4C — June 17 - July 21, 2022 — Owners, Developers & Managers — M id A tlantic Real Estate Journal
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Electrical Contracting/Lighting
By Timothy Menard, LYT How AI & machine learning are now reshaping the way transit systems move traffic patterns
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f the many ways arti- ficial intelligence (AI) and machine learning
and traffic trends and suggest optimum routing for drivers in real-time based on specific traffic conditions. As a result of drastically im - proved processing power, tran - sit system technologies are now used in various IoT (Internet of Things) devices to achieve real-time image recognition and prediction that took place in legacy data centers during the last half century. This new decentralized-focused architec - ture helps increase the imple - mentation of machine learning and AI. Today’s recognition algorithms offer enhanced insight on the mix of density, traffic and overall rate of flow. Furthermore, these optimized algorithms can leverage data points by region resulting in a streamline pattern to reduce traffic problems while redis - tributing flow more optimally. Municipal transit systems can then make better decision- making power, and the control system has a much higher de - gree of failure tolerance as was previously demonstrated in legacy hub-and-spoke systems. AI Is Already Impacting Transit Systems These technologies are al - ready being deployed around the country. As one example, the Santa Clara Valley Trans - portation Authority (VTA) in partnership with the City of San José has been piloting a cloud-based, AI-powered tran - sit signal priority (TSP) system that utilizes pre-existing bus- fleet tracking sensors and city communication networks to dynamically adjust the phase and timing of traffic signals to provide sufficient green clear - ance time to buses while mini - mally impacting cross traffic. Because the new platform leverages pre-existing infra -
structure, it required no ad - ditional hardware installations inside traffic signal cabinets or buses. And unlike traditional, location-based check-in and check-out TSP solutions, the platform processes live bus location information through machine learning models and makes priority calls based on estimated times of arrival. The platform has so far improved travel times on VTA’s route 77 by 18% to 20% overall, equat - ing to a five- to six-minute reduction in signal delay. The cloud-based transit sig - nal priority system combines asset management and auto - mation to produce a system capable of providing services to an entire region. Unlike hardware-based systems, this platform uses pre-existing equipment and leverages cloud technology to facilitate operations. This removes the need for vehicle detection hardware at the intersection because vehicle location is known through the CAD/AVL system. This enables both priority calls from greater distances away from signals and priority calls coordinated among a group of signals. Fur - thermore, the system provides real-time insights on which buses are currently receiving priority along with daily re - ports of performance metrics. The advanced transit signal priority systems available today 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 commu - nication medium. When a ve - hicle is within predetermined boundaries, the system places a request to the signal control - ler for prioritization. Since the depth of knowledge and inte - grated service platform – com - bined with our industry repu- tation as a hands-on, nimble and collaborative third-party provider – in selecting us for this assignment,” said J oseph Lowry , LMC senior vice presi - dent of acquisitions and busi - ness development. “This is par - ticularly gratifying because the client’s due diligence process was incredibly thorough, and the property owner met with a number of regional, national and international commercial real estate services firms.”
original systems used fixed detection points, signal con - trollers were configured with static estimated travel times. Since travel times are depen - dent on several environmental factors, the industry imple - mented 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. The platform allows cities to build upon current invest - ments in infrastructure to de - ploy city-wide TSP. To enable safe and secure connections with traffic signals, each city requires just 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 se - curely 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 con - figuration, the platform may receive vehicular data directly or via the city’s network using secure connections. Sophisticated Process for Prioritizing Traffic The system’s method of placing priority calls to traffic signals is more sophisticated and is not constrained to fixed- point locations. Unlike the current state-of-the-art of placing priority calls from the detection of buses at specific locations that starts a pre- programmed time of arrival, this platform uses a “vector - ized” approach. In mathemat - ics, a vector is an arrow rep - resenting a magnitude and a direction. In this platform’s software, the arrow points in Additionally, in Old Bridge, NJ, LMC was tapped as man - aging agent for The Shoppes at Old Bridge. Located on Rte. 9, the 125,000 s/f shopping center is home to Crunch Fit - ness and Worldwide Flooring Design Center, along with a mix of apparel stores, spe - cialty shops, restaurants and service providers such as Loft, Chico’s, Panera Bread and Lash Lounge, among others. The center’s new ownership is gearing up to launch a prop - erty repositioning/renovation. “Our new client recognized
the direction of the traffic light and the magnitude is the trav - el time. When the system is set up, traffic signals, bus routes, and bus stops all get a digital representation on this vector. This ends up producing a digi - tal geospatial map where soft - ware is then able to track bus progression along bus routes. This results in a system that can dynamically place transit calls regardless of its location. Instead, the system makes precise priority calls based on the expected time of arrival which is the basis for all TSP check-in calls supported by all signal controller vendors. And due to the nature of the tracking algorithm, any sig - nificant changes to ETA can be adjusted. For example, if a bus was predicted to skip a bus stop but didn't, the system will detect the change and adjust the priority call accordingly. The combination of AI, ma - chine learning and cloud- based technology all have great potential to not only improve the current mass transit system but reimagine it all together. This advanced technology is already proving how it can improve coordina - tion between GPS, naviga - tional apps, connected autos, and even taxi and ride-sharing services to efficiently combine into a single transit entity based on real-time data. In the not-too-distant future, it is expected that connected self-driving cars and trucks will be more prevalent on the roads and highways, offering even greater potential for AI to reduce both the duration and risk of rapid mobility. T imothy Menard is the founder and CEO of LYT, provider of cloud-based smart traffic solutions. MAREJ
are poised to improve modern life, the promise of impacting mass transit is significant. The world is much differ - ent compared
Timothy Menard
with the early days of the pandemic, and people around the world are again leveraging mobility and transit systems for work, leisure and more. Across the U.S., traditional mass transit systems includ - ing buses, subways and per - sonal vehicles have returned to struggling through gridlock, rider levels and congestion. However, advanced AI and machine learning solutions built on cloud-based platforms are being deployed to reduce these frustrations. Transportation Presents Exciting Opportunities With AI Transportation is one of the most important areas where modern AI provides a significant advantage over conventional algorithms used in traditional transit system technology. AI promises to streamline traffic flow and reduce conges - tion for many of today’s busiest roadways and thoroughfares. Smart traffic light systems and the cloud technology platforms they operate on are now de- signed to manage and predict traffic more efficiently, which can save a lot of money and create more efficiencies not only for the cities themselves, but for individuals also. AI and machine learning today can process highly complex data NEW YORK/NEW JER- SEY/PENNSYLVANIA — Commercial real estate ser - vices firm Levin Manage- ment Corporation (LMC) has been awarded two new property management assign - ments – on behalf of two new clients – continuing a period of sustained business momen- tum for the North Plainfield- based organization. In New York/New Jersey/ Pennsylvania, LMC was re - tained to manage a 25-property portfolio including five multi- tenant and 20 single-tenant net-
leased assets totaling 335,000 s/f. The addition strengthens LMC’s Long Island presence, especially, as most of the prop - erties are located in Suffolk and Nassau counties. Tenants span a range of categories in - cluding convenience, grocery, dining, home improvement, banking and pharmacy, among others. In addition to providing strategic consulting, LMC will apply its proven management standards to ensure consistent and high-quality operations throughout the portfolio. “This new client cited Levin’s the importance of engaging a strong manager with deep ex - perience operating properties under redevelopment – one that could provide a continual on-site presence and work to ensure a seamless experi - ence for tenants and shoppers throughout the process,” said Lowry. “The Shoppes at Old Bridge is a great addition to Levin’s management portfolio, and we look forward to utilizing our expertise in all aspects of property operations to maxi - mize the center’s value and appeal.” MAREJ LMC secures 25-property portfolio management assignments
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