Investigation of the impacts of shared autonomous vehicle operation in Halifax, Canada, using a dynamic traffic microsimulation model
Open accessAbstract
This study presents a novel sequential modeling framework of shared autonomous vehicle (SAV) operation in the Halifax transport network. The Halifax regional transport network model is used to generate business-as-usual traffic demand in the morning peak hours. A new module of SAV assignment upon trip request is introduced and integrated with the traffic microsimulation model to simulate the SAVs’ occupied and empty trips in the network. The proposed framework demonstrates the capability to evaluate service performance of SAVs with different level of fleet sizes in the network. Model results suggest that fleet size of 900 SAVs serves 20% of the total morning commute trip requests. Traffic condition is improved for the first hour of peak periods as average speed increases and total travel time requirement decreases during operation of SAV fleet in Halifax. The results provide insights into SAV system planning in accordance to anticipated challenges of SAV adoption in transportation network.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Car-sharing systems are found to increase congestion.
“On one hand, car-sharing systems are found to increase congestion (Alam & Habib, 2018;Zhao & Kockelman, 2018)”
From Shared autonomous vehicle services: A comprehensive review (2020) · cites this paper as Alam & Habib, 2018
Section 4.2.3 reports that traffic conditions degraded during the last two hours of Halifax’s morning peak when shared autonomous vehicles operated, possibly because of empty trips. But it also reports improved congestion in the first hour. The claim omits this time-dependent result and describes shared autonomous vehicles more broadly as car-sharing systems; no page number is available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Google Car: Driving Toward A Better Future?
Poczter, S. L., & Jankovic, L. M. (2013) · Journal of Business Case Studies (JBCS) · doi:10.19030/jbcs.v10i1.8324 · Cited by 106 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- Yeshodara NS, Nagojappa NS, Kishore N. Cloud based self driving cars. IEEE International Conference on Cloud Computing in Emerging Markets 2014
- Novak V. Google self-driving car. Doctoral dissertation, ВНТY 2016
- Fagnant DJ, Kockelman KM, Bansal P. Operations of a shared autonomous vehicle fleet for the Austin, Texas market. Transportation Research Record: Journal of Transportation Research Board 2015;2536:98-106
- Boesch, PM, Ciari F. Agent-based simulation of autonomous cars. Proceedings of the American Control Conference 2015;2588-2592
- Chen TD, Kockelman KM. Management of a shared autonomous electric vehicle fleet: implications of pricing schemes. Transportation Research Record: Journal of the Transportation Research Board 2016;2572:37-46
- Chen TD, Kockelman KM, Hanna JP. Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle and charging infrastructure decisions. Transportation Research Part A: Policy and Practice 2016;94:243-254
- Levin MW. Congestion-aware system optimal route choice for shared autonomous vehicles. Transportation Research Part C: Emerging Technologies 2017;82:229-247
- Park B, Schneeberger J. Microscopic simulation model calibration and validation: case study of VISSIM simulation model for a coordinated actuated signal system. Transportation Research Record: Journal of The Transportation Research Board 2003;185-192.
- Miller DM. Developing a procedure to identify parameters for calibration of a VISSIM model. Master's Thesis, School of Civil and Environmental Engineering, Georgia Institute of Technology 2005.
- Alam MJ, Habib MA, Quigley K. Vulnerability in transport network during critical infrastructure renewal: lessons learned from a dynamic traffic microsimulation model. Procedia Computer Science 2017;109:616-623
Cited by
Sorted by citations
Shared autonomous vehicle services: A comprehensive review
Narayanan, S., Chaniotakis, E., & Antoniou, C. (2020) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2019.12.008 · Cited by 691 · see citing works
- Authors
- Alam, M. J., & Habib, M. A.
- Affiliations
- Dalhousie University (CA)
- Corresponding author
- Habib, Muhammad Ahsanul
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 130
- Pages
- 496-503
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2801210630
- Cited by
- 40
- References
- 1
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 8 · 2023: 4 · 2022: 4 · 2021: 9 · 2020: 8 · 2019: 2
- FWCI
- 4.87
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Nova Scotia Department of Energy
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- shared autonomous vehicles, microscopic traffic simulation, empty trips, peak hour traffic, travel time
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
