By Jesse J. Manning, Vice President of Business Development
I’ve always been the kind of person who hates to do domestic chores – folding laundry, doing the dishes, and especially vacuuming the floor. Don’t ask me why. Maybe it’s because I believe there should be a better way of automating these time-consuming tasks while I work on better and more-interesting things. It’s not that I don’t know where the problems are. I can clearly see when the carpets are dirty or when the sink is full of dishes. I don’t have an issue gathering the data. I have an issue taking action to fix the problem – no time, no interest, and I’m just not that good at it.
Recently, we bought a Roomba at home. We’re quite a team, the Roomba and I. I’ve set it to vacuum the floors every other day at 1:00 pm, and if (through my powers of observation) I determine that the carpets need a little bit more attention, I take a few seconds to tweak the Roomba’s schedule or run it in a particular area. Together, we’ve managed to solve the problem of a dirty house. I collect data and adjust the Roomba to meet my needs, and the Roomba does the heavy lifting: the part I really don’t like to do.
Whether we know it or not, the Roomba Principle applies in so many areas of our everyday lives. In nearly every facet of life, tools have been developed to help us solve problems and free up our time to be more productive.
Given that basic reality, I’ve been somewhat surprised by a recent trend in the traffic industry that promotes investment in observation only. Over the last two years, tools that have been designed to automate data analysis and adjustment of traffic signal timing have been eclipsed by a demand for more and better data-collection systems. Don’t misunderstand: high-resolution data is an incredibly powerful ally in the traffic-signal optimization battle. With it, you can observe efficiencies and inefficiencies down to the smallest details and moments in time. Arrivals on red, arrivals on green, the delay of individual movements – all important data when it comes to better understanding how you can improve your signal timing. But data alone is a half-measure that does not provide the solution.
It’d be like observing my carpets with a microscope. If I collected data that showed 18 dirt particles per square inch within a two-and-a-half foot radius around the front door as compared to eight per square inch in front of the couch, I may know where I need to focus my cleaning efforts. But without my Roomba to follow up, I’m armed with a lot of really interesting yet useless data. My house guests would be less than impressed with reams of data about how dirty my floors are if I didn’t actually clean them.
Since becoming a part of the traffic industry, I’ve heard from hundreds of agencies about the lack of time, lack of staff and lack of resources when it comes to actually fixing signal timing challenges. Those challenges continue, and vendors in the traffic industry owe it to our agency partners to provide consulting, products, and services that matter and actually address their challenges. All the data in the world doesn’t make a corridor flow more efficiently. It won’t ease a frustrated motorist’s mind to know that you’ve got detail to the Nth degree on how poorly your signals are timed. An overabundance of information won’t reduce emissions or improve safety. Data in and of itself is a half-measure, and without the time / staff / resources to take action on the data, traffic industry vendors are doing a disservice to our agency partners by promoting the sizzle and forgetting the steak.
Data is important. We shouldn’t forget it. But we have an obligation to also provide you with the best tools in the industry to fix what the data shows is broken. Keep the solution in mind when deploying your next round of technology upgrades, because knowing your floors are dirty alone won’t solve the problem of cleaning them.
In many ways, government will never operate like a business. But if the goal is to replicate some of the more-efficient functions of a business, there is room for improvement. The three points above focus on streamlining decision-making processes, which seem to be a particular challenge for agencies regardless of location or size. However, if you include citizens in decision-making processes, set firm timelines for implementation of those decisions, and allow those seeking your business to do the heavy lifting, you’ll soon start hearing, “Now 
One of my favorite corridors we have developed technology for is the corridor that serves the 
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 
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
Before vehicles begin to drive people around, there are other technologies that are already on the market to help make motorists’ lives easier.
The Google driverless car, now called 
According to an article in 
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.
When 
Only the decision-making intersection determines the new period value. This value is based upon the following criteria:
Inductive loops have 
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.
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 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:
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.
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.
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.