How traffic signal priority technology contributes to increased urban mobility and improved property values

A recent study conducted by The Ohio State University has revealed that bus rapid-transit lines (BRT) can help increase the property values of multi-family locations in cities. The study was initially located in Ohio and Cleveland was one of the cities to show this type of property value improvement.

Specifically for Cleveland, the study showed that overall residential property values grew by nearly 15 percent near the Greater Cleveland Regional Transit Authority's seven-mile HealthLine along Euclid Avenue from downtown Cleveland through University Circle to East Cleveland. The rate of value improvement was approximately 41.5 percent when isolated to the value of multi-family residences.

This is one example of many urban locations that are making significant investments into transportation and transit systems across the country. These implementations are significant because they can link residents with critical infrastructure, such as schools and universities, hospitals and health networks, employment, retail shopping and recreational activities.

In addition to Cleveland, the study reviewed BRT systems in other regions, including Boston, Chicago, Eugene, Ore.; Everett, Wash.; Kansas City, Mo.; Los Angeles, Miami, Oakland, Calif.; Pittsburgh and Seattle.

How TSP assists BRT

BRT is different than traditional fixed-route bus lines since it offers dedicated service lanes, added frequency of routes, traffic signal priority and elevated platforms and stations that travelers can easily access.

The transit signal priority (TSP) system plays a significant role in providing the enhanced benefits of BRT. Many urban and municipal leaders today have been inquiring about this technology, what it is and how it works. This interest is now driven by the fact that BRT lanes currently utilize either radio or GPS-based TSP. However, AI/cloud-based TSP are now leading the way in helping cities improve their mass transit systems to alleviate gridlock and traffic congestion, improve on-time performance of mass transit networks, assist in the arrival of emergency vehicles and increase rider levels that have socioeconomic and environmental benefits.

How TSP works

Smart traffic light systems and the cloud technology platforms they operate on are now designed to manage and predict traffic more efficiently, which can save money and create more efficiencies not only for the cities themselves but also for drivers. Modern AI and machine learning technology can process highly complex data and traffic trends and suggest optimum routing for drivers in real-time based on specific traffic conditions.

Conventional TSP systems available typically consist of two parts: a unit in the traffic cabinet and another unit placed on the vehicle. The transit priority logic is the same, regardless of the detection and communication medium. When a vehicle is within predetermined boundaries, the system places a request to the signal controller for prioritization. Signal controllers were configured with static estimated travel times because the original systems used fixed detection points. Since travel times are dependent on several environmental factors, the industry implemented GPS based, wireless communication systems. With this method, vehicles found within detection zones replace the static detection points and the vehicle’s speed is used to determine arrival time.

As a result of improved processing power, transit system technologies can now take advantage of the huge gains made in the areas of AI and machine learning that were previously reserved for widely known tasks such as image recognition and apply them to longstanding traffic problems to generate insight on the mix of density, traffic and overall rate of flow in a region. Furthermore, these optimized algorithms can analyze large volumes of data to learn not only local traffic patterns but also cross-region traffic flows, resulting in the ability to redistribute traffic flow more optimally for all road users at all times of day. AI-powered TSP systems leverage the power of the cloud to track and learn the patterns of transit vehicles to inform intersections of the arrival of these vehicles, giving them frictionless travel along their routes while minimizing disruption to general traffic. Municipal transit systems can access these new insights from these systems to make better decisions that serve riders, their operations and their communities.

These smart traffic platforms allow cities to build upon current investments in infrastructure to deploy city-wide TSP, avoiding the need to add the bulky and expensive field equipment of conventional signal priority systems. To enable safe and secure connections with traffic signals, each city requires one device for use that is a computer that resides at the "edge" and serves as the protective link between city traffic signals and the platform. It is designed to securely manage the information exchange between traffic lights and the cloud platform. It is the only additional hardware necessary and depending on the existing city network configuration, the platform may receive vehicular data directly or via the city’s network using secure connections. Communities benefit from having smarter infrastructure that adapts to real-time traffic conditions instead of being stuck with statically programmed infrastructure that can quickly become ill-suited to the dynamic nature of traffic.

How TSP helps neighborhoods

The number of American cities currently facing a housing crisis increases by the day, generating movements to demand the densification of single-family zoned neighborhoods and the shift away from car-centric development patterns. Many densification efforts call for enhanced transit service to encourage residents to ditch cars in favor of transportation options that support denser development. Modern TSP systems can serve as a tool to rapidly expand and enhance transit service without costly transportation infrastructure improvements that might hinder densification efforts.

Now that TSP systems can run primarily in the cloud, the industry is entering a new era where transit agencies and partner traffic agencies can leverage data from cloud-based TSP systems to show communities where infrastructure improvements make sense. Perhaps most importantly, advanced TSP systems can also empower transit agencies to demonstrate meaningful progress toward climate emission goals by minimizing transit vehicle braking and idling time at red lights and utilizing transit vehicles more effectively in operations planning.

With these advanced TSP technologies in place, urban regions and community hubs can enjoy a renaissance of mobility options, connecting residents with desired transit locations in a more efficient and environmentally friendly way. What’s more, the improved way of life will help these communities flourish in ways that translate into prosperity for everyone.

Dustin Harber is LYT’s chief technology officer. Bringing more than 11 years of engineering experience in the automotive R&D space, he passionately shares LYT’s vision of providing communities with seamless, efficient transportation of all modalities.