Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand (2012) / Chapter Skim
Currently Skimming:
Pages 89-113
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 89... ...
89 C h a p t e r 4 This chapter discusses approaches for integrating models of user behavior in network modeling and simulation frameworks to support analysis and evaluation of pricing and other congestion-related measures. It also showcases the application of such integrated demand–network simulation procedures in an actual large-scale regional network: the New York City (NYC)
|
From page 90... ...
90 increasingly been used to realistically capture traffic dynamics in practice. Most state departments of transportation (DOTs)
|
From page 91... ...
91 memory storage for the various algorithmic components of these procedures, especially shortest-path calculation, traffic simulation, and traffic assignment. In addition, large-scale regional networks require large amounts of memory to store data for path calculation and traffic simulation, as well as traffic assignment.
|
From page 92... ...
92 Multiple Attributes (Criteria) in Highway Route-Choice Process The solution of the mode choice problem gives an equilibrium mode flow pattern, ymwt, "w ∈ W, t ∈ T, m ∈ M, which forms the input for a multiclass dynamic user equilibrium trafficassignment problem.
|
From page 93... ...
93 distance. This method is best applied directly at the path level (between given origin and destination)
|
From page 94... ...
94 (Vovsha, Donnelly, and Gupta 2008; Vovsha, Donnelly, and Chiao 2008; Chiao and Vovsha 2006; PB Consult, Inc. 2005; Vovsha et al.
|
From page 95... ...
95 Nested Logit-Based Mode Choice Model With the above time-dependent traveler O-D demand with individual characteristics for internal zones, the mode choice model is used to determine the mode to be chosen for each traveler according to his or her individual characteristics (e.g., income, auto ownership, age) and mode attributes (LOS)
|
From page 96... ...
96 The result of the mode choice model is to assign a mode to each traveler. Because the majority of traffic interaction in the transportation networks is vehicle to vehicle, especially for highway networks, it is necessary to map travelers to vehicles, especially for those ride-sharing travelers, according to occupancy levels.
|
From page 97... ...
97 The mode choice model, ride-sharing choice, and vehiclegeneration procedures result in a time-varying vehicle O-D demand pattern that consists of a set of multiclass vehicles with distinct individual origin, destination, departure time, occupancy level, VOT, and so forth, for the route choice and network simulation procedure. The next section describes the multidimensional simulation-based dynamic microassignment system used in the route choice and network simulation procedure in this study to assign routes to each vehicle.
|
From page 98... ...
98 Multidimensional Network Choice Model sections within Appendix A The following sections present an overview of a simulation-based iterative solution framework to solve the integrated model and present the some key issues in the integrated model, specifically route choice and path computations including reliability, a column-generation solution framework, and algorithms and challenges for large-scale applications.
|
From page 99... ...
99 4. Multidimensional simulation-based dynamic microassignment.
|
From page 100... ...
100 Step 1. Input and Initialization 1.1 Input: Time-dependent multimodal traveler O-D demand with individual characteristics (income, auto ownership, and purpose)
|
From page 101... ...
101 among travelers and should be set to a small value. Increasing the value of D in a small network may lead to inaccurate and unrealistic predictions of flow distribution patterns, whereas in a large-scale network the flow patterns will remain valid.
|
From page 102... ...
102 be obtained that differs considerably from the current one in a small network. When D is large, a lot of information may be lost when generating the least-cost path tree, resulting in inaccurate flow distribution patterns for a small network.
|
From page 103... ...
103 Figure 4.9. TransCAD model of the NYBPM network.
|
From page 104... ...
104 adjust the representation of geometric features of interchanges to support operational simulation and to assess, assign, and verify properties of junctions and specification of movements at junctions. The conversion also includes the preparation of the various required and optional input data files for the simulation.
|
From page 105... ...
105 • The Verrazano-Narrows Bridge, Bronx-Whitestone Bridge, Brooklyn-Battery Tunnel, Queens Midtown Tunnel, Throgs Neck Bridge, Triborough Bridge, Marine Parkway-Gil Hodges Memorial Bridge, Cross Bay Veterans Memorial Bridge, and Henry Hudson Bridge are the bridges and tunnels tolled in New York metropolitan area; and • The Tappan Zee Bridge, Bear Mountain Bridge, Kingston Rhinecliff Bridge, Mid Hudson Bridge, and Newburgh Beacon Bridge are the tolled bridges in New York State. Methodology for Calibration of Origin–Destination Demand for Dynamic Analysis Given static O-D demand information and time-dependent link measurements, the dynamic O-D demand estimation procedure aims to find a consistent time-dependent O-D demand table that minimizes the deviation between (1)
|
From page 106... ...
106 Numerical Experiments Scenario Definition The planning horizon is the morning period from 6:00 to 10:00 a.m. The departure time interval is 15 minutes.
|
From page 107... ...
107 Convergence Pattern of the Integrated Model The convergence of the algorithm is examined by the objective function of formulation described in Appendix A, specifically in Equation A.16, for the dynamic mode choice problem. This expression is a gap measure of the total square of the difference between assigned mode flows ymwt and expected mode flows qwt × pmwt(y)
|
From page 108... ...
108 of effectiveness is collected in all conducted experiments, in addition to the objective function Gap(r)
|
From page 109... ...
109 computational time is defined as the average time required by one operation in PAM for a root node (destination) of a TDLGC path tree relative to the average time in E1; this measure is used to explore the magnitude of the improvement.
|
From page 110... ...
110 Figure 4.19. Time-dependent mode share.
|
From page 111... ...
111 Figure 4.21. Mode share by income group.
|
From page 112... ...
112 prediction of the flow patterns on the network. To address this concern and investigate the impact of continuously distributed VOT across the entire population, numerical results regarding the toll road usage are presented in this section.
|
From page 113... ...
113 implementation techniques do not compromise the accuracy of flow pattern prediction. Summary of Network Modeling procedures The proposed integrated model framework is a demonstration of a trip-based integration of a well-calibrated mode choice model in practice and a simulation-based dynamic traffic microassignment model.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.