Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population

With the rise of smart cities, a number of new mobility services have emerged to drive changes in built environment policies. Up-to-date demand models are needed to capture the impact of these policies on emerging mobility-enabled travel patterns. The study explores modeling requirements to assess the impact of such built environment policies. A synthetic population of New York City with a tour-based nested logit mode choice model was developed with accessibility to bikesharing and ride hail services via smartphone ownership. The model results suggest Manhattanites have a value of time of $29/h, consistent with the literature. Smartphone ownership is positively influenced by income and negatively influenced by age, and in turn negatively impacts Citi Bike ridership relative to other modes available. The synthetic population is also applied to analyze two city-scale built environment scenarios: a hypothetical Amazon headquarter deployment and a Citi Bike service expansion. If Amazon succeeded in Long Island City, it would have increased the number of trips to/from that neighborhood by 239%, of which FHVs would grow by over 441% and transit by 294%. It would have led to an increase of peak morning trips from 5000 up to at least 8000. Citi Bike’s expansion plan would grow ridership by 92%, and if they were able to expand efficiently throughout NYC this would grow further to 210% over the baseline.

[1]  S. Kullback,et al.  Contingency tables with given marginals. , 1968, Biometrika.

[2]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .

[3]  Xinyu Cao,et al.  Examining the Impacts of Residential Self‐Selection on Travel Behaviour: A Focus on Empirical Findings , 2009 .

[4]  Yongping Zhang,et al.  Synthetic household travel survey data simulation , 2008 .

[5]  Chandra R. Bhat,et al.  Activity-based Travel Demand Analysis , 2011 .

[6]  Reid Ewing,et al.  Travel and the Built Environment: A Synthesis , 2001 .

[7]  Xin Ye,et al.  An Exploration of the Relationship Between Mode Choice and Complexity of Trip Chaining Patterns , 2007 .

[8]  Joseph Y. J. Chow,et al.  A longitudinal study of bike infrastructure impact on bikesharing system performance in New York City , 2020, International Journal of Sustainable Transportation.

[9]  Craig R. Rindt,et al.  The Activity-Based Approach , 2008 .

[10]  Ryuichi Kitamura,et al.  Generation of Synthetic Daily Activity-Travel Patterns , 1997 .

[11]  Yoram Shiftan,et al.  TOUR BASED TRAVEL DEMAND MODELING IN THE U.S. , 1997 .

[12]  J. Polak,et al.  Predicting new forms of activity/mobility patterns enabled by shared-mobility services through a needs-based stated-response method: Case study of grocery shopping , 2014 .

[13]  Chandra R. Bhat,et al.  The Impact of Demographics, Built Environment Attributes, Vehicle Characteristics, and Gasoline Prices on Household Vehicle Holdings and Use , 2009 .

[14]  Joseph Ying Jun Chow,et al.  21st International Symposium on Transportation and Traffic TheoryActivity-based market equilibrium for capacitated multimodal transport systems , 2015 .

[15]  Joseph Y. J. Chow,et al.  Informed Urban Transport Systems: Classic and Emerging Mobility Methods toward Smart Cities , 2018 .

[16]  Joseph Y. J. Chow,et al.  Spatial welfare effects of shared taxi operating policies for first mile airport access , 2017 .

[17]  Alan Borning,et al.  Microsimulation of Urban Development and Location Choices: Design and Implementation of UrbanSim , 2003 .

[18]  Mariela Alfonzo,et al.  Evaluation of the California Safe Routes to School legislation: urban form changes and children's active transportation to school. , 2005, American journal of preventive medicine.

[19]  Michel Bierlaire,et al.  Simulation based Population Synthesis , 2013 .

[20]  Paul Williamson,et al.  An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata , 2000 .

[21]  W.W. Recker,et al.  A MODEL OF COMPLEX TRAVEL BEHAVIOR: PART I. THEORETICAL DEVELOPMENT , 1985 .

[22]  Michael R. Chernick,et al.  An Introduction to Bootstrap Methods with Applications to R , 2011 .

[23]  K. Kockelman,et al.  Self-Selection in Home Choice , 2008 .

[24]  Chao Liu,et al.  Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach , 2014 .

[25]  Chao Liu,et al.  Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance , 2017 .

[26]  M. Ben-Akiva,et al.  A practical policy-sensitive, activity-based, travel-demand model , 2011 .

[27]  Matthew J. Roorda,et al.  A tour-based model of travel mode choice , 2005 .

[28]  Mark Bradley,et al.  Activity-Based Travel Forecasting Models in the United States: Progress since 1995 and Prospects for the Future , 2005 .

[29]  Cynthia Chen,et al.  Role of the built environment on mode choice decisions: additional evidence on the impact of density , 2008 .

[30]  Kay W. Axhausen,et al.  Population synthesis for microsimulation: State of the art , 2010 .

[31]  Ta Theo Arentze,et al.  Creating Synthetic Household Populations , 2007 .

[32]  Felipe F. Dias,et al.  A behavioral choice model of the use of car-sharing and ride-sourcing services , 2017 .

[33]  Chandra R. Bhat,et al.  Population Synthesis for Microsimulating Travel Behavior , 2007 .

[34]  W. Deming,et al.  On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known , 1940 .

[35]  D. Gargett,et al.  Population synthesis for travel demand forecasting , 2013 .

[36]  Kenneth A. Small,et al.  The Value of Time and Reliability: Measurement from a Value Pricing Experiment , 2001 .

[37]  Eric J. Miller,et al.  Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously , 2012 .

[38]  Karthik C Konduri,et al.  Enhanced Synthetic Population Generator That Accommodates Control Variables at Multiple Geographic Resolutions , 2016 .

[39]  Hui Lin,et al.  How do changes to the built environment influence walking behaviors? a longitudinal study within a university campus in Hong Kong , 2014, International Journal of Health Geographics.

[40]  Christof Teuscher,et al.  ActivitySim: large-scale agent-based activity generation for infrastructure simulation , 2009, SpringSim '09.

[41]  Reid Ewing,et al.  Travel and the Built Environment , 2010 .

[42]  Eric J. Miller,et al.  Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City , 2017 .

[43]  S. Fienberg An Iterative Procedure for Estimation in Contingency Tables , 1970 .

[44]  Michael G. McNally,et al.  The Four Step Model , 2007 .