Needless to say, commuters and drivers feel pained and stressed out when stuck in highway congestion. To combat this, transportation authorities turn to highway traffic management systems (HTMS).
Needless to say, commuters and drivers feel pained and stressed out when stuck in highway congestion. To combat this, transportation authorities turn to highway traffic management systems (HTMS), which can help reduce traffic and make highway usage a better experience for all.
It goes without saying
highways and freeways are vital transportation links between cities. In the U.S. alone, the total expressway length is 108,394 kilometers, according to the US Federal Highway Administration. Even within a large city, people use highways to go to work or travel to their intended destinations.
However, amid urbanization and an increasing car-buying population, demands for highway capacity often exceeds what’s available. This, then, leads to congestion, which is a major source of headache for drivers and commuters alike.
Consider the following statistics. Two stretches of freeway in Los Angeles made INRIX’s list of the most traffic-clogged U.S. corridors: On Interstate 10 between I405 and I110, drivers encounter delays of 19 minutes per day, and on I101, drivers hit daily delays of 13 minutes between Hollywood and Downtown L.A. Further, according to the 2019 Urban Mobility Report by The Texas A&M Transportation Institute, to reliably arrive on time for important freeway trips, travelers had to allow 34 minutes to make a trip that takes 20 minutes in light traffic.
As it turns out, highway congestion not only wastes time but also does damage to the environment. According to the University of California Transportation Center, if congestion reduces the average vehicle speed below 45 miles per hour, carbon dioxide emissions increase.
Causes of congestion
So what are some of the causes for highway congestion? There are several, ranging from obstacles on the road to weather impacts.
“A variety of reasons and causes for congestion include: bottlenecks at certain locations where the capacity of the roadway is reduced, for instance lane drop or work zones; obstacles in the roadway for example lost cargo from downstream vehicles, animals suddenly crossing the roadways, even people crossing highways at unauthorized locations; and weather impacts such as slush, snow, ice, and even rain, which can lead to different driving operations, governments and/or companies closing early leading to demands that are normally spread out over time and increased risk and occurrences of incidents,” said Joerg “Nu” Rosenbohm, Global Solutions Expert at
Kapsch TrafficCom. “Other causes include drivers not familiar with the roadways and making sudden lane or speed changes, badly placed variable message signs and messages shown on them, and large speed differentials that can also lead to sudden maneuvers that create backups."
Turning to HTMS
Given the economic and environmental impacts of highway congestion, combating it has become a top priority. Luckily, technologies have become more and more mature to help operators in this regard. Specifically, highway traffic management systems can come in handy to reduce traffic and optimize the highway infrastructure.
“From relatively simple things like orchestrating the sequence of seasonal road repair to more complex, intricate interventions that may re-route traffic flows, a highway traffic management system (HTMS) is essential to implement a comprehensive strategy for alleviating gridlock,” said Christian Chenard-Lemire, Team Lead for Intelligent Transport Systems at
Genetec.
“An
advanced traffic management system (for highway applications) can certainly reduce congestion and incidents. It maps the capacity of the highway with the real-time demand and provides vehicles with potential detours or recommendations to take prior to bottleneck locations or locations with already existing backups,” Rosenbohm said. “Additionally, demand exceeding capacity can be predicted by comparing historical and real-time data for the same conditions. Incident occurrences can also be predicted based on historical data (incident hot spots and historical volumes and speeds) and current, real-time traffic conditions (from detectors and floating car data) using pattern recognition methods, deep learning/machine learning and/or
AI.”