00965451

Component

https://doi.org/10.3141/1836-08

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

While information technology has facilitated the collection of never-before-seen quantities of data, these data have not always provided the information needed by transportation professionals to support sound decision making. Computational intelligence (CI) has great potential to support the needs of transportation professionals. CI is a result of synergy among information processing technologies such as artificial neural networks (ANNs), fuzzy sets, and genetic algorithms. As the number of CI applications to transportation problems grows, so does the need to evaluate these systems. The issue of validating and evaluating transportation CI applications is addressed. A case study that evaluates the effectiveness of two CI paradigms, case-based reasoning and ANNs, for estimating the benefits of real-time traffic diversion is presented. The case study illustrates the need for regarding validation and evaluation as a part of the development effort and the need for tuning the design parameters of CI paradigms.

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

English

p. 57-63

2003

Transportation Research Record

0309085721

Figures (1) ; References (34) ; Tables (2)

Benefits; Case based reasoning; Case studies; Decision making; Estimating; Evaluation; Fuzzy sets; Genetic algorithms; Neural networks; Real time information; Traffic diversion; Transportation planning; Validation

Computational intelligence

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
• MODULAR ARTIFICIAL NEURAL NETWORKS FOR SOLVING THE INVERSE TRANSPORTATION PLANNING PROBLEM
• 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
• USE OF LOCAL LINEAR REGRESSION MODEL FOR SHORT-TERM TRAFFIC FORECASTING