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Realistic Urban Traffic Simulation Tool CISC 499 Project Report

Muhammad R Muhaimin Student # 06127876

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Table of Contents

1. Introduction 3

2. Tools used 4

2.1 Open Street Map 4

2.1.1 Map Production Technique used in Open Street Map 4

2.2 Sumo 4

2.2.1 Features of SUMO 5

2.2.2 Included Applications in SUMO 5

3. How the program works 7

3.1 Map Conversion 7

3.2 Trips Generation 9

3.2.1 Program Configurations 9

3.2.2.1 Coordinate Based 9

3.2.2.2 District Based 11

3.3 Generating Trip File from routing table 12

3.4 Generating Flow File from routing table 13

4. Vehicle Movement Generation and data extraction 13

5. Conclusion 17

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1. Introduction:

Vehicle mobility is now becoming a key element for many areas of scientific data analysis. The data generated from vehicle mobility traces can be used in urban planning, telecommunications, and other disciplines that encompass human movement. There are some existing tools to perform such simulations. However, these tools are challenging to use for general users. Some of them require coding skills and familiarity with command line interfaces. Creating a simple simulation with different scenario can become very challenging and time consuming. To support this need, we develop a simple tool which researchers can use to easily generate vehicle mobility over a real map. To build this tool we are going to use

SUMO "Simulation of UrbanMObility" traffic simulation engine, Open Street Map and Java.

We are going to import designated map from Open Street Map and modify it using Java

and make it compatible to use with SUMO Simulator. User will be asked to give input of

origin and destination of vehicle as simple latitude and longitude co ordinate of the

vehicle or they can divide the map into districts and create trips between districts. The

file format would be simple text file or .CSV file. We will parse the file using Java and

send the coordinate to SUMO for creating the simulation and then save the result for

user to further use. Our main goal here is to automate the whole process. Generated

output for each stage will be used as input for next stage by program.

Figure 1: Vehicle Mobility Simulation Process

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2. Tools used

2.1 Open Street Map:

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the

world. Two major driving forces behind the establishment and growth of OSM have

been restrictions on use or availability of map information across much of the world and

the advent of inexpensive portable satellite navigation devices. Rather than the map

itself, the data generated by the OpenStreetMap project is considered its primary

output. This data is then available for use in both traditional applications, like its usage

by Craigslist and Foursquare to replace Google Maps, and more unusual roles, like

replacing default data included with GPS receivers.

2.1.1 Map Production Technique used in Open Street Map:

The initial map data were collected from scratch by volunteers performing systematic

ground surveys using a handheld GPS unit and a notebook, digital camera, or a voice

recorder. These data were then entered into the OpenStreetMap database. More

recently, the availability of aerial photography and other data sources from commercial

and government sources has greatly increased the speed of this work and has allowed

land-use data to be collected more accurately by the process of digitization. When

especially large datasets become available, a technical team manages the conversion

and import of the data. Open street map for any part of the world can be downloaded

from open street map server and later that can be used for map related to applications.

In my project designated area based on user input is selected for parsing from a large

area open street map.

2.2 Sumo

"Simulation of Urban MObility" (SUMO) is an open source, highly portable, microscopic

road traffic simulation package designed to handle large road networks. It is mainly

developed by employees of the Institute of Transportation Systems at the German

Aerospace Center. SUMO is licensed under the Plait allows to simulate how a given

traffic demand which consists of single vehicles moves through a given road network.

The simulation allows addressing a large set of traffic management topics. It is purely

microscopic: each vehicle is modelled explicitly, has an own route, and moves

individually through the network.

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2.2.1 Features of SUMO

Includes all applications needed to prepare and perform a traffic simulation

(network and routes import, DUA, simulation)

Simulation

Space-continuous and time-discrete vehicle movement

Different vehicle types

Multi-lane streets with lane changing

Different right-of-way rules, traffic lights

A fast openGL graphical user interface

Manages networks with several 10.000 edges (streets)

Fast execution speed (up to 100.000 vehicle updates/s on a 1GHz machine)

Interoperability with other application at run-time

Network-wide, edge-based, vehicle-based, and detector-based outputs

Supports person-based inter-modal trips

Network Import

Imports VISUM, Vissim, Shapefiles, OSM, RoboCup, MATsim, openDRIVE, and XML-

Descriptions

Missing values are determined via heuristics

Routing

Microscopic routes - each vehicle has an own one

Different Dynamic User Assignment algorithms

High portability

Only standard c++ and portable libraries are used

Packages for Windows main Linux distributions exist

High interoperability through usage of XML-data only

Open source (GPL)

2.2.2 Included Applications in SUMO

SUMO package comes with following applications:

Application Name

Short Description

SUMO The microscopic simulation with no visualization; command line application

SUMO-GUI The microscopic simulation with a graphical user interface

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NETCONVERT Network importer and generator; reads road networks from different formats and converts them into the SUMO-format

NETGENERATE Generates abstract networks for the SUMO-simulation

DUAROUTER Computes fastest routes through the network, importing different types of demand description. Performs the DUA

JTRROUTER Computes routes using junction turning percentages

DFROUTER Computes routes from induction loop measurements

OD2TRIPS Decomposes O/D-matrices into single vehicle trips

POLYCONVERT Imports points of interest and polygons from different formats and translates them into a description that may be visualized by SUMO-GUI

ACTIVITYGEN Generates a demand based on mobility wishes of a modeled population

Additional Tools

There are some tasks for which writing a large application is not necessary. Several solutions for different problems may be covered by these tools.

Additionally, the following unsupported contributions are included:

Application Name Short Description

TraCI4J A Java interface for connecting and extending information via TraCI by Enrico Gueli

Contributed/SUMO Traffic Modeler

A Java application for generating a demand based on mobility wishes by Leontios Papaleontiou

Several people have extended the SUMO package during their work and submitted their code. If not being a part of the release, these contributions are usually not tested frequently and may be outdated.

Further - closed source - applications and extensions exist and may be used within joined projects. They are:

Application Name

Short Description

NETEDIT A graphical network editor.

MESO A simulation which uses a mesoscopic queue-model, performing simulations up to 100 times faster than the pure microscopic simulation.

In my project the tools we are going to use are SUMO, SUMO-GUI, NETCONVERT, and

DUAROUTER.

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3. How the program works

3.1 Map Conversion:

Sumo Simulator do not support traditional map format. It only support NETMAP format

map. It is represented as an xml file. For using a map with Sumo Simulator firstly we

need to convert it to NETMAP format using sumo NETCONVERT tool. NETCONVERT can

only convert following files to NETMAP file

"SUMO native" XML descriptions (*.edg.xml, *.nod.xml, *.con.xml)

OpenStreetMap (*.osm.xml), including shapes

VISUM, including shapes and demands

Vissim, including demands

openDRIVE

MATsim

SUMO (*.net.xml)

Shapefiles (.shp, .shx, .dbf), e.g. ArcView and newer Tiger networks

Robocup Rescue League, including shapes

a DLR internal variant of Navteq's GDF (Elmar format)

For my project I have chosen Open Street Map because it is very detailed, easy to import and modify. Open Street Map can be downloaded as .osm file. In our scenario based on user input we will extract the required area from a bigger size map supplied initially with the program. Every latitude and longitude in open street map represented as a node. Following shows representation of a node

<node id="25779173" lat="41.8946481" lon="-87.6565662" user="NE2" uid="207745" visible="true" version="7" changeset="7788557" timestamp="2011-04-06T19:49:32Z"> <tag k="exit_to" v="Ohio Street east (Ohio/Ontario Feeder) (600 North)"/> <tag k="highway" /> <tag k="ref" v="50B"/> </node>

A collection of node represents a way. Following is a representation of a way <way id="73911354" user="Umbugbene" uid="567034" visible="true" version="3" changeset="10556633" timestamp="2012-02-01T08:24:09Z"> <nd ref="25779173"/> <nd ref="25779174"/> <tag k="highway" v="residential"/> <tag k="name" v="West 9th Street"/> </way>

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As in our scenario we are only dealing with vehicle transportation we removed all the Nodes and Ways that are not associated with any roads for faster computation. This Open Street Map then later transferred into NETMAP format using SUMO’s NETCONVERT tool. In NETMAP format, Node and Way relation are representing as Edge. For each corresponding Way in Open Street Map file there is at least one Node in edge file. The Nodes inside the Way are found inside the range of each Edge. Sometimes for representing away with lots of Nodes, NETMAP file uses multiple Edges. Here is the representation of an Edge

<edge id="73911354" from="25779173" to="25779174" priority="7" type="highway. residential"> <lane id="33262496#0_0" index="0" speed="27.78" length="110.20" shape="8801.40,2205.77 8911.59,2207.59"/> <lane id="33262496#0_1" index="1" speed="27.78" length="110.20" shape="8801.34,2209.07 8911.53,2210.89"/> </edge>

Other than representing relation between Way and Nodes, Edge also have attributes of various properties of lane in any highway. This file also contain information about traffic junction, traffic light rules etc.

In the next figure is a comparison between Open Street Map and NETMAP format of

map.

Node

(lat, lon) Ref

Way Ref

Net Convert

Edge

Ref as range Ref

Figure 2: Map conversion process

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3.2 Trips Generation:

3.2.1 Program Configurations:

Program configurations are provided in a separate file named config.txt. This file defines

various properties of the program. It has following property.

Maxlat Defines the latitude of upper right and upper left corners of map.

Minlat Defines the latitude of lower right and lower left corners of map.

Maxlon Defines the longitude upper right and lower right corners of the map.

Minlon Defines the longitude of upper left and lower left corners of the map.

UseTripData Boolean value determining whether accurate latitude and longitude value (provided in trips.xls) will be used or not

UseFlowReplication Boolean value determining whether flow file will be used or not

NumFlowReplication Number of cars for each vehicle in flow

UseDistrict Boolean value determining whether district will be used or not.

DivideByRow Number of rows when dividing district

DivideByColumn Number of column when dividing district

Debug Boolean value determining whether using debug mode or not

3.2.2 Input Processing:

3.2.2.1 Coordinate Based:

Figure 2: Map comparison

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In coordinate based trips generation input will be given in a file named trips.xls. Usually

this type of input is given to program when we want trip generation between accurate

source and accurate destination. The input file trips.xls has the following property.

LatSrc LonSrc LatDest LonDest Time # cars

44.2224 -76.4912 44.223 -76.492 30 10

When parsing the input, my program checks whether the given coordinate is a valid

road. Because as we are dealing here with vehicle mobility any coordinate which is not

in a road is an invalid coordinate to the program. If given coordinate is not a valid road,

our program will find the closest coordinate that is a valid road. For doing that initially

program parse all the Nodes from Open Street Map and save their coordinates. When

an invalid coordinate is given as an input program find the closest coordinate from the

list of coordinates and use that as input.

After this step program stores this coordinate in a routing table. The representation of

routing table is give below

Src CoOrd Dest CoOrd Time

44.2224, -76.4912 44.223, -76.492 30

Figure 3: Finding valid coordinate

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44.2224, -76.4912 44.223, -76.492 30

44.2224, -76.4912 44.223, -76.492 30

In this routing table we repeat same trip thrice because in our input file the user

indicated 3 cars will make a trip between this source and destination.

3.2.2.2District Based:

In District based Input the input will be given in a simple text file named dRoute.txt. This

file has following properties.

Src Dist. Dest Dist. Cars Time

2 3 3 40

Based on this input the program will generate random coordinates in source and

destination district, which we created by diving the maps based on program

configurations.

Figure 4: District based routing

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Randomly generated source and destination coordinates are replicated based on

number of trips between source and destination, and these trips data are stored in

routing table.

Src Coordinate. Dest Coordinate. Time

44.201,-76.491 44.223, -76.493 30

44.200,-76.501 44.221, -76.492 30

44.211,-76.511 44.220, -76.490 30

In later stages the program use this routing table to prepare trips.xml and flow.xml file

and which are passed to simulator.

3.3 Generating Trip File from routing table:

Trip file is a simple .xml file that represents the source and destination with edge. It has

the following property

The representation of a trip file is shown below

… more trip def <tripdef id = "30050" depart="0" from="4758294" to="39239215#0"/> … more trip def

Before generating trip file the program need to compute source Edge and Destination

Edge for each Source coordinate and Destination coordinate. In our routing table the

source and destination coordinates represent a node in Open Street Map. For each

entry in the routing table programs find the corresponding node in Open Street Map. At

next step the program finds the Way which contains our corresponding Node. As

NETMAP file Edge are related to Open Street Map's Way and Node at next step our

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program find the corresponding Edge in the NETMAP file related to Way and Node and

create a new routing table.

Src Edge Dest Edge Time

73911354 73911387 30

After this step program use this table to create Trip file.

3.4 Generating Flow File from routing table:

Flow file is very similar to trip file but it has two extra property of replicating vehicle and

create a flow of cars after a set time interval. Flow file has the following structure

The representation of a flow file is show below

….more flow def <interval begin="270" end="630"> <flow id = "1000049" from="4758241#0" to="51939951" number="5"/> </interval> …. more flow def

In the flow file program used the same table used in creating trip file and for creating

flow it uses the NumFlowReplication parameter provided by user to generate number of

replication for each file.

After this stage trip file and flow file are passed to simulator for creating simulation.

4. Vehicle Movement Generation and data extraction

For generating vehicle movement we pass the trip file and flow file to DUAROUTER tool

provided by SUMO. DUAROUTER imports different demand definitions, computes

vehicle routes that may be used by SUMO using shortest path computation; when called

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iteratively DUAROUTER performs dynamic user assignment (DUA). DUA router

generates definition of vehicles, vehicles types and routes and save them. In our

program we name it as rou.xml. Here is how this file is represented.

<routes xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="http://sumo.sf.net/xsd/routes_file.xsd"> <vehicle id="50010_0" depart="0.00"> <route edges="26232853#0 -23646718#4 -23646718#3 -23646718#2 "/> </vehicle> <vehicle id="50015_0" depart="0.00"> <route edges="116846056#0 116846056#1 116846056#2 116846056#4"/> </vehicle> </routes>

For each vehicle inside the route it contains its departure time and the edges that it

travels through the trip. At this point we have all the necessary elements to run our

simulation. NETMAP file contain maps and rou.xml contains vehicle information which is

essential for running the simulation. We referenced rou.xml and NETMAP from another

xml file named cfg.xml. This file contain the following

<configuration> <input> <net-file value="chicago.net.xml"/> <route-files value="chicago.rou.xml"/> </input> <time> <begin value="0"/> <end value="10000"/> </time> </configuration>

Initially we run this file with GUI Simulator to see a visual output of the program. It lets

user visualize the vehicle movement.

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For saving the simulation we use another tool provided by SUMO name vTypeProb

which saved our output as an .xml file. This file contains all the info related to our

simulation. Below is the representation of vTypeProb file

<timestep time="<COLLECTION_TIME>" id="<DETECTOR_ID>" vtype="<OBSERVED_TYPE>"> <vehicle id="<VEHICLE_ID>" lane="<LANE_ID>" pos="<POSITION_ON_LANE>" \ x="<X-COORDINATE>" y="<Y-COORDINATE>" \ lat="<LAT-COORDINATE>" lon="<LON-COORDINATE>" \ speed="<VEHICLE_SPEED>"/> ... further vehicles ... </timestep>

Figure 5b: Visual Simulation zoom-in view

Figure 5a: Visual Simulation zoom-out view

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At the very end we run an xml parser to parse this .xml file and save our output in a .csv

format file.

time vehicle_id lane Lat Lon x y speed count

0 1000010_0 56150114#0_0 44.223 -76.493 11446.65 2841.75 0 100

0 1000011_0 56150107_0 44.221 -76.494 11060.69 2353.29 0 100

0 1000012_0 59407145#0_0 44.212 -76.492 11326.85 2979.5 0 100

0 1000014_0 4630404#0_0 44.220 -76.491 11439.09 3696.55 0 100

The figure below show the whole simulation and data extraction process in my program

Figure 6: Simulation and Data extraction

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5. Conclusion

We believe this project can help scientific research by contributing vehicle mobility data.

Any researcher who needs vehicle mobility data can use the developed tool with

minimal knowledge of the underlying map convertors and microscopic vehicle

simulators. For example it can help urban planners measure the effect of vehicle

mobility in urban area in any change of city road network. Environmentalist can

measure CO2 footprint of any area by measuring carbon emission of each vehicle and

vehicle mobility in any particular area. I plan to improve the project in future as well.

Our future development plan includes implementing better algorithm for faster

computation, allowing user to provide their own road network and hosting the program

in an external server which enable user to run simulation based on their input and make

SUMO configuration file and simulation data available for download.