Informed-Urban-Transport-Systems-Classic-and-Emerging-Mobility-Methods-toward-Smart-Cities-by-Joseph-Y.-J.-Chow

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Informed Urban Transport Systems

PDF Free Download | Informed Urban Transport Systems Classic and Emerging Mobility Methods toward Smart Cities by Joseph Y. J. Chow

Contents of Informed Urban Transport Systems

  • Table 1.1 Elements of a transport system
  • Table 1.2 Additional elements for an urban transport system
  • Table 3.1 Costs of coffee shop queu
  • Table 3.2 Path-link incidence matrix for Exercise
  • Table 3.3 Test networks
  • Table 3.4 Commercial software and open source tools for traffic assignment
  • Table 3.5 Comparison of equilibrium and social optimal scenarios for downtown Toronto
  • Table 3.6 Different types of mobility as a service
  • Table 4.1 Modes used in scheduling portion of CEMDAP
  • Table 4.2 Cumulative arrivals and departures on link (u,w) for Exercise 4.4
  • Table 4.3 Activity data for Exercise 4.5
  • Table 4.4 mHAPP network data for Exercise 4.5
  • Table 4.5 Market schedule equilibrium assignment
  • Table 4.6 Stations on the A-Lefferts Blvd line
  • Table 5.1 Machine learning methods by similarity compiled by Brownlee
  • Table 5.2 Machine learning applications in urban transport
  • Table 5.3 Inverse optimization advances and applications
  • Table 5.4 Median arrival times by activity type from 2001 California Household Travel Survey
  • Table 5.5 Prior and calibrated capacity parameters for freight flows in the airports in California in 2007
  • Table 5.6 Summary of model calibration and validation
  • Table 5.7 Illustration of parameter estimation using Algorithm 5.2 for example from Section 4.5
  • Table 5.8 Scenarios evaluated in example
  • Table 5.9 Estimated parameters and significance tests for multinomial and mixed multinomial logit model
  • Table 5.10 Estimated shares (MNL) vs actual shares of route choices when link 3 is closed
  • Table 5.11 Comparison of uninformed prior vs optimal invariant common prior
  • Table 5.12 Comparison of performance measures
  • Table 6.1 Illustration of the differential privacy criterion evaluation
  • Table 6.2 Sample of 20 individual incomes to illustrate effect of differential privacy on consumer surplus
  • Table 6.3 Summary of 10 simulated queries of income data with differential privacy for E¼{1000, 100}
  • Table 6.4 Simulated origins and destinations
  • Table 6.5 Travel times and demand
  • Table 6.6 Travel times for Exercise 6.5
  • Table 7.1 Network design problems covered in this chapter
  • Table 7.2 Popular software packages for solving network design problems
  • Table 7.3 Sorted savings for all node pairs in descending order
  • Table 7.4 Solutions to MCLP for Exercise 7.7
  • Table 7.5 Server locations at time t and t+ 1 (without and with relocation costs)
  • Table 7.6 Solution to Exercise 7.11
  • Table 7.7 Example equilibrium solutions
  • Table 8.1 Summary of solution for Exercise 8.2
  • Table 8.2 Twelve “in-the-money” simulated path values for t¼{9, 10}
  • Table 8.3 Summary of sensitivity analysis for Exercise 8.6
  • Table 8.4 Summary of fixed-flexible switching example for Fig. 8.7 in Guo et al. (2017)
  • Table 8.5 Simulation of 20 sample paths
  • Table 8.6 Exercise values for each option in sequence
  • Table 8.7 Exercise decisions
  • Table 8.8 Summary of exercise, deferral, option values, and decisions for each sequence in whole
  • Table 8.9 Summary of CR policy decisions as a function of σ
  • Table A.1 Transportation research h-index rankings by institution for (left) all years up to 2016, and (right) for papers published in 2007–16
  • Table B.1 UML diagrams (Ambler, 2017)
  • Table B.2 Selection of ITS performance measures
  • Table C.1 Example transition matrix
  • Table D.1 Input data for assortment problem example

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