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Illustration by Magictorch

wide-area networkThe use of cellular or GPS probes is contentious, but provides a great opportunity for the traffic industry to take a step closer to true real-time traffic information. Could a fusion of sources get us there more quickly?

AN EvALUATION OF PROBE SOURCES FOR REAL-TIME TRAFFIC INFORMATION

PROBESFOCUSby B.M.R. Heijligers, TNO, the Netherlands

ere in the Netherlands, traffic jams are a fact of everyday life, yet most of us continue to use our

cars to commute. If this trend continues unchecked, we could become the first country in the world to unite the morning rush hour with the afternoon peak. Still,

we have adjusted pretty well to these circumstances and the traffic jams

themselves aren’t actually the biggest cause

of irritation.

But not knowing how long you’re going to be stuck in a jam, whether there is a faster alternative route, or driving into unexpected traffic are constant sources of irritation. Wouldn’t it be nice to be able to measure traffic on all the roads in an area, understand and predict how it will evolve in the next few hours and suggest a best time for departure and route to drive?

TNO is one of the technology institutes working to make this vision a reality, by exploring the potential of deriving traffic information from cellular phone technology (Cellular Floating Car Data or CFCD), as well as combining

this with fusion technologies that leverage many traffic-related

data inputs.

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cEllulaR tRaffic data tyPESIn analyzing CFCD technologies, it is important to have a sound understanding of their capabilities, limitations and differences when compared to GPS-enabled probe vehicle technologies and traditional road sensor loops. There are actually several competing cellular traffic data technologies, but each works on the principle that a cell phone has the capability to seamlessly move through a vast array of GSM/GPRS network antenna without the connection being dropped. This is achieved by continually keeping track of all the antennae (or cells) that a cell phone ‘sees’ and from whichever one it receives the strongest signal.

Some CFCD systems utilize the moment that a cell phones changes from one cell to another (the handover) as input for a traffic algorithm, while others try to triangulate the position of a phone by comparing the signal strength from the surrounding cells to their known positions, or even measure how

long the radio signal needs to travel (timing advance) from the phone to the antenna. But all of these techniques only work for phones that are ‘in-call’. This generally means that the overnight availability of CFCD data is poor. That said, 99% of all traffic congestion occurs between 06.00-22.00 anyway.

The biggest difference between CFCD technology and GPS-enabled probe vehicle technology is that the accuracy of GPS probe positioning enables precise vehicle speeds on specific road segments. This means less filtering and an overall higher yield per GPS-enabled probe, although this is potentially offset by the vast number of cell phones in vehicles at any one time on the roads.

Why aren’t we all using cellular floating car data right now? Well, it has actually been a long-standing promise of cellular traffic data technology providers to deliver broad road network coverage, low cost, reliable traffic information. Is it time to cash in? The industry seems to think so. In the past two years, we have seen regional systems become operational in Israel (Cellint, Decell and others), the Netherlands (TomTom), Belgium (ITIS), as well as numerous trials across Europe and in the USA (AirSage, IntelliOne, ITIS and others). TomTom has even promised to provide a national service to its Dutch portable navigation users in 2007.

PROmiSing RESultSAt the ITS Europe conference in Aalborg in June, Cellint, TomTom, ITIS and Vodafone TrafficOnline presented results from their latest trials. The cost and coverage benefits compared to infrastructure-based solutions were clear. The accuracies of the derived traffic speeds varied between 5-20km/h and were shown to depend on the market share of the network carrier in the area and the volume of the traffic on the road.

But not everyone agrees that CFCD is ready for prime time. In the USA, the Florida DOT sponsored a Travel Time Estimation Using Cell Phones for Highways and Roadways study earlier this year, investigating trials and data from five CFCD technology providers. The research team found that “cell phone technology is viable and mature under the normal conditions of free traffic flow for travel-time estimations… however, it is not accurate in congested traffic conditions, where the data is more important than in the few-flow traffic conditions, and the accuracy decreases rapidly as the congestion increases…”

One shared sentiment regarding the delayed uptake of CFCD concerns the difficulties in objectively comparing floating car data to other sensor systems. Loop sensors measure spot velocities of the traffic flow, but can’t identify the same car

The above is the Data Fusion diagram: 1) Data from multiple probe and sensor sources; 2) Spatial filtering and outlier detection, traffic state calculation; 3) Temporal filtering and alignment, confidence measurement; 4) High accuracy, low latency traffic flow information

TNO is developing new CFCD algorithms, data fusion to predict congestion and autonomous network management using intelligent agents to improve driver information and management solutions

Travel time and traffic congestion information is valued by road users and traffic managers

TRAFFIC TECHNOLOGY INTERNATIONAL AUGUST/SEPTEMBER 2007 59

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“the cost and coverage benefits compared to infrastructure-based solutions were clear”

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a paradigm shift?In terms of collecting vehicle probe data at local, regional and national levels, vII could certainly prove to be groundbreaking for surface transportation. Ram Kandarpa from Booz Allen Hamilton explains further

Under direction of the vII National Working Group, several vII-based applications (or use cases) have been selected for deployment during the initial roll-out of the vII network and vII-enabled vehicles – applications that continue to be the focus of ongoing development efforts.

One such application that is central to the vII concept is the traveler information application. The objective of vII-enabled traveler information applications is to provide location- and situation-relevant information to travelers in their vehicles, using the vII network services and WAvE communications standards. Traveler information would be derived from vII vehicle probe data obtained through the vII network’s Probe Data Service (PDS), as well as from traditional traffic-monitoring systems and other emerging probe technologies (including cell phone probes) and provide geographically relevant information to vehicles via the vII network’s Advisory Message Distribution Service (AMDS). Traveler information

would be delivered to vehicles based on a standardized ‘language’, consisting of message sets, data frames, and data elements.

The vII-based traveler information applications will include, among others, the following types of information:Traffic information: The applications would include provisions for broadcasting basic traffic information on defined roadway links within proximity to an RSE. Examples of traffic information would include average

a mile down the road. The average speeds between sensors have to be estimated from the velocities at the loop locations, which is error-prone due to the amorphous nature of traffic flows. Shockwaves (also known as the harmonica effect in traffic flows) and incidents remain undetected until the backwash reaches a loop location.

StREngtHS and wEaknESSESCFCD isn’t subjected to this problem. It measures the true time it takes for a single vehicle to travel a given distance. These measurements of travel time are theoretically superior to loop measurements. But in its

strength lies also its potential weakness. Before a travel time can be measured, a car has to physically travel that piece of road. This takes time. If there are enough probe vehicles available and the system is optimally configured, these movements can be projected on shorter road segments and alleviate this time delay.

The success of this approach varies between technologies, but is primarily determined by the number of cell phone probe vehicles. Cellint’s TrafficSense showed that in high-penetration circumstances they can limit the time delay to one minute, while the same system in a different pilot using a cell phone carrier with less market share exhibited delays of four minutes.

Depending on the application for the traffic data, the requirements are different. Studies from the Technical University Delft regarding the effect of time delays in traffic management situations have shown that the effectiveness of network measures remained largely unaffected by delays below six minutes. Significant control was lost when the delays became larger than nine minutes.

However, even instantaneous information is of limited use to a driver who wants travel- time advice for a three-hour journey. By the time that the user has traveled the first part, the road conditions would have changed to such an extent that the original estimate has become outdated, or even an alternative route would have been better. Here, data fusion and predictions come into play. To give appropriate information to the driver, the travel information has to be based on

travel speeds, travel time, and other measures of traffic density (for example, ‘percent utilization’). The vII-enabled vehicle’s OBE would then receive, store and ‘assemble‘ the roadway link data to convey the ‘local’ roadway traffic conditions to the driver. Although automakers and other device manufacturers will employ different strategies to this end, it is envisioned that the roadway link information might be overlaid on a GIS map database and displayed to the driver. Alternative methods of conveying traveler information to the driver could be based on predefined threshold events or incidents, combined with voice annunciation, such as exception-based reporting.Incident information: The applications would also provide for incident reporting and would

Traveler information: probe data collection… … and advisory message distribution

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Evaluating traffic flow data sources, including cellular, GPS, sensors and fusion technologies

cEllulaR gPS SEnSORS fuSiOn

Location precision

Low (>250-550m) High (<30m)High (point locations)

High

Major benefitLow-cost area

coverageVery accurate in high-flow traffic

Very accurate volume/occupancy

Best of all worlds

Major limitation

Low accuracies in dense road

networks

Low overnight availability

Accuracies in urban canyons

Number of probes for arterials

Low travel-time accuracies

High infrastructure maintenance

Cooperation required

Technical stage of evolution

Research with pilot deployments

Emerging with major US, UK,

and Japan deployments

Established technology

deployed globally

Emerging with major US, UK

and Japan deployments

Slowdown detection latency

1-5 mins (penetration)

1-5 mins (penetration)

30 sec-10 mins (sensor spacing)

Best of all worlds

“loop sensors measure spot velocities of the traffic flow, but can’t identify the same car a mile down the road”

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include event-driven messages relevant to a particular point location or roadway segment. Examples include location of an accident, blocked lane, and other types of localized traffic disruptions. Incident information may also include more widespread broadcasts related to emergency events. Local signage: Local signage messages are intended to convey information that is temporary or periodic (i.e. may vary with time of day, day of week, etc). Examples would include school zone warnings and associated speed limits, work zone warnings/speed limits, cautionary warnings in place due to special events or conditions, such as reduced speed due to surface conditions or fog. Detour information and road closures are also examples of local signage.

In addition to the above, it is envisioned that other applications, such as road weather information and integrated corridor management, will evolve into what could arguably be labeled as the next-generation traveler information applications enabled through vII. The information content generated might be repackaged and delivered to traditional traveler advisory systems, such as websites, DMS, 511 systems, highway advisory radio (HAR) and personal electronic devices.

Responsibilities for developing the vII-enabled traveler information applications

are spread among both public and private entities. The public sector traveler information applications can be differentiated from private sector traveler and navigation assistance applications in that the informational messages are delivered unencrypted via the open-standard WAvE Short Message (WSM) format, as currently outlined in SAE J2735.

In contrast, private sector traveler information applications would be encrypted and likely delivered via a propriety language.

Additionally, unlike the private sector applications, it is not envisioned that the public sector traveler information applications would provide for maintaining a communication session as the vehicle moves from one RSE to the next. Rather, all messages from a particular RSE would be broadcast to all vehicles within range of that RSE.

At this point in the vII program, the vII-based traveler information use case has been developed, and efforts are currently under way to develop and test the applications as part of the vII Proof-of-Concept (POC) testing, planned in Detroit, Michigan later this year. Results of the testing are expected to help answer the key technical, economic, deployment and policy-related questions.

“intelligently combining all of the best available data inputs is the ideal solution, rather than one technology”

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what the traffic will be like once he arrives at a road segment – and not that those before him were stuck in traffic for 20 minutes.

There is work under way at TNO that is looking to perfect a system that can predict how traffic congestion will evolve based on all data available, including using weather radar to determine whether it rains for every mile of the road, or whether a significant accident has closed half of the lanes. This will result in notable improvements for driver information and network management solutions.

A similar approach of data fusion is being employed in research efforts in Germany to improve the core algorithms for cellular floating car data. The University of Stuttgart is developing the ‘Do-It’ project – funded by the German Federal Ministry of Economics and Technology – that includes the departure times of buses and trains. This

The future may lie in fusing data from probes

speed if there are cars, or even entire lanes, that travel at twice the speed of others – as can occur in near-congested conditions? Is it useful to demand an average travel time of 25km/h with a 95% accuracy, which translates to a maximum error of 1.25km/h, if 25% of the traffic manages an average speed greater than 32km/h and another 25% manages only at most 18km/h? Even with a perfect measurement system, one would have to measure over 95% of all vehicles to statistically achieve this accuracy.

You could argue that in these cases the travel time numerical values are less relevant than the actual classification of the traffic flow, especially where traffic management applications are concerned. Here, again, data fusion becomes an important tool. Instead of just calculating a numerical average and relying on the individual to properly interpret this, data fusion can classify the traffic flow and determine if this slowdown is an isolated event, part of an emerging congestion, or the effect of an accident with more serious congestion down the line. At the very least, accuracy requirements from the various DOTs should acknowledge the various traffic situations.

cOmBining fORcESSuch challenges do not mean that we should just stick to physical road sensors and forget cellular or GPS floating car data. All of the technologies have their advantages and disadvantages, so intelligently combining all of the best available data inputs is the ideal solution, rather than point solutions using one technology.

For example, the Dutch Department of Transport started building a road management system 30 years ago based upon a network of induction loops. A set of dual induction loops are integrated in the road surface every 500m on all major highways for every lane. This has led to a very detailed system that dynamically changes the speed limit upstream if congestion is detected ahead. Of course, when induction loops break down, the road needs to be physically closed off so they can be replaced, thereby possibly causing the congestion that it is specifically designed to detect. The cost of extending this system to the secondary road system is also prohibitive

information is then used to better separate the cell phones moving in these vehicles from the normal traffic on the road.

cOmPlicatEd mattERSBut there are still other complications with CFCD. How do you describe a road situation with a single meaningful average

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the best of all worldsWhen it comes to real-time traffic information, a fusion of probe and static sensors offer the optimum solution. Inrix’s Oliver Downs details the science

No single source of data can provide the highest accuracy real-time traffic information. As a result, we at Inrix employ sophisticated data fusion methodology, together with intelligent management of our data portfolio, to produce the most accurate traffic data available across the broadest road network coverage.

In 2006, we launched a real-time traffic service across the USA that optimally combines data from the world’s largest GPS network of probe vehicles with traditional road sensors using our traffic data Fusion Engine. This approach requires detailed understanding and management of all data sources. In the case of GPS probes, we pioneered the management of fleet composition – long-haul trucks, local service vehicles, airport shuttles, limos and taxis, and passenger vehicles – to provide data-point density and average vehicle behavior consistent with the overall population of vehicles on the road at any given time. Furthermore, it is important to maximize coverage by time throughout every 24-hour period, seven days a week, for which there is sufficient information to provide a measurement or estimate of real-time traffic flow conditions.

In competition with these needs are the needs of the businesses and fleets providing the data. Using more data or reporting at too high a frequency from any given vehicle or fleet incurs unnecessary data costs. We have worked closely with our data providers to optimize the data that their vehicles provide to us. In the case of several of our multi-year exclusive data providers, we have actually specified components of the firmware loaded on each in-vehicle device, so that an optimal package of data and rate of data supply, including logic based around information from the vehicle bus, can be achieved. In addition to the vehicle location, heading and speed pulled directly from the GPS and/or vehicle bus, this uniquely allows us to obtain contextual information about the activity and status of the vehicle in real time beyond the 30m accuracy of the GPS measurements themselves. More important even than the physical number of vehicles – in Inrix’s case over 650,000 vehicles under contract currently – this overall richness of the information a vehicle reports maximizes the density of useable probe data points, which ultimately governs the quality of traffic information that can be achieved.

Probe networks (both GPS and cellular) suffer from the paradox that low sample sizes can occur in two different situations: when overall vehicle volume is low and traffic is under low-density free-flow conditions; and due to statistical sample size fluctuations and limited density of probes relative to other vehicles. It is actually a little-discussed fact that all

sensor modalities, (loop detectors, radar detectors, toll tags, GPS probes and cellular probe technology) suffer from the same problem in the case of low traffic volume and density. For example, many public sector loop sensor networks simply set a threshold value on occupancy measured at a detector, below which estimated speed will be set to a ‘free-flow’ or speed-limit value. However, given other contextual information about current conditions, it is possible to employ Bayesian methods to make a statistical decision about whether low sample size, volume or occupancy measurements are indicative of free-flow conditions or a low-data situation. An example based on observed statistical sample size at a probe penetration of 1.4% is shown below.

Data fusion allows the combination of all available data, rather than just ‘selection of the best data source’. A well-known principle is that you never throw away useful information. That said,

different types of data (loop detectors, radar detectors, data from GPS probes and cellular phone probes) have limitations (i.e. latency, location accuracy, etc) and errors. In fact, each individual measurement from each source has its own independent error or uncertainty measurement. The principle of never throwing away useful information implies that there must exist some optimal combination of all the available data, subject to its respective error measurements, such that the error of that combination is even less than that of the single lowest-error measurement – this is the ‘minimum variance estimator’. The minimum variance estimator combines all available data values, weighting each in inverse proportion to its error. When combined with sophisticated detection of outlier data points, this results in maximum-accuracy real-time traffic data.

Oliver Downs is principal scientist at Inrix. He has 10 years’ experience in advanced Bayesian predictive modeling, machine-learning algorithm design, and is a pioneer in quantum-inspired optimization algorithms

“the demand for area-based traffic information has been growing”

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Figure 1: Inferred traffic volume given observed sample size

Inferred traffic volume

Observed sample size

Likelihood

(even though similar traffic information is required to conduct proper network management), so the optimal solution would be to add cellular and/or GPS vehicle probe data to the overall road management system.

making HEadlinESMost headlines concerning CFCD systems have been about possible privacy issues. This is understandable – privacy issues make good headlines. But is there any truth in them? As the entire GSM system depends on the capability to find a specific cell phone anywhere in the country (or in the world for that matter), using this information to generate traffic information adds nothing new or compromising to the system. On the contrary, traffic providers receive anonymized versions of the data, which makes it impossible for them to identify the original phone. Ultimately, the road user will benefit if a carrier makes the data available, as road authorities will be able to create better traffic management systems and individual drivers will have better travel-time estimates available to them. Early results from TomTom show that 75% of the users are even willing to share their GPS position in return for better travel information.

Why should cellular providers still make their network data available if it is not part of their core business and they run the risk of becoming the target of a media scare? The demand for area-based traffic information has been steadily growing, both for individual drivers and road authorities. This means that financially sound business models may become more feasible. Moreover, the positioning technology allows the carriers to provide additional location-based services to their customers. People could even ‘opt in’ to receive customized information based on their location, route and traffic conditions. The possibilities are all there – the question is, who will be brave enough to be the first? n

Björn Heijligers is a senior consultant with the Mobility & Logistics business unit of TNO in the Netherlands, and has an extensive background in cell phone-based and other traffic probe technologies. TNO was founded in 1932 as a non-profit technology institute and created for a single mission: to apply scientific knowledge with the aim of strengthening the innovative power of industry and government

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Real-time traffic information is the Holy Grail of ATIS. But PBS&J’s

Pete Costello thinks we still have many more miles to cover

round 10 years ago, the ITS Joint Program Office developed a video to help explain ITS. In its

truest essence, the production detailed, ITS is information (comprised of sets of data) overlaid onto the physical reality of the transportation network. Those of us who focus on traveler information think that ‘real-time traffic information’ is ATIS’s Holy Grail. We want travelers to have as much information available about what is happening on all modes of the transportation network so that they can make an informed decision about their commute and other travels. Unfortunately, we are not quite there yet.

a daRk mattERAccording to USDOT’s ITS Deployment Tracking Database[1], 63 metropolitan areas in the USA reported in 2006 that they have only 38% of their freeway miles under traffic surveillance. At a guess, perhaps half of this surveillance is achieved with CCTV deployments and the rest through automated detection via loops and other sensors. Whatever the assumption as to surveillance method,

are we there yet?

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most of the freeway miles are not covered. And what about surveillance outside of the metropolitan areas? There is little to no data available in rural areas: most of the USA is ‘dark’ as to real-time traffic information. Will this be addressed through probe technology? Data from fleets or cellular phones moving around the transportation network will likely be able to ‘light up’ more of the country, but is that data ubiquitous enough to allow ‘operations’ of the transportation network and generate real-time traffic information? That is still to be determined according to the technology evaluations.

There are initiatives under way, such as VII and Clarus (aggregating surface transportation weather information) that

may eventually enable every vehicle on the network to provide ‘probe’ and weather data. But are public sector agencies preparing themselves for the onslaught of data that will be flowing to them continuously? An educated guess would suggest not.

To borrow a phrase from a former colleague, Rick Schuman, agencies will go from being relatively ‘data poor’ to ‘data rich’ in the next few years as the transportation network gets ‘lit up’. The important point to remember, though, is that regardless of from where an ATIS draws data today, there is certainly more to come in the future. So it’s important to establish and maintain a platform that is flexible and scalable.

Agencies should take a step back and look at the Traveler Information Value Chain to see if they are prepared for additional data and give themselves a grade of ‘passing’ or ‘deficient’. A specific list of definitions lays the groundwork for what constitutes the Traveler Information Value Chain[2]:Data collection: The creation and

maintenance of an infrastructure capable of providing the data required by an ATIS and operation and maintenance of the data infrastructure. Data sharing: This is the provision of data to, or the receipt of additional data from, external sources. Data fusion: This involves the collation and integration of data from a number of different sources into a unified data stream.Information dissemination: The unified data source is converted to information and packaged or combined with other information to provide value propositions that can be marketed (sold) to consumers, provided for free, or provided at no charge to the consumer with advertising or

sponsorship underwriting, or support for the cost of delivery.Marketing: The target users for the information must be made aware of some basic information regarding the use of and access to the information, including benefits and use value and associated costs, if any.Customer satisfaction: This is a measure of how products and services supplied meet or surpass customer expectation, seen as a key performance indicator.

intElligEncEThe Traveler Information Value Chain is meant to be a feedback loop so that input from customers – such as ‘you do not have information on my trip/road’ – leads to additional data collection and so forth. What makes transportation intelligent is how data is gathered, assimilated and then delivered as information to travelers.

Public sector agencies are strongest in the data collection and information dissemination links in the chain, but it is

the rest of the links that need to be worked more. We have all heard horror stories about the ‘next town over’ not sharing data because of legacy systems, incompatible formats, etc, or the fact that they do not want travelers being ‘dumped on their roads’. As is much the case in ITS, it is not technology but ‘institutional issues’ that prevent goals from being reached. And ATIS’s goals, if anyone is developing a list/scorecard/dashboard, should include gathering real-time data on the entire transportation network. How many state DOTs are ready for that? Probably some. How many transit agencies? Probably just a handful to a few.

Unfortunately, traveler information services are not developed in a vacuum while time stands still. The business environment in which ATIS operates is continually changing. BMW’s Jan Urbahn said at the 2007 ITS America Annual Meeting in Palm Springs that customers in Europe who pay for real-time traffic data ask, “If I pay for this service, why am I stuck in a jam?” Good point. These customers also spend significant time on rural roads that have no or little coverage/data. How are we going to deal with these issues/expectations in the USA as ATIS develops?

miSSiOn-cRitical SERvicE In a few years, it is believed that ATIS will be a ubiquitous service, essential to travelers and mission critical to transportation operators (see Traveler Information Systems in 2013: Will It Finally Be Advanced?)[3]. Organizations need to develop requirements for more ATIS data today – and then implement systems to fulfill those requirements to deliver appropriate information to travelers.

According to the USDOT, based on data from current road projects, each mile of new roadway costs more than US$30 million. For a fraction of this, ATIS can provide travelers with decision-quality information on activities on the network and the transportation system as a whole. ATIS will also increase the touch points and interactions that agencies have with customers. Delivering value to citizens through ATIS can lead to increased customer satisfaction and a positive feeling for the agency among citizens, and support for the agency’s funding desires moving forward.

We are not yet there with real-time traffic information. In fact, we still have a long way to go. n

Pete Costello is a program manager with PBS&J – a consulting firm internationally recognized as experts in ITS. He can be emailed at petecostello@pbsj.com or contacted by telephone on +1 407 806 4440, or toll-free at +1 800 284 5182

References[1] www.itsdeployment.its.dot.gov[2] The definitions were adapted from the Advanced Traveler Information Systems text book, written by Bob McQueen, Rick Schuman and Dr Kan Chen, published by Artech House in 2002[3] www.pbsj.com/its2013/pdfdocs/ 05080_ITS-wp02_V8.pdf

“most of the uSa is dark as to real-time traffic information”

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