交通燈真是傻死了！

在等綠燈等得搓火的時候，你或許會一邊用手指敲著方向盤，一邊盯著一直不變的紅燈這么暗自抱怨。

要讓史蒂夫?史密斯博士說，這種看法字面上完全成立。

他解釋說：“按常規交通管理系統的工作方式，你需要提前對定時系統進行預編程。但系統一安裝好就會開始老化。而交通模式與流量一直在持續變化。”

隨著人口與地形的變化，對一個十字路口進行重新編程的成本可能需要數千美元，因此，許多城市根本沒有考慮這樣做。那些令你陷入交通堵塞的信號模式，可能在三五年前便已過時。

由此產生的低效率會擴展到整體經濟。德州農工大學交通研究所的《城市交通報告》發現，2011年，美國的交通擁堵成本總計為1210億美元，而在1982年僅為240億美元。

史密斯希望改變這種狀況。他不僅是卡耐基梅隆大學的研究教授，也是初創公司Surtrac的老板。這家公司正在將先進的人工智能技術與規劃應用于交通管理。Surtrac的智能交通信號依靠先進的傳感器和強大的處理技術，每隔幾秒鐘（而非幾年時間）就更新一次信號模式，以此響應交通流量的變化和意外事件，如交通事故或道路封閉等。自2012年以來，該公司一直在匹茲堡的東利伯蒂社區進行試點研究。

史密斯迅速指出，動態交通信號的理念并非他的發明。洛杉磯早在籌備1984年奧運會期間，便安裝了最早的動態交通信號系統，現在該系統被稱為“自動交通監控和控制系統”。目前，該系統仍在通過一臺強大的中央計算機，協調超過4500個交通信號燈。這臺計算機用越來越少的人力投入，做出及時的決策。

但史密斯認為，集中管理是洛杉磯系統的弱點之一。與之相反，Surtrac由每一個交通信號燈做出自己的交通管理決策。這些信號燈會與鄰近的系統分享信息，但不存在集中控制，也沒有主動的人為管理。

史密斯表示：“在試點研究開始幾個月之后，穿過我們研究區域的一條主干道被切斷。交通模式發生了巨大的變化——但我們沒有做任何事情。”

結果顯示，交通秩序變得高度協調。

史密斯解釋說：“通過與鄰近信號系統的通信，信息可以多躍程傳播。因此，你可以通過一種分散式途徑建立一個合理的長遠規劃。”

Surtrac信號合作的方式，與螞蟻或鳥相同。這種方式被稱為“蟲群戰略”，常被用于協調多架自動無人機執行各種任務，如調查研究和救援等。這種方式的優勢包括可擴展性——額外增加信號燈非常容易，持久性——一個單元出現故障，剩余部分會自動調整以做出補救。

Surtrac在匹茲堡取得的成果非常顯著。史密斯稱，試點區域內的出行時間縮短了25%，這在很大程度上得益于空轉時間減少了40%。而這也意味著排放減少21%。盡管Surtrac的結果僅來自24個交通信號燈，但它的效果遠遠超過洛杉磯系統減少交通擁堵16%的成果。試點得到了當地基金會和公私合營機構的資助，今年夏天將擴大規模，新增25個信號燈。

隨著試點規模的擴大，Surtrac自適應技術的效果將得到最好的檢驗。匹茲堡首先將重新調整常規模型的信號燈計時，在Surtrac安裝其智能系統的同時，該設置就處于運行狀態。這意味著從一開始，這套智能系統就跟常規設置進行比較。

史密斯對結果充滿信心。他說道：“傳統觀念認為，自適應信號不適合密集的城區。我們必將證明這種觀念是錯誤的。”（財富中文網）

譯者：劉進龍/汪皓

審校：任文科

Traffic lights are dumb.

Maybe you’ve muttered it to yourself, in a moment frustration, as you tap the steering wheel and stare at a persistent red.

As Dr. Steven Smith points out, it’s literally true.

“The way conventional systems work, you preprogram those timing systems in advance,” he explains. “But as soon as you install them, they start to age. Patterns and flows are continually evolving.”

Reprogramming a single intersection as population and geography shifts costs thousands of dollars—so some municipalities rarely get around to it. The signal patterns keeping you stuck in traffic may be three to five years out of date, if not more.

The resulting inefficiencies multiply into the broader economy. The Texas A&M Transportation Institute’s Urban Mobility Report found that in 2011, congestion cost the U.S. an aggregate $121 billion dollars, up from just $24 billion in 1982.

Smith is hoping to change that. On top of his position as a research professor at Carnegie Mellon, he’s the head of Surtrac, a startup that’s applying cutting-edge artificial intelligence and planning to traffic management. Surtrac’s smart traffic signals rely on advanced sensors and powerful processing to update their patterns, not every few years, but every few seconds, in response to shifting traffic volumes and unexpected events like accidents or road closures. They’ve been deployed in a growing pilot study in Pittsburgh’s East Liberty neighborhood since 2012.

Smith is quick to point out that he didn’t invent the concept of dynamic traffic signaling. Los Angeles installed one of the earliest such systems, now known as the Automated Traffic Surveillance and Control system, in preparation for hosting the 1984 Olympics. It’s still busy coordinating over 4,500 traffic lights through a powerful central computer, which makes timing decisions with less and less human input.

Smith is convinced, though, that centralization is a weakness of systems like the one in Los Angeles. Instead, Surtrac lets each signal make its own traffic management decisions. Though the signals share information with their neighbors, there’s no central control, and no active human management.

“A few months after we originally installed the pilot, one of the major arteries through our area got cut off,” Smith says. “Traffic patterns changed completely—we didn’t do a thing.”

What emerges, though, can look highly coordinated.

“By communicating with your neighbors, information can propagate multiple hops,” explains Smith, “So you can actually end up building a reasonably long-horizon plan in a distributed way.”

Surtrac’s signals cooperate in much the same way groups of ants or birds do in nature. This approach, sometimes referred to as “swarming,” has become frequently discussed as a way to coordinate groups of autonomous drones for tasks such as surveying or search and rescue. Advantages of the approach include scalability—additional lights can be added very easily—and durability—one unit can go down, and the rest will automatically adjust to compensate.

The results Surtrac has achieved in Pittsburgh are dramatic. Smith says travel times in the pilot area have been reduced over 25%, thanks mostly to a 40% reduction in idling. That also translates into a 21% emissions reduction. Those results significantly edge out the 16% congestion reduction achieved by the Los Angeles system, though Surtrac’s results are from only 24 traffic lights. The pilot, funded by local foundations and public-private partnerships, will be expanded this summer to include 25 more signals.

With the expansion, Surtrac’s adaptive technology will get the best test so far of its effectiveness. The city will first be re-timing the lights on a conventional model, and that setup will run while Surtrac installs its smart system. That means they’ll be measured against a conventional setupat its freshest.

Smith is confident of the outcome. “A lot of the conventional wisdom is that adaptive signals can’t work in dense urban areas,” he says. “We’re definitely proving that wrong.”