Hossein Jula

Collision avoidance analysis for lane changing and merging

One of the riskiest maneuvers that a driver has to perform in a conventional highway system is to merge into the traffic and/or to perform a lane changing maneuver. Lane changing/merging collisions are responsible for one-tenth of all crash-caused traffic delays often resulting in congestion. Traffic delays and congestion, in general, increase travel time and have a negative economic impart. We analyze the kinematics of the vehicles involved in a lane changing/merging maneuver, and study the conditions under which lane changing/merging crashes can be avoided. That is, given a particular lane change/merge scenario, we calculate the minimum longitudinal spacing which the vehicles involved should initially have so that no collision, of any type, takes place during the maneuver. Simulations of a number of examples of lane changing maneuvers are used to demonstrate the results. The results of this paper could be used to assess the safety of lane changing maneuvers and provide warnings or take evasive actions to avoid collision when combined with appropriate hardware on board of vehicles.

Truck route planning in nonstationary stochastic networks with time windows at customer locations

Most existing methods for truck route planning assume known static data in an environment that is time varying and uncertain by nature, which limits their widespread applicability. The development of intelligent transportation systems such as the use of information technologies reduces the level of uncertainties and makes the use of more appropriate dynamic formulations and solutions feasible. In this paper, a truck route planning problem called stochastic traveling salesman problem with time windows (STSPTW) in which traveling times along roads and service times at customer locations are stochastic processes is investigated. A methodology is developed to estimate the truck arrival time at each customer location. Using estimated arrival times, an approximate solution method based on dynamic programming is proposed. The algorithm finds the best route with minimum expected cost while it guarantees certain levels of service are met. Simulation results are used to demonstrate the efficiency of the proposed algorithm

Vehicle Route Guidance Systems: Classification and Comparison

Route guidance in vehicular roadways has become an important and emerging method of congestion alleviation. The proliferation of low cost electronics such as sensors, wireless communication, and computing equipment has now made large scale vehicle navigation practical. The advanced computational equipment available has made performing complex algorithms in real time possible, the key to the operation of vehicle route guidance systems. While route guidance systems fundamentally strive for similar optimum operation, several important differences exist in the design of these systems. This paper presents an investigation of some of the main distinctions between these systems. Much work has been done in this field in the past, which is presented here as a classification and comparison of such systems

Real-Time Estimation of Travel Times Along the Arcs and Arrival Times at the Nodes of Dynamic Stochastic Networks

Route planning in uncertain and dynamic networks has recently emerged as an active and intense area of research, both due to industry needs and technological advances. This paper investigates methods to predict travel times along the arcs and estimate arrival times at the nodes of a stochastic and dynamic network in real time. It is shown that, under fairly mild conditions, the developed travel and arrival time estimators are unbiased and that the error variance of the arrival time estimator is bounded. Simulation results are used to demonstrate the efficiency of the proposed algorithm.

A “plug-n-play” computationally efficient approach for control design of large-scale nonlinear systems using co-simulation

Recently, there has been a growing interest towards simulation-based control design (co-simulation), where the controller utilizes an optimizer to minimize or maximize an objective function (system performance) whose evaluation involves simulation of the system to be controlled. However, existing simulation-based approaches are not able to handle in a computationally efficient way large-scale optimization problems involving hundreds or thousands of states and parameters. In this paper, we propose and analyze a new simulation-based control design approach, employing an adaptive optimization algorithm capable of efficiently handle large-scale control problems. The convergence properties of the proposed algorithm are established. Simulation results exhibit efficiency of the proposed approach when applied to large-scale problems. The simulation results employ two large-scale real-life systems for which conventional popular optimization techniques totally fail to provide an efficient simulation-based control design.

On the Limitations of Linear Models in Predicting Travel Times

Traffic congestion is growing in major cities, and, consequently, delays are becoming more frequent. Route guidance systems can significantly reduce delays by assisting drivers in finding alternative routes. Due to simplicity and scalability, the linear predictors have been an essential part of route guidance systems in predicting the future travel times. This paper investigates the limitations of linear predictors, and proposes a switching model which can predict travel times with less error. Real world data is used to evaluate the proposed switching predictor and compare the results with commonly used linear predictors.

Comparing different technologies for containers movement in marine container terminals

The explosive growth of freight volumes has greatly increased the work of seaports and urged the port authorities to adopt advanced technologies to cope with the booming container ships. Linear motor conveyor systems (LMCS) and automated guided vehicle systems (AGVS) are the two candidate of automation systems that can be used to improve the performance of yard operations. In this paper, simulation models of LMCS and AGVS employed in marine container terminals are developed to investigate the effect of automation and different cargo handling technologies. Yard performance measures are evaluated by multi-attribute decision making method to determine the optimal number of vehicles needed to be deployed. The simulation results show how the performance of the terminal increases when LMCS/AGVS are deployed. The differences between the two technologies, LMCS and AGVS, are also investigated using the developed simulation models.

A simulation approach for performance evaluation of proposed automated container terminals

Several high-density automated container terminal (ACT) systems are proposed and designed to meet the future projection made by several ports. A microscopic simulation model is developed and used to simulate the different ACT systems for the same operational scenario. The performance results and characteristics of the ACT systems are used as inputs to a cost model in order to compare their cost for meeting the demand for high capacity. The simulation results show that the proposed ACT systems can meet the future capacity demand of ports by requiring less land and at lower cost depending on various factors, such as the cost of land, labor, etc., that are described and analyzed. The implementation of the proposed automated concepts will require additional studies where the labor issues and concerns about job losses due to automation need to be addressed.