Travelers' Rationality in Anticipatory Online Emergency Response

In this study, the simulation of traffic demand and flow behaviors is integrated with optimization of emergency resource allocation to explore benefits to the travelers and emergency responders and to save lives, money, and time. This research fills the gaps in the rationality of travelers when unexpected events occur and improves the myopic dispatching of emergency vehicles. Online optimization model is extended for a decisionmaking of whether to change the allocation of emergency resources when the traveler rationality exceeds boundary. The non-myopic model considers future expected delay based on traffic flow dynamics. The choice parameters of traveler are estimated from probe vehicle data and loop detector data in the real-world transportation network. Data-driven path-size logit model illustrating traveler’s route choice changes before and after incident occurrence is integrated to traffic simulation software. A boundedly rational travelers’ choice indicate a better dispatching of emergency vehicles thereby reduce traffic delays to the network. The lookahead algorithm in an easy interface can train responders with less frustration of going back and forth due to less efficient response strategy. This project envisions a new era in which an optimal resource allocation adapts to external events effectively and anticipates the future learning from the past to produce effective solutions.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; References; Tables;
  • Pagination: 91p

Subject/Index Terms

Filing Info

  • Accession Number: 01740075
  • Record Type: Publication
  • Report/Paper Numbers: CATM-2019-R4-NCAT
  • Contract Numbers: 69A3551747125
  • Created Date: May 14 2020 2:59PM