Understanding the relationship between road users and the roadway infrastructure in Ghana.

Ghana exemplifies the contribution of road crashes to mortality and morbidity in Africa, partly due to a growing population and increasing car ownership, where fatalities have increased by 12 to 15 % annually since 2008 (National Road Safety Authority (NRSA), 2017). The study described in this paper focused on understanding driver behavior at unsignalized junctions in the Ashanti Region of Ghana. Understanding driver behavior at unsignalized junctions is particularly important since failure to stop or yield can seriously affect vulnerable road users. The study's objectives were to develop relationships between driver behavior and junction characteristics. Understanding the characteristics that lead to determining what factors influence a driver's behavioral response at rural junctions provides information for policy makers to determine the best strategies to address these behaviors. The study evaluated stopping behavior at rural junctions. Driver behavior was extracted from video views of ten junctions in the Ashanti Region of Ghana. A total of 3,420 vehicles were observed across all ten junctions during data collection before any analysis was conducted. The type of stop was selected as a surrogate measure of safety. Logistic regression was used to model stopping behavior at the selected junctions. The analysis showed drivers were more likely to stop when going straight (versus a left turn) and left turning vehicles were more likely to stop than right turning vehicles. Additionally, single unit trucks and tro-tros were more likely to stop than other vehicle types. Drivers were also much more likely to stop when channelization, intersection lighting, or speed humps were present. Drivers at junctions with 4-approaches were also more likely to stop than those with 3 approaches. The results from this research contribute valuable information about what factors contribute to positive safety behaviors at rural junctions. This provides guidance for safety professionals to select solutions and can be a valuable tool to predict the economical effectiveness of solutions to addressing junction safety in low- and middle-income countries (LMIC) such as Ghana. The results can also provide insight and recommendations to Ghanaian road safety agencies and launch sustainable efforts to raise community awareness toward decreasing road crash fatalities in Ghana.

Evaluation of driving behavior on rural 2-lane curves using the SHRP 2 naturalistic driving study data.

INTRODUCTION: Over half of motor vehicle fatalities are roadway departures, with rural horizontal curves being of particular interest because they make up only a small share of the system mileage but have a crash rate that is significantly higher than tangent sections. However the interaction between the driver and roadway environment is not well understood, and, as a result, it is difficult to select appropriate countermeasures. METHOD: In order to address this knowledge gap, data from the SHRP 2 naturalistic driving study were used to develop relationships between driver, roadway, and environmental characteristics and risk of a road departure on rural curves. The SHRP 2 NDS collected data from over 3,000 male and female volunteer passenger vehicle drivers, ages 16-98, during a three year period, with most drivers participating between one to two years. A Roadway Information Database was collected in parallel and contains detailed roadway data collected on more than 12,500 centerline miles of highways in and around the study sites. RESULTS: Roadway data were reduced for rural 2-lane curves and included factors such as geometry, shoulder type, presence of rumble strips, etc. Environmental and traffic characteristics, such as time of day, ambient conditions, or whether the subject vehicle was following another vehicle, were reduced from the forward roadway video view. Driver characteristics, such as glance location and distraction were reduced from the driver and over the shoulder videos. CONCLUSIONS: Logistic regression models were developed to assess the probability (odds) of a given type of encroachment based on driver, roadway, and environmental characteristics. At the point this study was undertaken, crashes and near crashes were not yet available and only around 1/3 of the full SHRP NDS dataset could be queried. As a result, the likelihood of crossing the right or left lane line (encroachments) and speeding were used as dependent variables.

Comparison of on-road emissions for hybrid and regular transit buses.

Hybrid technology offers an attractive option for transit buses, since it has the potential to significantly reduce operating costs for transit agencies. The main impetus behind use of hybrid transit vehicles is fuel savings and reduced emissions. Laboratory tests have indicated that hybrid transit buses can have significantly lower emissions compared with conventional transit buses. However the number of studies is limited and laboratory tests may not represent actual driving conditions, since in-use vehicle operation differs from laboratory test cycles. This paper describes an on-road evaluation of in-use emission differences between hybrid-electric and conventional transit buses for the Ames, Iowa transit authority, CyRide. Emissions were collected on-road using a portable emissions monitoring system (PEMS) for three hybrid and two control buses. Emissions were collected for at least one operating bus day. Each bus was evaluated over the same route pattern, which utilizes the same driver. The number of passengers embarking or disembarking at each stop was collected by an on-board data collector so that passenger load could be included. Vehicle emissions are correlated to engine load demand, which is a function of factors such as vehicle load, speed, and acceleration. PEMS data are provided second by second and vehicle-specific power (VSP) was calculated for each row of data. Instantaneous data were stratified into the defined VSP bins and then average modal emission rates and standard errors were calculated for each bus for each pollutant. Pollutants were then compared by bus type. Carbon dioxide, carbon monoxide, and hydrocarbon emissions were higher for the regular buses across most VSP bins than for the hybrid buses. Nitrogen oxide emissions were unexpectedly higher for the hybrid buses than for the control buses.

Driver's lane keeping ability with eyes off road: Insights from a naturalistic study.

Many studies have shown that driver inattention can influence lane-keeping ability. The majority of studies on lane keeping have been conducted in controlled on-road networks or in simulated environments. However, few studies have examined lane-keeping ability in naturalistic settings for the same purpose. In this current study, the relationship between driver inattention and lane keeping ability was examined using naturalistic data for 24 drivers. Driver inattention was placed into two categories based on whether drivers were looking forward toward the roadway (inattention with eyes-on-road) or not looking forward (inattention with eyes-off-road) while engaged in a secondary task. Repeated measures regression models were used to account for within-subject correlations. The results showed that, after accounting for driving speed and lane width, the eyes-off-road significantly increased the standard deviation of lane position (SDLP). The findings from this study are consistent with other studies that show that the amount of time drivers spend looking away from the road can impact drivers' ability to maintain their lane position. Additionally, this paper demonstrates how driver inattention can be examined with real world data while accounting for the roadway, environment, and driver behavior.

Evaluation of Iowa's graduated driver's licensing program.

The effectiveness of Iowa's graduated driver's licensing (GDL) program was evaluated for a 4-year period before and after implementation in 1999. Since some changes had occurred in the crash reporting format, changes in crash rates for younger drivers were compared to those for 35-44-year-old drivers (middle-age group of drivers) who were used as a control group. After implementation of GDL, the 14-, 16- and 17-year-old age groups experienced a greater decrease in crash rate than the middle-age control group while 15-year-old experienced a smaller decrease. This suggests that the crash rate for 15-year-old drivers may actually have increased when downward trends were adjusted for. Iowa's GDL program allows holders of the instruction permit to travel unaccompanied to and from school and school-endorsed activities after obtaining a minor school license. Fifteen-year-old with minor school licenses account for up to 26.7% of 15-year-old license holders yet represent up to 74.8% of 15-year-old drivers involved in crashes (depending on the year) from 1998 to 2004. As a result, 15-year-old drivers with minor school licenses are involved in 7.2-8.9 times more crashes, are 7.7 times more likely to have one or more sanctions, and are 4.8 times more likely to receive one or more moving convictions than their peers with a regular instruction permit. This help may explain why 15-year-old drivers did not seem to benefit from implementation of the GDL program in Iowa.

Use of n-fold cross-validation to evaluate three methods to calculate heavy truck annual average daily traffic and vehicle miles traveled.

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.

Evaluating speed differences between passenger vehicles and heavy trucks for transportation-related emissions modeling.

Heavy-duty trucks make up only 3% of the on-road vehicle fleet, yet they account for > 7% of vehicle miles traveled in the United States. They also contribute a significant proportion of regulated ambient emissions. Heavy vehicles emit emissions at different rates than passenger vehicles. They may also behave differently on-road, yet may be treated similarly to passenger vehicles in emissions modeling. Input variables to the MOBILE software, such as average vehicle speed, are typically specified the same for heavy trucks as for passenger vehicles. Although not frequently considered in modeling emissions, speed differences between passenger vehicles and heavy trucks may influence emissions, because emission rates are correlated to average speed. Differences were evaluated by collecting average and spot speeds for heavy trucks and passenger vehicles on arterials and spot speeds on freeways in Des Moines, IA, and Minneapolis/St. Paul, MN. Speeds were compared by study site. Space mean speeds for heavy trucks were lower than passenger vehicle speeds for all of the arterials with differences ranging from 0.8 to 19 mph. Spot speeds for heavy trucks were also lower at all of the arterial and freeway locations with differences ranging from 0.8 to 6.1 mph. The impact that differences in on-road speeds had on emissions was also evaluated using MOBILE version 6.2. Misspecification of average truck speed is the most significant at lower and higher speed ranges.