Microsimulation of an Autonomous Taxi-System in ... - Amazon AWS

Microsimulation of an Autonomous Taxi-System in Munich

Poster #5

Florian Dandl, Klaus Bogenberger – Munich University of the Federal Armed Forces

Autonomous Taxis (aTaxis) Big Players „Mercedes targets Silicon Valley rivals with robo-taxis by 2023“

„BMW and Intel to bring a fleet of selfdriving cars to the road by the end of 2017“

http://www.wired.co.uk/article/bmw-intel-driverless-tech-ces-2017

http://www.wired.co.uk/article/bmw-intel-driverless-tech-ces-2017

„Waymo taps Phoenix for a big public test of Google self-driving car tech“

„Self-driving Ubers: the world‘s first selfdriving Ubers are on the road in Steel City“

https://www.forbes.com/sites/alanohnsman/2017/04/25/waymotaps-phoenix-for-a-big-public-test-of-google-self-driving-cartech/#6a602eda6141

Research Questions and Framework

? •

•

„By 2030, individually owned ICE vehicles will still represent 40% of the vehicles in the U.S. vehicle fleet, but they will provide just 5% of passenger miles.“

inbound traffic

within-area trips O/D entries 10%

Traffic Volume

Travel Times

100%

modified private-vehicle O/D matrix (for all scenarios)

study area

possible requests (for aTaxis or for public transportation)

#(aTaxi requests) ------------------------------ 50% #(possible requests)

aTaxi Operation

Investigation of Traffic Impacts

other trips O/D entries

90%

transit traffic

vehicles might be tracked by users  vehicle early, in-time or with small delay at customer location Incidents, creation of queues, traffic lights might cause longer delays ? re-routing ? re-assignment

First Results

original private-vehicle O/D matrix (base simulation)

outbound traffic

Traffic Situation

Travel Times: Estimated TT ≠ Driven TT

… „Transport-as-a-Service four to ten times cheaper per mile than buying a new car and two to four times cheaper than operating an existing vehicle in 2012.“

traffic impact of induced empty trips impact of delay on fleet performance

intra-city traffic

https://www.uber.com/cities/pittsburgh/self-driving-ubers/

Possibly Disruptive Technology

First Simulation Scenarios

60%

70%

80%

90%

100%

Operation Schematics

Customer 1 Origin

Destination

Implementation of Autonomous Taxi System in a Traffic Microsimulation

scenario

50

60

70

80

90

100

# aTaxi requests [x 1000]

20

24

28

32

36

40

fleet size

2000

2400

2800

3200

3600

4000

Customer 2 Origin

Destination

Selection of Model Assumptions •

Fleet Operator

Network Information: • • • • •

Autonomous ride of vehicle to customer Autonomous ride of vehicle with customer on board

3 request data sets per scenario generated by Poisson processes

Limitations and Planned Extensions •

ri = (request-time, start-location, destination)

Search for nearby vehicles and assignment of „optimal“ vehicle

Boundary of study area

25 km x 30 km 294 OD-centroids 2573 km total road length max. ~ 180k trips per hour 1238 aTaxi OD-locations (in residential and side roads)

• •

customer accept estimated waiting times of up to 10 minutes

•

each aTaxi serves only a single request at a time (no ride-pooling)

•

requests are unknown to the fleetoperation algorithm until request-time (on-demand-mobility)

•

vehicle, which will be available first at start-location of request, is assigned (can be in use at request-time!)

fleet size waiting times environmental impact empty vehicle mileage electrification ride-pooling financials …

Authors: • • • • • • •

Kockelman, Fagnant, Chen, Liu, … Frazolli, Spieser, Samaranayake, … Shaheen, Greenblatt, … Axhausen, Ciari, Boesch, Nagel, … Pavone, Zhang, Rossi, … Jung, Jayakrishnan, … …

Average Fleet Velocity [km / h]

Average Private Vehicle Delay [s / km]

50

7677 (19)

155 (1)

11.2 (0.1)

25.0 (0.1)

71.9 (1.6)

60

7678 (14)

185 (0)

10.7 (0.1)

25.0 (0.1)

70.3 (0.5)

70

7669 (4)

213 (0)

10.4 (0.1)

24.9 (0.1)

72.0 (0.3)

80

7682 (19)

245 (2)

10.1 (0.0)

24.6 (0.1)

72.4 (0.2)

90

7700 (10)

271 (1)

9.8 (0.1)

24.3 (0.1)

74.1 (1.6)

100

7715 (19)

303 (1)

9.6 (0.1)

24.2 (0.1)

75.1 (0.9)

Basis

7948 (8)

-

-

-

74.3 (0.4)

•

Investigation of More Realistic Traffic and Imperfect Knowledge on Fleet Performance • estimated waiting time: ETA computed by fleet operator based on estimated travel times

• real waiting time:

aTaxi demand is model input (high uncertainty, sensitivity analysis necessary)

• effective waiting time:

simplifications in current fleet-operation algorithms: • OD-routing by traffic simulator • does not consider or treat delays • global assignment not considered • vehicles stay put until request assignment

time from request until actual vehicle arrival

1) real waiting time for served requests 2) penalty time for unserved requests  performance measure

…

Interface: Traffic Microsimulation & aTaxi Module

Previous Studies • • • • • • • •

Ratio of Empty Mileage [%]

long computation times, rule-of-thumb: cpu time [base scenario] + cpu time [link-level aTaxi simulation]

•

Research Questions:

aTaxi Mileage [1000 km]

average and standard deviation (in parantheses) of three replications with different seeds for private and aTaxi trip generation

http://www.rethinkX.com

simulation time period 05:00 – 11:00

Scenario

Private Vehicle Mileage [1000 km]

Simplified Travel Times: •

•

Perfect knowledge: the exact arrival time of vehicles is known to the operator

Aimsun API

Perfect operation: all vehicles drive exactly as long as predicted

aTaxi module

Traffic Situation

Travel Times

aTaxi Operation

aTaxi arrives at its destination

aTaxi enters next street section

update of link-level travel times for fleet computations

recording customer and vehicle statistics

update of vehicleavailability estimations

treatment of new customer requests

start of queued jobs

end of a time-step aTaxi starts driving in traffic simulation

trips to customers trips with customer on bord

recharging / refueling periodic relocations * * implementation in future framework

re-routing * delay-management of queued jobs * update of link-level travel times for fleet computations based on fleet-FCD *

NoMicroSim_75pc: time-independent link-level travel times NoMicroSim_TT: time-dependent link-level travel times (5 min updates)