A Scalable Algorithm for Locating Distribution Centers on Real Road Networks

The median problem is a type of network location problem that aims at finding a node with the total minimum demand weighted distance to a set of demand nodes in a weighted graph. In this research, an algorithm for solving the median problem on real road networks is proposed. The proposed algorithm, referred to as the multi-threaded Dijkstra’s (MTD) algorithm, is then used to optimally locate Wal-Mart distribution centers on the 28-million node road network of the United States with the objective of minimizing the total demand weighted transportation cost. The resulting optimal location configuration of Wal-Mart distribution centers improves the total transportation cost by 40%

Scalable Heuristic for Locating Distribution Centers on Real Road Networks

Poster 1

Scalable Heuristic for Locating Distribution Centers on Real Road Networks

The median problem is a type of network location problem that aims at finding a node with the total minimum demand weighted distance to a set of demand points in a weighted graph. In this research, an algorithm for solving the median problem on real road networks is proposed. The proposed algorithm, referred to as the Multi-Threaded Dijkstra’s (MTD) algorithm, is used to locate Walmart distribution centers on the 28-million node road network of the United States with the objective of minimizing the total demand weighted transportation cost. The resulting optimal location configuration of Walmart distribution centers improves the total transportation cost by 46%

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

The random nature of traffic conditions on freeways can cause excessive congestions and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use machine learning approaches to develop a novel real-time prediction model for ramp metering. We evaluate the potentials of our approach in providing promising results by comparing it with a baseline traffic-responsive ramp metering algorithm.Comment: 5 pages, 11 figures, 2 table

Efficient Algorithms for Solving the Median Problem on Real Road Networks

Location theory is a well-established and active research area. The main objective in location theory is to find the best locations for facilities to meet supply and demand constraints and minimize total cost. The median problem (also known as the 1-median problem) is a special case in location analysis that aims at locating a facility such that the sum of demand weighted distances to a set of demand points is minimized. This research is composed of three main parts. In the first part, two algorithms for obtaining optimal solutions to the median problem on real road network graphs are proposed. The first algorithm known as the multi-threaded Dijkstra’s (MTD) algorithm, is inspired by the bidirectional Dijkstra's algorithm. The second algorithm is based on exhaustive graph search (EGS) and uses Dijkstra's shortest path algorithm to exhaustively calculate the distance from every demand point to all nodes in the graph. The proposed algorithms are used to evaluate several facility location scenarios that vary with respect to the size of the study area and the number of demand points on that network. The results show that both algorithms are capable of obtaining optimal solutions and that their performance is influenced by factors such as the number of demand points and the size of the area in which the demand points are located. In the second part of this research, an improved variant of the MTD algorithm known as the MTD-i algorithm is presented. The MTD-i algorithm preforms less search than the MTD algorithm to find an optimal solution. An experiment is conducted to compare the performance of the MTD-i algorithm to that of the MTD and EGS algorithms in terms of the average percentage of nodes explored and the average run time. The results show that the MTD-i algorithm always outperforms both the MTD and EGS algorithms in terms of the percentage of nodes explored in all scenarios tested. However, the average run time of the algorithms is heavily influenced by the experimental factors. In the last part of this research, a methodology that applies the MTD algorithm to locate public transit stops on real road network is presented. The proposed methodology finds the optimal location for transit stops so that the total weighted walking distance from the internal point of census blocks to the transit stops is minimized

Proof Of Concept: GTFS Data As A Basis For Optimization Of Oregon’sRegional And Statewide Transit Networks

Assessing the current state of health of individual transit networks is a fundamental part of studies aimed at planning changes and/or upgrades to the transportation network serving a region. To be able to effect changes that benefit both the individual transit networks as well as the larger transportation system, organizations need to develop meaningful strategies guided by specific performance metrics. A fundamental requirement for the development of these performance metrics is the availability of accurate data regarding transit networks. Prior to 2005, transit data was not readily available. This situation complicated the assessment of single transit networks, let alone performing a state-wide or region-wide study. The advent of the General Transit Feed Specification (GTFS) changed this constrained landscape and motivated transit operators to release their schedules and route information to third party developers. In this report, the development work conducted to create an open source software tool to help the Oregon Department of Transportation\u27s Public Transit Division gain a better understanding and more efficient utilization of existing state-wide transit networks is described. The final product, referred to as the Transit Network Analysis software tool, incorporates GTFS data and census data as its main inputs and can be used to visualize, analyze and report on the Oregon transit network