AUM2020 Session 3- Emerging insights into autonomous driving

Duration: 2 hours 2 mins

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Description:	1. Urban sprawl or reurbanization? An agent-based simulation of autonomous vehicles and household relocation Prof Rolf Moeckel (Technical University of Munich) 2. Large-scale, shared autonomous vehicle simulations with geofences, stop aggregation, and parking restrictions Prof Kara Kockelman (University of Texas at Austin) Discussant: Prof Robert Cervero (University of California Berkeley) Session Chair: Mr Steve Denman (University of Cambridge)

Description:

1. Urban sprawl or reurbanization? An agent-based simulation of autonomous vehicles and household relocation
Prof Rolf Moeckel (Technical University of Munich)

2. Large-scale, shared autonomous vehicle simulations with geofences, stop aggregation, and parking restrictions
Prof Kara Kockelman (University of Texas at Austin)

Discussant: Prof Robert Cervero (University of California Berkeley)
Session Chair: Mr Steve Denman (University of Cambridge)


Created:	2020-11-03 09:56
Collection:	Martin Centre AUM2020: Modelling the New Urban World
Publisher:	University of Cambridge
Copyright:	The Martin Centre
Language:	eng (English)
Keywords:	AUM2020; Emerging insights into autonomous driving; Modelling the New Urban World; Martin Centre; Session 3;


Abstract:	1. To explore the impact of autonomous vehicles on land use development, an integrated land use/transport model was used to model resulting changes in travel demand and household relocation. Assumptions were carefully reviewed in a series of sensitivity tests. While congestion increases substantially with autonomous vehicles, the model suggests little impact on land use patterns. Urban sprawl induced by a lower sensitivity to commute times is in part balanced out because urban dwellings become more attractive as autonomous vehicles can park themselves elsewhere. The presentation will introduce the methodology and present model results. 2. Large-scale modeling of the future of shared autonomous vehicles (SAVs) is necessary to appreciate various model assumptions and policy implications. Geographical constraints on an SAV fleet, operational modifications to accommodate trip request aggregation at stops, and the inclusion of parking search behavior in large regions are studied to identify potential benefits. Artificially increasing trip density within a region or at stops is expected to help improve fleet performance, but the question remains - by how much? Similarly, the bias in fleet metrics from not including parking behavior is also worth quantifying. The presentation will discuss the use of POLARIS and MATSim to quantify such policy effects for different regions and varying samples of trips simulated.

Abstract:

1. To explore the impact of autonomous vehicles on land use development, an integrated land use/transport model was used to model resulting changes in travel demand and household relocation. Assumptions were carefully reviewed in a series of sensitivity tests. While congestion increases substantially with autonomous vehicles, the model suggests little impact on land use patterns. Urban sprawl induced by a lower sensitivity to commute times is in part balanced out because urban dwellings become more attractive as autonomous vehicles can park themselves elsewhere. The presentation will introduce the methodology and present model results.

2. Large-scale modeling of the future of shared autonomous vehicles (SAVs) is necessary to appreciate various model assumptions and policy implications. Geographical constraints on an SAV fleet, operational modifications to accommodate trip request aggregation at stops, and the inclusion of parking search behavior in large regions are studied to identify potential benefits. Artificially increasing trip density within a region or at stops is expected to help improve fleet performance, but the question remains - by how much? Similarly, the bias in fleet metrics from not including parking behavior is also worth quantifying. The presentation will discuss the use of POLARIS and MATSim to quantify such policy effects for different regions and varying samples of trips simulated.

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