lec24_lec1_12_6p

Lec24_lec1_12_6p - Traffic Effective Route Guidance Forecast Number of cars will increase further in Traffic Networks Fact Infrastructure will not

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Unformatted text preview: Traffic Effective Route Guidance Forecast : Number of cars will increase further in Traffic Networks Fact : Infrastructure will not be enhanced to the same extent Remedy : Improve the efficiency of traffic by other means Lectures developed by Andreas S. Schulz and Nicol´ as Stier May 12, 2003 c 2003 Massachusetts Institute of Technology 3 c 2003 Massachusetts Institute of Technology 2002 Urban Mobility Study Outline (http://mobility.tamu.edu/ums) • Lecture 1 “The bad news is that even if transportation officials do all the right things, the likely effect is that congestion will continue to grow. . . ” • Total congestion “bill” in 2000 was $67.5 billion Route Guidance; User Equilibrium; System Optimum; User Equilibria in Networks with Capacities. (= 3.6 billion hours delay + 5.7 billion gallons gas) • Lecture 2 1982 2000 time penalty for peak period travelers 16 hours 62 hours c 2003 Massachusetts Institute of Technology 4 Constrained System Optimum; Dantzig-Wolfe Decomposition; Constrained Shortest Paths; Computational Results. c 2003 Massachusetts Institute of Technology 1 Problem The Context • Olaf Jahn (Research Assistant). People travel (between 6% and 19%) too much because of an unfavorable selection of their route. • Rolf H. M¨ ohring (Principal Investigator). Collaboration with and support by DaimlerChrysler, Berlin. (Beccaria & Bolelli 1992, L¨osch 1995) • Nicolas Stier (Research Assistant). • Andreas S. Schulz (Principal Investigator). Supported by General Motors Innovation Grant and SMA. c 2003 Massachusetts Institute of Technology 5 c 2003 Massachusetts Institute of Technology 2 Shortest Path Routing Potential Remedies • Toll systems • Dynamic traffic signal control • Park and Ride • Traveller information systems Improved network performance, but . . . (Kaufman et al. 1991, Lee 1994) c 2003 Massachusetts Institute of Technology Route Guidance 9 c 2003 Massachusetts Institute of Technology 6 Shortest Path Routing II . . . the same simulations show the performance decreases Route Guidance as soon as many cars use the system. c 2003 Massachusetts Institute of Technology Route Guidance 10 c 2003 Massachusetts Institute of Technology Route Guidance 7 Proposed Solutions In-Car Navigation Systems • Multiple path routing: – k shortest paths – random perturbation • Feedback control: – iterative computation of shortest paths • Traffic assignment: – user equilibrium – system optimum – a new approach c 2003 Massachusetts Institute of Technology Route Guidance 11 c 2003 Massachusetts Institute of Technology Route Guidance 8 Modeling Assumptions Reality Our Model • microscopic → individual vehicles • macroscopic → one abstract measure → exact position, speed → traffic flow • dynamic → consider time → on a single point at any time • static → time independent → simultaneously at any point of the path selfish users central planner the goal optimize own travel time optimize system...
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This note was uploaded on 12/04/2011 for the course ESD 15.094 taught by Professor Jiesun during the Spring '04 term at MIT.

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Lec24_lec1_12_6p - Traffic Effective Route Guidance Forecast Number of cars will increase further in Traffic Networks Fact Infrastructure will not

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