Nothing frustrates a commuter more than hitting traffic
congestion on the way to work. And nothing's more maddening to a
commercial truck driver than seeing the navigation system report normal
traffic on the interstate while he's stalled and wasting
$4-plus-per-gallon diesel fuel for a half-hour as police clear a
traffic accident. Such are the tribulations triggered by poor traffic
information.
Despite progress this decade in providing more up-to-date traffic information to auto and commercial truck drivers - whether via a Web portal, a Global Positioning System (GPS) device, a mobile phone, a tech-savvy radio or TV traffic advisory service - vast improvements are still needed to help forewarn drivers to find alternate routes when a traffic accident snarls the freeway.
The data provided via these services haven't yet reached a level of quality on which drivers can fully count to deliver accurate travel information. Today, individual solutions rely too heavily on a single or very limited number of data sources to produce real-time information. It's an approach that may result in substantial data gaps and inaccurate information. But real value will emerge when these various data sources are dynamically analyzed and proactively monitored.
Torturous Travels
One key indicator underscores the necessity for improved real-time traffic data: commuting time. In the U.S., for instance, the average commuting time has climbed to 25.1 minutes in 2005, from 22.4 minutes in 1990 and 21.7 minutes in 1980, according to the U.S. Census Bureau, periodic report on commuting, 2005. In major cities, of course, those average times are much higher, including 34.2 minutes one-way in New York.
But U.S. commuters don't have it the worst. In Canada, the average commute time has increased by nine minutes since 1992, with commuters now spending 63 minutes getting to and from work in cities across the country. Toronto tops the list, with the average round trip taking 79 minutes, according to Statistics Canada.
What's the cost to commuters of those longer driving times? Consider that a 10 percent reduction in an hour-long commute - just six minutes per day - would provide an entire weekend's worth of free time per year. Further, that extra 30 to 50 hours of driving time a year, per person results in higher fuel consumption, lost productivity at work and poorer air quality.
Longer commutes interfere with living and working conditions, raising stress levels. Less time is available for discretionary leisure activities with the family. Long-term effects on health must still be investigated but it does contribute to increased absenteeism at work. And then there's the stress of paying as much as $500 a year, maybe more, at the pump.
What's the Problem?
So, why aren't updated traffic data getting to commuters? While road sensors have become more sophisticated - involving microwave radar, infrared sensors, ultrasonic detectors and passive acoustic devices that can be attached to bridges, overpasses and lighting structures - such roadway sensors and travel-speed detectors cover less than seven percent of all major roads in the U.S. Some individual urban areas have zero coverage. Those percentages are much different in many other countries.
That coverage is simply not sufficient to provide the real-time traffic information that drivers, commercial and otherwise, require today. And since the information trend this decade has centered around collecting speed information on the road and travel time calculation, the inadequate data hurt performance.
But it's more complicated than that. Even with advances in GPS and other probe data collection, speed calculation is not the only measure of advanced traffic information. At the most extreme, when a highway or road must close for a bridge collapse or repair, police stop traffic for miles before that bridge. The road sensors show no cars traveling so that suggests traffic is flowing freely. A more sophisticated modeling engine is necessary to determine when to reduce the weight that traffic models give sensors or GPS flow data. Here's another complication. Chicago employs an excellent road sensor system, but when snow is falling at a rapid two inches an hour, the sensors don't provide a good estimate of travel times. They're only one source of travel speed.
In addition, solid reports of incidents are still essential. Collecting speed data can pinpoint major traffic slowdowns. But unless the cause of the slowdown is determined - a major hazardous materials spill or simply a flock of chickens getting loose - the impact model will be imprecise, yielding a less meaningful travel time forecast and uncertain advisories to drivers.
Wanted: Multiple Data Sources
Traffic and travel experts increasingly recognize the need to use multiple sources of real-time traffic data to develop dynamic and meaningful travel advisories. They're realizing that no silver bullet is going to emerge to provide all the real-time data essential to delivering reliable traffic information. Still, technology advances are arriving which, when coupled with other means of compiling traffic data, are improving substantially the flow of travel advisories and information that help drivers.
Already, a number of companies are moving swiftly into the broad area of floating vehicle data to calculate road speeds. In the U.S., cell probes, GPS probes and eventually tracking data from cell phones are all being researched and evaluated. Key variables include data sampling requirements, latency and "denoising" of extraneous data samples.
In Shanghai, cell phones are being used as part of what is considered the world's largest cellular-probe system to develop accurate real-time traffic information. The anonymous mobile phone position and signaling data in China Mobile's GSM network are collected and converted into travel time and speed information for hundreds of miles of major highways and surface streets in the city. Millions of Shanghai subscribers will soon receive reliable traffic information to help them deal with that city's major traffic congestion problem. The state-of-the-art cell-probe deployment is helping support logistics management for the 2008 Beijing Olympic Games and the 2010 World Expo in Shanghai.
No Silver Bullet
As more companies go global, they increasingly want to obtain reliable real-time traffic information in many locations. They need that information to ensure that time-critical packages are delivered. For instance, a growing number of companies want accurate traffic information to help them manage their supply chains and fulfillment.
The accuracy of traffic information today is probably where weather forecasting was in 1990, so there's plenty of catching up to do before extremely reliable traffic and travel data will be available. Still, the gap is closing fast, and traffic science will accelerate the process. But a single silver bullet won't provide the answer. And unless we take a new approach to end the reliance on a silver bullet data source, the data gap will persist.
The real remedy will involve using myriad sources of traffic and weather data - some already available, some being tested now and some still to be developed - and combining them to forge one dynamic source of real-time traffic information. Then the promise of Dynamic Traffic Assignment, the ability to more accurately predict traffic flow across the entire road network and intelligently advise drivers of alternate routes, can become a reality.
And when that time arrives, it will give all drivers peace of mind.
Despite progress this decade in providing more up-to-date traffic information to auto and commercial truck drivers - whether via a Web portal, a Global Positioning System (GPS) device, a mobile phone, a tech-savvy radio or TV traffic advisory service - vast improvements are still needed to help forewarn drivers to find alternate routes when a traffic accident snarls the freeway.
The data provided via these services haven't yet reached a level of quality on which drivers can fully count to deliver accurate travel information. Today, individual solutions rely too heavily on a single or very limited number of data sources to produce real-time information. It's an approach that may result in substantial data gaps and inaccurate information. But real value will emerge when these various data sources are dynamically analyzed and proactively monitored.
Torturous Travels
One key indicator underscores the necessity for improved real-time traffic data: commuting time. In the U.S., for instance, the average commuting time has climbed to 25.1 minutes in 2005, from 22.4 minutes in 1990 and 21.7 minutes in 1980, according to the U.S. Census Bureau, periodic report on commuting, 2005. In major cities, of course, those average times are much higher, including 34.2 minutes one-way in New York.
But U.S. commuters don't have it the worst. In Canada, the average commute time has increased by nine minutes since 1992, with commuters now spending 63 minutes getting to and from work in cities across the country. Toronto tops the list, with the average round trip taking 79 minutes, according to Statistics Canada.
What's the cost to commuters of those longer driving times? Consider that a 10 percent reduction in an hour-long commute - just six minutes per day - would provide an entire weekend's worth of free time per year. Further, that extra 30 to 50 hours of driving time a year, per person results in higher fuel consumption, lost productivity at work and poorer air quality.
Longer commutes interfere with living and working conditions, raising stress levels. Less time is available for discretionary leisure activities with the family. Long-term effects on health must still be investigated but it does contribute to increased absenteeism at work. And then there's the stress of paying as much as $500 a year, maybe more, at the pump.
What's the Problem?
So, why aren't updated traffic data getting to commuters? While road sensors have become more sophisticated - involving microwave radar, infrared sensors, ultrasonic detectors and passive acoustic devices that can be attached to bridges, overpasses and lighting structures - such roadway sensors and travel-speed detectors cover less than seven percent of all major roads in the U.S. Some individual urban areas have zero coverage. Those percentages are much different in many other countries.
That coverage is simply not sufficient to provide the real-time traffic information that drivers, commercial and otherwise, require today. And since the information trend this decade has centered around collecting speed information on the road and travel time calculation, the inadequate data hurt performance.
But it's more complicated than that. Even with advances in GPS and other probe data collection, speed calculation is not the only measure of advanced traffic information. At the most extreme, when a highway or road must close for a bridge collapse or repair, police stop traffic for miles before that bridge. The road sensors show no cars traveling so that suggests traffic is flowing freely. A more sophisticated modeling engine is necessary to determine when to reduce the weight that traffic models give sensors or GPS flow data. Here's another complication. Chicago employs an excellent road sensor system, but when snow is falling at a rapid two inches an hour, the sensors don't provide a good estimate of travel times. They're only one source of travel speed.
In addition, solid reports of incidents are still essential. Collecting speed data can pinpoint major traffic slowdowns. But unless the cause of the slowdown is determined - a major hazardous materials spill or simply a flock of chickens getting loose - the impact model will be imprecise, yielding a less meaningful travel time forecast and uncertain advisories to drivers.
Wanted: Multiple Data Sources
Traffic and travel experts increasingly recognize the need to use multiple sources of real-time traffic data to develop dynamic and meaningful travel advisories. They're realizing that no silver bullet is going to emerge to provide all the real-time data essential to delivering reliable traffic information. Still, technology advances are arriving which, when coupled with other means of compiling traffic data, are improving substantially the flow of travel advisories and information that help drivers.
Already, a number of companies are moving swiftly into the broad area of floating vehicle data to calculate road speeds. In the U.S., cell probes, GPS probes and eventually tracking data from cell phones are all being researched and evaluated. Key variables include data sampling requirements, latency and "denoising" of extraneous data samples.
In Shanghai, cell phones are being used as part of what is considered the world's largest cellular-probe system to develop accurate real-time traffic information. The anonymous mobile phone position and signaling data in China Mobile's GSM network are collected and converted into travel time and speed information for hundreds of miles of major highways and surface streets in the city. Millions of Shanghai subscribers will soon receive reliable traffic information to help them deal with that city's major traffic congestion problem. The state-of-the-art cell-probe deployment is helping support logistics management for the 2008 Beijing Olympic Games and the 2010 World Expo in Shanghai.
No Silver Bullet
As more companies go global, they increasingly want to obtain reliable real-time traffic information in many locations. They need that information to ensure that time-critical packages are delivered. For instance, a growing number of companies want accurate traffic information to help them manage their supply chains and fulfillment.
The accuracy of traffic information today is probably where weather forecasting was in 1990, so there's plenty of catching up to do before extremely reliable traffic and travel data will be available. Still, the gap is closing fast, and traffic science will accelerate the process. But a single silver bullet won't provide the answer. And unless we take a new approach to end the reliance on a silver bullet data source, the data gap will persist.
The real remedy will involve using myriad sources of traffic and weather data - some already available, some being tested now and some still to be developed - and combining them to forge one dynamic source of real-time traffic information. Then the promise of Dynamic Traffic Assignment, the ability to more accurately predict traffic flow across the entire road network and intelligently advise drivers of alternate routes, can become a reality.
And when that time arrives, it will give all drivers peace of mind.
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