Rhythm Engineering Blog

Autonomous Vehicle Technology – The Future of Traffic Engineering is Happening Now

By Dr. Reggie Chandra, PE

Traffic engineering in the future will be intimately connected to how the cars of the future operate.  We are less than a decade away from seeing this microchip technology, which will equip vehicles to monitor what is happening all around them and act accordingly.

How Did We Get Here?

The Kurzweil Curve reveals the power of technology will keep growing exponentially, according to Ray Kurzweil. His prediction is that by 2050, you will be able to buy a device with the computational capacity of all mankind for the price of a nice refrigerator today.1 Kurzweil continues that within the past 60 years, life in the industrialized world has changed almost beyond recognition except for living memories from the first half of the 20th Century. This pattern will culminate in unimaginable technological progress in the 21st Century, leading to a singularity.1

Singularity is defined as an era in which our intelligence will become increasingly non-biological and trillions of times more powerful than it is today — the dawning of a new civilization that will enable us to transcend our biological limitations and amplify our creativity.2

Already examples exist of this foresight in the car of the future technology. The Wall Street Journal reports that self-driving cars, or the autonomous vehicle, are on the horizon. Widespread embrace of self-driving vehicles could eliminate 90% of all auto accidents in the U.S., prevent up to $190 billion in damages and health-costs annually and save thousands of lives, according to a new report by consulting firm McKinsey & Co, as reported by WSJ. The report, compiled after interviews with dozens of industry officials, also predicts mass adoption of auto-piloted vehicles beginning in about 15 years and initial implementations early next decade.3

According to Reuters, fully autonomous vehicles could make up nearly 10% of global vehicle sales, or about 12 million cars a year, by 2035. The Boston Consulting Group added manufacturers and suppliers are rapidly rolling out new hardware designed to speed adaptation of self-driving systems. Carmakers Mercedes-Benz®, Audi® and BMW® demonstrated vehicles with various autonomous capabilities to show attendees at this year’s Consumer Electronics Show in Las Vegas. Daimler’s chief executive, Dieter Zetsche, and Audi’s chief technology officer, Ulrich Hackenberg, said they expect various autonomous systems to be rolled out in stages over the next five to 10 years.4

Emerging Technologies

Before vehicles begin to drive people around, there are other technologies that are already on the market to help make motorists’ lives easier.

  1. The Waze app:“Helps motorists navigate traffic better. Drivers enter their destination and the app displays several route options, enabling drivers to avoid accidents, traffic jams and roads affected by weather. It will also update the driver on changes in traffic.”6
  2. Traffic Light Assist: “Promises to help motorists make every green light. Using both live and predictive data beamed into the vehicle’s navigation unit via onboard Wi-Fi, local data sources provide information about traffic light patterns. The in-car system uses that data and the motion of the car to predict exactly how long it will be until the green light turns red.  Audi® has been testing this system in Europe as well as in Las Vegas, NV.”7
  3. Mobileye: “Reduces the risks of traffic accidents, thus saving lives. The EyeQ® chip (now in its 3rd generation) performs detailed interpretations of the visual field in order to anticipate possible collisions with other licensed vehicles, pedestrians, animals, debris and other obstacles. The product also detects roadway lanes, road boundaries, and barriers, as well as reads traffic signs and traffic lights. This technology is currently being tested with several carmakers.”8
  4. Cruise System: “Available for installation on your 2012 or newer Audi A4 or S4, although there is a wait list now. The technology, called RP-1, uses a combination of sensors, radar, and cameras to drive the vehicle. Using advanced computer vision and obstacle detection, the RP-1 keeps the car in its lane and a safe distance from vehicles in front of it.”9

The future looks like it is moving toward self-driving vehicle technology through advanced microchip technology. But how does it work?

Autonomous Vehicle Technology

The Google driverless car, now called Waymo, uses an array of detection technologies, including sonar devices, stereo cameras, lasers, and radar, according to an article in Extreme Tech. The light detection ranging (LIDAR) system is at the heart of object detection, according to Google engineers. It’s highly accurate up to a range of 100 meters, and although there is several detection technologies on the car that work at greater distances, they do not have the kind of accuracy that LIDAR can provide. The article states the LIDAR system can rotate 360-degrees and take up to 1.3 million readings per second, making it the most versatile sensor on the car. Mounting it on top of the car ensures its view isn’t obstructed.10

Several carmakers have incorporated various technologies into a functioning autonomous vehicle. In an article titled “The Six Things I Learned From Riding in a Google Self-Driving Car” published in The Oatmeal, the author declares that human beings are terrible drivers with a plentitude of human errors; therefore self-driving cars can eliminate these errors. The Google car is programmed to act like “nervous student driver,” so it takes things slow and deliberate. The writer also notes that the technology, while still a work in progress, is wanted/needed “like…yesterday.”11

Volvo® has announced plans to test 100 self-driving vehicles on city streets by 2017. The development team outlined the “one-of-a-kind” pilot scheme that will see “ordinary people” in self-driving cars in an uncontrolled urban environment.12

Has this technology been tried out in a real world setting? Yes. The University of Michigan has designed a Safety Pilot Model Deployment, which is a scaled-down version of a future in which all vehicles will be connected. The model deployment experiment will discover how well connected vehicle safety technologies and systems work in a real-life environment with real drivers and vehicles. It will test performance, usability, and collect data to better understand the safety benefit of a larger scale deployment.13

According to an article in The MIT Technology Review, some of the results from the University of Michigan study are in. “After studying communication records for those vehicles, National Highway Traffic Safety Administration (NHTSA) researchers concluded that the technology could prevent more than half a million accidents and more than a thousand fatalities in the United States every year. The technology stands to revolutionize the way we drive,” says John Maddox, a program director at the University of Michigan’s Transportation Research Institute.14

Shortly after the Ann Arbor trial ended, the U.S. Department of Transportation announced that it would start drafting rules that could eventually mandate the use of car-to-car communication in new cars, according to the article in The Review. “More than five million crashes occur on U.S. roads alone every year, and more than 30,000 of those are fatal. The prospect of preventing many such accidents will provide significant impetus for networking technology.”14

What’s Next?

This journey began with the rapid growth in microchip technology and the how it will initially impact traffic engineering. Starting with driver-assistance technology currently on the market, to vehicle technology advancements, and finally a real-world scenario that brings of all these functions together, a world with self-driving cars is not that far away. However, as this microchip technology continues to advance, traffic signal technology lags far behind. What can be done to catch the industry up? That is exactly what Rhythm Engineering is working on.

 


 

About the Author

Dr. Reggie Chandra, PE, PTOE spent a large portion of his career as a public traffic engineer focused on optimizing and synchronizing signals. He grew frustrated with the tools available for him to perform his job. Dr. Chandra knew the traffic signal technology had fallen decades behind, creating crowded and unsafe roadways, smog, and wasted time and fuel. He also knew traffic engineers alone didn’t have all the answers.

In 2005, Dr. Chandra set out to find a solution. In February of 2008, his team flipped the switch on an artificially intelligent, digital, adaptive traffic signal system that could optimize signals to automatically adapt to traffic in real time.

Since its launch, In|Sync has become the most widely adopted adaptive traffic control systems in the United States. More U.S. traffic agencies select In|Sync than any other adaptive traffic control system, making it the fastest growing such system in U.S. history. As of January 2015, In|Sync is the solution of choice for more than 1500 intersections in 29 states. Independent studies prove that In|Sync reduces stops by up to 90%, cuts fuel consumption and emissions up to 30%, and even reduces accidents by up to 30%. Dr. Chandra currently serves as the Founder, President and CEO of Rhythm Engineering, LLC. The company has ranked twice on the Inc. 500 list of the fastest growing private companies in the U.S.

Born and raised in India, Dr. Chandra came to the United States with his wife Jenny at the age of 27 to pursue the American dream. He earned a bachelor’s degree in civil engineering, a master’s degree in traffic engineering (Univ. of Florida) and a Ph.D. in organizational leadership (Regent University).

In 2012, Dr. Chandra released his first book, Shades of Green: Why Traffic Signals Frustrate You and What You Can Do to Fix Them. This book explains how traffic signals work and how we can fix the problem of unsynchronized traffic signals.

Dr. Chandra enjoys international travel with his friends and family, and finding ways to give back to society such as making dreams come true for adults facing life-threatening illness via The Dream Foundation, and also supports the Community Services League.

Citations

  1. Kurzweil, Ray (2001, March, 7) The Law of Accelerating Returns http://www.kurzweilai.net/the-law-of-accelerating-returns
  2. Kurzweil, Ray (2006) Viking Press, New York, NY – http://www.kurzweilai.net/the-law-of-accelerating-returns
  3. Ramsey, Mike (2015) published in The Wall Street Journal, Self Driving Cars Could Cut Down on Accidents, Study Says http://www.wsj.com/articles/self-driving-cars-could-cut-down-on-accidents-study-says-1425567905?KEYWORDS=self-driving+cars
  4. Reuters, (2015) published in Fortune, 12 million driverless cars to be on the road by 2035, study says http://fortune.com/2015/01/08/12-million-driverless-cars-to-be-on-the-road-by-2035-study-says/
  5. https://www.waze.com
  6. http://www.autoblog.com/2014/01/09/audi-traffic-light-assist-ces-2014/
  7. http://www.mobileye.com/
  8. http://www.getcruise.com
  9. Whitwam, Ryan (2014) published in Extreme Tech, How Google’s self-driving cars detect and avoid obstacles http://www.extremetech.com/extreme/189486-how-googles-self-driving-cars-detect-and-avoid-obstacles
  10. Inman, Matthew, (2015) published in The Oatmeal, 6 things I learned from driving in a Google Self-driving Car http://theoatmeal.com/blog/google_self_driving_car
  11. Bryant, Ross (2015) published in dezeen magazine, Volvo announced “one–of-a-kind” public tests for self-driving cars http://www.dezeen.com/2015/02/24/volvo-public-testing-self-driving-cars-2017-gothenburg/
  12. http://safetypilot.umtri.umich.edu
  13. Knight, Will (2015) published in Technology Review, Car-to-Car Communication: A simple wireless technology promises to make driving much safer. http://www.technologyreview.com/featuredstory/534981/car-to-car-communication/
Rhythm EngineeringAutonomous Vehicle Technology – The Future of Traffic Engineering is Happening Now
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A Quick Look at Traffic Sensors

By Wayne Simmons

As traffic signals needed to become more efficient and allow more traffic though an intersection, the industry realized that pre-set timed lights alone would never be good enough, even with different timings per day. The only way to streamline the process was to know which vehicle movement would best serve the current demand.

Because putting a traffic engineer on every corner wasn’t a possibility, controllers needed to have more smarts to make the best decisions. To make smarter decisions about how to run the lights in the intersection, controllers needed to know about the vehicles at the intersection. To solve this, in the early ‘60s, induction loops were deployed as detectors.

Induction loops use a simple interaction of metal, magnetism and electricity to detect large metal objects. They have many advantages: they are simple, cheap and reliable. However, they have some serious drawbacks.

Sometimes, loops can miss smaller vehicles, like motorcycles or bicycles. In addition, as modern cars get smaller and consist of more plastic than metal, they become harder to detect. Finally, loops are expensive to install (or reinstall), because they are embedded in the road. This leaves them vulnerable to unrelated road work, which can damage them or leave them inoperable.

Enter the Age of Video Cameras.

While research using video detection cameras was done as early as the ‘70s, use of video detection systems was not widely spread until cheap, reliable cameras became available in the ‘90s and 2000s.

Video cameras solve many problems that loop detectors face. Because video detection is not based on the presence of metal mass, smaller vehicles can be detected. Cameras are also easier and less expensive to install and maintain, because they attach to mast arms, poles or nearby street lights.

Video detection systems provide an extra added benefit: they give operators the ability to see what is really going on at the intersection in real time. However, they brought along new classes of problems too like sun glare, fog and other weather conditions that can make vehicles detection difficult.

Other modern traffic detection options range from thermal cameras to radar, as well as other advanced sensors. All of these options provide a more accurate understanding of real-time traffic needs that help run an intersection more efficiently. Each category of detector has their own strengths and weaknesses. However, one deficiency has never been addressed by any of these detectors since loops were first deployed.

Loops can only detect presence or not, meaning they can only tell a controller if a vehicle is above them, or not. This is good information, but it isn’t enough information to run the intersection in the most efficient manner. To know how to serve the greatest number of vehicles, the controller needs to know how many vehicle requests are at each approach. Loops can answer: “Yes someone is waiting,” but they can’t easily tell how many. So how can a controller know which phase serves the longest queue of waiting vehicles?

Adaptive Traffic Control with Video Processing

In|Sync does special video processing, looking at the images of the cars waiting on each approach to calculate which movement best serves the waiting vehicles. It then places calls to the controller to serve those phases.

In|Sync continuously analyzes queue levels and wait times, placing calls to the controller for the appropriate phases. Using this method, In|Sync streamlines intersection operation, even with the same controller running the lights. In|Sync uses cameras, other modern detectors, historical data, as well as loops to detect, verify and serve actual traffic demand.

All detectors have weaknesses, but unless you can afford to place a professional traffic light operator at each busy intersection to assess traffic and run the signals, you will have to rely on automatic detectors of some kind.

Knowing this, why wouldn’t you prefer a more advanced system that can understand demand better and put it to use when trying to move traffic safely and efficiently though the intersection?

Finally, all of this focuses on a single intersection. For busy corridors, traffic engineers must consider coordinating signal timing between each intersection. That’s something In|Sync excels at as well but, that’s a topic for another time.

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Traffic Signals: The Troubling Truth

As a typical motorist who hates sitting at long red lights, it is reassuring to know that there is so much that occurs behind the scenes to control traffic congestion.  As we learned in the first blog (7 Traffic Myths Debunked), there are a number of reasons we get into a jam!

Here’s a ‘crash course’ in traffic engineering which will illuminate the real reasons traffic signals seldom function as we want them to.

Essentially, traffic signals can be considered to have 3 major parts: 1.) Traffic signal heads, 2.) Vehicle sensors or detectors, and 3.) Traffic signal controllers and cabinets.  As with a police officer directing traffic in a busy intersection, these parts must work together to make the best decisions for optimal traffic flow. Thinking of the officer analogy, this makes sense: his brain would be the signal controller; his eyes the sensor; and his hands the signal head. He must effectively observe, make decisions regarding signals, and communicate them to motorists to ensure their safe and efficient travel; this is the same way traffic signals work.

1. Traffic Signal Heads

As pedestrians and motorists, we’re all extremely aware of signal heads; they are generally mounted on mast-arms or hung on wires spanning the intersection (aptly called “span wires”) and have three color-coded indications. As many of us have known since playing rousing games of “Red Light Green Light” as youngsters, these lights let us know when to stop and go, respectively. The middle, amber—or more commonly “yellow”—light alerts us to prepare to stop.

Most of us only think of this traffic signal component when expressing our frustration with red lights. When we must abruptly stop at yet another intersection, we’re more likely to shake our fists at that stubborn red light than any other part of the traffic signal system. In addition, these are often the most updated component of the three-part system.  In fact, most signal heads now use Light Emitting Diode (LED) lights in order to conserve energy. While signal heads may have the greatest presence in the public view of traffic signals, they are not as critical in signal optimization as the other two components.

(image source)

2. Vehicle Sensors

The next most infamous component is the subject of many of the myths described in the first blog: the vehicle sensor. The vehicle sensor detects the presence of vehicles at the stop bar of each lane, though it is neither a laser curtain nor a buried scale. The information collected by the sensor is binary, reading either “yes, at least one vehicle is present” or “no, not even one vehicle is present.”

Depending on the technology utilized, a sensor may or may not recognize the presence of more than one car. Once collected, this information is sent to the traffic signal controller for processing. The sensor is usually an inductive loop located beneath the pavement, though newer video technologies are growing in popularity. Two other less popular methods of detection include radar-based detection and detection using wireless magnetometers. Regardless of its form, the role of the vehicle sensor is always to detect cars and communicate that information to the signal controller.

…role of the vehicle sensor is always to detect cars and communicate that information to the signal controller.

Most intersections currently employ inductive loop sensors. Electromagnetic induction-based detectors use coils of wire under each lane. These coils are commonly referred to as “inductive loop detectors” or “loops.” This method works based on the laws of electromagnetic induction; when a mass of metal is moved inside a coil of wire, electricity or an electric pulse is generated. With these sensors, the vehicle is the mass of metal that moves inside the coil of wire buried under the lane. The detector constantly monitors the electric pulse in the loop and informs the traffic signal controller about the presence or absence of a car. Since induction-based detection relies directly on the laws of physics, if configured properly, it offers the most reliability.

While inductive loop sensors provide reliable feedback, installation and maintenance are costly and time consuming. To install loops, the pavement on each lane is cut with a saw in the shape of the loop. Loop wires are then installed into the saw cut and sealed. Over time, pavement moves and as a result, loop wires are broken. Regular pavement maintenance projects also destroy loops, necessitating premature reinstallation.

Furthermore, this methodology is limited: it does not know whether there is one vehicle or a string of vehicles waiting. Loop sensors can only detect the presence of cars idling at the stop bar. As a consequence, loop sensors do not allow for more complex traffic mitigation, which could give preference to busier streets in order to relieve traffic congestion. If you have seen a solitary car on the side street given priority (and green indication) over a long line of cars approaching on the main street, this limitation is likely the reason.

Another detection method is based on video image processing. This method has gained popularity because its installation is less intrusive and its maintenance is easier. In video based detection, cameras are mounted on mast arms from which vantage point they record approaching traffic. 

These (video) sensors are the little white, tubular objects sticking up from the signal mast-arm and staring at you. Fear not, these sensors aren’t recording your speed or your steering wheel drum solo!

The cameras send real-time images to an image processor. When pixels in the image don’t change over time, the processor saves them as a “learned” background image. The processor compares the real-time image with the learned image by literally subtracting the real-time image from the learned background image. If objects are left over from this subtraction process, the processor reports that a new car is present in its detection zone.

Installing cameras on mast arms is the only invasive step for deploying video image processing detection. If a camera fails, unlike replacing the loop, where the pavement has to be saw cut again, it is relatively easy to replace the failed part. However, video detection is susceptible to camera issues such as being blinded by the sun, fog, and snow. Additionally, shadows of moving objects and reflected light from wet pavements can generate false detection triggers. These false detection triggers bring up green lights even when there are no cars waiting to be served. Fortunately, software exists to readily compensate for these issues, thereby eliminating these issues and optimizing video detection sensors.

3. Traffic Signal Controller/Cabinet

If you’ve ever wondered about the purpose of the metal box at the corner of every major intersection, aside from being a public bulletin board or convenient place to unload an unwanted wad of gum, it is actually the traffic signal cabinet. The cabinet houses the signal controller and other hardware required to run the traffic signal. Based on input from the sensors set up at every intersection and the programming done by a traffic engineer, the signal controller determines the amount of green or “go” time given to each movement of cars. As such, it functions as the brains of the operation—aside from the traffic engineer, of course.

While they may look similar from the outside (i.e., big, metal boxes, possibly covered with graffiti), not all traffic signal controllers run in the same fashion. In fact, there are three modes of signal operations in the popular analog traffic signal controller. Most traffic signals operate using one of the following modes: pre-timed, actuated or synchronized.

The simplest of the three modes is the pre-timed mode. In the pre-timed mode, each vehicle movement is given a predetermined amount of time regardless of vehicle demand. Vehicle sensors are not required at these intersections because the system runs on a set timer rather than responding to the presence or absence of cars at the stop bar. Synchronization between signals is possible with the pre-timed mode; however, this method is very inefficient in terms of mitigating traffic or serving the needs of commuters and is not recommended.

Coming in as the polar opposite of the pre-timed mode is the actuated mode. In the actuated or “free” mode, there are no timing plans or synchronization between lights.

Each signal gets input from its own sensors and changes signal indication (i.e., the color of the light on the signal head) in a sequential fashion, regardless of what other signals on this main route are doing.

That is, with actuated signals, each intersection is something of a “first come, first served” affair: green lights and red lights are distributed according to the presence or absence of cars detected by the sensors at each individual intersection.

The advantage of the second mode, actuated mode, is that delay—that pesky wait time—on the side street is minimal during periods when traffic flow is light. The disadvantage is that delay experienced at the intersection as a whole will increase as motorists on the main street will need to stop while vehicles on the side street are being served. Also, there will not be any synchronization between signals. This lack of coordination will cause a substantial increase in stops, vehicle emission, delay, and fuel consumption. Thus, while isolated signals may effectively operate in actuated mode, it is not efficient to operate a string of signals in actuated mode.

The final and most efficient option is the synchronized mode. In this mode, the signal rests in green for the main street unless vehicles are present in the side street. This design takes into account the greater volume of traffic travelling through main streets and distributes more “go” time to that thoroughfare. Often, agencies may not install sensors on the main street because the signal will always revert to the default of giving green time to vehicles travelling on that street. The assumption is that more vehicles will always be present on the busier corridor.

Additionally, it is possible to synchronize multiple signals in an arterial in this mode, thereby allowing even greater customization for the needs of commuters along that route.

The Holy Grail every traffic engineer seeks is an efficiently installed and maintained system that allows for flexibility depending on changes in traffic patterns; thus, the synchronized traffic signal is the preferred option for effectively mitigating traffic congestion in city driving conditions.

Synchronizing semi-actuated signals allows whole movements of vehicles along a specific route to travel in what are called “green tunnels.” A green tunnel is a passageway of green lights enabling a group, or “platoon,” of vehicles to travel uninterrupted by congestion-causing red lights. Moreover, all signals that are synchronized must have the same cycle length. The cycle length is defined as the time it takes the timer within the controller to turn 360 degrees. In terms of what you see as a motorist, a cycle length is the time it takes to move from the beginning of green light to the end of a red light for your main street. Within that duration, the controller is required to sequentially serve all movements that have a demand. While this information may seem technical, we’ve all seen it in action. In reality, where an intersection fails to serve all movements sequentially—that is, give each direction of traffic its share of green lights—the resulting chorus of car horns would alert you of the lapse.

The cycle is further divided into “splits,” where splits are the percentage of a cycle allocated to a particular vehicle movement. Think of it this way: at an intersection of a main thoroughfare and a smaller side street, the main road is going to get the larger “split” of the cycle or the larger cut of green time. There is also one fixed point in the cycle that can be considered as a baseline and referenced. Offsetting this fixed point from one intersection to another synchronizes signals. If you’ve ever sung a tune like “Row, Row, Row Your Boat” in rounds, you can imagine the first singer as establishing the baseline upon which the other singers find their rhythm and melody. Much like when singing in rounds, synchronizing traffic signals in this fashion allows for a more complex, harmonious flow of traffic.

How Signals Communicate

To delve even deeper, traffic signals should be interconnected to form a network to ensure optimal performance.  Since roadways already function as a network, it makes sense for the technology responsible for directing traffic on these roads to communicate the same way.  Traffic issues don’t occur in isolation; if they did, we wouldn’t need to listen to the traffic reports during our morning or evening commutes.

Read an Engineer’s Perspective on the Signal Communication Problems

Traffic runs best when signals are interconnected and work together in a synchronized manner. Common media used for interconnection include wireless, fiber-optic cable, and twisted pair copper conductors. Often, this communication network covers the entire city and terminates at the office of the traffic engineer. Interconnection provides the engineer with remote access to the signal but synchronization is not perfect. Nevertheless, the overall effect of networking signals maximizes the efficiency of signal performance.

Summary

We hope you’ve begun to understand some of the technical reasons behind the difficulty of optimizing traffic signals.  The foundational equation behind most of the software models is flawed, and furthermore, the process of synchronizing the signals based upon those flawed equations is itself a time consuming and expensive endeavor. 

Since synchronizing traffic signals in this traditional manner is so inefficient, between 70 and 90 percent of traffic signals in the United States are not synchronized.  In fact, the National Transportation Operation Coalition grades U.S. traffic signal operation a grade D. 

However, it’s not the engineering limitations alone that earn us such a dismal grade.  Rather, a number of social and political factors prevent traffic engineers and city planners from making significant and technologically feasible improvements to traffic signal operation.  I will explore these limitations in greater detail in later blogs. For now, read more on the latest technology used for traffic control at www.rhythmtraffic.com.

Bo LaisTraffic Signals: The Troubling Truth
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7 Traffic Signal Myths Debunked

 

Have you ever wondered how we process traffic signal information? It all starts with our early driving experiences and actually, over time, we learn to automatically block out traffic signals during our daily commute. That is, we block out the signals until we get caught at lengthy, numerous red lights over a relatively short distance. At that time, our focus then turns to the nuisance they cause – long delays to our final destination, road rage, and misconceived notions about the benefits of traffic signals (or lack thereof).

Click here to read about Oscar’s story.

In reality, the purpose of traffic signals is to solve traffic conflicts. We all want to be in the same place at the same time, but this would only lead to accidents, severe traffic jams, and ultimately, chaos.

To help you and those motorists out there, there are a number of preconceived ideas about traffic control that are absolutely incorrect and lead to frustration and unnecessary stress. I’d like to share the 7 myths that motorists actually believe about stoplights and other traffic signals.

Myth #1: The Flasher. If you flash your car’s high beams at a stoplight, it will turn from red to green more quickly.

Reality: Traffic light sensors do not detect headlights; they use other means to detect traffic at a light. Emergency light sensors read encoded and proprietary infrared signals from special emitters installed in emergency vehicles.

Myth #2: The Pusher. If you push the pedestrian crossing button multiple times or in a set pattern, you can trigger a green light faster.

Reality: While many of us are guilty of pushing the cross button over and over, it has no impact on how quickly the signal changes. When you push the button, the event gets recorded in the memory of the traffic signal controller (just as if you push an elevator button). This signal is then used to time the light change, nothing more.

Myth #3: The Weight-Builder. The amount of weight present at an intersection triggers a green light.

Reality: The weight of a vehicle has nothing to do with triggering a green light indication. Vehicle presence is detected by inductive loop technology, which works on the principle of electromagnetic induction, and all that is necessary is a vehicle having sufficient iron in the metal for detection and stopping over the inductive loop which signals the traffic controller that there is traffic waiting at the intersection.

The only vehicles potentially affected may be motorcycles or mopeds, but this can be overcome by drivers pulling near the corner of the lane near the stop bar painted at the intersection.

Myth #4: The Unseen. Traffic lights are changed by tripping an invisible curtain that covers only a section of the lane.

Reality: Vehicles trigger the inductive loop (see Myth #3), and as long as they have sufficient metal and stop in the right spot – just before the stop bar, the thick white line painted on the pavement that signals to motorists where they should stop in order to be effectively detected by the traffic controller.

Stop too far past or before the bar and the pavement sensors can’t detect your presence. As a result, motorists who do not stop at the stop bar generally end up waiting longer at intersections!

In order to be detected, motorcycles and bicycles also must stop before the stop bar. The in-pavement detectors are most sensitive at the corners. So, motorcycles have a better chance of being detected if they stop at the corner of the lane just behind the stop bar too.

Myth #5: Remote Controlling. You can turn the stoplight green through the use of a universal television remote.

Reality: You cannot program a remote with a special code in order to change traffic signals. This myth stems from an Internet spoof and holds no truth. Sensors associated with preemption systems are programmed to only detect certain infra-red signals from emergency vehicles and cannot be fooled or tricked into activating a green light for passenger vehicles; and for good reason.

Imagine the chaos if every impatient driver with access to a Radio Shack, took it upon himself to direct traffic according to his whims!

Myth #6: Big Brother Is Stopping You: Governments or cities purposely implement policies that do not allow traffic to efficiently flow through intersections.

Reality: Most traffic lights are poorly timed and inefficient because transportation agencies don’t have the personnel or financial resources to update their timing plans or implement newer traffic technologies that could reduce delay at intersections. Without experienced personnel or money for updates and improvements, cities are unable to improve the efficiency of their traffic control systems and motorists, by default, are stuck wasting time and fuel at red lights.

Myth #7: The Safety Patrol. Traffic signals always reduce collisions.

Reality: The key word here is “always”. Traffic signals do help prevent collisions, but since only 40% of collisions occur at intersections, and drivers often get into accidents by trying to beat a red light or disobeying traffic rules, the truth is that poorly timed signals will not eliminate human actions, and therefore, will not eliminate all accidents.

Nevertheless, optimizing traffic signals to mitigate traffic conflicts is in the best interest of everyone. Coordinating traffic signals can reduce driver frustration, cut down on the number of cars running red lights, and decrease the number of traffic accidents occurring at our intersections.

Bottom line:
Motorists form their own opinions based on urban myths about traffic signals and controls. Our job, as traffic experts, is to minimize the number of traffic aggravations experienced by motorists.

The ultimate goal is signal optimization for each and every thoroughfare – this can be accomplished through synchronized traffic signals, vehicle detection systems, and communication between intersections. Learn more about the latest technology used for traffic control at www.rhythmtraffic.com

Bo Lais7 Traffic Signal Myths Debunked
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