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4 driver behavior can be clearly viewed regarding decision errors, performance errors, inattention, and recognition errors. The following limitations still exist: 1. Some studies had fewer video cameras installed compared with other studies. For example, the Automotive Collision Avoidance System (ACAS) FOT and the RDCWS FOT conducted by the University of Michigan Transportation Research Institute (UMTRI) had only two video cam- eras: one facing the driver and the other facing the front view. In these cases, the data sets are limited because traffic conditions beside and behind the subject vehicles are not available. The video frequencies of these UMTRI studies were set relatively low because the original research purposes were not driver-behavior oriented. Consequently, the causal factors of safety-related events are not viewable. 2. Image glare was a typical problem with video data. Some data sets have issues with glare that sometimes make it difficult to make judgments regarding driver behavior. 3. Accidental cable unplugging or malfunction caused incompleteness or errors in data. Although linear interpolation can solve some of the missing data problems, in many cases such problems were not easily detected or corrected. 4. Driver identification is an issue worthy of attention. In a naturalistic driving study, it is not uncommon for the equipped car to be driven by drivers other than the appointed partici- pant. Although the video data can be manually viewed afterward to differentiate drivers in data reduction, it is more efficient if an automatic identification process can be used to tag each trip recorded with driver information so that the data analysis can avoid unnecessary biases. 5. Existing data sources lack a sufficient sample size of crash events to identify changes in driver behavior and the impact of these changes on nonrecurring congestion. The data collection effort in SHRP 2 Safety Project S07, In-Vehicle Driving Behavior Field Study, will offer a unique data set that can be used for further analysis. Recommendations To improve the quality of video data in future data collection efforts of this kind (i.e., designed to investigate the reduction of nonrecurring congestion through modifying driver behavior), there are several recommendations. First, the procedure to recruit participants needs to be carefully designed. It is ideal to include a comprehensive population of drivers ranging evenly across every age, income, and occupation cat- egory. When recruiting participants, it is crucial to make it clear that driver information is vital for the research. To better identify drivers, two methods can be used: 1. A formal statement needs to be included in the contract to make the signer the exclusive driver of the vehicle. 2. A touch-screen device can be installed onboard to collect information before and after each trip. The touch-screen equipment can be designed so that a customized interface will be dis- played to the driver to input trip-related information by selecting certain check boxes. The before trip information-collecting interface may consist of a list of the first names of house- hold members for the driver to select from as passengers, a list of trip purposes, weather con- ditions when the trip started, and any information about why the driver selected the time of departure. The after trip information-collecting interface may include an "original trip pur- pose changed" option, a "route choice changed" option, and a "crashes happened en route" option. Necessary hardware can be designed to connect the input touch screen with the engine so that the driver can start the engine only after the information is input. To ensure safety while driving, the device should be disabled while the vehicle is in motion to prevent driver distrac- tion. One concern with this approach is that it reminds drivers that they are being monitored and thus may deem the study nonnaturalistic.

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5 Second, to serve the research purpose, certain data are more important than others. The following four categories are imperative: 1. Basic onboard equipment should include devices that collect the following data: video; vehicle network information (speed, brake pedal, throttle, turn signal); global positioning system (GPS) data (latitude, longitude, heading); X, Y, and Z acceleration; distances between the subject and surrounding objects; lane location information (X, Y, Z); driver behavior (seat belt usage, lights on or off); and yaw rate. 2. The video cameras should shoot at least five views: front, back, right, left, and the driver. The resolution should be high enough to identify ongoing traffic conditions, weather conditions, and the driver's hand movements and facial expressions. Correction of sun glare to improve video quality is available when needed. 3. The frequency setting should be high enough that the video is continuous, the acceleration and deceleration of the vehicles clearly recorded, and the reaction times recorded and measured. The recommended minimum frequency for GPS devices is 1 Hz and for all other equipment, 10 Hz. 4. To improve the versatility of the data so that the data can be used in other related research, vehi- cle performance parameters such as engine speed, throttle position, and torque should be recorded. Third, the data collection system needs to run for an additional 10 minutes after the engine is turned off in case an accident occurs. During the data reduction, data collection usually halted as soon as the driver stopped the vehicle. Because it is important to observe the traffic conditions being affected by a safety-related event, additional data are required after a driver turns off the engine. One concern is that if some malfunction to the subject vehicle occurs (in case of an accident), gathering data may cause a safety hazard. This issue needs further investigation. Fourth, to improve linking vehicle data with external data, it is ideal to standardize the format for time and location information. For vehicle data, the synchronized GPS clock should be used rather than local computer time for better connection of the data with external traffic, crash, work zone, and weather data. For external data, some states have their database built on the milepost sys- tem. The conversion of mileage post locations to a standard latitude and longitude should be con- ducted ahead of time. Fifth, because a limited number of crashes--especially severe accidents that affected traffic conditions--occurred in all the candidate data sets, certain adjustments are needed to create a sta- tistically significant database. A longer data collection effort or more drivers involved in the study would be ideal. For example, SHRP 2 Safety Project S07, In-Vehicle Driving Behavior Field Study (a 2,500-Car Study), which will soon be conducted, is a quality candidate. Another solution is simulation, which can be used to compensate for data shortage. Sixth, additional analysis of existing data is required to study typical levels of variability in driver departure times and in trip travel times and the level of variability in driver route choices. A char- acterization of this behavior is critical in attempting to quantify and develop travel time reliability measures and to understand the causes of observed travel time reliability. The data may be augmented with tests on a driving simulator to study the impact of travel time reliability on driver route-choice behavior. Finally, although numerous studies have used video cameras to gather data, an ideal starting point is a compiled data source list that summarizes existing video-involved studies with specifi- cations of data collected, limitations of data usage, and access issues. Such a list will help prevent redundancy in future investigation efforts.