UAM-SUMO - Urban Air Mobility in SUMO

Introduction

TBD

How to use

Prerequisites

• sumo installed (requires version released on 01.08.2024 or newer, 1.20 contains a problem that was fixed later on)
• rtree installed (see requirements.txt)
• python installed

Adding UAM hubs to network

1. valid .sumocfg with sumo network and at least one route and additionals file specified (unzipped files)
2. decide on uam hub coordinates, fitting to sumo network. For example using netedit
3. run "createUamHubs.py" with desired sumocfg and coordinates. For example: py .\createUamHubs.py <path_to_sumocfg>\sim.sumocfg 6200 2000 8500 3600 6300 5150 will create UAM hubs at (6200, 2000), (8500, 3600) and (3600, 5150). Alternatively add --help for more info.
4. new sumocfg with added hubs is called <hub_count>_uam_hubs_<previous_name>.sumocfg

Keep in mind that the specified coordinates should not be further apart from all others, than specified in uamHubConfig.py. Hubs are only connected when they are within the uam_hub_connection_radius of another UAM hub.

Many UAM hub specific variables can be configured in uamHubConfig.py to adjust the creation of the UAM hubs. These changes are only applied upon calling createUamHubs.py anew.

Running the simulation

Run the simulation by calling py uamTraCI.py. Without any further arguments, the default settings configured in the simConfig.py will be used. For the available command line parameters, run py uamTraCI.py --help.

Custom scenarios, for example ones created using createUamHubs.py, can be run by adding the --scenario_path <path_to_scenario> command line option.

If further adjustments to the parameters used during the simulation are desired, edit simConfig.py as needed.

Add LLM support

Currently, vehicles defined in the route file are converted to UAM customers, with a chance of uam_density. To achieve this, the vehicles start and destination point are looked at. If the start and/or destination edge don't allow for pedestrian access, an alternative edge in a small radius (see simConfig.py) is looked for (find_alternative_edge()). Once a valid start and end point are found, we call traci.simulation.findIntermodalRoute(start_edge, dest_edge, modes="taxi"), to generate stages for a potential intermodal route from start to dest. If one of the stages contains an UAM taxi, travel by Air Taxi is faster than walking. For more detail, see create_uam_customers().

Assuming we don't want to convert vehicles for LLM, but add additional pedestrians to the simulation, we need a start and a destination. The function we use to calculate our intermodal route is traci.simulation.findIntermodalRoute(). Passing modes="taxi" searches for routes using "walking" and "taxi" (in our case Air Taxi). More can be added. This function requires the start and destination in the form of an edge. To find an edge near a location, we can use net.getNeighboringEdges(), where net = sumolib.net.readNet(net_file), with a radius of our choosing.

findIntermodalRoute returns a list of "stages", which have to be looked at first. (Note: a stage containing taxi travel for example can only be generated if a valid taxi exist in the simulation at the time the function is called, likewise for other travel modes). Stage object contain the following information: stage: {type, vType, line, destStop, edges, travelTime, cost, length, intended, depart, departPos, arrivalPos, description}).

If no stages are returned, there is no valid route from start to dest. If there is only one stage returned, no change in mode will happen and only the vType will be used. Lastly, the returned list contains multiple stages: Information like estimated travelTime and the type of travel can be extracted from each stage element to use in the decision-making of the LLM. These stage objects can be altered at will. For example if 3 stages in the following order are generated: passenger, taxi(Air Taxi), passenger, but the LLM instead wants to walk to the UAM hub, we can create a new stage instead of the first stage, using the given information. Keep in mind that the selected edges have to allow the desired vType. We can extract the first and the last edge of the initial car route, and generate a new one for pedestrians using traci.simulation.findRoute(), given the start end destination edge allow the new vType.

Lastly we need to create the new entity and add the desired stages. For example for pedestrians that can be achieved by calling traci.person.add(new_id, start_edge, pos=0, depart=current_time) and traci.person.appendStage(new_id, stages). This should be identical with other vTypes.

If changes to the route/stages are desired afterward, for example after a pedestrian arrives at the destination UAM hub, the previously mentioned functions can be called again and used to alter the pedestrian. Currently, the following stage transitions are registered in my code:

• pedestrian stopped walking and started waiting -> arrived at UAM hub
• pedestrian stopped waiting and started flying -> entered Air Taxi
• pedestrian stopped flying and started walking -> left Air Taxi
• pedestrian left simulation -> arrived at destination

Currently, these are used for logging purposes, but can be extended with LLM decision-making if so desired.

Licenses

• SUMO