TEMPLATE DESIGN © Issues and Challenges in Route Guidance: Mr. Tremaine Rawls, Mr. Timothy Hulitt, Dr. Fatma Mili Norfolk State University, Jackson State University, Oakland University About Route GuidanceReactiveExperimental Comparison Conclusions and Future Work Time Dependency Partial Reference List In its narrowest sense, Route Guidance is the set of directions given to drivers to help them reach their desired destination. The Department of Transportation (DOT) defines Route Guidance in a much broadly manner as “a driver decision aid that uses knowledge about a traffic network to provide advice that facilitates travel between an origin and a destination.” Based on 2005 data from research at Texas Transportation Institute (TTI), Overall, traffic congestion costs the U.S. economy $78 billion a year, wasting 2.9 billion gallons of fuel and robbing commuters of 4.2 billion hours, the study found. Background Route Guidance can be thought of as the problem of finding the shortest path in a directed graph. Given a geographical area of interest, the roads and their intersections are represented by edges and vertices. The vertices are points of intersections where drivers make decisions; the edges are road segments between two intersection points. Challenges Scalability Criteria used to select the path Time-variation in the data ---regular traffic patterns Accounting for non-regular traffic disturbances Difficulty in evaluating solutions Consistency Infrastructure required The Decreasing Order of Time (DOT) Algorithm is time dependent, and the assumptions are: Availability of historical data reflecting regular daily traffic patterns There is constant travel time starting from the last time interval The concept of First-In-First-Out (FIFO), that a trip starting at a later time will arrive later. (9, 10) KPH Travel time: 1.6 mins KPH Travel time: 1.6 mins. (12, 13) KPH Travel time: 12 secs KPH Travel time: 24 secs. Edges Interval 1: 4:30 pm—7:30 am Interval 2: 7:30 am—8:30 am The Farver algorithm is reactive and assumes that a driver already has an optimal route constructed using historical data. The algorithm receives regular updates about the state of the roads, and reacts to evidence that unexpected events made the selected route sub-optimal. The idea behind Farver to redirect drivers in response to unexpected slow downs without over-reacting. It uses a splitting method to spread out vehicles. Predictive Predictive algorithms construct the shortest path based upon real- time information. These algorithms, unlike the DOT, do not utilize historical data to obtain the optimal route. Predictive algorithms collect regular updates about the state of the roads and if an event takes place that might hinder traffic, such as a congestion or accident, the algorithm changes the path based on real-time communication. 1.DoT, Federal Highway Administration, “Development of Human Factors Guidelines for Advanced Traveler Information Systems and Commercial Vehicle Operations” Publication No FHWA-RD Jing-Chao Chen “Dijkstra’s Shortest Path Algorithm”, Journal of Formalized Mathematics, Vol 15, uncs.shinshu-u.ac.jp/mirror/mizar/JFM/pdf/graphsp.pdf 3.Jennifer Farver and Ismail Chabini, “A Vehicle-Centric Logic for Decentralized and Hybrid Route Guidance,” Massachusetts Institute of Technology Fatma Mili and Bill Herbert: “State of the Art vs. State of Practice” Kristina Höök, Route Guidance Issues 8.Ismail Chabini, “Algorithms & High Performance Computing for Dynamic Shortest Paths and Analytical Dynamic Traffic Assignment Models,” Massachusetts Institute of Technology 9.Intelligent Transportation Systems of America (ITS) 10.Texas Transportation Institute (TTI) 11.Forbes Informative Website Los Angeles I-405 & I-605 Interchange. This intersection on the San Diego Freeway cost 18 million hours of delay TRANSIMS  The Transportation Analysis Simulation System was developed at the Los Alamos National Laboratory.  TRANSIMS consists of different modules such as a population synthesizer, activity generator, route planner and micro simulator. Also, each module correlates with each other. Have a clear understanding of the key issues in route guidance. Gain knowledge of the solutions proposed. Elicit the relationship between the different solutions. Develop criteria for comparing the different algorithms. Comparison through analysis and simulation.