{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:10:41Z","timestamp":1774894241518,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of North Carolina at Charlotte"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The initial hype around Automated Vehicle (AV) technologies has subsided, and it is now being realized that near-term deployment of AV technologies will be in the form of low-speed shared automated shuttles in geofenced districts with a high density of trip demand. A concept labeled \u2018Automated Mobility Districts\u2019 (AMD) has been coined to define such deployments. A modeling and simulation toolkit that can act as a decision support tool for early-stage AMD deployments is desired for answering the questions such as (i) for a series of given conditions, such as the amount of travel demand and automated shuttle fleet configuration, what is the expected mode split for shared automated vehicle (SAV) services? (ii) for that mode share of SAVs, what level-of-service and network performance can be anticipated? To answer these research questions, an innovative and integrated framework of multi-mode choice and microscopic traffic simulation model is presented to obtain the equilibrium of mode split for various modes in AMDs, based on real-time traffic simulation data. The proposed framework was tested using travel demand and road network data from Greenville, South Carolina, considering a car, walk, and two SAV on-demand ridesharing modes in a proposed AMD. Results from the study demonstrated the efficacy of the proposed framework for solving the mode split equilibrium in an AMD. In addition, sensitivity analyses were conducted to understand the impact of factors such as waiting times and fleet resources on mode share equilibrium for SAVs.<\/jats:p>","DOI":"10.3390\/s22208020","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"8020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Mode Split Equilibrium Microsimulation Approach for Early-Stage On-Demand Shared Automated Mobility"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-1178","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte, Charlotte, NC 28223, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8993-3813","authenticated-orcid":false,"given":"Jinghui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Transportation Engineering, Shanghai Jiao Tong University (SJTU), Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqiu","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte, Charlotte, NC 28223, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Traffic & Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","unstructured":"Boudette, N.E. (The New York Times, 2019). Despite high hopes, self-driving cars are \u2018way in the future\u2019, The New York Times, p. 17."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102766","DOI":"10.1016\/j.trd.2021.102766","article-title":"How to measure the impacts of shared automated electric vehicles on urban mobility","volume":"93","author":"Nemoto","year":"2021","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_3","unstructured":"Hou, Y., Young, S.E., Garikapati, V., Chen, Y., and Zhu, L. (November, January 29). Initial Assessment and Modeling Framework Development for Automated Mobility Districts. Proceedings of the ITS World Congress 2017: Integrated Mobility Driving Smart Cities, Montreal, QC, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"79968","DOI":"10.1109\/ACCESS.2019.2920232","article-title":"System Design and Optimization of In-Route Wireless Charging Infrastructure for Shared Automated Electric Vehicles","volume":"7","author":"Mohamed","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/0361198120925273","article-title":"Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles","volume":"2674","author":"Zhu","year":"2020","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stocker, A., and Shaheen, S. (2019). Shared Automated Vehicle (SAV) Pilots and Automated Vehicle Policy in the US: Current and Future Developments, Springer International Publishing.","DOI":"10.1007\/978-3-319-94896-6_12"},{"key":"ref_7","unstructured":"Pero, J. (2022, July 24). Self-Driving Shuttles Have Arrived in NYC: Optimus Ride Begins Trials at Brooklyn Navy Yard. Available online: https:\/\/www.dailymail.co.uk\/sciencetech\/article-7334167\/Self-driving-shuttles-arrived-NYC-Optimus-Ride-begins-trials-Brooklyn-Navy-Yard.html."},{"key":"ref_8","unstructured":"Sebastian, B. (2020, February 06). Columbus Is First City in U.S. with Autonomous Shuttles in Residential Areas. Available online: https:\/\/www.forbes.com\/sites\/sebastianblanco\/2020\/02\/06\/columbus-is-first-city-in-us-with-autonomous-shuttles-in-residential-areas\/#66b542e66d7f."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.trc.2017.06.020","article-title":"Congestion-aware system optimal route choice for shared autonomous vehicles","volume":"82","author":"Levin","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, L., Garikapati, V., Chen, Y., Hou, Y., Aziz, H.M.A., and Young, S. (2018). Quantifying the Mobility and Energy Benefits of Automated Mobility Districts Using Microscopic Traffic Simulation. International Conference on Transportation and Development 2018, American Society of Civil Engineers.","DOI":"10.1061\/9780784481530.010"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhu, L., Zhao, Z., and Wu, G. (2021). Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study. Sustainability, 13.","DOI":"10.3390\/su13052483"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1073\/pnas.1611675114","article-title":"On-demand high-capacity ridesharing via dynamic trip-vehicle assignment","volume":"114","author":"Samaranayake","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.trc.2013.12.001","article-title":"The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios","volume":"40","author":"Fagnant","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.trb.2016.03.009","article-title":"Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state\u2013space\u2013time network representations","volume":"89","author":"Mahmoudi","year":"2016","journal-title":"Transp. Res. Part B Methodol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3563","DOI":"10.1109\/TSG.2016.2635025","article-title":"Optimal Routing and Charging of an Electric Vehicle Fleet for High-Efficiency Dynamic Transit Systems","volume":"9","author":"Chen","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s11116-016-9729-z","article-title":"Dynamic ridesharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas","volume":"45","author":"Fagnant","year":"2018","journal-title":"Transportation"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13290","DOI":"10.1073\/pnas.1403657111","article-title":"Quantifying the benefits of vehicle pooling with shareability networks","volume":"111","author":"Santi","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ruch, C., Horl, S., and Frazzoli, E. (2018, January 4\u20137). AMoDeus, a Simulation-Based Testbed for Autonomous Mobility-on-Demand Systems. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569961"},{"key":"ref_19","unstructured":"Kay, W.A., Andreas, H., and Kai, N. (2016). The multi-agent transport simulation MATSim, Ubiquity Press."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Maciejewski, M., Bischoff, J., H\u00f6rl, S., and Nagel, K. (2017). Towards a Testbed for Dynamic Vehicle Routing Algorithms. Communications in Computer and Information Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-60285-1_6"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/15568318.2020.1847368","article-title":"An optimization-based planning tool for on-demand mobility service operations","volume":"16","author":"Aziz","year":"2022","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1177\/0361198120962491","article-title":"Use of shared automated vehicles for first-mile last-mile service: Micro-simulation of rail-transit connections in Austin, Texas","volume":"2675","author":"Huang","year":"2021","journal-title":"Transp. Res. Rec."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.trpro.2018.12.173","article-title":"Exploring the Impact of User Preferences on Shared Autonomous Vehicle Modal Split: A Multi-Agent Simulation Approach","volume":"37","author":"Kamel","year":"2019","journal-title":"Transp. Res. Procedia"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.trc.2017.01.010","article-title":"User preferences regarding autonomous vehicles","volume":"78","author":"Haboucha","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"119792","DOI":"10.1016\/j.techfore.2019.119792","article-title":"Modeling Americans\u2019 autonomous vehicle preferences: A focus on dynamic ridesharing, privacy & long-distance mode choices","volume":"150","author":"Gurumurthy","year":"2020","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.tra.2019.12.004","article-title":"Preference heterogeneity in mode choice for car-sharing and shared automated vehicles","volume":"132","author":"Zhou","year":"2020","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1007\/s11116-017-9811-1","article-title":"Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation","volume":"44","author":"Liu","year":"2017","journal-title":"Transportation"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Young, S., and Lott, J.S. (2020). The Automated Mobility District Implementation Catalog: Insights from Ten Early-Stage Deployments, National Renewable Energy Lab. (NREL). NREL\/TP-5400-76551.","DOI":"10.2172\/1659783"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lopez, P.A., Wiessner, E., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y.-P., Hilbrich, R., Lucken, L., Rummel, J., and Wagner, P. (2018, January 4\u20137). Microscopic Traffic Simulation using SUMO. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569938"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wegener, A., Pi\u00f3rkowski, M., Raya, M., Hellbr\u00fcck, H., Fischer, S., and Hubaux, J.-P. (2008, January 14\u201317). TraCI. Proceedings of the 11th Communications and Networking Simulation Symposium on-CNS \u201808, Ottawa, ON, Canada.","DOI":"10.1145\/1400713.1400740"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1177\/03611981211044726","article-title":"Simulation-Based Dynamic Traffic Assignment with Continuously Distributed Value of Time for Heterogeneous Users","volume":"2676","author":"Tian","year":"2022","journal-title":"Transp. Res. Rec."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1287\/trsc.37.3.278.16042","article-title":"Time-dependent, label-constrained shortest path problems with applications","volume":"37","author":"Sherali","year":"2003","journal-title":"Transp. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.trc.2018.10.018","article-title":"Transit-oriented autonomous vehicle operation with integrated demand-supply interaction","volume":"97","author":"Wen","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_34","unstructured":"Edmonds, E. (2022, July 24). Annual Cost to Own and Operate a Vehicle Falls to $8,698, Finds AAA (2015 Your Driving Costs). Available online: https:\/\/newsroom.aaa.com\/2015\/04\/annual-cost-operate-vehicle-falls-8698-finds-aaa-archive\/."},{"key":"ref_35","unstructured":"Schrank, D., Eisele, B., Lomax, T., and Bak, J. (2015). 2015 Urban Mobility Scorecard, INRIX, Inc."},{"key":"ref_36","unstructured":"Bhat, C.R. (2022, July 24). Travel Demand Forecasting: Parameters and Techniques. National Cooperative Highway Research Program (NCHRP) Report 716, Available online: https:\/\/ntrl.ntis.gov\/NTRL\/dashboard\/searchResults\/titleDetail\/PB2012108697.xhtml."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"78","DOI":"10.3141\/2528-09","article-title":"Transportation Routing Map Abstraction Approach","volume":"2528","author":"Zhu","year":"2015","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1049\/iet-its.2016.0287","article-title":"Road network abstraction approach for traffic analysis: Framework and numerical analysis","volume":"11","author":"Zhu","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1007\/s11116-018-9923-2","article-title":"The impact of ride-hailing on vehicle miles traveled","volume":"46","author":"Henao","year":"2019","journal-title":"Transportation"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:27Z","timestamp":1760144307000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,20]]},"references-count":39,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22208020"],"URL":"https:\/\/doi.org\/10.3390\/s22208020","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,20]]}}}