERNAME}/manta/LivingCity/glew/include/ -I/usr/local/cuda-9.0/include  -L/opt/l
ocal/lib -lopencv_imgcodecs -lopencv_core -lopencv_imgproc -lcudart -lcuda -g -G
-lgomp LivingCity/traffic/b18CUDA_trafficSimulator.cu -o
${OBJECTS_DIR}b18CUDA_trafficSimulator_cuda.o

After this modification, sudo make clean and sudo make -j must be run.

Please keep in mind that this alteration slows the program down. For more information about cuda-gdb, please refer to the official Website and Documentation.

Testing

In order to run all tests you should first move to manta/LivingCity

cd LivingCity

and then run

sh runAllTests.sh

Benchmarking / profiling

In order to obtain a profiling of each component of the simulation, you should run

python3 LivingCity/benchmarking/runBenchmarks.py

If you wish to specify the name of the benchmark outputs and/or the number of iterations, just run:

python3 LivingCity/benchmarking/runBenchmarks.py --name={name_of_benchmark} --runs={number_of_iterations_to_run}

The script will run LivingCity the specified number of times while polling the system resources. For each component, its resource and time consumption will be saved into a csv file, a plot and a xls file in manta/LivingCity/benchmarking/. The profiling of each version is encouraged to be stored in here.

Versions correspond to the repository's tags. In order to create a new tag, just run

git tag v0.x.0
git push --tags

Acknowledgments

This repository and code have been developed and maintained by Pavan Yedavalli, Ignacio Garcia Dorado, Krishna Kumar, and Paul Waddell. This work heavily derives from Ignacio Garcia Dorado's Automatic Urban Modeling project.

If this code is used in any shape or form for your project, please cite this paper accordingly:

P. Yedavalli, K. Kumar, and P. Waddell, “Microsimulation Analysis for Network Traffic Assignment (MANTA) at Metropolitan-Scale for Agile Transportation Planning,” arXiv:2007.03614 [physics], Jul. 2020, Available: http://arxiv.org/abs/2007.03614.

Thank you!