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Proceedings ArticleDOI

FreeSim_Mobile: A novel approach to real-time traffic gathering using the apple iPhone™

01 Dec 2010-pp 57-63
TL;DR: A preliminary application for the iPhone™ that uses the built-in GPS receiver along with the web capabilities utilizing a V2I architecture to send a continuous flow of data to a central server where FreeSim, a real-time traffic simulator, applies the proportional model algorithm to determine the time to traverse a roadway in order to report in real- time the current flow of traffic.
Abstract: In this paper, we present a preliminary application for the iPhone™ [2] that uses the built-in GPS receiver along with the web capabilities utilizing a V2I architecture to send a continuous flow of data to a central server where FreeSim [13–15], a real-time traffic simulator, applies the proportional model algorithm [18] to determine the time to traverse a roadway in order to report in real-time the current flow of traffic. At the University of Alaska, Anchorage, we currently have vehicle tracking devices installed in 80 probe vehicles that traverse the Anchorage area. The high cost associated with vehicle tracking devices makes it difficult to penetrate a large vehicular network on limited funds, so we must look towards other available technologies, such as the constantly-expanding cellular network. In this paper we look at the iPhone™ 3G capability of reporting accurate and reliable locations by describing our sample application and comparing its reported GPS accuracy to the existing vehicle probes we have. We will then present a study of its performance of calculating an accurate traffic flow where a chosen section of roadway was driven. Drivers equipped with an iPhone™ 3G cellular phone and a vehicle tracking device manually timed how long it took to travel along the test road section. The vehicle tracking devices report speed and location every 10 seconds whereas the iPhone™ is capable of reporting the location every second, though we were receiving it every eight seconds. From this data, we calculated the amount of time to traverse the test roadway section using the proportional model algorithm and compared it to the actual amount of time it took to traverse the test roadway section. We found that the vehicle tracking device had an average error factor of 4.43% from the actual time to traverse the roadway section (as determined by the stopwatch), whereas the iPhone™ was found to have an error factor of 4.18%. The outcome of the case study is used to determine that the iPhone™ is relatively as accurate as a vehicle tracking device, though it is important to note that the iPhone™ is more limited than a device attached to a vehicle in the data it can obtain to only reporting its location.
Citations
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Proceedings ArticleDOI
18 Nov 2011
TL;DR: The case study furthers the feasibly that mobile phones can act as an alternative means for probe vehicles as the iPhone 4 and Motorola Droid X are relatively as accurate as a vehicle tracking device.
Abstract: In this paper, we present a comparison of the Samsung Galaxy S™ [1], the Motorola Droid X™ [2], and the iPhone 4™ [3] using the real-time vehicle tracking application FreeSim_Mobile [4,5]. Using the built-in GPS receiver and the web capabilities of these smart phones, coupled with a V2I architecture, we are able to send a continuous flow of location data to a central server for processing by FreeSim [6–8], a real-time traffic simulator. The proportional model algorithm [9] is then used on this data to determine the time to traverse a section of roadway in order to report in real-time the current flow of traffic. In this paper we analyze the capability of two different Android-based [10] phones, the Samsung Galaxy S and the Motorola Droid X, on reporting accurate and reliable locations and compare them to a vehicle tracking device and the iPhone 4, which has been previously shown to be as accurate as a vehicle tracking device [4,5]. Drivers equipped with a Samsung Galaxy S, Motorola Droid X, iPhone 4, and vehicle tracking device manually timed how long it took to travel along a 0.98 mile/1.58 kilometer section of roadway. All four devices reported their location and the vehicle tracking device also reported the current speed. The amount of time to traverse the test section of roadway was determined using the proportional model algorithm [9] and compared to the actual amount of time it took to traverse the test section of roadway as manually timed. The results of the vehicle tracking device had an average error factor of 0.79% from the actual time to traverse the section of roadway, whereas the Samsung Galaxy S was 4.59%, the Motorola Droid X was 0.84%, and the iPhone 4 was found to have an error factor of 0.60%. The case study furthers the feasibly that mobile phones can act as an alternative means for probe vehicles as the iPhone 4 and Motorola Droid X are relatively as accurate as a vehicle tracking device.

65 citations

Journal ArticleDOI
TL;DR: Performance evaluation and testing results demonstrate clearly that the proposed clustering schemes provide better performance for target tracking applications as compared to other cluster-based algorithms.

51 citations


Cites methods from "FreeSim_Mobile: A novel approach to..."

  • ...In [47] an application based on iPhone’s GPS receiver is proposed....

    [...]

Journal ArticleDOI
TL;DR: A driving cycle for a selected bus route with supercapacitor buses deployed has been developed and it is shown that driving characteristics of buses was significantly different from other worldwide bus driving cycles.

36 citations

Proceedings ArticleDOI
05 Jun 2011
TL;DR: In this article, a comparison between the Apple iPhone 3G and the iPhone 4 was made using the real-time vehicle tracking application FreeSim_Mobile. The results showed that the 3G had an average error factor of 4.94% from the actual time to traverse the section of roadway (as determined by the stopwatch).
Abstract: In this paper, we present a comparison between the Apple iPhone 3G™ [2] and the iPhone 4™ [2] using the real-time vehicle tracking application FreeSim_Mobile [24]. The built-in GPS receiver and web capabilities of the iPhone™, coupled with a V2I architecture, are used to send a continuous flow of data to a central server for processing by FreeSim [13–15], which is a real-time traffic simulator. The proportional model algorithm [18] is then used to determine the time to traverse a roadway in order to report in real-time the current flow of traffic. At the University of Alaska Anchorage, we currently have vehicle tracking devices installed in 80 probe vehicles that traverse the Anchorage area. Due to the high cost associated with vehicle tracking devices, it is difficult to penetrate a large vehicular network on a finite amount of money, so we must look towards other available technologies, such as the constantly-expanding cellular network. In this paper we look at the iPhone 4™ capability of reporting accurate and reliable locations and compare it to the recent study of the iPhone 3G™ [24]. Drivers equipped with an iPhone 4™ cellular phone and a vehicle tracking device manually timed how long it took to travel along a 0.99 mile/1.59 kilometer section of roadway. The vehicle tracking device and the iPhone 4™ report speed and location every 10 seconds whereas the iPhone 3G™ reported every 8 seconds [24]. From this data, we calculated the amount of time to traverse the test section of roadway using the proportional model algorithm [18] and compared it to the actual amount of time it took to traverse the test section of roadway as manually timed. We found that the vehicle tracking device had an average error factor of 4.94% from the actual time to traverse the section of roadway (as determined by the stopwatch), whereas the iPhone 4™ was found to have an error factor of 1.10%. The outcome of the case study is used to determine that the iPhone 4™ has higher accuracy than a vehicle tracking device, though it is important to note that the iPhone™ is more limited than a device attached to a vehicle since it can only report its location. If paired with another third party OBD device, however, it can also send the same information as a vehicle tracking device.

22 citations

Proceedings ArticleDOI
01 Nov 2012
TL;DR: A Popular Mid-range Android Smartphone served as GPS sensor in a vehicle as traffic probe and FreeSim, a macroscopic/microscopic simulator, was used to automatically reflect traffic estimates through a browser for dissemination.
Abstract: In this research, we present and study a practical Transportation System solution to the congestion concerns in a big city like Metro Manila Philippines. A Popular Mid-range Android Smartphone served as GPS sensor in a vehicle as traffic probe. Client application software was designed to generate GPS location updates with timestamps every second once it approached a designated roadway only. No additional client hardware and wireless infrastructure is needed since the system utilized the cellular system. FreeSim, a macroscopic/microscopic simulator, was used to automatically reflect traffic estimates through a browser for dissemination. Proportional computation was integrated into FreeSim which reflected traffic status in real time. The tests had an average error percentage of 2.08% and should open related researches for a true and practical intelligent transportation system in Metro Manila.

11 citations


Cites background from "FreeSim_Mobile: A novel approach to..."

  • ...They were able to collect locations every 8 seconds with timestamps through the cellular network to a central server [4]....

    [...]

  • ...It enables vehicles travelling within a transportation system to have the ability to remain in communication with a central system [4, 6]....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.
Abstract: Although much research has been done over the decades on the formulation of statistical regression models for road traffic relationships, they have been largely unsuitable due to the complexity of traffic characteristics. Traffic engineers have resorted to alternative methods such as neural networks, but despite some promising results, the difficulties in their design and implementation remain unresolved. In addition, the opaqueness of trained networks prevents understanding the underlying models. Fuzzy neural networks, which combine the complementary capabilities of both neural networks and fuzzy logic, thus constitute a more promising technique for modeling traffic flow. This paper describes the application of a specific class of fuzzy neural network known as the pseudo outer-product fuzzy neural network using the truth-value-restriction method (POPFNN-TVR) for short-term traffic flow prediction. The obtained results highlight the capability of POPFNN-TVR in fuzzy knowledge extraction and generalization from input data as well its high degree of prediction capability as compared to traditional feedforward neural networks using backpropagation learning.

194 citations

Proceedings ArticleDOI
25 Aug 2001
TL;DR: This study suggests that real-time speed and travel time estimates derived from single-loop detector data assuming a common g-factor for all detectors in the district can be in error by 50 percent, and so they are of little value to travelers.
Abstract: Presents the PeMS algorithms for the accurate, adaptive, real-time estimation of the g-factor and vehicle speeds from single-loop detector data. The estimates are validated by comparison with independent, direct measurements of the g-factor and vehicle speeds from 20 double-loop detectors on I-80 over a three-month period. The algorithm is used to process data from all freeways in Caltrans District 12 (Orange County, CA) over a 20-month period beginning January 1998. Analysis of those data shows that the g-factors for different loops in the district differ by as much as 100 percent, and the g-factor for the same loop can vary up to 50 percent over a 24-hour period. Many transportation districts now post real-time speed and travel time estimates on the World Wide Web. Those estimates often are derived from single-loop detector data assuming a common g-factor for all detectors in the district. This study suggests that those estimates can be in error by 50 percent, and so they are of little value to travelers. The use of the PeMS algorithm will reduce those errors.

194 citations


"FreeSim_Mobile: A novel approach to..." refers methods in this paper

  • ...However, using an adaptive algorithm to cope with the fluctuation of congestion significantly reduces the error percentage [8]....

    [...]

01 Aug 2008
TL;DR: The super vehicle detection (SVD) algorithm for how a vehicle can find or become a super vehicle of a zone and how super vehicles can aggregate the speed and location data from all of the vehicles within their zone to still ensure an accurate representation of the network are described.
Abstract: In this paper, the vehicle-to-vehicle-to-infrastructure (V2V2I) architecture, which is a hybrid of the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) architectures is described. The V2V2I architecture leverages the benefits of fast queries and responses from the V2I architecture, but with the advantage of a distributed system with no single point-of-failure from the V2V architecture. In the V2V2I architecture, the transportation network is broken into zones in which a single vehicle is known as the Super Vehicle. Only Super Vehicles are able to communicate with the central infrastructure and all other vehicles can only communicate with the Super Vehicle responsible for the zone they are currently traversing. The Super Vehicle Detection (SVD) algorithm for how a vehicle can find or become a Super Vehicle of a zone is described and how Super Vehicles can aggregate the speed and location data from all of the vehicles within their zone to still ensure an accurate representation of the network is discussed. An analysis using FreeSim is performed to determine the trade-offs experienced between accuracy and bandwidth based on the number of edges that comprises a zone, in addition to describing the benefits of the V2V2I architecture over the pure V2I or V2V architectures. (a) For the covering entry of this conference, please see ITRD abstract no. E217226.

139 citations


"FreeSim_Mobile: A novel approach to..." refers background in this paper

  • ...Probe data by itself during peak traffic times can cause prediction errors to increase due to more probes in close proximity to each other [1]....

    [...]

Proceedings ArticleDOI
04 Jun 2008
TL;DR: In this paper, the authors describe the V2V2I architecture, which is a hybrid of the vehicle-to-vehicle (V2V) and V2I architectures, and perform an analysis using FreeSim to determine the trade-offs experienced based on the size and number of zones within a transportation network.
Abstract: In this paper, I describe the vehicle-to-vehicle-to-infrastructure (V2V2I) architecture, which is a hybrid of the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) architectures. The V2V2I architecture leverages the benefits of fast queries and responses from the V2I architecture, but with the advantage of a distributed architecture not having a single point-of-failure from the V2V architecture. In the V2V2I architecture, the transportation network is broken into zones in which a single vehicle is known as the super vehicle. Only super vehicles are able to communicate with the central infrastructure or with other Super Vehicles, and all other vehicles can only communicate with the super vehicle responsible for the zone in which they are currently traversing. I describe the super vehicle detection (SVD) algorithm for how a vehicle can find or become a super vehicle of a zone and how super vehicles can aggregate the speed and location data from all of the vehicles within their zone to still ensure an accurate representation of the network. I perform an analysis using FreeSim to determine the trade-offs experienced based on the size and number of zones within a transportation network and describe the benefits of the V2V2I architecture over the pure V2I or V2V architectures.

131 citations

Journal ArticleDOI
TL;DR: In this article, the adaptive space division multiplexing (ASDM) protocol is proposed for safety-related intervehicle communication (IVC) networks, which requires no control messages and provides message delivery guarantees.
Abstract: Current link-layer protocols for safety-related intervehicle communication (IVC) networks suffer from significant scalability and security challenges. Carrier sense multiple-access approaches produce excessive transmission collisions at high vehicle densities and are vulnerable to a variety of denial of service (DoS) attacks. Explicit time slot allocation approaches tend to be limited by the need for a fixed infrastructure, a high number of control messages, or poor bandwidth utilization, particularly in low-density traffic. This paper presents a novel adaptation of the explicit time slot allocation protocols for IVC networks. The protocol adaptive space-division multiplexing (ASDM) requires no control messages, provides protection against a range of DoS attacks, significantly improves bandwidth utilization, and automatically adjusts the time slot allocation in response to changes in vehicle densities. This paper demonstrates the need for and the effectiveness of this new protocol. The exposures of the current proposals to attacks on availability and integrity, as well as the improvements effected by ASDM, are analytically evaluated. Furthermore, through simulation studies, ASDM's ability to provide message delivery guarantees is contrasted with the inability of the current IVC proposals to do the same

109 citations