This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
A New Approach of Generating Travel Demands for Smart Transportation Systems Modeling
Technical Paper
2020-01-1047
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs. To address this data gap, we propose a new approach of travel demand generation based on households. Census data provide the demographic information for each household (e.g., number of adults and kids, income and education, vehicle ownership etc.). The travel demands of each individual in the household are modeled as chains of trips with spatial and temporal details that match the travel patterns of the individual’s demographic profile. The trip chains also consider multi-person trips, accounting for group traveling of the individual with others within and outside of the households. Using Miami as a case study city, we demonstrate the feasibility and validity of the proposed approach. The proposed approach can be applied to any city using publicly available data as inputs.
Authors
Topic
Citation
Wen, R., Jiang, Z., Liang, C., Telenko, C. et al., "A New Approach of Generating Travel Demands for Smart Transportation Systems Modeling," SAE Technical Paper 2020-01-1047, 2020, https://doi.org/10.4271/2020-01-1047.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 |
Also In
References
- Sperling , D. Three Revolutions: Steering Automated, Shared, and Electric Vehicles to a better Future 2018
- Fulton , L. , Mason , J. , and Meroux , D. 2017
- Fagnant , D.J. and Kockelman , K.M. Dynamic Ride-Sharing and Fleet Sizing for a System of Shared Autonomous Vehicles in Austin, Texas Transportation 2018
- Yong , J.Y. , Ramachandaramurthy , V.K. , Tan , K.M. , and Mithulananthan , N. A Review on the State-of-the-Art Technologies of Electric Vehicle, its Impacts and Prospects Renewable and Sustainable Energy Reviews 49 365 385 2015
- Fleming , K. and Singer , M. April, 2019
- Stiglic , M. , Agatz , N. , Savelsbergh , M. , and Gradisar , M. Enhancing Urban Mobility: Integrating Ride-Sharing and Public Transit Computers and Operations Research 2018
- Gurumurthy , K.M. and Kockelman , K.M. Analyzing the Dynamic Ride-Sharing Potential for Shared Autonomous Vehicle Fleets Using Cellphone Data from Orlando, Florida Computers, Environment and Urban Systems 2018
- Wang , Y. , Zheng , B. , and Lim , E.P. Understanding the Effects of Taxi Ride-Sharing - A Case Study of Singapore Computers, Environment and Urban Systems 2018
- Mattia , G. , Guglielmetti Mugion , R. , and Principato , L. Shared Mobility as a Driver for Sustainable Consumptions: The Intention to Re-Use Free-Floating Car Sharing Journal of Cleaner Production 2019
- Gärling , T. , Gärling , A. , and Johansson , A. Household Choices of Car-Use Reduction Measures Transportation Research Part A: Policy and Practice 34 5 309 320 2000
- Ferguson , E. Demographics of Carpooling Transportation Research Record 1496 142 150 1995
- Kim , S. and Ulfarsson , G.F. Travel Mode Choice of the Elderly: Effects of Personal, Household, Neighborhood, and Trip Characteristics Transportation Research Record 1894 117 126 2004
- Taylor , B. , Chin , R. , Melanie , C. , Dill , J. et al. 2015
- Achtnicht , M. German Car Buyers’ Willingness to Pay to Reduce CO2 Emissions Climatic Change 113 3-4 679 697 2012
- Ziegler , A. Individual Characteristics and Stated Preferences for Alternative Energy Sources and Propulsion Technologies in Vehicles: A Discrete Choice Analysis for Germany Transportation Research Part A: Policy and Practice 46 8 1372 1385 2012
- Wang , S. , Fan , J. , Zhao , D. , Yang , S. et al. Predicting Consumers’ Intention to Adopt Hybrid Electric Vehicles: Using an Extended Version of the Theory of Planned Behavior Model Transportation 43 1 123 143 2016
- Liu , P. , Guo , Q. , Ren , F. , Wang , L. , et al. Willingness to Pay for Self-Driving Vehicles: Influences of Demographic and Psychological Factors Transportation Research Part C: Emerging Technologies August 2018 306 317 2019
- Kelly , J.C. , MacDonald , J.S. , and Keoleian , G.A. Time-Dependent Plug-In Hybrid Electric Vehicle Charging Based on National Driving Patterns and Demographics Applied Energy 94 395 405 2012
- Mustafa , R. and Zhong , M. Challenges and Opportunities in Applying High-Fidelity Travel Demand Model for Improved Network-Wide Traffic Estimation: A Review and Discussion The Open Transportation Journal 2014
- Rasouli , S. and Timmermans , H. Uncertainty in Travel Demand Forecasting Models: Literature Review and Research Agenda Transportation Letters 4 1 55 73 2012
- Rasouli , S. and Timmermans , H. Activity-Based Models of Travel Demand: Promises, Progress and Prospects International Journal of Urban Sciences 18 1 31 60 Jan. 2014
- Bhat , C.R. , Guo , J.Y. , Srinivasan , S. , and Sivakumar , A. Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns Transportation Research Record 1894 57 66 2004
- Pendyala , R.M. , Kitamura , R. , Kikuchi , A. , Yamamoto , T. et al. Florida Activity Mobility Simulator Overview and Preliminary Validation Results Transportation Research Record 1921 123 130 2005
- Miller , E.J. and Roorda , M.J. Prototype Model of Household Activity-Travel Scheduling Transportation Research Record 1831 114 121 2003
- Baqueri , S.F.A. , Adnan , M. , Kochan , B. , and Bellemans , T. Activity-Based Model for Medium-Sized Cities Considering External Activity-Travel: Enhancing FEATHERS Framework Future Generation Computer Systems 2019
- Kim , C. and Parent , O. Modeling Individual Travel Behaviors Based on Intra-Household Interactions Regional Science and Urban Economics 2016
- Ho , C. and Mulley , C. Intra-Household Interactions in Transport Research: A Review Transport Reviews 35 1 33 55 2015
- Lemp , J.D. , McWethy , L.B. , and Kockelman , K.M. Transportation Research Record 1994 80 88 2007
- Lokhandwala , M. and Cai , H. Dynamic Ride Sharing Using Traditional Taxis and Shared Autonomous Taxis: A Case Study of NYC Transportation Research Part C: Emerging Technologies 2018
- Çolak , S. , Alexander , L.P. , Alvim , B.G. , Mehndiratta , S.R. et al. Analyzing Cell Phone Location Data for Urban Travel: Current Methods, Limitations, and Opportunities Transportation Research Record 2526 126 135 2015
- Currans , K.M. and Clifton , K.J. Using Household Travel Surveys to Adjust ITE Trip Generation Rates Journal of Transport and Land Use 2015
- Consult , P.B. 1 51 2005
- U.S. Census Bureau 2018
- U.S. Department of Transportation 2009
- U.S. Department of Transportation, F. H. A. 2017
- U.S. Census Bureau Using the American Community Survey Summary File: What Data Users Need to Know 2018
- Axhausen , K.W. and Gärling , T. Transport Reviews Activity-Based Approaches to Travel Analysis: Conceptual Frameworks, Models, and Research Problems Activity-Based Approaches to Travel Analysis 12 4 323 341 1992
- Krueger , R. , Rashidi , T.H. , and Rose , J.M. Preferences for Shared Autonomous Vehicles Transportation Research Part C: Emerging Technologies 69 343 355 Aug. 2016
- Al-Alawi , B.M. and Bradley , T.H. 2013
- Abraham , H. , Lee , C. , Brady , S. , Fitzgerald , C. , et al.
- Duncan , A. Powers of the Adjacency Matrix and the Walk Matrix The Collection 9 4 11 2004
- https://gis-mdc.opendata.arcgis.com/ 2019
- https://bcgis.broward.org/GISData.htm 2019
- National Academies of Sciences, Engineering, and Medicine Federal Statistics, Multiple Data Sources, and Privacy Protection: Next Steps Washington, DC The National Academies Press 2017
- Warren , D.L. , Glor , R.E. , and Turelli , M. Environmental Niche Equivalency Versus Conservatism: Quantitative Approaches to Niche Evolution Evolution 62 11 2868 2883 2008
- WSP Parsons Brinckerhoff 170 November 2017