Little is publicly known about the effects that a rough road surface can have on the motion of a motor vehicle. Gorski Consulting has recently conducted testing with school buses to supplement the growing results of testing already posted to this website on this issue.
On April 5, 2021 an article was posted to the Gorski Consulting website describing the results of testing with a GMC 18-passenger school bus that was conducted on March 4, 2021 on Wharncliffe Road in London, Ontario. At that time we indicated that the results of further testing would be released regarding the same bus as well as testing on March 25 and 26, 2021 with a full-size International school bus. We have now completed analysis of the second round of testing with the GMC 18-passenger school bus and we will present the results in the present article.
In brief, the condition of a road surface is capable of being determined by driving over the surface with a test vehicle while using the accelerometer and gyro sensors of an iPhone accompanied by multiple video cameras. A large amount of testing has been reported on the Gorski Consulting website in the Road Data webpage which confirms this conclusion. Since the testing involved a single passenger car (a 2007 Buick Allure) a reasonable concern is that the data may be different when a vehicle of a different structure and suspension is used. It is fortunate that recently Gorski Consulting has been able to conduct additional testing with two school buses to assess whether the methodology might also be applicable with such totally different vehicles.
The first school bus testing involving a GMC 18-passenger school bus was reported in the previous article of April 5, 2021 entitled “Testing of School Bus Response to Irregular Road Surface Conditions”. We encourage readers to explore this article which can be found on this webpage. This article provided the results of testing on March 4, 2021 on Wharncliffe Road in London, Ontario. The present article will report on the results using the same school bus from testing on March 4, 2021 on Wellington Road in London, Ontario.
The table below shows the results of the Wharncliffe Road testing. Each set of bars represents a time of 30 seconds of travel. As reported previously, a poor road surface would be expected to produce average motions of 0.0500 radian per second or higher. Although several road segments approached this threshold none surpassed it.
The next table shows the results of the Wellington Road testing. An usual occurrence took place when the school bus was northbound on Wellington Road and approaching Hill Street, just south of London’s downtown district. An usually large amount of longitudinal motion took place as indicated by the height of the blue bar shown in the table. The average Longitudinal Rotation was 0.0517 and the Lateral was 0.0345.
The table below shows a closer view of the motions of the bus that took place over that 30 second interval of travel. During this time the bus was travelling at 47 km/h and was slowing to about 28 km/h as it approached Simcoe Street. The largest amount of motion was in a sample of about 200 readings represented by the green oval shown below.
Approximately 200 samples were taken from the motion data and these are displayed in the table below. Again, these data come from Wellington Road between Hill St and Simcoe Street. They reveal the very large effect on the GMC school bus. Within these 200 readings, comprising about 6.7 seconds of travel, the Longitudinal Rotation was 0.0867 while the Lateral Rotation was 0.0435. Thus the Longitudinal Rotation is well above the 0.0500 threshold that indicates a poor road surface. And the Lateral Rotation is just below that threshold.
We visited Wellington Road on April 12, 2021 to examine the surface between Hill Street and Simcoe Street. the photo below shows a view of the northbound lanes of Wellington Road taken from the south side of the intersection with Hill Street. This is approximately where the motion of the school bus began to be excessive. There is nothing particularly obvious about the character if the surface that would signify a problem. Yet watching northbound vehicles one could them bouncing up and down.
The photo below is a view of the northbound lanes of Wellington Road just north of the intersection with Grey Street. Here the surface problems become more visible even though it is difficult to capture such characteristics through photos alone. The curb lane has become sagged, particularly in the area where the left wheels of vehicles would travel.
The next photo shows a northward view of the northbound lanes of Wellington closer to the sagged portion of the curb lane just north of Grey Street. Even though it is difficult to depict the elevation change with still photos, the sagging can still be visually detected.
The next photo shows a view of the same area of Wellington Road but looking southward. Again the dark van is located in the approximate area where the sagging is most prominent. In fact the surface throughout this areas of Wellington Road exhibited similar characteristics.
The purpose of this testing was to explore whether the motions of an 18-passenger school bus could be used to reliably detect road surface problems. We observed large values of motion of the in the data obtained from the iPhone’s gyro sensors. We then travelled to the site where these large motions were detected. By observing the exaggerated motions of passing vehicles, and observing the obvious sagging of the road surface, we confirmed that the motion data made sense. The data was reporting the poor road surface conditions that existed.
This result is not surprising. We have conducted hundreds of similar tests on road segments throughout southern Ontario and the results have been posted on the Road Data page of the Gorski Consulting website. Those tests confirm the reliability of the methods and results.
Further analysis will be reported in the near future from testing conducted with a full-size school bus on March 25 and 26, 2021. We expect that this methodology will reliably report the motions of this large vehicle. Such large buses have been known to bounce considerably more that passenger cars and light duty vehicles but it will be interesting to see exactly what the data will show.
The condition of road surfaces can be easily identified using the accelerometer and gyro sensors of a smartphone. This has been demonstrated in testing performed by Gorski Consulting using an iPhone and multiple video cameras attached to a 2007 Buick Allure passenger car. Data from such testing is shown on the Road Data webpage of this Gorski Consulting website. The longitudinal and lateral motions of the test vehicle contained in this database were determined to be good indicators of the condition of a road surface. Factors that affect these values include the test vehicle’s speed and the suspension/body of the vehicle.
Qualities of a road segment are obtained by various municipal and state/provincial governments. Vehicles equipped with high-speed profilers are used to identify the physical layout of a road surface. These road segments are given a International Roughness Index (IRI) value. Unless one is a member of such agencies there is no practical way to obtain similar data. And government agencies are not willing to make this data publicly available. For those who are unable to obtain such data there is no objective way to question whether a road surface is adequate or needs repair except to conduct expensive laser scans for limited distances. The use of vehicle motion data provides that objective test, not focusing on the micro geometry of the surface, but focusing on what responses it creates in the motion of the test vehicle.
There are drawbacks to motion data. Motion data varies depending on the speed of the vehicle. It also likely varies from one vehicle to another. How much variance is related to vehicle differences is generally unknown because our testing has involved only one vehicle: a 2007 Buick Allure passenger car. It would be useful to examine the motions of vehicles that are very different from passenger cars.
Recently Gorski Consulting has gained the opportunity to study the motions of school buses. This is helpful because school buses are different in structure and suspension than a typical passenger car. Thus this enables the opportunity to study inter-vehicle differences in motion data. Two school buses were used in the testing:
- 2012 GMC 18-Passenger School Bus
- 2012 International 72-Passenger School Bus
These buses are shown in the photos below.
Two tests were performed with the GMC School Bus on March 4, 2021 in London, Ontario. In one test the bus was driven along a northbound route of White Oaks Road, Southdale Road, Wharncliffe Road and Western Road. In a second test the bus was driven northbound along White Oak Road, Southdale Road, Wellington Road and King Street. Two tests were also performed with the full-size, 2012 International School Bus on March 26, 2021. Both tests were performed eastbound from Pack Road, Bostwick Road, Exeter Road and White Oak Road.
In this article we will focus on the first test with the 2012 GMC 18-passenger school bus.
Previous testing was performed on Wharncliffe Road In London Ontario on March 31, 2014 using the 2007 Buick Allure passenger car. At that time several segments of the road surface were in disrepair. The results of the March, 2014 testing are posted on this Gorski Consulting website on the Road Data webpage. For convenience the data is reprinted in the following table.
The last two columns in the above table report the extent of lateral and longitudinal motion of the test vehicle in terms of radians per second. One radian is equal to 57.3 degrees. It may be recalled from previous discussions that the magnitude of vehicle motion can be evaluated according to three levels of severity:
- Rotation values up to 0.0200 rad/sec indicate a road surface that produces mild effects on the vehicle’s motion and therefore the surface is in good condition.
- Rotation values between 0.0200 and 0.0500 indicate a road surface that produces moderate levels of vehicle motion indicating that some portions the road segment could be substandard.
- Rotation values above 0.0500 indicate that the road segment contains major surface problems that could be a factor in the stability and safety of a travelling vehicle.
In the above table values in green indicate a low vehicle motion, below 0.0200 rad/sec and therefore a good quality road surface. The values in red indicate a high level of vehicle motion, above 0.0500 rad/sec and therefore a poor road surface. It can be seen that, as the test vehicle passed north of Duchess Ave, its motions became excessive and therefore the road surface was poor. Subsequent to the testing in 2014 the segments of Wharncliffe Road that contained the worse conditions were repaved. Thus at the time of the re-testing with the GMC School Bus in March, 2021, the road surface conditions were improved.
The testing on March 4, 2021 was conducted along the route shown in the following figure. The testing commenced near the intersection of White Oaks Road and Bradley Ave. Northbound travel along White Oaks Road was transferred for a brief distance onto Southdale Road before proceeding northward along Wharncliffe Road up to Oxford Street. Although the testing continued past Oxford we have limited the discussion up to Oxford.
The two tables below show the results of the March 4, 2021 testing over a period of 930 seconds (15.5 minutes). For reasons beyond our control the testing had to be performed during a morning rush hour. Thus in several instances the bus had to be brought to a stop, sometimes to wait for a red traffic signal. At other times the rush hour congestion caused the bus to stop in a line of stopped traffic. During these stoppages the recording indicated very low motions, as noted in the two tables. In contrast the testing in 2014 occurred in non-rush hour conditions and there was little interruption in the test vehicle’s travel.
Looking back at the testing from March 14, 2014 it was noted that the passenger car experienced a very large jolt as it crossed the north junction of the Thames River bridge. This disruption can be seen in the chart shown below. The chart shows the car’s travel over a time of 20 seconds. There is a smaller jolt in the longitudinal rotation at about 6-7 seconds into the chart which is probably the vehicle passing over the south junction of the bridge. Then there is the very large jolt at about 15 seconds which was caused when crossing the north junction of the bridge.
The above chart is broken down into greater detail in the next chart where we display the vehicle’s motion over a one-second time interval as it passes over the north junction of the bridge. The very large jolt occurs over a relatively short time and there is only one spike that approaches 2.4000 radians per second. Recall that the values being presented here are the rates of motion. In other words, how fast is that motion? So the value of 2.4000 radians per second means that, over a very short time, of perhaps less than a 10th of a second, the vehicle’s motion was 2.4000 radians per second. In fact, the reaction of the car occurred over a time of about 1/3 of the chart, or about 1/3 of a second and the longitudinal motion was generally in the range of 0.5000 radians per second – still very large when compared to the rest of the motion data.
The table below shows the data from the above chart in numerical form. Here we can clearly see what motion values were detected at each of the 30 samples of the 1 second time interval. We can see that only one sample (#1826.57) displayed a value of 2.3960 radians per second. The Standard Deviation value at the bottom of the table shows that, on average, the deviations were 0.5366 Longitudinal and 0.0942 rad/sec Lateral. So there was a single sample of a very high value that made the visual chart (above) seem more dramatic than it was. Yet, the rest of the data still suggests a very large reaction of the vehicle to the character of the road at the north bridge junction.
To see what caused this motion, the five photos below are frames taken from video during the March 31, 2014 testing. These views are looking through the windshield of the passenger car. They begin as the car approached the bridge at its south junction and carries on past the north junction.
There is nothing obvious in the above photos that could warn a driver of these large effects on the vehicle motion. This demonstrates a fact from previous studies that, in most situations, drivers are unable to detect many dangerous road surface features until it is too late to take any meaningful action.
Returning to the testing of March 4, 2021, the two charts below show the motions of the school bus as it travelled northbound through a similar location on Wharncliffe Road. Since this testing was preliminary only two video cameras were used; one showing the speedometer and one pointing forward through the windshield. Thus the precise surface feature that caused the motions could not be determined. In contrast 9 video cameras were used in the testing of March 31, 2014. Thus in 2014 there were several views that provided detailed information about the position of the test vehicle and the conditions of the surface that caused the motion.
The first chart shows the data as the bus passes underneath the CNR bridge and then reaches The Ridgeway crossroad. Our table shows that during this 30 seconds of travel the Longitudinal Rotation was 0.0449 and the Lateral was 0.0450 radians per second. The values are elevated but not into the red category (above 0.0500) that would suggest major road surface problems.
The next chart shows the motion in the vicinity of the crossing of the Thames River bridge. A large spike in the Lateral motion of over 0.4000 radians per second occurs right at the beginning of the chart, then there is a smaller spike at about 10-11 seconds followed by a larger, third spike around 16-17 seconds that rises just over 0.3000 radians per second. The last (third) spike is likely from riding over the north bridge junction. Although it is very large it is nowhere close the very large spike of 2.4000 radians per second that occurred in the 2014 testing.
When viewed in a graphic form, the data can give an exaggerated appearance of the vehicle motion when a single sample reports a very high value. When sampling rates are 30 or more per second, a single sample does not say much about the effect on the vehicle. Thus we need to look at a range or several samples to conclude that an effect on the vehicle motion is sufficient to be of concern.
Also there appears to be something unusual between the motions of the test vehicles in the two testing dates in the vicinity of the north bridge junction. In March 31, 1014 the data showed that the car experienced a very high longitudinal motion whereas in the March 4, 2021 data the Bus experienced a high lateral motion. These differences could related to repaving of the surface yet they remain puzzling. When using an iPhone to collect data it is possible to position the phone in different orientations. Also it is possible to mis-read the title of a column of data and what is meant by the author of the app. Thus it would not be too uncommon if the data in the March 31, 2014 data was misinterpreted such that the longitudinal and lateral motions were reversed. Our tests with the iPhone confirm that the interpretation of the March 4, 2021 is correct.
A quick check of the video during the 2014 testing indicates that the top of the iPhone was oriented to the left (toward the driver). In contrast, in 2021 the top of the iPhone was oriented toward the front of the vehicle. Thus the two phones were oriented with a 90 degrees difference. Thus what is reported as Longitudinal in one dataset will be lateral in the other, and vise versa. Reviewing the datasets it appears we made the correct adjustment for the differences in orientation of the iPhone, so the differences do not appear to be an error on our part. Yet the differences are so opposite that we will remain alert for possible explanations.
Meanwhile, the following 15 figures are frames taken from video during the testing of March 4, 2021. These views commence as the school bus is travelling under the CNR underpass and terminate as the bus is passing over the north junction of the Thames River bridge. These views provide some guidance about the conditions experienced during the March 2021 testing.
This preliminary testing with a 2012 GMC 18-passenger school bus has revealed that motion data can be generated that appears to be reliable in depicting the quality of a road surface. It is similar to that produced with a passenger car test vehicle in the sense that the motions are consistent with what would be expected. Travelling over a rougher section of road surface has produced higher levels of motion of the bus. When the bus has been brought to a stop we see that the motion becomes very minimal.
The only peculiar finding is with respect to the motions that occurred when the Bus travelled over the north junction of the Thames River bridge on Wharncliffe Road. In previous testing conducted ion March 31, 2014 a passenger car sustained very high levels of longitudinal motion when crossing this junction. In contrast the testing with the Bus produced a high lateral motion when crossing over the same junction. It is unclear if these differences occurred because of repaving of the surface between 2014 and 2021. It could also mean that the difference is related to the test vehicle differences.
This is the first test conducted with a school bus and it is expected that further testing will occur. We hope to discuss the results of our second test with the same school bus. Also two additional tests were performed with a full-size school bus on March 25, 2021 and hopefully there will be an opportunity to post the results in the near future.
Gorski Consulting has completed a review of 8 years of photos of cyclists riding on or adjacent to roads in London, Ontario. The years 2013 to 2020 showed the characteristics of the riders, their actions and the safety of the roads on which they travelled. For the purposes of this article we will focus on one aspect of the cyclist population: their gender. Following this we will make some general comments about the characteristics of cyclists in the the City of London Ontario.
Like many cities in North America London Ontario is embarking on an ambitious change in its roadway network which will include electric vehicles, greater emphasis on mass transit and a greater focus on active transportation, particularly cycling. With respect to cycling little information is publicly available regarding the composition of this population and if efforts to create cycling infrastructure will achieve a higher level of usage. In particular, no information appears to exist regarding the gender of cyclists and whether this may be a factor with respect to increasing the cycling population. Thus Gorski Consulting has reviewed its historical data of photos taken over the past 8 years along London’s roads to extract this cyclist gender data.
The table below shows the results of our documentation of 1351 cyclists who were observed riding on, or adjacent to, the City’s streets for these past 8 years.
There were 66 observations where it was not possible to identify the gender of the rider. This was because the photos may have been from a longer distance, the view was from behind the rider, the clothing and cycle characteristics were not clarifying, or other reasons.
Of the remaining 1285 observations it can be seen that 1091 riders were male and only 194 were female. This results in the observation that 84.9% of observed cyclists were male. This is a very large difference.
The City of London has come to the belief that it will be successful in increasing the cycling mode of transportation from its current value of 1% to a minimum of 5%, but ideally up to 20 to 25%. This may be difficult to achieve if only the male half of the City’s population is involved in cycling.
The difference in cyclist gender is even greater when examining the winter months. Below is a table that summarizes observed cyclists in the four winter months (December through to March).
Again, the smaller set of 275 observations where gender was possible to identify reveals an even greater gender disparity. There were 257 observations of males and only 18 observations of female riders. This results in observations of 93.5% male riders. Thus it would appear that very few females ride on London’s roads in winter months.
London’s city politicians and their staff are not ones to accept advice or data from outside sources. Yet an abundance of such help is available, often free of charge. An example of this was a very detailed report submitted by its Cycling Advisory Committee in 2019 which was initially viewed as a threat to the City’s cycling plans. One politician even claimed that the Committee had stepped out of its bounds. While critical in some aspects, the CAC report was well researched and professionally written. Whatever disagreements resulted, it was clear that detailed data were needed to understand what actions need to be taken in the future.
A recent option was proposed by the Ontario government which would allow cargo cycles to ride within those Ontario cities that allowed their operation. London’s decision on this matter would be helped if they had good quality information about how such cargo cycles would operate with respect to efficiency and safety. Without official clearance the City already has a wide variety of cyclists hauling various cargos within mini-trailers, and otherwise. However, provincial regulations would allow these units to be as wide as 2.2 metres. This is not much wider than a typical small car which might be about 2.5 metres in width. Would this be a problem? How wide is a typical cycling lane in the City of London and what problems could this cause? This demonstrates the need for detailed data. The Province of Ontario has focused on cargo cycles as a narrow group that would be employed by larger commercial entities without considering the wider scope of riders who might transport goods.
The provincial regulations would also prevent cargo cycles from being altered. What does that mean for current cyclists with mini-trailers. Will this regulation stop this segment of the population from using and altering their mini-trailers?
Presently the cyclist population can be divided into obvious categories of riders. There are those who ride for recreational purposes and who are often of a higher income. And there are those who ride because it is essential for their survival. The purchase of groceries and transportation of materials cannot be done with a motor vehicle because many of these riders cannot afford such a luxury. So they use their cycles in innovative ways. It is important to understand what those innovations are and if legislation will interfere with these essential travels.
At Gorski Consulting we have stepped up our efforts to provide data about cycling in London and South-Western Ontario. As governments in North America are rapidly changing their roadway networks to create more access to active transportation, it is believed that the cycling mode of transportation will also see a rapid increase. This is good for correcting climate change and improving heath. However, uninformed change can have its detrimental effects. Good quality data remains the cornerstone of good change.
In the last month we have been examining our historical photos of cyclists who were observed riding in London, Ontario. We decided to select the years 2013 through 2020 as the range of our study. The focus has been on cyclists who were observed riding next to or within a city roadway. Thus we have excluded observations of cyclists on dedicated paths and lanes that are not adjacent to a roadway. We have also not included the data from our focused studies where we set up video cameras at a specific location and documented for several hours at that specific location. Thus the observations that were included were those where cyclists were seen riding along or adjacent to any roadway regardless of the location in the City.
We have just completed the first three years worth of data from 2013, 2014 and 2015. An interesting finding is the percentage of males versus females riding on London’s roads. The table below shows the results of these observations.
Of the 522 cyclists in the above table 426 were male and 79 were female. Each year the data indicates that about 85 percent of observed cyclist were male.
London Ontario, like most cities, is expecting a very large increase of cyclists in the next 10 years as a result of its intention to reduce its carbon emissions. There are some loose indications that the present cycling mode of transportation in London is about 1% of its total. The City expects to increase this to at least 5%, but if it is to meet its climate change commitments the needed percentage is likely to be in the range of 20 to 25%.
The disparity in gender becomes even larger when winter road conditions are examined. In the next table we look at observed cyclists just in the winter months (December through March).
Of the 101 cyclists where gender could be established, 96% percent of riders were male.
The above tables demonstrate a potential problem. Is it likely that females will be difficult to entice to ride a cycle on a City street? Will this cause a difficulty in achieving the required increase in ridership? Why do females appear to be less inclined to ride a cycle along City streets? What is the purpose of male cyclists that causes them to ride along City streets and is that purpose different for females? Are unsafe conditions one of the reasons why females are not observed along City streets? Are weather conditions a factor? Answers to these questions will be needed if we can make an efficient transformation in mode of transportation.
We will have further to add once we have completed our analysis of data for the years 2016 to 2020.
A Highway Traffic Act cannot solve all of our road safety problems. We need detailed collision information in order to recognize the relative risk of unorthodox human behaviours on our road systems.
As shown in the above photo, an elderly male rider has determined that his best option is to ride his medical scooter the wrong way on Brydges Street in east London, Ontario. This is so even though the sidewalk next to him may be a viable option. It is understood that vehicles should not be driving the wrong way on a city street yet there is some reason to believe that, for smaller mass vehicles such as bicycles and scooters, the rider’s ability to see and react to oncoming traffic could be improved when the rider faces that traffic. There are conflicting beliefs about what would be the safest approach.
This is why it is important to study the details of collisions. And it is important that collisions be properly documented with sufficient objective details so that a proper assessment can be made of their causes. When investigators do not possess the proper training or experience, or when they lack sufficient time and resources to complete a proper investigation, the final product produces error and variance in the collision data. That error and variance is often not understood or recognized by those conducting mass-data analysis. The end result is that we do not fully understand what needs to be done to reduce the risk of collision, injury and death.
Many persons who are deserving of charges are not captured by typical investigations of motor vehicle collisions.
Look at the scenario above which commences when traffic is diverted due to a closed lane. The driver of the BMW SUV has been forced into the left lane along with all eastbound drivers at this Oxford Street location in London, Ontario. When this occurs vehicles become more compressed and visibility ahead becomes more limited.
In the next photo we see that an elderly rider of a limited-mobility scooter is seen in the centre, left-turn lane, and it appears he is intending to cross from north to south. It is possible that our BMW driver could have seen the scooter since, looking at the shadow caused by the vehicle ahead, there would appear to be a clear line of sight. But the BMW driver may not be focused on that area of the road. The BMW driver may be focused on the upcoming opening in the right lane and the opportunity to pass the vehicle ahead using that right lane.
Looking at the photo below it is clear that other eastbound vehicles are moving into the right lane after passing the area of construction. Or perhaps an eastbound vehicle has stopped in the left lane to allow the elderly scooter driver an opportunity to complete his crossing.
Indeed the photo below shows that the BMW driver steers into the right lane. And the elderly driver of the scooter is no longer in the median. So where is the scooter rider? In front of the white SUV? In front of the BMW SUV? Will this result in a death? Note that we see the illumination of brake lights in the white SUV on the left as well as the BMW on the right.
Fortunately the photo below shows that the elderly scooter rider passes through the intersection and the crisis is resolved.
However, consider who would be to blame if the scooter rider was struck.
Most likely the elderly rider of the scooter would be blamed for attempting to cross a busy, four-lane road at a dangerous location.
But what about the driver of the White SUV? Did this driver stop and cause the scooter driver to understand that it was safe to cross?
And what about the BMW SUV? Could a collision be avoided if the driver was more patient or more attentive to the surroundings?
How would we make these assessments?
Event Data Recorders (EDRs) can help. Modern vehicles are equipped with electronic modules that constantly monitor the motion of equipped vehicles. When a sudden change in speed occurs that resembles a collision the module will begin to store the data a few seconds preceding the event as well as for a short time afterward. But the change in vehicle motion must be of a sufficient magnitude to “wake up” the system. Impacts with pedestrians or bicycles would generally not be sufficient to wake up the system. And given the relatively small mass of the scooter and rider a similar situation would occur.
If the BMW struck the scooter and a recording was made, then there might be information about the BMW’s pre-impact speed, if and when braking was applied, and other important facts such as whether the BMW driver sped up by stepping on the accelerator pedal when he ought to have detected the scooter. But the timing of these events cannot always be determined from EDR data.
For example, the EDR can not tell an investigator the precise location of the white SUV that was in front of the BMW. So an investigator would not know the degree to which that white SUV prevented the BMW driver from detecting the scooter rider. Proving that the driver of the white SUV deliberately stopped to allow the scooter to pass might be difficult. In all likelihood the driver of the BMW would be absolved of any wrong-doing because the BMW driver “had the right-of-way”. The right-of-way is an ugly term coded in Highway Traffic Acts throughout North America. It allows for many judicial, bad decisions to be made expediently and unjustly.
We can also ask another question: Why would the City of London not face charges in a situation like this? Imagine that an employee of the City’s transportation department had sufficient training and data to understand that there were frequent attempts by pedestrians, cyclists and medical scooter drivers to cross this portion of Oxford Street. Imagine that this employee’s manuals told him/her that certain thresholds were met for the installation of some form of traffic control. Imagine that there were discussions with City politicians about costs and that this ultimately resulted in postponing of such traffic controls. Should the consequences of a death or serious, permanent injury be placed solely upon the drivers of the motor vehicles and scooter? Should the City also bear some responsibility?
In many instances there are multiple factors that, in their combined influence, determine whether a collision will occur and the magnitude of its consequences. Some understanding of these many factors must be had and considered. Collisions are complex matters but their causes are rarely, correctly identified. No education, experience, honorary titles and medals, or fancy equipment can improve this failing if there is no genuine interest in pursuing the truth. Too often cause for motor vehicle collisions is determined using simplistic logic that sounds true, rudimentary calculations that mimic science, and a belief by the general public that there is a Wizard of Oz behind the curtain who has all the answers.
Why is it irrational to see a pedestrian walking within a travel lane with their back to traffic? Let us consider the photo below which shows a pedestrian walking on a cycling lane.
Is this dangerous? Not only is his back to traffic but the next photo shows that he is wearing headphones. Is there anything wrong with that?
If we understand current beliefs toward cycling safety there is apparently nothing dangerous about pedestrians walking in this manner. Even though, through our childhood years, we were told exactly the opposite: walking on the road with your back to traffic is dangerous.
So why is it also just as dangerous for cyclists to ride on the edge of a road with their backs to traffic? If an impact occurs with a motor vehicle do we seriously believe that the cyclist will be better protected than the pedestrian?
The cyclist shown below has difficulty seeing vehicles approaching from behind. While a mirror would help, in many cases typical commuter cyclists do not ride with mirrors.
As demonstrated in the photo below the cyclist below must turn his head over his shoulder and this is not ideal for observing dangers behind or new dangers that might develop in front.
Let us consider a further example. What if a mother was pushing a baby carriage in the same location where we showed the pedestrian above. Would that be dangerous? Consider the view below. Would we consider that the mother and baby in the carriage would safe walking in a cycling lane with their backs to traffic?
What about the child next to the mother walking with the small cycle? Do we seriously believe that this child would be safe riding on their small cycle at the edge of lane of motor vehicle traffic, or in a painted cycling lane? What is the safety difference when we exchange these pedestrians with an adult cyclist?
Some adults who ride in cycling lanes will transport their children in flimsy mini-trailers towed behind the cycle such as the example below. Little do the realized is that, if they were struck from behind the first thing to be struck would be the mini-trailer.
It is important to recognize that these mini-trailers are low to the ground. This means that they are more difficult for drivers to detect. In congested areas drivers may be able to see the taller cyclist but may not be able to see the mini-trailer. And because these mini-trailers are not frequently used drivers do not expect them to exist behind a cycle. Thus drivers may believe they have enough time and distance to avoid striking the cyclist only to discover at the last moment that there is a mini-trailer attached behind the cyclist.
The stiffest portion of motor vehicle is at a low level: the bumper level. And the most vulnerable portion of a child’s body is at the head level. While seated in a flimsy mini-trailer the head of a child is exactly where the stiffest portion of a motor vehicle would make contact. Why is this so wise?
Why has our society continued to create these dangerous conditions for cyclists? In many instances cyclists would be better off riding facing traffic if they are to ride on a roadway or in a painted cycling lane. At least there would be a greater opportunity to attempt an evasive motion should a motor vehicle stray into the cyclist’s path. But this is not a solution. The solution must be to change our understanding of cyclist safety and remove cyclists from such dangerous conditions.
As regrettable as they are, collisions involving celebrities are a time when a large segment of society is interested in knowing what happened. And this can be a valuable opportunity to educate those who may otherwise be unreachable. But those opportunities are almost always lost. The details that could be used to provide that education are kept from the public. Such, unfortunately, will likely be the reality in the latest celebrity collision involving Tiger Woods.
It was reported that Tiger Woods, perhaps the most known and talented golfer of the current generation, was involved in a single vehicle collision on Hawthrone Boulevard in suburban Los Angeles, California on the morning of February 23, 2021. Some Googlemaps images below should clarify where the collision site was located.
The Google view below shows the centre of Los Angeles on the upper right and the orange circle in the centre shows the location of the collision site.
The view below outlines the path that Tiger Woods would have taken if he travelled along several kilometres of Hawthorne Boulevard up to where the collision occurred at the orange circle. Note that there would have been many sharp horizontal curves along this path that were much sharper than the one approaching the collision site. We would what to know how vertical curves (up-grades and down-grades) might have related to the safety of the roadway. Whenever horizontal and vertical curves are combined this can be a challenging environment for many drivers, especially when environmental factors such as rain or snow are introduced. It was noted that the roadway was dry at the time of the Woods’ crash.
The view below shows the collision site with a measurement taken from the centre median to the approximate final rest position of the SUV. This distance is in the general range of 130 metres. This distance is not exceptionally large. But we have no information about what events occurred prior to the median impact.
The news media provided many photos of damaged SUV lying on its side and we were told that this was a rollover collision.
The use of the term “rollover’ to describe the collision is a misnomer. While Woods’ SUV obviously came to a stop on its side the most important characteristic on the damaged SUV was the major frontal crush that occurred at a low level. This is why Woods reportedly sustained his leg injuries. The rearward displacement of the front wheels was an important identifier of the large amount of kinetic energy that was dissipated in this region. The right front wheel was pushed back further than the left-front, again indicating this is where the greatest force was concentrated.
But there was also minimal crush of the hood. And the left-front fender also sustained very little crush. Such facts help to identify how the frontal impact occurred. This damage is more common when a vehicle plows into an embankment. Such an embankment impact would occur at a typical T-intersection where a driver fails to detect the end of the road and drives through the intersection striking the embankment that might exist on the opposite side of the road. The greater crush at the right front would indicate that the vehicle was leading with its right front corner when the impact occurred. The force was likely oriented upward from the ground as if the SUV was diving down into the earth. Obviously Tiger Woods did not travel through such a T-intersection so we would want to know what conditions existed causing an impact of a similar nature.
There were other areas of damage to the rear corners of the SUV which were not as severe as the frontal impact. While it was stated that the SUV rolled over several times there is little evidence on the vehicle exterior to support that claim. While it is possible for a vehicle to make isolated contacts with the ground and the lifting off the ground, it would be rare that scrapes and scratches to the painted surfaces would not exist. Also the side roof rails of the vehicle do not appear to have been damaged and such damage would be very common in a rollover. Again, we are not saying that multiple rollover events did not take place, but the evidence of the exterior of the vehicle requires that further explanations be provided.
As typical, police and news agencies have provided little information about the path of the SUV from the road to its final rest position so the specifics of how and why the collision occurred cannot be known. Some comments were reportedly made by investigating police that no tire marks were found in the northbound lane preceding the SUVs impact with the centre median. Such a fact is not surprising. Modern electronic stability control (ESC) systems that would exist on the Hyundai Genesis SUV would become activated preventing the yaw-type rotation that would have occurred if such systems did not exist. Such activation would prevent any tire marks from occurring that could be detected by the naked eye. Many events could have occurred for several hundred metres before the impact of the centre median and we would be completely unaware of them. Fortunately modern-day vehicles are equipped with event data recorders (“Black Boxes”) that would capture a variety of data for several seconds leading up to the crash. Some vehicles can also capture snap-shots of those events. This is why news media should be asking police to release this data for public consideration. But, as has been customary, such useful information is unlikely to reach the public.
Many lessons could be learned during this time when the public’s interest is high. As an example, an understanding could be had that modern safety systems are geared for typical impacts where horizontal forces exist or where lower-severity rollover forces exist. But these systems are not well-adjusted to impacts where the force is applied upward from the floor pan. Air bags protect the head and chest areas. Three-point seat-belts also protect much of the upper body. But there is little done to protect the lower portion of an occupant’s body. Generally, leg injuries are not very life-threatening.
Using a coding scheme such as the Abbreviated Injury Scale (AIS) we can code the severity of injuries according to this six-point scale whereby level “6” would be untreatable (decapitation for example) and level “1” might be a soft-tissue neck stain. For injuries to the lower extremities the highest magnitude of injury could be coded a level “3”, or serious, if a femur is fractured. And with other uncommon complications even a level “4” code might be possible. But these would be quite uncommon. It is generally not possible to sustain a level “5” or “6” lower-extremity injury. So those agencies whose mission it is to prevent death are not as eager to focus on lower extremity injuries even though they may be quite debilitating.
Injury causation must be a part of our societal understanding and training so that we can better select how we function and what we do to prevent injury. This understanding and training is continually lost when essential information about collisions is kept from the public.