00965444

Component

https://doi.org/10.3141/1836-01

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

Surface terrain information is needed to economically site new or relocate existing infrastructure facilities and make final design plans. Field surveying and photogrammetric mapping are the methods most widely used to acquire these data. However, these methods are time- and resource-intensive, as significant data collection and reduction are needed to provide the level of detail necessary for facility location and design. Light detection and ranging (LIDAR) is a relatively new alternative technology for obtaining terrain information more efficiently. With LIDAR, data can be collected under a variety of environmental conditions, including low sun angle, cloudy skies, and even darkness, resulting in expanded windows for data collection. Although less accurate than photogrammetric mapping, LIDAR can help expedite the highway location and design process by providing designers with preliminary terrain information earlier in the process. Presented is a proposed methodology for using LIDAR in conjunction with photogrammetric mapping to speed up highway location and design activities, including estimates of time and cost savings.

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

English

p. 1-6

2003

Transportation Research Record

0309085721

Figures (3) ; References (18) ; Tables (3)

Highway design; Highways; Laser radar; Location; Mapping; Photogrammetry; Terrain evaluation

Design; Highways; Planning and Forecasting; I21: Planning of Transport Infrastructure

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 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
• TOWARD MORE EFFECTIVE TRANSPORTATION APPLICATIONS OF COMPUTATIONAL INTELLIGENCE PARADIGMS
• USE OF LOCAL LINEAR REGRESSION MODEL FOR SHORT-TERM TRAFFIC FORECASTING