Traffic State Estimation Applications for Eclipse MOSAIC

This repository contains applications for Floating Car Data (FCD) based Traffic State Estimation (TSE) to be used with Eclipse MOSAIC. Included metrics are partly based on a paper by Yoon et al.¹. Furthermore, we provide additional MOSAIC configuration files to be used with the BeST Scenario², to reproduce and validate results and develop your own TSE metrics.

Documentation

The picture above depicts the fundamental structure and functioning of this application suite. The functionality is split across two applications TseServerApp and the FcdTransmitterApp. The FcdTransmitterApp is a vehicle application that will periodically send FCD messages to the TseServerApp. The server application is responsible for processing received FCD.

The repository is structured into two main packages, com.dcaiti.mosaic.app.fxd and com.dcaiti.mosaic.app.tse trying to separate code that is concerned with the transmission and reception of messages (i.e., FCD) and code that is concerned with measuring the traffic state from the received FCD (i.e., TSE).

Usage

If this is your first time encountering Eclipse MOSAIC, please refer to the Documentation and Tutorials to get a grasp of MOSAICs' core functionalities and working principles.

To use the applications, you need to build them with Maven by calling the following command in the root directory of this repository:

mvn clean install

This will download all required dependencies and package the applications into a jar file (traffic-state-estimation-<version>.jar). To use the allplications with your scenario, copy the generated jar-file from the target directory to the application directory of your scenario and follow the subsequent steps to properly configure all simulators accordingly.

Mapping

To deploy the applications with your scenario, you need to adjust the mapping_config.json accordingly. Below is an exemplary configuration that you can adjust to your needs. Additionally, the bundled mapping file (configs/best-scenario/mapping/mapping_config.json) can be used directly in conjunction with the BeST Scenario.

{
    "prototypes": [
        {
            "name": "YourSumoVehicleType",
            "applications": [
                "com.dcaiti.mosaic.app.fxd.FcdTransmitterApp"
            ],
            "weight": 1.0
        },
        {
            "name": "YourMosaicPrototype"
        }
    ],
    "vehicles": [
        {
            "startingTime": "1 s",
            "maxNumberVehicles": 1,
            "route": "0",
            "types": [
                {
                    "name": "YourMosaicPrototype",
                    "applications": [
                        "com.dcaiti.mosaic.app.fxd.FcdTransmitterApp"
                    ]
                }
            ]
        }
    ],
    "servers": [
        {
            "name": "TrafficStateEstimationServer",
            "group": "YourServerGroup",
            "applications": [
                "com.dcaiti.mosaic.app.tse.TseServerApp"
            ]
        }
    ]
}

Cell Simulator

Additionally, when using servers with MOSAICs cell simulator, you need to adjust the network.json and configure the server group. Below is an example of how this could look like. This can also be found in configs/best-scenario/cell/network.json.

{
    ...
    "servers": [
        {
            "id": "SampleServer",
            "uplink": {
                "delay": {
                    "type": "ConstantDelay",
                    "delay": "1 ms"
                },
                "transmission": {
                    "lossProbability": 0.0
                }
            },
            "downlink": {
                "unicast": {
                    "delay": {
                        "type": "ConstantDelay",
                        "delay": "1 ms"
                    },
                    "transmission": {
                        "lossProbability": 0.0
                    }
                }
            }
        }
    ]
}

Application Configuration

There are two application configuration files to consider for the application suite, FcdTransmitterApp.json and the TseServerApp.json, which are used to configure the vehicles and the server respectively.

FcdTransmitterApp.json

{
    "receiverId" : "server_0",
    "collectionInterval": "1s",
    "transmissionInterval": "30s"
}

TseServerApp.json

{
      "fcdDataStorage": {
        "type": "FcdDatabaseHelper",
        "inMemory": false
    },
    "databasePath": null,
    "databaseFileName": null,
    "isPersistent": false,
    "unitRemovalInterval" : "60min",
    "unitExpirationTime" : "30min",
    "traversalBasedProcessors": [
        {
            "type": "SpatioTemporalProcessor",
            "spatialMeanSpeedChunkSize": "15m"
        }
    ],
    "timeBasedProcessors": [
        {
            "type": "ThresholdProcessor",
            "triggerInterval": "30min",
            "defaultRedLightDuration": "45s",
            "minTraversalsForThreshold": 10,
            "recomputeAllRtsmWithNewThreshold": false
        }
    ]
}

Write all FCD into the database For different purposes it can be useful to write all received FCD Records into the database. To achieve this, you can add the FcdWriterProcessor to your list of timeBasedProcessors.

{
    "timeBasedProcessors": [
        {
            "type": "FcdWriterProcessor",
            "triggerInterval": "30min"
        }
    ]
}

Evaluation Utilities

Within the evaluation directory, we bundled python scripts for reading and preprocessing simulation data. The usage_example Jupyter Notebook should explain the usage of the methods.

You can install required dependencies by generating a virtual environment and calling the following command:

python -m pip install -r requirements.txt

Footnotes

1. Yoon, Jungkeun; Noble, Brian; Liu, Mingyan. Surface street traffic estimation. In: Proceedings of the 5th international conference on Mobile systems, applications and services. 2007. S. 220-232 ↩

2. Schrab, K., Protzmann, R., Radusch, I. (2023). A Large-Scale Traffic Scenario of Berlin for Evaluating Smart Mobility Applications. In: Nathanail, E.G., Gavanas, N., Adamos, G. (eds) Smart Energy for Smart Transport. CSUM 2022. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-031-23721-8_24 ↩