Rhythm Engineering Blog

The Success of the Kansas City Streetcar

By Ryan Broomfield, Systems Architect

As a resident of downtown Kansas City, Missouri and a Systems Architect at Rhythm Engineering, I am filled with an enormous sense of pride and satisfaction to be making a difference nationally and also locally in my own community.

One of my favorite corridors we have developed technology for is the corridor that serves the Kansas City Streetcar. The Streetcar is a project that has exceeded every projection and has been an instrumental addition to the ongoing revitalization of Kansas City’s downtown. Original estimates of riders were highly ambitious and put daily ridership at 2,700. Incredibly, ridership has beat all expectations and averages over 5,000 per day resulting in numerous expansion initiatives and funding approval for additional cars.

Not only has the Streetcar been successful based on its own projections, it is one of the most successful streetcar projects in the country beating out similar projects with both free ridership and paid fares in cities across the U.S., including Atlanta, Washington D.C. and Seattle.

The system has become a national example to other cities of how a streetcar can be successful and for locals it is a strong source of KC pride. Additionally, the system has earned rave reviews from riders about being clean and well-lit, as well as being the transportation method of choice for downtown entertainment options.

Rhythm Engineering has contributed to this project by being the adaptive provider of choice for the traffic signals located along the Streetcar route. Balancing vehicle, pedestrian and streetcar demand in the heart of a major city’s urban grid is a challenging task for any adaptive system, and In|Sync delivers proven results.  

In|Sync accomplishes this feat by incorporating Transit Signal Priority (TSP) into its core algorithm. Many other TSP solutions take a naive approach and simply extend green intervals, create a forced early return to green, or, in some cases, abandon coordination and go into a transition state. In|Sync works with TSP differently in that we balance TSP demand as another type of important demand, rather than performing a deliberately responsive action.  It also doesn’t abandon coordination to accomplish this goal.

While this approach is less direct, it has resulted in significant improvements for the Streetcar’s travel time since it began public operation in May of 2016. A significant, measured reduction in travel time for the Streetcar was achieved over the baseline operation of In|Sync’s adaptive technology when measured with before/after studies. Here are some travel time studies that were performed on the Streetcar corridor with TSP enabled and without TSP enabled.

Personally, it has been a pleasure to ride the streetcar. I have used it for easy access to cherished Kansas City landmarks, such as Union Station and World War 1 Memorial, all the way down to the historic River Market, where fresh produce can be bought at the Farmers Market. Along the way, there are a variety of wonderful businesses, some old, some new, that benefit from the increased exposure of the streetcar. It’s absolutely worth spending a few days to discover some new favorite haunts, destination spots and storefronts.

Overall, we are all proud at Rhythm Engineering to have been a part of such a successful project and to have delivered on our mission of helping citizens get to their destinations faster and safer. We look forward to contributing to the future expansion and success of the Kansas City Streetcar.

Rhythm EngineeringThe Success of the Kansas City Streetcar
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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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In|Sync’s Dynamic Period Explained

By Lisa Honeyman, Technical Writer

In|Sync 1.6 introduced many improvements that expanded features that were already established in In|Sync 1.4. Dynamic period is one of those features that has been an integral part of In|Sync’s method for proactively serving demand during changing traffic patterns. With In|Sync 1.6, we’ve incorporated feedback regarding In|Sync 1.4’s dynamic period and built an improved solution that responds to changing traffic conditions in a more effective manner.

In|Sync uses dynamic periods to determine when, how and how much a period can change, ensuring that In|Sync can constantly adjust to shifting traffic patterns. It requires that two conditions exist:

  • All intersections have the same period
  • All intersections in a configuration belong in a coordination group

Once these conditions are met, you can enable the dynamic period feature, define limits on how much a period can change and specify a reference intersection where global offsets can be applied. In|Sync then determines whether a change in period is necessary, the type of change and the amount of change by using a three-step approach.

Step 1 – Each intersection evaluates miscellaneous time left in the previous two periods

In|Sync Processors at each intersection evaluate how much miscellaneous time occurred in the previous two periods at the intersection. Miscellaneous time is excess time remaining at the end of the period after all demand for all phases has been served at least once.

  • If an intersection reports that the previous two periods did not have any available time, the intersection initially votes to increase the period.
  • If the previous two periods reported more than 20 seconds of available miscellaneous time, the intersection initially votes to decrease the period.
  • Otherwise, the intersection initially votes to keep the existing period.

 Step 2 – Each intersection compares Maximum Average Vehicle Density against the previous time period

The In|Sync Processor at each intersection then goes further and does the following analysis based on the maximum average vehicle density over the previous time period (dynamic period occupancy):

  • If the initial vote is to increase period length and:
    • Dynamic Period Occupancy is 5% or less, the vote is changed to decrease the period.
    • Dynamic Period Occupancy is greater than 5% and less than 20%, the vote is changed to keep the existing period.
    • Otherwise, the vote remains to increase the period.
  • If the initial vote is to keep the existing period length and:
    • Dynamic Period Occupancy is less than 20%, the vote is changed to decrease the period.
    • Dynamic Period Occupancy is between 20 – 50%, the vote remains to keep the existing period.
    • Otherwise, the vote is changed to increase the period.
  • If the initial vote is to decrease the period length, the vote is to decrease the period.

Step 3 – Collect votes from intersections and determine period change

When dynamic period is enabled, In|Sync designates one intersection within the tunnel to be the decision maker regarding dynamic period changes. This intersection’s In|Sync Processor receives the votes from each intersection and ultimately makes two decisions: the type of period change and how much it should change. This evaluation happens:

  • Once every 60 seconds
  • Only if all processors are online
  • Only if there is not a period change already proposed
  • After two full periods have elapsed since a period change

 Determine period change type

The decision-making intersection changes the dynamic period based on the following criteria:

  • If all processors vote to decrease, then the outcome is to DECREASE
  • If some processors vote to decrease and some processors remain the same, then the outcome is to REMAIN THE SAME
  • If any processor votes to increase, then the outcome is to INCREASE

 Determine period change value

Only the decision-making intersection determines the new period value. This value is based upon the following criteria:

  • If the vote is increase and there has never been a dynamic period change, increase the period by ¼ from the current period.
  • If the vote is increase and the previous dynamic period change was an increase, increase the period by ½ from the current period.
  • If the vote is increase and the previous dynamic period was a decrease, increase the period length by ¼ from the current period.
  • If the vote is decrease and there has never been a dynamic period change, decrease the period by ¼ from the current period.
  • If the vote is decrease and the previous dynamic period change was a decrease, decrease the period by ½ from the current period.
  • If the vote is decrease and the previous dynamic period change was an increase, decrease the period by ¼ from the current period.

After determining the type of period change and how much it should change, the deciding intersection processor sends the new period length to each intersection in the tunnel, which is applied to the next period. This process is continual, meaning that when dynamic period is enabled, In|Sync applies this analysis every period, continually adapting the length of the period to best accommodate current traffic demand.

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Adaptive Detection Cameras: Is it a Smart Traffic Management Choice for Your City?

By Thomas Officer, Technical Coordinator

The Operations Department at Rhythm Engineering has often been asked why we use cameras instead of induction loops in order to recognize vehicle presence for our adaptive traffic control systems, In|Sync. Today I’ll answer that questions.

Learn more about In|Sync’s detection camera options

Many cities use induction loops to provide information on traffic flow in order to adjust traffic signals and improve vehicle movement across corridors. Traffic professionals can all agree that the induction loop system is affective — loops provide valuable information to traffic engineers and professionals at intersections around the world — however, loops aren’t always the best answer.

With many systems, loops provide a limited amount of information because they can only detect vehicles that are directly above them. Often times motorcycles or bicycles who are at the intersection go unnoticed — a problem that is frequently addressed in the traffic industry. And while there are always advancements in the traffic industry that have improved loops over time, another problem comes up: money.

Inductive loops have a very high cost of installation because the loops need to be installed within the roadway — a process that can cost a lot. But it’s more than just money; a large construction project can take up time and disrupt normal traffic for motorists — meaning a community has to deal with the changes and frustrations that come with updating the intersection.

These are just some of the reasons are why traffic professions should consider a shift in detection technology, and it is also why Rhythm Engineering decided to develop other options of adaptive traffic management in the first place. While inductive loops get the job done, cameras could do it better.

In|Sync is Rhythm’s Adaptive Traffic Control System that utilizes the In|Sync Processor with detection cameras in order to respond to actual vehicle demand at the intersection. With high-quality cameras, our algorithm can best detect vehicle presence at any intersection and respond to actual demand at the intersection in real time. (Learn more about In|Sync here).

One major advantage of adaptive cameras over induction loops is that it does not require a civil engineering firm or an outside company to install, unlike with in-road loops. With In|Sync, your city technicians can easily install the cameras in a quick and efficient manner, wire them up to the intersection cabinet and allow the Rhythm Engineering Operations team to configure the cameras. (Learn more about installation and In|Sync deployment here).

Once installed, In|Sync will immediately start responding to real-time traffic flow. The detection system will begin adapting signals in a more efficient and safer manner by recognizing the actual vehicle presence in real time.

Rhythm’s In|Sync cameras are a more intelligent way to decrease congestion while increasing the efficiency of traffic flow. While inductive loops work, the cost of installation and the possibility of missing vehicles are risks that your intersection might not want to take. As you know, we as motorist want to get to point A & B faster, with all green signals and with less traffic jams — all without the hassle of construction. In|Sync makes that happen.

Not only can In|Sync cameras be used to control traffic lights by way of detecting vehicles, but it will also recognize bicycles and motorcycle riders at the intersection stop bar. This technology transmits information over IP addresses to your controller, which allows more effective and advanced traffic control of signal lights.

By using In|Sync in your detection library, you are increasing the amount of information you gather about traffic flow at any particular intersection. In comparison to inductive loops, you can cut installation costs in a major way by allowing your own technicians to install In|Sync. And down the line, Rhythm always provides the support needed to insure that your cameras will work at a superior performance level, that your traffic safety and speeds will increase, and that your citizen complaints with go down.

Rhythm EngineeringAdaptive Detection Cameras: Is it a Smart Traffic Management Choice for Your City?
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In|Sync Transit Signal Priority Functionality

Transit Signal Priority (TSP) is one of the most cost-effective approaches to enhancing the effectiveness and efficiency of transit operations. However, while many TSP implementations have minimal impact on non-transit vehicle travel times, there are cases where TSP deployments have created additional delay for non-transit vehicles. In|Sync’s goal is to actively handle TSP requests to both improve transit travel time efficiency and minimize impact to normal traffic operations. It does this through the use of several priority strategies.

In|Sync supports continuous TSP signals captured through recognized I/O devices. It

does not support TSP devices that produce a pulsating or intermittent signal — but pulsating signals can be converted to a continuous signal within the Priority software.

With In|Sync, traffic professionals can change TSP priority at an intersection based on the goals for the configuration, all because In|Sync treats TSP as another form of demand. It doesn’t cause traditional “transition” scenarios that some TSP solutions generate, and all coordination, early release settings, geometry restrictions, etc. are adhered to while TSP is active.

In addition, In|Sync employs a real-time, active priority strategy in only responding to actual TSP calls from an intersection in order to serve that transit vehicle. It does not support a passive strategy in which signals are adjusted based on pre-defined transit schedules.

TSP at a Local Intersection Level

In|Sync handles TSP on a local intersection level, and not at a global level. This means that if a TSP call is received at one intersection, it doesn’t affect the time along a tunnel across intersections. In|Sync’s behavior is determined by how TSP is configured within In|Sync. When TSP is activated, In|Sync performs a combination of the following behaviors:

  1. Prioritize the currently active TSP phase.

In|Sync serves the phase in which the TSP signal call is received. It does this by prioritizing the sequence or by determining where extra time exists in the period and allowing extra servicing of the phase receiving the TSP call. This feature allows In|Sync to flexibly choose to prioritize the TSP phases with the goal of minimizing impact to other movements.

  1. Extend the currently active TSP phase.

If time in the period and configuration allows for additional service of the TSP phase without impacting other phases, In|Sync automatically extends the currently active TSP phase. This feature allows In|Sync to “hold” the TSP phase longer than originally intended to allow the transit vehicle to get through the intersection.

  1. Create demand on a phase when TSP is active but no vehicle queue exists.

In|Sync brings up the empty movement when no vehicles exist in anticipation of the arrival of the transit vehicle.

  1. Eliminate extensions on non-TSP phases during TSP actuation.

In|Sync schedules time to service other phases at the intersection but does not hold additional time for late arrivals or higher-than-anticipated demand. This promotes an “early return” to green for the TSP phase. In|Sync then determines which phase demands service and serves that phase.

  1. Gap out non-TSP phases during TSP actuation.

In|Sync terminates any active non-TSP phases at the intersection, allowing the system to then determine which phase requires service. This method heavily promotes an “early return” to green for the TSP phase.

In|Sync gives priority to the TSP movement as part of its normal green time allocation. It does not step out of its plan to serve a TSP movement and then struggle to get back in step with other intersections; instead, it stays coordinated throughout the TSP call and service. In normal operating conditions, the In|Sync algorithm chooses which phase pair to serve based on how many vehicles are waiting and how long they have been waiting, where more vehicles and longer wait times equals higher priority. When configuring TSP inputs within In|Sync, you assign the same type of priority of service to each type of TSP signal. In|Sync has four different TSP levels: Off, Low, Medium and High.

OFF:

No special treatment

LOW:

When a TSP signal is received, the phase receiving the TSP signal is extended if it is currently being served and time exists in the configuration. This serves the TSP phase sooner and allocates more time to the phase.

MEDIUM:

When a TSP signal is received, the phase receiving the TSP signal is extended if currently being served and time exists in the configuration. It also stops any other phase from extending beyond its scheduled time during a TSP call. This causes the system to serve other movements faster, cutting them off at their scheduled time, even if additional vehicles are present for that movement.

HIGH:

When a TSP signal is received, the phase receiving the TSP signal is extended if currently being served and time exists in the configuration. It also cuts the scheduled time for other non-TSP phases to the minimum green. This priority stops service on other phases before all demand for that movement is served.

Now that you know how In|Sync’s TSP functionality works, learn more about In|Sync’s additional operating features by visiting the website here. Want to know how In|Sync has changed traffic for other communities around North America? View case studies here.

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What SB 743 Meant for the Traffic Industry

By Justin P. Schlaefli, PE TE PTOE 

On September 27, 2013, the way we look at transportation in California changed. On that date, Governor Jerry Brown signed SB 743 into law, sending shockwaves through the transportation industry — which will be felt for years to come.

SB 743 involves a major change to the CEQA Guidelines requiring the use of Vehicle Miles Traveled (VMT) metrics rather than level of service based metrics for measuring vehicle impacts on transportation facilities. The intent of this change is to reduce reliance on the automobile by encouraging development in “Transit Priority Areas” as well as remove disincentives to infill development and reduce greenhouse gas emissions. While indeed serving critical goals, this change has secondary effects which influence the viability of many roadway improvement projects as well as impacting infrastructure funding mechanisms that California has relied on for decades.

While the Governor’s Office of Planning and Research (OPR) has been engaged in drafting and re-drafting CEQA Guidelines for the past several years, the transportation industry is still catching up. Current versions of the draft guidelines include the following key points:

  • Development within one-half mile of an existing major transit stops may be presumed to have a less than significant transportation impact. This mean development of this type may not be required to fund transportation infrastructure improvements.
  • Transit, bike and pedestrian projects may be presumed to have a less than significant transportation impact making them easier to construct.
  • Induced travel effects of major roadway capacity expansion projects are required to be analyzed meaning that road extensions and widenings may no longer be an infrastructure “improvement” but may have an “impact” under CEQA. This could impact the funding and viability of many infrastructure projects.
  • Safety impacts of road widening projects may be considered significant

As a result of many of these changes, funding and planning for transportation and infrastructure projects must be revised to comply with the new rules and priorities. The transportation industry is already headed in this direction, but important questions remain. Fundamentally, the challenge of the future transportation professional will be how to do more with less. It is unfortunate that the characteristics that often occur in the highest congestion locations also occur in areas which are most impacted by the changes of SB 743.

For example, major transit lines often occur in areas with significant density and congestion. Additionally, infill development typically occurs in areas which are already built-out and often experience congestion. A large part of the funding for local transportation projects has historically come from impact fees driven by the rules of CEQA. With those rules changing, large projects in congested areas could avoid paying these fees or avoid constructing roadway infrastructure. Alternatively, proposed roadway infrastructure could be considered to cause a CEQA impact and therefore be rendered infeasible or have a significantly increased cost.

This leads to the conclusion that we must increase the effectiveness of the existing road system. This must be done in a way that is sensitive to all roadway users. This must also be done in a way that avoids costly projects which lack funding or which induce VMT or cause safety concerns for other modes of transportation.

We must do more with less.

A solution which enhances safety, considers the needs of all roadway users, is cost effective and which avoids major induced VMT is ideal. This means that the transportation professional must be willing to go beyond the “traditional” road improvement projects of the past several decades and must add more tools in the tool box. The successful transportation professional of the future will recognize that creative solutions and funding options will keep residents and stakeholders happy. The shelf life of our existing playbook is about to expire … time for a new set of plays!

 

About the author:

Justin P. Schlaefli, PE TE PTOE is President of Urban Systems Associates, Inc. based in Southern California. He has over 16 years of experience in the transportation industry working for both public and private clients. Questions about SB 743 or transportation solutions which might be the most effective under likely future guidelines, please contact Justin@urbansystems.net

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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.

Rhythm EngineeringA Quick Look at Traffic Sensors
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In|Sync’s Distributed Architecture

By Grant Niehus, Director of Operations

From the very start of In|Sync’s history, it was designed to be a robust and scalable, adaptive traffic control platform. We envisioned that the system would positively impact millions of drivers a day, bringing loved ones to their destination faster and safer.

But to accomplish that goal, we had come up with an architecture that could be deployed quickly and without limits. By designing a system that utilized a distributed architecture we could overcome two significant challenges that adaptive systems encountered at the time:

1. System degradation during communication breaks

2. Limits to the number of intersections that could be added

Traditional adaptive systems relied on a central server to send operational instructions on a per-cycle-basis or even worse once every 5-15 minutes. Not only was this way of doing things slow and inefficient — because the “adaptive” changes were always behind — but more importantly, if the communication from the central server to the intersection was broken, the intersection could no longer operate adaptively.

In|Sync took a different approach and placed the adaptive logic at the intersection level instead of at a Traffic Management Center (TMC). This allowed In|Sync and the adaptive system to continue to operate with 100% efficiency even when a communication outage occurred between the TMC and the field. Events such as internet service provider outages, fiber breaks and network routing issues are becoming every day occurrences and designing a system to mitigate these real possibilities has been something we have worked very hard on.

The other advantage to a distributed network design is that it puts the computational load on the individual equipment in the cabinet rather than on a single server in the TMC. This allows the system to scale to a theoretically unlimited amount of intersections. Each intersection uses its own local, real-time data to alter the signal state and does not rely on network access back to the TMC. From an intersection network of 100+ intersections to a corridor crossing multiple jurisdictions, In|Sync is a solution that can be deployed without limits.

Over the years, we have used our knowledge gained from our 2,500+ intersections deployed and put it into our newest software. I am pleased to share that our brand-new software platform, In|Traffic and InSync 1.6, continues the distributed architecture design that we believe is a pillar to In|Sync’s success and are excited to roll it out to all of our users.

Learn more about In|Sync here.

Rhythm EngineeringIn|Sync’s Distributed Architecture
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Are You Ready for a Revolution? The History and Future of Traffic Lights

By Jesse Manning, Vice President of Business Development

The modern traffic signal is an ever-present fact of life for motorists, but controlling traffic flow through green and red indicators was an idea pioneered long before motor vehicles were the standard mode of ground transportation. In 1868, J.P. Knight — a British engineer and inventor — developed a traffic signal in order to reduce the number of accidents on busy London streets, where horse-drawn carriages and carts, along with pedestrians, ruled the roads. Knight’s invention used semaphores to signal which directions of traffic should stop and which should proceed through an intersection, and at night, the police-operated device used red- and green-colored gas lamps to indicate the same instructions.

Knight’s traffic signal was abandoned in 1869 after one of the gas lamps exploded, injuring the traffic control officer, and traffic signals wouldn’t appear in London again for 50 years. But by the early-1900s in the United States, the growing number of motor vehicles sharing streets with horse-drawn traffic and pedestrians forced other inventors into action. From Lester Wire’s first electric traffic signal in Salt Lake City in 1912 to Garrett Morgan’s famous patent in 1923, America led the way in the development of modern traffic control devices. We had no other choice. The rapid adoption of automobiles as a preferred method of travel spurred the innovation of control devices that were desperately needed by municipal and state governments adjusting to a new normal.

Historically, however, automobiles and infrastructure have operated independently of each other. The driver has interpreted the language of the infrastructure and — at risk to himself and others — may ignore the direction of traffic lights and posted directions, limits and warnings if he so chooses. Even vehicle detection systems, which can make our signals operate more responsively and effectively, simply interpret vehicle presence and behavior.

In 2017, 105 years after the first electric traffic signal was installed in Utah, we are again facing a revolution in traffic that presents both industry and government with challenges and opportunities. True autonomous, driverless vehicles are far from science fiction, and the first freight and transit applications are being piloted around the country. Infrastructure can no longer afford to be passive, and since innovation rarely waits for regulation, governments cannot afford to take a wait-and-see approach.

Fortunately, connected vehicles — those that feature advanced communication capabilities but rely on traditional driver operation — will serve as a bridge between the familiar past and a driverless future. Standards for transferring data to and from vehicles and infrastructure are being set, and traffic technology innovators are working with states and municipalities across the United States to provide better information to drivers from the infrastructure, and to the infrastructure from the cars on the road. While those parties are working to determine which data sets are of most use to both motorists and traffic control systems, there’s no doubt that the connected-vehicle revolution is upon us. Over the next decade, we’ll see exceptionally rapid development and deployment of connected technologies at rates not experienced by the traffic industry in 100 years.

For traffic professionals, it’s critical to begin considering the impact of connected vehicles and infrastructure into both short- and long-term management plans. The industry may still be developing solutions — answers to questions that seemed outlandish just a decade ago — but we should begin to prepare today by researching and investing in powerful, modular platforms that are specifically designed to bridge the gap between traditional solutions and cars of the future.

Because it won’t be long before the most common motorist complaint changes from, “I’m sitting on the side street and no one is on the main street,” to, “Why aren’t your signals providing my new car with real-time travel-time information and signal status?”

Rhythm EngineeringAre You Ready for a Revolution? The History and Future of Traffic Lights
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Virginia Solved Their Traffic Congestion with Adaptive Traffic Control

The state of Virginia had a problem. Across multiple jurisdictions, traffic flow issues were rampant. Each corridor and signal was different, with unique structure, volume, speed limit and control need; but they needed a solution that would solve traffic congestion across the state. What could be done? The answer was simple: adaptive traffic control.

Between 2011 and 2013, Rhythm Engineering worked with the Virginia Department of Transportation (VDOT) and installed In|Sync adaptive traffic control at 111 intersections, comprising 13 corridors in 11 jurisdictions. The goal was for In|Sync to help reduce delay and improve overall traffic safety as compared to the existing static time of day plans that weren’t working for those backed-up intersections.

“We have a little over 3,000 traffic signals that we operate and maintain from VDOT,” said Michael Clements, PE, VDOT Traffic Signal and Arterial System Program Manager. “Most of our corridors in Virginia are running time-of-day plans, so they’re based off of historical traffic data.”

While historical data provides information about vehicles, often times traffic patterns change before time-of-day plans can be updated. In many jurisdictions, signal timing plans are only updated every five years, and traffic patterns can change a lot over that lengthy period of time. Instead of relying on outdated data, Virginia needed a system that could adapt to the changing traffic flows driving through their intersections. In|Sync was the answer.

As an adaptive traffic control system, In|Sync is programed to respond to the actual demand at the intersection. This means, the system will prioritize movements based on the volume of vehicles. It does this by detecting vehicle presence and making real-time adjustments to phase sequencing to more efficiently serve demand.

 

 

The Problems Virginia Needed Solved

Many cities in the state of Virginia were having issues with increased traffic demand and backed up intersections. Before technological advances, the only answer to an increase in vehicle volume was expanding streets in order to accommodate traffic demand.

“We don’t have the option of expanding at all, so getting those facilities in is difficult,” said Justin Hall, Traffic Manager of Winchester, VA.

Due to an increased number of commuters to Winchester or out of Winchester to Washington D.C., peak volume times and increases in summer time traffic patterns, Winchester had a traffic volume their roads couldn’t handle. Winchester is full of historical buildings and was already tightly packed, so expanding roadways wasn’t an option.

Widening the roadways also wasn’t a favorable option in the County of Albemarle. While they had the space to widen, the budget was was creating another problem.

“With a lot of lane widenings you have to buy right of way and they can get very expensive,” said Dennis Rooker, Albemarle County Board of Supervisors (2001-2013), County of Albemarle. “You can expect to pay $10 million a mile for adding one lane, at least. Now we’re looking at a widening project on route 29 North that I think is a couple of miles long, and it’s projected to cost $30 million.”

Rather than invest in a long-term construction project, Rooker found In|Sync adaptive traffic control system to be a better option.

“I’m very much in favor of looking at ways to improve traffic flow and deal with capacity issues that don’t involve laying new pavement,” said Rooker.

In other areas of Virginia, sometimes the issue couldn’t possibly be solved with a widening project because it wasn’t the vehicles that were the problem.

“Our biggest challenges right now are with pedestrians,” said Gigi O’Donnell, Traffic Signal Supervisor in Charlottesville, VA. “Almost every single intersection has pedestrian signals.”

Cities Across Virginia Signed Up for In|Sync

VDOT approached several cities and asked them to join in on the In|Sync deployment, including Winchester, Albemarle County and Charlottesville, among many others. Each city did their own research and were all impressed with the results.

“I think I had a constituent send me some information on In|Sync,” Rooker added. “I looked at some of the third-party evaluations of the In|Sync system, and I became convinced that it would be something that would probably enhance the transportation here.”

“I looked into it and what attracted me most was that it was real-time coordination,” said O’Donnell of Charlottesville.

“We thought that we would partner with Virginia DOT and see if this system would work for us,” replied Don DeBerry, PE, a Transportation Engineer in Lynchburg, VA, whose community also deployed In|Sync with VDOT.

In|Sync Deployments Brought Immediate Results

Over the next three years, In|Sync adaptive traffic control system was deployed at all 111 intersections chosen by VDOT. During that time, extensive amounts of data were collected in order to judge how In|Sync had impacted the corridors it was deployed on. Michael Fontaine, PhD, PE, Associate Principal Research Scientist was one of the lead analysts from the Virginia Transportation Research Council who evaluated the impact of In|Sync adaptive traffic control.

“We definitely saw benefits on the corridors where we deployed, both in terms of moving people safer and faster,” said Fountaine.

All intersections were impacted by the installation and the results were noteworthy in terms of stops, average speed, travel time and accidents. Overall, there was up to a 67% decrease in stops, a 58% increase in average speed, a 36% decrease in travel time and a 17% decrease in total accidents collectively across Virginia. Compared to the previous time-of-day plans, In|Sync’s adaptive technology was positively impacting the state of Virginia.

“We certainly have enough experience to know that with the initial implementation and the corridors are operating much more efficiently than we could have timed them,” responded DeBerry of Lynchburg, VA.

The data clearly illustrated how drastic the improvements were once In|Sync was in place, but the comments from the community were even more telling.

“Citizens were very excited about it. I don’t think I had a single negative comment from a citizen,” noted Rooker of Albemarle County. “I had many people comment that they thought it was one of the better things we had tried to move forward in the county on the transportation side for years.”

The same went for Winchester: “Everything we’ve been hearing has been positive about In|Sync in the two corridors that we have,” said Hall.

Safety Improved Across the State

Beyond the improvements in traffic flow, the safety benefits of installing In|Sync were apparent across Virginia.

“On the safety side, we looked at 47 intersections around the state where we had at least one to two years of data after In|Sync was activated,” said Fountaine. “On average we saw a statistically significant reduction of about 17% in total crashes.”

Imagine what 17% fewer crashes looked like to every city involved in the Virginia deployment of In|Sync. That is countless lives saved and hundreds of thousands of dollars saved from the cost of car accidents.

“We’ve definitely seen an improvement in traffic flow, and it looks like we’re going to see a significant reduction in our crashes. So I’d say job well done, guys.” DeBerry of Lynchburg said.

Overall, the benefits of the system were numerous and better than VDOT could have expected. From constituents to traffic engineers and managers, the positive impacts successfully improved the traffic environment across Virginia.

“It’s just unbelievable,” said O’Donnell of Charlottesville about their In|Sync deployment. “I ride down that way every day now.”

After all the data was reviewed, the Virginia Transportation Research Council also determined the benefit/cost ratio of their entire In|Sync deployment. They found an average ratio of over 8 after just a year of deployment, meaning almost 8 times as many benefits were accrued on those corridors than the cost to install the system initially. That is in one year alone. Overall they found an annual benefit vs. cost of $34.6 million.

“We have quite positive results with the system,” said Clements of VDOT. “Positive enough that we continue to keep installing it.”

Rhythm EngineeringVirginia Solved Their Traffic Congestion with Adaptive Traffic Control
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