Coefficient Of Variation In Speed Research Articles

Effects of operating parameters on start performance of compression ignition engine by using high-pressure direct-injection pure methanol fuel

Methanol was one of the research hotspots in internal combustion engines over recent years. The application of methanol is a promising approach to transport decarbonization due to low carbon content. However, the utilization of methanol in compression ignition (CI) engines faces some challenges as a result of low reactivity and high latent heat of vaporization, such as cold start and operation stability. Furthermore, the impact mechanisms of operating parameters on cold start and operation stability in CI engine have not been understood deeply. In this work, the effects of operating parameters on average cycle of start and coefficient of speed variation (CoVspeed) of idle were studied on a CI engine fueled by pure methanol fuel. Results show that the injection strategy of the minimum average starting cycle number is the pre-main injection, which includes lower injection pressure, −4 °CA ATDC (degree crank angle after top dead center) main timing, smaller pre mass, and larger pre-main interval. The injection strategy of the minimum CoVspeed of idle is the pilot-pre-main injection, which includes lower injection pressure, −4 °CA ATDC main timing, smaller pre mass, larger pre-main interval, smaller pilot mass, and shorter pilot-main interval. Under the same operating conditions, the average cycle of start is 12 and 5 for methanol and diesel respectively. The CoVspeed of idle is 6.72% and 0.53% for methanol and diesel respectively. When coolant temperature is 30 °C, the minimum intake temperature is 120 °C. Upon adjusting the coolant temperature to 80 °C, the minimum intake temperature decreases to 80 °C.

A traffic dynamic operation risk assessment method using driving behaviors and traffic flow Data: An empirical analysis

Presently, traffic safety analysis is mainly based on accident-based indicators, but they are incidental and small probability events, making safety analysis based on accident-related data difficult to achieve a comprehensive application in network size, and to realize risk assessment before accidents occur. To this end, this study innovatively develops a metric named Traffic Dynamic Operation Risk (TDOR) based on aggressive driving behaviors (ADBs) and traffic flow data for traffic safety evaluation. And Non-Negative Matrix Factorization (NMF) is adopted to explore the quantitative risk under the coupling of multiple risk factors. The results show that: 1) segments with high TDOR are always concentrated in a certain area and in a spatial distribution of Gaussian, and the peaks of these distributions are almost the assesses of transportation hubs or complex intersections; 2) traffic volume and coefficient of speed variation are the significant risk factors at ordinary sections and branches, while traffic volume and speed difference between upstream are the largest contributions at intersections; 3) roads with more accidents are associated with a higher TDOR, but the relationship between the two is not completely linearly correlated. The experimental results show that it is reasonable and feasible to use ADBs and traffic flow parameters to quantify traffic safety risks. Compared with other matrix factorization methods, the importance of variables based on NMF is more interpretable and more consistent with the change pattern of risk variables. The method proposed in this paper can change the previous traffic risk assessment pattern based on accident-related indicators and provide a basis for pre-accident risk prevention.

Assessing driver behavior in work zones: A discretized duration approach to predict speeding

Higher speeds in work zones have been linked to an increased likelihood of crashes and more severe crash outcomes. To enhance safety, speed limits are often reduced in work zones, aiming to create a steady flow of traffic and safer traffic operations such as merging and flagging. However, this speed reduction can also lead to abrupt speed changes, resulting from sudden braking or acceleration, increasing the risk of crashes. This disruption in speed and flow results increases the likelihood of rear-end crashes. Ensuring driver compliance with the reduced speed limits and traffic flow operations is challenging as work zones may cause frustration and lead to more instances of speeding. Therefore, proactively predicting speeding events in work zones can be crucial for the safety of both workers and road users, as it enables the implementation of speed enforcement measures to maintain and improve driver compliance in advance. In this study, we employ the duration-based prediction framework to forecast speeding occurrences in work zones. The model is used to identify significant predictors of speeding including visibility, number of lanes, posted speed limit, segment length, coefficient of variation in speed, and travel time index. Among these variables, the number of lanes, posted speed limit, and coefficient of variation of speed are positively associated with speeding. On the other hand, visibility, segment length, and travel time index are negatively associated with speeding. Results show the model’s predictive accuracy is higher for speeding events with shorter durations between consecutive occurrences. The model predicted speeding within 61% of the actual epoch when speeding events within 5 h of one another were considered for validation. This indicates that the model is more effective for road segments and work zones where speeding occurs more frequently. The prediction framework can be a great asset for agencies to improve work zone safety in real-time by enabling them to proactively implement effective work zone enforcement measures to control speeding and to stay prepared, preventing potential hazards.

Exploring pathways from driving errors and violations to crashes: The role of instability in driving

This study explores how different driving errors, violations, and roadway environments contribute to safety–critical events through instability in driving speed. We harness a subsample (N = 9239) of the naturalistic driving study (NDS) data collected through the Second Strategic Highway Research Program (SHRP2). From a methodological standpoint, we use the safe systems approach relying on path analysis to jointly model outcomes. This accounts for the potential correlation between unobserved factors associated with both instability in driving speed and epoch (video stream) outcomes, i.e., baseline or event-free driving, near-crashes, and crashes. Tobit and ordered Probit regressions are estimated to model the coefficient of variation (COV) of speed and epoch outcomes, respectively. Results from the Tobit model indicate that driving errors and violations are associated with instability in the driving speed of the subject driver (COV of speed). The Probit model reveals that driving errors, violations, and instability in driving speed are associated with higher chances of crashes and near-crashes. Our key finding is that driving errors and violations not only induce event risk directly but also indirectly through instability in driving speed. For instance, recognition errors associate with higher crash risk by 6.78 % but this error is accompanied by instability in driving speed, which further increases event risk by 4.73 %, bringing the total increase in risk to 11.51 %. Moreover, significant correlations were found between unobserved factors reflected in the error terms of the two models. Ignoring such correlations can lead to inefficient parameter estimates. Based on the findings, practical implications are discussed, which can lead to effective countermeasures that effectively reduce crash risk.

Development of the peanut precision fertilization control system based on threshold velocity algorithm

In view of the fact that the current ground wheel velocimetry of the peanut precision fertilizer control system cannot solve the phenomenon of ground wheel slippage, and signal interference and delay loss cannot be excluded by BeiDou positioning velocimetry, a set of peanut precision fertilizer control system was designed based on the threshold speed algorithm. The system used STM32F103ZET6 microcontroller as the main controller, and touch screen for setting the operating parameters such as operating width, fertilizer type, and fertilizer application amount. The threshold speed algorithm combined with BeiDou and ground wheel velocimetry was adopted to obtain the forward speed of the tractor and adjust the speed of the DC drive motor of the fertilizer applicator in real time to achieve precise fertilizer application. First, through the threshold speed algorithm test, the optimal value of the length N of the ground wheel speed measurement queue was determined as 3, and the threshold of the speed variation coefficient was set to 4.6%. Then, the response performance of the threshold speed algorithm was verified by comparative test with different fertilization amounts (40 kg/hm2, 50 kg/hm2, 60 kg/hm2, 70 kg/hm2) under two speed acquisition methods of ground wheel speed measurement and threshold speed algorithm (combination of Beidou single-point speed and ground wheel speed measurement) in different operation speeds (3 km/h, 4 km/h, 5 km/h). The response performance test results showed that the average value of the velocimetry delay distance of the BeiDou single-point positioning velocimetry method was 0.58 m, while the average value of that with the threshold velocity algorithm was 0.27 m, which decreased by 0.31 m and indicated more accurate with the threshold velocity algorithm. The field comparison test for fertilizer application performance turned out an over 96.08% accuracy rate of fertilizer discharge by applied with the threshold speed algorithm, which effectively avoided the inaccurate fertilizer application caused by wheel slippage and raised the accuracy of fertilizer discharge by at least 1.2% compared with that of using the ground wheel velocimetry alone. The results showed that the threshold speed algorithm can meet the requirements of precise fertilizer application. Keywords: peanut, precision fertilizer planter, BeiDou velocimetry, ground wheel velocimetry, threshold speed algorithm, control system DOI: 10.25165/j.ijabe.20221505.7342 Citation: Fan Y Q, Yu Y, Shang S Q, Gu T Y, Hu Y R, Diao L S, et al. Development of the peanut precision fertilization control system based on threshold velocity algorithm. Int J Agric & Biol Eng, 2022; 15(5): 116–122.

Investigating the influence of time pressure on overtaking maneuvers and crash risk

The research conducted on overtaking maneuver for evaluating drivers’ safety showed adverse effects of urgency on driving performance and decision making. Therefore, a driving simulator study was designed to examine driving performance of the drivers and its implication on overtaking and crash probabilities under increasing time pressure conditions. Eighty-eight participants data were analyzed in the current study. Three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) were considered for analyzing driving performance of the drivers while executing overtaking maneuvers. The driving performance was assessed using minimum time-to-line crossing and coefficient of variation in speed to dissect the safety margin adopted by the drivers while overtaking the lead vehicle. Further, minimum time-to-line crossing and coefficient of variation in speed were considered as explanatory variables to investigate their influence on overtaking and crash probabilities. Parametric survival analysis and Generalized Linear Mixed Models (GLMM) were used to assess the driving performance, overtaking and crash probabilities. The parametric survival analysis showed that minimum time-to-line crossing reduced by 36.7% and 63.8% in LTP and HTP driving conditions, respectively. The GLMM results revealed that coefficient of variation in speed increased by 3.437% in HTP (no significant effect in LTP) as compared to NTP driving conditions. Further, the GLMM results showed that overtaking and crash probabilities decreased with increment in minimum time-to-line crossing and coefficient of variation in speed values. Additionally, it was observed that male drivers took risky decisions than female drivers. Nevertheless, the comparative analysis revealed that male drivers were less prone to crashes than female drivers. Overall, it can be inferred that the drivers take risky decisions with increment in time pressure to complete the driving task, even at the expense of their own safety which exposed them to high likelihood of crashes.

Measuring the Cognitive Workload During Dual-Task Walking in Young Adults: A Combination of Neurophysiological and Subjective Measures.

Background: Walking while performing a secondary task (dual-task (DT) walking) increases cognitive workload in young adults. To date, few studies have used neurophysiological measures in combination to subjective measures to assess cognitive workload during a walking task. This combined approach can provide more insights into the amount of cognitive resources in relation with the perceived mental effort involving in a walking task.Research Question: The objective was to examine cognitive workload in young adults during walking conditions varying in complexity.Methods: Twenty-five young adults (mean = 24.4 ± 5.4) performed four conditions: (1) usual walking, (2) simple DT walking, (3) complex DT walking and (4) standing while subtracting. During the walking task, mean speed, cadence, stride time, stride length, and their respective coefficient of variation (CV) were recorded. Cognitive workload will be measured through changes in oxy- and deoxy-hemoglobin (ΔHbO2 and ΔHbR) during walking in the dorsolateral prefrontal cortex (DLPFC) and perceived mental demand score from NASA-TLX questionnaire.Results: In young adults, ΔHbO2 in the DLPFC increased from usual walking to both DT walking conditions and standing while subtracting condition. ΔHbO2 did not differ between the simple and complex DT and between the complex DT and standing while subtracting condition. Perceived mental demand gradually increased with walking task complexity. As expected, all mean values of gait parameters were altered according to task complexity. CV of speed, cadence and stride time were significantly higher during DT walking conditions than during usual walking whereas CV of stride length was only higher during complex DT walking than during usual walking.Significance: Young adults had greater cognitive workload in the two DT walking conditions compared to usual walking. However, only the mental demand score from NASA-TLX questionnaire discriminated simple from complex DT walking. Subjective measure provides complementary information to objective one on changes in cognitive workload during challenging walking tasks in young adults. These results may be useful to improve our understanding of cognitive workload during walking.

The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

The purpose of this study is to investigate the impact of the truck proportion on surrogate safety measures to explore the relationship between truck proportion and traffic safety. The relationship between truck proportion and traffic flow parameters was analyzed by correlation and partial correlation analysis, and the value of the 85th percentile speed minus the 15th percentile speed (85%V–15%V) and the speed variation coefficient were selected as surrogate safety measures to explore the impact of truck proportion on traffic status. The k-means algorithm and the support vector machine were employed to evaluate traffic status on a freeway under different truck proportions in different periods. The major results are that the relationship between truck proportion and the value of 85%V–15%V and the speed variation coefficient is consistent in different aggregation periods. With increasing truck proportion, the value of 85%V–15%V, as well as the speed variation coefficient, increases initially and then decreases. In addition, the traffic flow status tends to be dangerous when the truck proportion ranges from 0.4 to 0.6 and when the value of 85%V–15%V and the speed variation coefficient are above 42 km/h and 0.223, respectively. While the truck proportion is from 0.1 to 0.3 and from 0.7 to 0.9, the traffic flow is relatively safe on the condition that the value of 85%V–15%V and the speed variation coefficient were under 42 km/h and 0.223, respectively. Therefore, the relationship between truck proportion and traffic safety could be well revealed by two surrogate safety measures, that is, the value of 85%V–15%V and the speed variation coefficient. In addition, the k-means algorithm and the support vector machine can well reveal the impact of truck proportion on traffic safety in different periods. The findings of this study indicate a need for decreasing the disturbance of mixed traffic and the impact of the truck proportion on traffic safety status.

Analysis of traffic flow with micro-cars with respect to safety and environmental impact

This study analyzes the characteristics of traffic flow in the presence of micro-cars in a vehicle mix. A two-lane multi-cell traffic cellular automaton (TCA) model is formulated to simulate mixed traffic flow comprising conventional passenger cars and micro-cars. The segments of an urban highway and arterial road, both with two lanes and measuring 700 m in length, are simulated; the latter includes an intersection delay with a signal cycle at the midpoint. Traffic flows with different proportions of micro-cars are investigated in the simulation. Four aspects of traffic conditions are calculated: the number of lane changes, the number of decelerations, the coefficient of speed variation (which may be indicative of safety performance), and the HC, CO, and NOx emissions (as a measure of environmental impact). The simulation results suggest that mixed flow with micro-cars leads to higher frequencies of lane-changing on both highways and arterial roads, although the incremental change on the latter is smaller. With the introduction of micro-cars on the highway, the frequency of decelerations increases in free flow and decreases in congested flow; however, on the arterial road, it decreases the frequency of decelerations and has an insignificant impact on free flow. The introduction of micro-cars has a similar impact on the coefficient of speed variation. Under free flow conditions on highways, the introduction of micro-cars has a negative influence on the three aforementioned parameters related to safety. However, for free flow on arterial roads or congested flow on highways and arterial roads, the results are inconclusive because the effect of micro-cars is contradictory in terms of the three parameters. Vehicle emissions, such as HC, CO, and NOx, increase during free flow on highways, but are always lower on arterial roads.

Monitoring heart rates to evaluate pacing on a 75-km MUM.

To examine pacing among twelve males on a 75-km mountain ultra marathon (MUM) and to determine whether pacing relates to final performance. Speed and heart rates (HR) were measured continuously using a HR monitor and a global position system device. An Index of Pacing (IP) was calculated by dividing the average race speed by the speed on the first race segment. In addition, percentage (%) of heart rate reserve (HRres), coefficient of variation (CV) in speed and in percentage of HRres were analyzed throughout the race. Performance time was correlated with IP (r=-0.88, P<0.01), % of HRres (r=- 0.72, P<0.05), and CV in % of HRres (r=0.80, P<0.05), but not with CV in speed (r=-0.12, P=0.9). On the entire race, evolution of HR was not dependent on the elevation gain. Tracking HR is a safer way to rate pacing than speed tracking on a hilly course.

Functional Role of Movement and Performance Variability: Adaptation of Front Crawl Swimmers to Competitive Swimming Constraints.

To study the variability in stroking parameters between and within laps and individuals during competitions, we compared and modeled the changes of speed, stroke rate, and stroke length in 32 top-level male and female swimmers over 4 laps (L1-L4) in 200-m freestyle events using video-derived 2-dimensional direct linear transformation. For the whole group, speed was greater in L1, with significant decreases across L2, L3, and L4 (1.80 ± 0.10 vs 1.73 ± 0.08; 1.69 ± 0.09; 1.66 ± 0.09 · s-1, P < .05). This variability was attributed to a decrease in stroke length (L2: 2.43 ± 0.19 vs L4: 2.20 ± 0.13m, P < .05) and an increase in stroke rate (L2: 42.8 ± 2.6 vs L4: 45.4 ± 2.3 stroke · min-1, P < .05). The coefficient of variation and the biological coefficient of variation in speed were greater for male versus female (3.9 ± 0.7 vs 3.1 ± 0.7; 2.9 ± 1.0 vs 2.6 ± 0.7, P < .05) and higher in L1 versus L2 (3.9 ± 1.3 vs 3.1 ± 0.1; 2.9 ± 0.9 vs 2.3 ± 0.7, P < .05). Intra-lap speed values were best represented by a cubic (n = 38), then linear (n = 37) and quadratic model (n = 8). The cubic fit was more frequent for males (43.8%) than females (15.6%), suggesting greater capacity to generate higher acceleration after the turn. The various stroking parameters managements within lap suggest that each swimmer adapts his/her behavior to the race constraints.

Improving idle performance of a hydrogen-gasoline rotary engine at stoichiometric condition

Because of the unusual structure chamber compared with traditional engines, gasoline rotary engine always encounters partial burning and increased noxious emissions at the idle. Hydrogen-addition could enhance the characteristics of combustion and emissions of sparked-ignited engines at idle. A modified gasoline rotary engine with electronic spark control and fuel (gasoline and hydrogen) port-injection system was developed to study the impact of hydrogen-addition on idle performance of a gasoline rotary engine. A hybrid electronic control unit was invented to manage the spark, fuel injection, hydrogen volume fraction of the total intake and overall excess air ratio. In this study, the engine was operating at the idle speed of 2400 rpm and stoichiometric conditions. The hydrogen volume fraction was gradually varied from 0% to 6.8%. Results showed that the coefficient of variation in speed and fuel energy flow rate were both decreased after the hydrogen-addition. Flame development and propagation periods were shortened owning to hydrogen-addition. The peak chamber temperature was enhanced after the hydrogen-addition due to the high adiabatic flame temperature of hydrogen. Cooling loss was dropped when hydrogen was added into gasoline. HC, CO and CO2 emissions were reduced by 79.4%, 86.0% and 25.9% when hydrogen volume fraction were raised from 0% to 6.8%.

Impact of Congestion and Traffic Flow on Crash Frequency and Severity: Application of Smartphone-Collected GPS Travel Data

Mobility and safety are the two greatest priorities within any transportation system. Ideally, traffic flow enhancement and crash reductions could occur simultaneously, although their relationship is likely complex. The impact of traffic congestion and flow on road safety requires more empirical evidence to determine the direction and magnitude of the relationship. The study of this relationship is an ideal application for instrumented vehicles and surrogate safety measures (SSMs). The purpose of this paper is to correlate quantitative measures of congestion and flow derived from smartphone-collected GPS data with collision frequency and severity at the network scale. GPS travel data were collected in Quebec City, Quebec, Canada, and the sample for this study contained data for more than 4,000 drivers and 20,000 trips. The extracted SSMs, the congestion index (CI), average speed ( V), and the coefficient of variation of speed (CVS) were compared with crash data collected over an 11-year period from 2000 to 2010 with the use of Spearman’s correlation coefficient and pairwise Kolmogorov–Smirnov tests. The correlations with crash frequency were weak to moderate. CI was shown to be positively correlated with crash frequency, and the relationship to crash severity was found to be nonmonotonous. Higher congestion levels were related to crashes with major injuries, whereas low congestion levels were related to crashes with minor injuries and fatalities. Surprisingly, V was found to be negatively correlated with crash frequency and had no conclusive statistical relationship to crash severity. CVS was positively correlated with crash frequency and statistically related to increased crash severity. Future work will focus on the development of a network screening model that incorporates these SSMs.

Analysis of Sideswipe Collision Precursors Considering the Spatial-Temporal Characteristics of Freeway Traffic

AbstractTo avoid bias caused by the high percentage of rear-end collisions in the generic models, only the observed sideswipe collisions on Highway I-5 in the central Puget Sound, Washington State, area are used to analyze the matched case-control logistic regression model. Considering the spatial-temporal characteristics of traffic flow and short-term variation of the sideswipe collision occurrence, a comprehensive analysis of sideswipe collision occurrence and its relationship with the freeway flow across lanes and detector locations are studied. The results imply that sideswipe collisions are more likely to occur at straight and level segments of multilane freeways in off-peak hours. High average occupancy and low average flow and speed variance upstream of collision location tend to increase the probability of sideswipe collision in congested scenarios. In contrast, high average speed and coefficient of variation in speed, low speed variance and coefficient of variation in occupancy, and high average ...

Modeling the Effects of Rainfall Intensity on the Heteroscedastic Traffic Speed Dispersion on Urban Roads

The heteroscedasticity refers to a collection of random variables with a subpopulation that have different dispersions from others. The variable dispersion could be quantified by measures of statistical dispersion such as standard deviation or coefficient of standard deviation. This study aims to model the effects of rainfall intensity on the heteroscedastic traffic speed dispersion on urban roads. The traffic and rainfall intensity data were collected by a selected video traffic detector and its nearest rainfall station in Hong Kong, respectively. The coefficient of variation of speed (CVS) was employed to measure the vehicular traffic speed dispersion. The analysis shows that the empirical values of CVS typically range from 0.05 to 0.2 at different traffic densities and rainfall intensities, and the exponential function provides a good fit to traffic speed data under both dry and rain conditions. A generalized function of CVS with the effects of rainfall intensity is proposed, calibrated, and validated with different sets of empirical data. The calibration and validation results show that the proposed generalized function of CVS fits well with the empirical data. The empirical findings and the generalized function of CVS proposed in this study may benefit for assessing and modeling the level-of-service performance of urban roads in Pacific Rim cities similar to Hong Kong with relatively high annual rainfall intensity. Language: en

Clinical Impact of Speed Variability to Identify Ultramarathon Runners at Risk for Acute Kidney Injury.

BackgroundUltramarathon is a high endurance exercise associated with a wide range of exercise-related problems, such as acute kidney injury (AKI). Early recognition of individuals at risk of AKI during ultramarathon event is critical for implementing preventative strategies.ObjectivesTo investigate the impact of speed variability to identify the exercise-related acute kidney injury anticipatively in ultramarathon event.MethodsThis is a prospective, observational study using data from a 100 km ultramarathon in Taipei, Taiwan. The distance of entire ultramarathon race was divided into 10 splits. The mean and variability of speed, which was determined by the coefficient of variation (CV) in each 10 km-split (25 laps of 400 m oval track) were calculated for enrolled runners. Baseline characteristics and biochemical data were collected completely 1 week before, immediately post-race, and one day after race. The main outcome was the development of AKI, defined as Stage II or III according to the Acute Kidney Injury Network (AKIN) criteria. Multivariate analysis was performed to determine the independent association between variables and AKI development.Results26 ultramarathon runners were analyzed in the study. The overall incidence of AKI (in all Stages) was 84.6% (22 in 26 runners). Among these 22 runners, 18 runners were determined as Stage I, 4 runners (15.4%) were determined as Stage II, and none was in Stage III. The covariates of BMI (25.22 ± 2.02 vs. 22.55 ± 1.96, p = 0.02), uric acid (6.88 ± 1.47 vs. 5.62 ± 0.86, p = 0.024), and CV of speed in specific 10-km splits (from secondary 10 km-split (10th – 20th km-split) to 60th – 70th km-split) were significantly different between runners with or without AKI (Stage II) in univariate analysis and showed discrimination ability in ROC curve. In the following multivariate analysis, only CV of speed in 40th – 50th km-split continued to show a significant association to the development of AKI (Stage II) (p = 0.032).ConclusionsThe development of exercise-related AKI was not infrequent in the ultramarathon runners. Because not all runners can routinely receive laboratory studies after race, variability of running speed (CV of speed) may offer a timely and efficient tool to identify AKI early during the competition, and used as a surrogate screening tool, at-risk runners can be identified and enrolled into prevention trials, such as adequate fluid management and avoidance of further NSAID use.

Pacing by Winners of a 161-km Mountain Ultramarathon

To examine pacing among the most successful runners in the 161-km Western States Endurance Run (WSER) to determine if variations in segmental speed relate to performance, ambient temperature, and calendar year. Segmental speed and coefficient of variation (CV) in speed were analyzed for 10 race segments of 24 races from 1985 through 2013. Segmental speeds did not differ between the eventual winners and lead runners and only differed between the 1st and 2nd finishers in the 2nd half of the race. Mean CV in speed was lower (P < .01) for the winners (12%) than for the other top-5 finishers (14-15%). CV in speed was related (r = .80, P = .006) to finish time for the fastest 10 finish times at the WSER. Multiple linear-regression analysis revealed mean CV in speed for the top-5 runners to be related to maximum ambient temperature (coefficient =.14, P < .05) and calendar year (coefficient = -.086, P = .034). Mountain trail running is characterized by wide variations in speed, but the fastest times are achieved when speed fluctuations are limited. This is generally accomplished by the winners remaining relatively close behind the lead runners before taking the lead in the middle half of the race, and then avoiding slowing as much as the other top runners in the latter race stages. Variations in speed increase with high ambient temperatures, and the small decrease in segmental speed variability among top runners across the nearly 30 y of this study suggests that the best runners have improved at pacing this race.

Evaluation of the Impacts of Speed Variation on Freeway Traffic Collisions in Various Traffic States

Objective: To evaluate the impacts of speed variation on the likelihood of traffic collision in various types of traffic states in a freeway section. Methods: The overall traffic state is divided into free flow (FF), congested traffic (CT), back of queue (BQ), and front of queue (FQ) according to the speed at up- and downstream detector locations. Based on 448 crash recordings from a freeway stretch in California, the logistic regression model is used to estimate the impacts of average speed (AS) and standard deviation of speed (SD) as well as coefficient of speed variation (CSV) for each traffic state separately. Results: For the overall state, both the SD and CSV are significantly related to the traffic collisions. For the separate states, the model estimates vary in different states: in FF, the CSV is positively related to collisions, whereas the SD is not a good indicator for collisions; in CT and BQ, the SD and CSV have positive impacts on the collision likelihood (especially for rear-end collisions); in FQ, the speed variation indexes were not found to have significant impacts. The coefficients of SD and CSV were found to be different in distinct traffic states. The impact of AS was significant only in FF and BQ. Conclusions: Considering the overall traffic as one state could hide important relationships between speed variation and collisions. The impacts of speed variation on collision likelihood are different in various traffic states. The SD and CSV are effective surrogate safety measures for traffic collisions in CT and BQ but may not be good measures in FF and FQ. The AS is an accident determinant in FF and BQ but not a contributing factor in other states. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Real-time prediction of visibility related crashes

More researchers started using real-time traffic surveillance data, collected from loop/radar detectors (LDs), for proactive crash risk assessment. However, there is a lack of prior studies that investigated the link between real-time traffic data and crash risk of reduced visibility related (VR) crashes. Two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data are advantageous for predicting VR crashes; LDs or AVIs. Thus, this study attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data are better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression models using the historical crashes, LDs and AVI data. Regarding the model estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5–10min prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5–10min prior to the crash time, affected the likelihood of VR crash occurrence. The results showed that both LDs and AVI systems can be used for safety application (i.e., predicting VR crashes). It was found that up to 73% of VR crashes could be identified correctly. Argument concerning which traffic data (LDs or AVI) are better for predicting VR crashes is also provided and discussed.

Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data

This study investigated the effect of the interaction between roadway geometric features and real-time weather and traffic data on the occurrence of crashes on a mountainous freeway. The Bayesian logistic regression technique was used to link a total of 301 crash occurrences on I-70 in Colorado with the space mean speed collected in real time from an automatic vehicle identification (AVI) system and real-time weather and roadway geometry data. The results suggested that the inclusion of roadway geometrics and real-time weather with data from an AV I system in the context of active traffic management systems was essential, in particular with roadway sections characterized by mountainous terrain and adverse weather. The modeling results showed that the geometric factors were significant in the dry and the snowy seasons and that the likelihood of a crash could double during the snowy season because of the interaction between the pavement condition and steep grades. The 6-min average speed at the crash segment during the 6 to 12 min before the crash and the visibility 1 h before the crash were found to be significant during the dry season, whereas the logarithms of the coefficient of variation in speed at the crash segment during the 6 to 12 min before the crash, the visibility 1 h before the crash, as well as the precipitation 10 min before the crash were found to be significant during the snowy season. The results from the two models suggest that different active traffic management strategies should be in place during these two distinct seasons.