00965449

Component

https://doi.org/10.3141/1836-06

http://scholar.google.com...Mark&publication_year=2003

Because major capacity-expansion projects are very unlikely in the coming years, transportation planners need to view the existing infrastructure as fixed and to start thinking about how much development the current system can sustain. This line of thinking, which involves deriving land use limits from infrastructure capacity, requires solving the inverse of the typical transportation planning problem. Modular artificial neural networks (ANNs) were developed for solving the inverse transportation planning problem. ANNs were designed to predict zonal trip ends, given the traffic volumes on the links of the transportation network. Computational experiments were performed to study the effect on ANN accuracy of three factors: transportation network size, variability in training data, and ANN topology. ANNs were shown to be quite capable of capturing the relationship between link volumes and zonal trip ends for both small and medium-sized transportation networks and for degrees of variability in the training data. Modular ANNs with one or two hidden layers appeared to outperform other ANN topologies.

This paper appears in Transportation Research Record No. 1836, Initiatives in Information Technology and Geospatial Science for Transportation.

English

p. 37-44

2003

Transportation Research Record

0309085721

Figures (9) ; References (6) ; Tables (1)

Highway capacity; Land use planning; Neural networks; Sustainable development; Traffic volume; Transportation planning

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Nov 7 2003 12:00AM

• ADJUSTING TRIP RATES IN THE CROSS-CLASSIFICATION TABLE BY USING THE FUZZY OPTIMIZATION METHOD
• APPLICATION OF LIGHT DETECTION AND RANGING TECHNOLOGY TO HIGHWAY SAFETY
• BILEVEL FORMULATION FOR OPTIMAL TRAFFIC-INFORMATION DISSEMINATION
• BUILDING AND MAINTAINING A STATEWIDE TRANSPORTATION FRAMEWORK
• COMMON DATA FORMAT ARCHIVING OF LARGE-SCALE INTELLIGENT TRANSPORTATION SYSTEMS DATA FOR EFFICIENT STORAGE, RETRIEVAL, AND PORTABILITY
• DESIGN OF ARCHIVAL TRAFFIC DATABASES: QUANTITATIVE INVESTIGATION INTO APPLICATION OF ADVANCED DATA MODELING CONCEPTS
• DEVELOPED INCIDENT DETECTION ALGORITHM COMPARED WITH NEURAL NETWORK ALGORITHMS
• EXPLORING IMPUTATION TECHNIQUES FOR MISSING DATA IN TRANSPORTATION MANAGEMENT SYSTEMS
• FUZZY-NEURAL NETWORK TRAFFIC PREDICTION FRAMEWORK WITH WAVELET DECOMPOSITION
• INTEGRATION OF LIGHT DETECTION AND RANGING TECHNOLOGY WITH PHOTOGRAMMETRY IN HIGHWAY LOCATION AND DESIGN
• INTEGRATION OF SPATIAL POINT FEATURES WITH LINEAR REFERENCING METHODS
• LEARNING A CAUSAL MODEL FROM HOUSEHOLD SURVEY DATA BY USING A BAYESIAN BELIEF NETWORK
• OPTIMIZATION OF DYNAMIC NEURAL NETWORK PERFORMANCE FOR SHORT-TERM TRAFFIC PREDICTION
• REAL-TIME COORDINATED SIGNAL CONTROL THROUGH USE OF AGENTS WITH ONLINE REINFORCEMENT LEARNING
• ROUGH SETS AND PROBABILITY MASSES FOR DEMPSTER-SHAFER DATA FUSION AT A TRAFFIC MANAGEMENT CENTER
• SPATIOTEMPORAL OBJECT DATABASE APPROACH TO DYNAMIC SEGMENTATION
• TOWARD MORE EFFECTIVE TRANSPORTATION APPLICATIONS OF COMPUTATIONAL INTELLIGENCE PARADIGMS
• USE OF LOCAL LINEAR REGRESSION MODEL FOR SHORT-TERM TRAFFIC FORECASTING