If you are a native to Houston or have lived here for years, it will not surprise you to learn the city’s traffic is getting worse. Houston consistently ranks as a growing city. It is popular for its well-paying jobs and relatively low cost of living. Unfortunately, that means there are a lot of people trying to get to and from work each day, leading to congestion, delays, and accidents. It is only natural then that Houston traffic apps have become the saving grace for commuters.
Various apps offer real-time information about delays, enabling you to choose the safest and fastest route through traffic. Yet as an experienced commuter, you know there may come a time that you are in an accident. It does not matter how much you check the traffic and avoid congestion or construction — other people’s negligence behind the wheel can cost you time, energy, and health. An understanding of the traffic app ecosystem, issues with interoperability, as well as the long-game toward enhancing the technology ecosystem via public-private partnerships helps us contextualize the future roadmap of traffic mobility solutions.
Today, traffic jams are popping up unexpectedly in previously quiet neighborhoods around the country and the world. The problem began when smartphone apps like Waze, Apple Maps, and Google Maps came into widespread use, offering drivers real-time routing around traffic tie-ups. An estimated 1 billion drivers worldwide use such apps. And the problem is getting worse. City planners around the world have predicted traffic on the basis of residential density, anticipating that a certain amount of real-time changes will be necessary in particular circumstances.
Here’s how the apps evolved. When navigation capabilities moved to apps on smartphones, the navigation system providers began collecting travel speeds and locations from all the users who were willing to let the app share their information. Originally, the system providers used these GPS traces as historical data in algorithms designed to estimate realistic speeds on the roads at different times of day. They integrated these estimates with the maps, identifying red, yellow, and green routes—where red meant likely congestion and green meant unrestricted flow.
As the historical records of these GPS traces grew and the coverage and bandwidth of the cellular networks improved, developers started providing traffic information to users in nearly real time. Estimates were quite accurate for the more popular apps, which had the most drivers in a particular region. And then, around 2013, Here Technologies, TomTom, Waze, and Google went beyond just flagging traffic jams ahead. They began offering real-time rerouting suggestions, considering current traffic on top of the characteristics of the road network. That gave their users opportunities to get around traffic slowdowns, and that’s how the chaos began.
Now online navigation apps are in charge, and they’re causing more problems than they solve. The apps are typically optimized to keep an individual driver’s travel time as short as possible; they don’t care whether the residential streets can absorb the traffic or whether motorists who show up in unexpected places may compromise safety.
On its face, real-time rerouting isn’t a problem. Cities do it all the time by changing the signal, phase, and timing of traffic lights or flashing detour alerts on signs. The real problem is that the traffic management apps are not working with existing urban infrastructures to move the most traffic in the most efficient way. To compound the “selfish routing” problem, each navigation application provider—Google, Apple, Waze (now owned by Google)—operates independently.
Each provider receives data streamed to its servers only from the devices of its users, which means that the penetration of its app colors the system’s understanding of reality. If the app’s penetration is low, the system may fall back on historical traffic speeds for the area instead of getting a good representation of existing congestion. So we have multiple players working independently with imperfect information and expecting that the entire road network is available to absorb their users in real time.
We may have recently benefited from these shortcuts, but it’s doubtful that we’re winning the long game. To do that takes thinking about the system as a whole and perhaps even considering aggregate fuel consumption and emissions. Only then can we use these rerouting algorithms for the benefit of all citizens and our environment. What we really want is a socially optimum state in which the average travel time is minimized everywhere. How do we merge the app-following crowds with an engineered flow of traffic that at least moves toward a socially optimized system, using the control mechanisms we have on hand?
We can begin by pooling everyone’s view of the real-time state of the road network. But getting everybody in the data pool won’t be easy—some players like Google and Apple have massive back-office digital infrastructures to run these operations, while many cities have minimal funding for advanced technology development. Without the ability to invest in new technology, cities can’t catch up with these big technology providers and instead fall back on regulations, such as lowering the speed limits on residential streets.
The real challenge with traffic control is the enormous scale of the problem. Using the flood of data from app users along with the data from city sensors will require a new layer of data analytics that takes the key information and combines it, anonymizes it, and puts it in a form that can be more easily digested by government-operated traffic management systems.
Solving both the technical and non-technical issues will require research and public-private partnerships before we can assemble this cooperative ecosystem. We must convince the app makers that if they share information with one another and with city governments, the rerouting algorithms could consider a far bigger picture, including information from the physical infrastructure, such as the timing schedule for traffic lights and meters and vehicle counts from static sensors, including cameras and inductive loops.
This data sharing would make their apps better while simultaneously giving city traffic planners a helping hand. As a first step, we should form public-private partnerships among the navigation app providers, city traffic engineering organizations, and even transportation companies like Uber and Lyft. Sharing all this information would help us figure out how to best reduce congestion and manage our mobility. If you happen to be in a car accident due to such conditions, do not hesitate to reach out to our car accident lawyers at The Hadi Law Firm. We have years of experience helping individuals recover the maximum compensation for their injuries through insurance settlements or winning personal injury claims in court.