Saif Eddin Jabari

Evaluation of corridor traffic management and planning strategies that use microsimulation: A case study

The California SR-41 corridor simulation project is presented as a case study of how to utilize microscopic traffic simulation for planning purposes. Two of the most important components of preparing simulation models for planning purposes are emphasized: origin-destination (O-D) matrix calibration and peak spreading for long-term testing (e.g., 20-year horizons) to overcome unrealistic network gridlock. With streamlining of the O-D calibration process, it is shown that the proposed model reproduces count and travel-time information collected from the field. Incorporating peak spreading as a result of congestion for long-term scenarios is also shown to yield performance improvements in the models and overcome network gridlock issues common to such applications.

Heuristic Solution Techniques for No-Notice Emergency Evacuation Traffic Management

When responding to unanticipated emergency events, time is of the essence. This paper proposes a heuristic algorithm for staged traffic evacuation, referred to as HASTE, which is shown to approximate a solution to the cell transmission-based many-to-one dynamic system optimum (DSO) traffic assignment problem. The proposed algorithm does not contain traffic holding, is fast enough for online applications, and produces evacuee routing schedules as its output. As an application of HASTE, a mixed 0-1 integer programming extension to the DSO is proposed to identify critical signalized intersection locations in the network for deployment of a limited number of police officers aimed at improving network throughput and further minimizing evacuee exposure time to the hazard. For the combined problem, a genetic algorithms-based solution procedure is proposed that uses HASTE for solution fitness. Efficiency and quality of the heuristic strategies are demonstrated via numerical experiments for moderately sized problems.

Sensor Placement with Time-to-Detection Guarantees

We present a novel and effective method for determining the placement of sensors so as to be able to satisfy probabilistic constraints on the time-to-detection of an incident. Indeed, with the wealth of real-time traffic data available today, an important new goal of intelligent traffic management systems is incident detection with time-to-detection guarantees, in particular on expressways with large distances between sensors. This goal drives investment decisions in new sensor deployment, hence making the topic a pressing need for traffic management. The method we provide makes use of a probabilistic formulation of traffic behavior and incident localization to determine the minimum spacing of sensors needed to achieve the time-to-detection goal with a specified probability.

Traffic state estimation using stochastic Lagrangian dynamics

Abstract This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. We utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman–Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and our results indicate good agreement with out-of-sample data.