Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore

By K. Spieser, Kyle Treleaven, Rick Zhang, Emilio Frazzoli, D. Morton, Marco Pavone. 2014

473 citations recorded by the source corpus.

The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of personal transportation in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.

Subfields: Automotive Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/3ef6ddcae8e61be5fae1da5b1c512eafedc8d4df

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Citations in the corpus

Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks (2018, background): “size to 10% in Berlin, and to one third in Singapore if autonomous taxi services also replaced traditional public transport (Bischoff & Maciejewski, 2016; Spieser et al., 2014).”
Discussing the “positive utilities” of autonomous vehicles: will travellers really use their time productively? (2019, background): “In a poll of 109 travel survey Spieser et al. (2014) Singapore 30% of car driver VOT Childress, Nichols, Charlton, and Coe (2015) Seattle, WA 65% of car driver VOT (for high-income travellers only) Davidson and Spinoulas (2015) Brisbane, Australia 95–75% of car driver VOT (for lower level AVs);…”
Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies (2017, background): “(8) modeled the potential replacement of all modes of personal transportation by a fleet of SAVs in the city of Singapore, and found that mobility needs of the entire population may be met with one-third of the total number of passenger vehicles currently in operation.”
Policy and society related implications of automated driving: A review of literature and directions for future research (2017, background): “Also, Chen, Balieu, and Kringos (2016), Boesch, Ciari, and Axhausen (2016) Fagnant and Kockelman (2014, 2016), Zhang et al. (2015) and Spieser et al. (2014) offered estimations about a replacement rate of conventional vehicles by shared automated vehicles that varies between three and fourteen.”
Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, methodology): “Existing tools are largely limited to two commonly studied regimes: where AVs are few enough as to not affect the surrounding trafﬁc dynamics [3], [4], [5], or so ubiquitous as to become a coordination problem [6], [7], [44].”
Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies (2018, methodology): “Most studies (e.g. Bischoff & Maciejewski, 2016; Chen et al., 2016; Fagnant et al., 2015; Martinez & Viegas, 2017; Spieser et al., 2014) investigated a replacement of all or a specific share of current private vehicle trips by SAVs and suggest that this could substantially (up to around 90”
Fully automated vehicles: A cost of ownership analysis to inform early adoption (2017, background): “Nearly all the researchers focus on one specific impact of automation (e.g. Fagnant and 16 Kockelman 2014, Spieser et al. 2014 for shared mobility, European Transport Safety Council 2016 17 looking at safety impacts, Miller 2015 investigating impact on insurance industry, etc.) while others 18…”
The social dilemma of autonomous vehicles (2015, background): “e then, Autonomous Vehicles (AVs) such as the Google Car covered thousands of miles of real-road driving [3]. AVs promise worldchanging beneﬁts, by increasing trafﬁc efﬁciency [4], reducing pollution [5], and This is the author’s version of the work. It is posted here by permission of the AAAS for p”
Assessing the long-term effects of autonomous vehicles: a speculative approach (2016, background): “For example, many studies have examined the effects of AVs on the efficiency of traffic flow (e.g. Ioannou & Chien, 1993, Fagnant & Kockelman, 2013) or their impact on safety and car sharing (e.g., Pavone, Smith, Frazzoli, & Rus, 2012, Spieser et al., 2014).”
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment (2017, background): “Muchof the ﬂeet management literature for MoD systems considers the case of ride-sharing without pooling requests, focusing on ﬂuid approximations (6), queuing based formulations (7), case studies in speciﬁc regions [e.g., Singapore (8)], and operational considerations for ﬂeet managers (9).”
Regulating TNCs: Should Uber and Lyft set their own rules? (2019, background): “We model a company that owns and operates a fleet of autonomous vehicles to provide autonomous mobility on demand (AMoD) service [48, 49], and compare it to a ride-hailing service with human drivers.”