This laboratory is intended to provide the necessary tools for analysis of transportation systems from various perspectives, such as travel prediction models as tools for transportation system planning, traffic operations and performance and highway safety. Both graduate and undergraduate students use the transportation lab mainly in research projects. In addition the lab aims to enrich transportation studies in relevant courses with practical hands on experience.
 

 

Academic advisor: Hillel Bar-Gera
 
 
 
 
Undesirable Driving EventsS
In this study we use an advanced in-vehicle technology to measure undesired driving events such as extreme braking and accelerating, sharp cornering and sudden lane changing - in real driving situations. Data includes more than 100,000 trips (and growing…). We study the usefulness of multiple drivers' properties in predicting the occurrence of undesired driving events: Drivers' attitudes towered road safety, subjective opinions about organizational attitudes towards road safety, risk taking behaviors and demographic properties such as age and gender. Analytic methods in this research include maximum likelihood parameter estimation of a Poisson-Gamma (Negative Binomial) model, negative Binomial regression, factor analysis, etc.
​Researchers: Oren Musicant, Hillel Bar-Gera, Edna Schechtman
Applicable macroscopic dynamic traffic assignment model of large scale transportation networks
 
Models of traffic assignment are used for predicting traffic patterns of large scale transportation networks, typically for the purpose of evaluating roadway system performance. Transportation networks are dynamic in their nature as can be seen from figure 1, representing the maps of flow and speed as a function of time and space along the south bound direction of the Ayalon Freeway in Israel. Each color depicts a different level of flow and speed in each time space interval. The speed map shows clearly the formation of a queue and its dissipation process. Accumulated queues and their related phenomena have a significant impact on the behavior of traffic along the roadway system, but they cannot be treated in static models that assume a steady state in terms of traffic flows and travel times. The objective of this research is the development of a generalized analytical continuous flow dynamic model that will be capable of providing solutions for dynamic traffic assignment problems of real transportation networks.
 
Researchers: Michal Nitzani, Hillel Bar-Gera

 

 

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