Moshe Ben-Akiva | |
|---|---|
משה בן־עקיבא | |
| Born | Moshe Emanuel Ben-Akiva |
| Known for | Discrete-choice theory; DynaMIT traffic simulator |
| Awards | IATBR Lifetime Achievement Award (2006); Jules Dupuit Prize (2007); IEEE ITS Outstanding Application Award (2011); INFORMS Robert Herman Award (2017) |
| Academic background | |
| Alma mater | Technion – Israel Institute of Technology (BS); MIT (SM, PhD) |
| Thesis | 'Discrete-choice models of trip generation and destination choice' (1973) |
| Academic work | |
| Discipline | Transportation systems analysis,econometrics |
| Institutions | Massachusetts Institute of Technology |
| Doctoral students | Joan L. Walker |
| Notable works | Discrete Choice Analysis (1985) [1] |
| Website | www |
Moshe Emanuel Ben-Akiva is an Israeli-American engineer who holds the Edmund K. Turner Professorship of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT). He is noted for pioneering discrete-choice methods in travel-demand modelling and for co-creating DynaMIT,a real-time traffic-management simulation platform. [2]
Ben-Akiva moved to the United States,obtaining an SM in 1971 and a PhD in transportation systems in 1973 from MIT. [2] His doctoral research laid the foundations for the textbook Discrete Choice Analysis. [1]
Immediately after completing his doctorate,Ben-Akiva joined the MIT faculty as an assistant professor;he was promoted to full professor in 1981 and named Edmund K. Turner Professor in 1996. [2] He founded and directs MIT’s Intelligent Transportation Systems Laboratory,whose DynaMIT software is used for real-time traffic prediction and was recognised with the IEEE Intelligent Transportation Systems Outstanding Application Award. [3] [4]
Ben-Akiva has supervised more than fifty doctoral dissertations and teaches graduate subjects in discrete-choice analysis,demand modelling and dynamic traffic assignment. [5]
Working at the interface of engineering and economics,Ben-Akiva introduced random-utility models that underpin modern activity-based demand forecasting. His subsequent integration of choice models with dynamic traffic assignment led to the microsimulation tools MITSIM and DynaMIT,which combine real-time sensor data with behavioural models to forecast congestion and test control strategies. [6] Since the 2010s his group has blended machine-learning techniques with discrete choice to improve forecasts for on-demand mobility and urban freight systems. [5]
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