Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 62
62 reliability. At the same time, using the expert reductionist's Table 7.4. Weather Station Input Data evaluations of each epoch as a gold standard, the proportion Row Minute Rain_ of agreement between the expert and each rater was calcu- Number GPS of Day Today lated for each test. This inter-rater test between expert and regular data reductionists was completed on the initial reduc- 57809 `2007-04-26 17:29:38.367' 1439 8.382 tion for 6, 12, and 18 months of data reduction. The results 57810 `2007-04-26 17:29:38.367' 1439 8.382 indicated an intra-rater reliability of 99% for all three tests. 57811 `2007-04-26 17:29:38.367' 0 0 The average inter-rater reliability score for this task was 92.1%. Discrepancies are mediated by a third, senior-level 57812 `2007-04-26 17:29:38.367' 0 0 researcher (2). Similar quality control procedures were used by the UMTRI research team. Two researchers initially viewed and coded a small portion of the alerts indepen- dently. The coded data were compared to decide a percentage according to local time (shown as 17:29 as of Coordinated of agreement; each researcher then independently coded the Universal Time [UTC]). To address this problem it was remaining alerts. A third researcher examined their coding determined that the offset was a constant value that was loca- results and determined the degree of consistency in coding. tion specific. An offset was allocated to each location in com- The results showed a high level of agreement, which testi- puting the precipitation rate to account for this error. fied to the efficiency and consistency of the data reduction For traffic count and traffic condition, crash, and work dictionary. The researchers then jointly viewed and recoded zone data, the quality and availability differ from state to all video to modify factors that had not been agreed on. Each state. As introduced in Chapter 6, some states have more of these meticulous steps guarantees that the data reduction continuous traffic count stations than others. For example, is under control. Virginia has maintained a traffic count database containing traffic data from more than 72,000 stations, among which 470 are continuous. West Virginia has only 60 continuous- Other Data Sources count stations. Some states have more complete crash or Besides the possible risks and problems that exist in vehicle work zone databases (e.g., New Jersey and Michigan) and data, the availability and quality of environmental data-- others maintain only general crash records from police specifically, weather data, traffic count, crash, and work reports. When linking vehicle data to outside data sources, zone data--are worthy of attention. Accurate weather data special attention should be paid to variations in data quality. are available at ASOS stations. Only vehicle data that were col- In summary, data elements designed to accomplish the lected at locations close enough (e.g., within 15 mi) to ASOS objectives of the original research study may not be suitable stations can be associated with the weather data observed for a study of driver behavior that causes nonrecurring con- there. Another source of weather data is the Road and Weather gestion. As discussed in this chapter, if certain modifica- Information System (RWIS). RWIS is a combination of tech- tions can be feasibly executed, a candidate data set can be more nologies that uses historic and current climatological data valuable to serve the research goal of this study. Table 7.5 to develop road and weather information. The information lists the possible modifications that can be made for each can- is then sent to road users and decision makers. RWIS usu- didate data set to render them more compatible with this ally includes an environmental sensor system, a model to research goal. develop forecasts, and a dissemination platform to publish As illustrated in the last row of Table 7.5, one more data. More than 10 DOTs in the United States have started potential data set will be available in the near future. Being or plan to start RWISs. When vehicles were far from ASOS the most extensive naturalistic travel data collection effort, stations or RWIS, the only source of weather information SHRP 2 Safety Project S07 will include high-quality video was the weather variable coded in the reduced data by data data and other in-vehicle data. With accurate location and reductionists. time information, Project S07 data can be easily linked with Even at locations where weather data are available, risks other external environment data. Once integrated with that the data have errors in them still exist. In the example other external data sets, the data set from Project S07 will shown in Table 7.4, the variable Rain_Today Field, which be the most valuable candidate data set for studying non- records the cumulative rainfall since 12:00 a.m., is reset to recurring congestion and its relationship to driver behav- zero. The controller times do not necessarily coincide with ior. Details of Project S07 are discussed in Chapter 8 in the the local time, and thus resetting has to be done at 13:29, section on recommendations.