CRII: CPS: Emerging Markets and Myopic Decision-Making in Multi-Modal Transportation Systems: Modeling and Validation
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In this project, we aim to create high-fidelity models, validated with real-world data, of mixed- mode travel decisions and emerging mobility markets. A growing subset of travelers make decisions informed by apps that optimize (mixed-mode) routes based on user-defined preferences. Locally optimized solutions tend to cause inefficiencies that are exacerbated by risk-sensitivity (arising from endogenous and exogenous uncertainties) in travelers. Traditional rational, utility maximization models tend not to capture these effects, particularly in short-horizon decisions that leave little time for cogitation and points of reference play a primary role in choice. We aim to 1) learn models of traveler decision-making that account for risk- sensitivity and 2) develop models of market structures (e.g., ride-sharing platforms) that capture traveler valuations of mobility modes.
The proposed research extends rational models of travel decisions by leveraging prospect theory for cap- turing risk-sensitivity and bounded rationality for capturing information asymmetries and myopia. These concepts will be integrated into an inverse reinforcement learning framework for which we seek online algo- rithms in support of control/incentive design. Moreover, we will develop technical approaches to modeling mobility market structures that include new queuing game-theoretic models that capture risk-sensitivity. The proposed work will benefit from multi-modal transit data available via municipal and industry partners for testing and validation within a well-developed testbed.
If successful, this project will expose areas where municipalities can adjust their management strategies to supplement the sharing economy in addressing the service gap in an equitable way. Through our collaboration with the Seattle Department of Transportation and industry partners Swiftly and IDAX, there is potential to translate results to practice. Graduate and undergraduate students will be heavily involved in the project and will have ample opportunity to engage with the community through our municipal partners as well as be exposed to the jobs of tomorrows smart cities.
Submitted by Lillian Ratliff
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In this project, we aim to create high-fidelity models, validated with real-world data, of mixed- mode travel decisions and emerging mobility markets. A growing subset of travelers make decisions informed by apps that optimize (mixed-mode) routes based on user-defined preferences. Locally optimized solutions tend to cause inefficiencies that are exacerbated by risk-sensitivity (arising from endogenous and exogenous uncertainties) in travelers. Traditional rational, utility maximization models tend not to capture these effects, particularly in short-horizon decisions that leave little time for cogitation and points of reference play a primary role in choice. We aim to 1) learn models of traveler decision-making that account for risk- sensitivity and 2) develop models of market structures (e.g., ride-sharing platforms) that capture traveler valuations of mobility modes.
The proposed research extends rational models of travel decisions by leveraging prospect theory for cap- turing risk-sensitivity and bounded rationality for capturing information asymmetries and myopia. These concepts will be integrated into an inverse reinforcement learning framework for which we seek online algo- rithms in support of control/incentive design. Moreover, we will develop technical approaches to modeling mobility market structures that include new queuing game-theoretic models that capture risk-sensitivity. The proposed work will benefit from multi-modal transit data available via municipal and industry partners for testing and validation within a well-developed testbed.
If successful, this project will expose areas where municipalities can adjust their management strategies to supplement the sharing economy in addressing the service gap in an equitable way. Through our collaboration with the Seattle Department of Transportation and industry partners Swiftly and IDAX, there is potential to translate results to practice. Graduate and undergraduate students will be heavily involved in the project and will have ample opportunity to engage with the community through our municipal partners as well as be exposed to the jobs of tomorrows smart cities.