The Synchronization of Traffic Signals by Mixed-integer Linear Programming
The Synchronization of Traffic Signals by Mixed-integer Linear Programming

Author: John D. C. Little

Publisher:

Published: 1966

Total Pages: 14

ISBN-13:

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Traffic signals can be synchronized so that a car, starting at one end of a main artery and traveling at preassigned speeds, can go to the other end without stopping for a red light. The portion of a signal cycle for which this is possible is called the bandwidth for that direction. Ordinarily the bandwidth in each direction is single. For this case we formulate the arterial problem as a mixed-integer linear program: Given (1) an arbitrary number of signals, (2) the fraction of the cycle that is red at each signal, (3) upper and lower limits on signal period, (4) upper and lower limits on speed each way between adjacent signals, (5) limits on change in speed, and (6) a constant of proportionality between the two bandwidths; find (1) a common signal period, (2) speeds between signals, and (3) the relative phasing of the signals so as to maximize the sum of the bandwidths. A branch and bound algorithm is developed for solving the given mixed-integer linear program by solving a sequence of ordinary linear programs. The problem of synchronizing a network of signals is also formulated. The resulting program consists of the arterial programs for the individual streets plus a set of further constraints that arise because the streets connect together to form closed loops.

The Data Science Design Manual
The Data Science Design Manual

Author: Steven S. Skiena

Publisher: Springer

Published: 2017-07-01

Total Pages: 456

ISBN-13: 3319554441

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This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)