David Fajardo

Automated Intersection Control: Performance of Future Innovation Versus Current Traffic Signal Control

Congestion is one of the biggest challenges faced by the transportation community; congestion accounted for an estimated

A Predictive Spatial Model to Quantify the Risk of Air-Travel-Associated Dengue Importation into the United States and Europe

87.2 billion in losses in 2007 alone. Transportation professionals need to go beyond capacity expansion projects and explore novel strategies to mitigate traffic congestion. Automated intersection management is a novel strategy that has the potential to greatly reduce intersection delay and improve safety. Although the implementation of such a system is contingent on the development of automated vehicles, competitions such as the Grand Challenge and Urban Challenge of the Defense Advanced Research Projects Agency have shown that this technology is feasible and will be available. Therefore, the development of the infrastructure and associated control methods required to exploit fully the benefits of such technology at the system level is critical. This research explores one such innovative strategy, an automated intersection control protocol based on a first-come, first-served (FCFS) reservation system. The FCFS reservation system was shown to reduce intersection delay significantly by exploiting the features of autonomous vehicles. Microscopic simulation experimental results showed that the FCFS reservation system significantly outperformed a traditional traffic signal in reducing delay.

Dynamic lane reversal in traffic management

The number of travel-acquired dengue infections has been on a constant rise in the United States and Europe over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue contributes to the increasing number of dengue cases. This paper reports results from a network-based regression model which uses international passenger travel volumes, travel distances, predictive species distribution models (for the vector species), and infection data to quantify the relative risk of importing travel-acquired dengue infections into the US and Europe from dengue-endemic regions. Given the necessary data, this model can be used to identify optimal locations (origin cities, destination airports, etc.) for dengue surveillance. The model can be extended to other geographical regions and vector-borne diseases, as well as other network-based processes.

Inferring Contagion Patterns in Social Contact Networks with Limited Infection Data

Contraflow lane reversal — the reversal of lanes in order to temporarily increase the capacity of congested roads — can effectively mitigate traffic congestion during rush hour and emergency evacuation. However, contraflow lane reversal deployed in several cities are designed for specific traffic patterns at specific hours, and do not adapt to fluctuations in actual traffic. Motivated by recent advances in autonomous vehicle technology, we propose a framework for dynamic lane reversal in which the lane directionality is updated quickly and automatically in response to instantaneous traffic conditions recorded by traffic sensors. We analyze the conditions under which dynamic lane reversal is effective and propose an integer linear programming formulation and a bi-level programming formulation to compute the optimal lane reversal configuration that maximizes the traffic flow. In our experiments, active contraflow increases network efficiency by 72%.

Inferring Contagion Patterns in Social Contact Networks Using a Maximum Likelihood Approach

The spread of infectious disease is an inherently stochastic process. As such, real time control and prediction methods present a significant challenge. For diseases which spread through direct human interaction, (e.g., transferred from infected to susceptible individuals) the contagion process can be modeled on a social-contact network where individuals are represented as nodes, and contacts between individuals are represented as links. The model presented in this paper seeks to identify the infection pattern which depicts the current state of an ongoing outbreak. This is accomplished by inferring the most likely paths of infection through a contact network under the assumption of partially available infection data. The problem is formulated as a bi-linear integer program, and heuristic solution methods are developed based on sub-problems which can be solved much more efficiently. The heuristic performance is presented for a range of randomly generated networks and different levels of information. The model results, which include the most likely set of infection spreading contacts, can be used to provide insight into future epidemic outbreak patterns, and aid in the development of intervention strategies.

Inferring Infection-Spreading Links in an Air Traffic Network

AbstractThe spread of infectious disease is an inherently stochastic process. As such, real-time control and prediction methods present a significant challenge. For diseases that spread through direct human interaction, the contagion process can be modeled on a social contact network where individuals are represented as nodes, and contact between individuals is represented as links. The objective of the model described in this paper is to infer the most likely path of infection through a contact network for an ongoing outbreak. The problem is formulated as a linear integer program. Specific properties of the problem are exploited to develop a much more efficient solution method than solving the linear program directly. The model output can provide insight into future epidemic outbreak patterns and aid in the development of intervention strategies. The model is evaluated for a combination of network structures and sizes, as well as various disease properties and potential human error in assessing these pro...

Two-Phase Model of Ramp Closure for Incident Management

The objective of this paper is to present a network-based optimization method for identifying links in an air traffic network responsible for carrying infected passengers into previously unexposed regions. The required data include individual infection reports (i.e., when the disease was first reported in a region), travel pattern data, and other geographic properties. The network structure is defined by nodes and links, which represent regions (cities, states, countries) and travel routes, respectively. The proposed methodology is novel in its attempt to replicate an outbreak pattern atop a transportation network by exploiting regional infection data. The problem parallels a related problem in phylodynamics, which uses genetic sequencing data to reconstruct the most likely spatiotemporal path of infection.

Dynamic Traveling Salesman Problem in Stochastic-State Network Setting for Search-and-Rescue Application

Temporary on-ramp closure has been proposed as a strategy to reduce the impact of severe incidents on freeway facilities; however, to date no rigorous procedure has been made available to provide guidance on how such a technique should best be used. In particular, one must decide which ramps to close and for how long. A two-phase approach is proposed to answer these questions. The first phase is macroscopic in nature and predicts how motorists will reroute in response to any ramp closure and recommends which ramps should be closed. The second phase uses microsimulation to study the vicinity of the incident in greater detail, more fully accounting for dynamic traffic phenomena and attempting to answer the question of how long these ramps should be closed. From a computational standpoint, the first phase is designed to run as quickly as possible to allow the ramp closure policy to be enacted as the second phase begins, since the results of the second phase are not needed until later. This procedure is demonstrated by using a fictitious incident in the El Paso region of Texas.

Finding Minimum-Cost Dynamic Routing Policies in Stochastic-State Networks with Link Failures

The problem presented in this paper was motivated by the need for a solution to be used in a search-and-rescue application and is formulated as a dynamic traveling salesman problem in a stochastic-state network setting. This problem formulation features a full-recourse decision framework and stochastic demands that are revealed only through direct observation. This problem is defined in a stochastic-state network setting, which allows the modeling of implicitly correlated demand stochasticity. The problem is then formulated as a Markovian decision process, and, finally, a heuristic solution is provided. The heuristic solution is based on a two-stage stochastic program with recourse solved on a set of aggregated networks generated by the use of an aggregating function. Subsets of the feasible solutions obtained at each stage are fixed, and the heuristic is used iteratively to further refine the routing policy.

A combinatorial algorithm and warm start method for dynamic traffic assignment

The focus of this research is to develop minimum-cost dynamic routing policies that can identify connecting paths between nodes in a stochastic-state network. In this context, the stochastic element of the network is the network structure, that is, the set of links that exist under each realization of the network state. It is assumed that information about the true network state can be gathered only endogenously through the routing decisions themselves. As such, the objective becomes finding a dynamic policy that accounts for information gathered en route that minimizes the cost of detection of a viable path between a given origin and destination. An exact solution method, based on a Markovian decision process, is presented, and then a heuristic based on an aggregating function of the network is developed.