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Title: Behavioral Modeling of On-Demand Mobility Services: General Framework and Application to Sustainable Travel Incentives

Abstract

This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with inter-consumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modeling framework is essential for accounting the impacts of real-time on-demand system's dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based context-aware stated preference survey. Through model estimation, lower values of time are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. These results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. The proposed methodology is flexible to be applied to model other on-demand mobilitymore » services such as ride-hailing services and the emerging mobility as a service.« less

Authors:
 [1];  [1];  [2];  [1];  [3]; ORCiD logo [4];  [5];  [1]
  1. Massachusetts Institute of Technology
  2. Technical University of Denmark
  3. Delft University of Technology
  4. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  5. Singapore-MIT Alliance for Research and Technology (SMART)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
U.S. Department of Energy, Advanced Research Projects Agency-Energy (ARPA-E)
OSTI Identifier:
1580027
Report Number(s):
NREL/JA-5400-75649
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Transportation
Additional Journal Information:
Journal Volume: 46; Journal Issue: 6
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; smart mobility; on-demand incentives; travel behavior; stated preference; sustainability

Citation Formats

Xie, Yifei, Danaf, Mazen, Azevedo, Carlos Lima, Akkinepally, Arun Prakash, Atasoy, Bilge, Jeong, Kyungsoo, Seshadri, Ravi, and Ben-Akiva, Moshe. Behavioral Modeling of On-Demand Mobility Services: General Framework and Application to Sustainable Travel Incentives. United States: N. p., 2019. Web. doi:10.1007/s11116-019-10011-z.
Xie, Yifei, Danaf, Mazen, Azevedo, Carlos Lima, Akkinepally, Arun Prakash, Atasoy, Bilge, Jeong, Kyungsoo, Seshadri, Ravi, & Ben-Akiva, Moshe. Behavioral Modeling of On-Demand Mobility Services: General Framework and Application to Sustainable Travel Incentives. United States. doi:10.1007/s11116-019-10011-z.
Xie, Yifei, Danaf, Mazen, Azevedo, Carlos Lima, Akkinepally, Arun Prakash, Atasoy, Bilge, Jeong, Kyungsoo, Seshadri, Ravi, and Ben-Akiva, Moshe. Mon . "Behavioral Modeling of On-Demand Mobility Services: General Framework and Application to Sustainable Travel Incentives". United States. doi:10.1007/s11116-019-10011-z.
@article{osti_1580027,
title = {Behavioral Modeling of On-Demand Mobility Services: General Framework and Application to Sustainable Travel Incentives},
author = {Xie, Yifei and Danaf, Mazen and Azevedo, Carlos Lima and Akkinepally, Arun Prakash and Atasoy, Bilge and Jeong, Kyungsoo and Seshadri, Ravi and Ben-Akiva, Moshe},
abstractNote = {This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with inter-consumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modeling framework is essential for accounting the impacts of real-time on-demand system's dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based context-aware stated preference survey. Through model estimation, lower values of time are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. These results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. The proposed methodology is flexible to be applied to model other on-demand mobility services such as ride-hailing services and the emerging mobility as a service.},
doi = {10.1007/s11116-019-10011-z},
journal = {Transportation},
number = 6,
volume = 46,
place = {United States},
year = {2019},
month = {6}
}

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