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Password generation and authentication in the OTP system data will be encrypted and decrypted by SIM card and by the phone service provider.
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In the recent years, it has become readily more accepted that smart mobile phones with GPS or A-GPS enabled device, or even Cell-ID enabled, among the commuters, can be used as traffic sensor, which complements other traditional sensors. This development is pursued in the efforts of reducing or avoiding traffic jams. Consequently, this paper attemp...
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Context 1
... advantages of this method are, GPS coordinates and private keys are never communicated, and the data's privacy is enhanced using OTP (Fig. 6, shows the OTP system). This is the first proposed privacy enhancement. The whole application installation and operation is depicted in Fig. 5. Lamport [36] has shown that there are advantages using his modified one way function. First, it makes the intruder's efforts in reading the password file from the system useless (because it ...
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... Our work has addressed a number of these aspects, as well as, to continue our previous research with smartphone as nonintrusive traffic sensor [16], which will include the police force mobile application as an important additional feature. This related research includes: vehicle traffic monitoring, modelling [17], vehicle travel distance-travel time prediction [18] and vehicle counting [19] [20]. ...
... Besides, public users can also use this application to check whether their vehicle has been issued a ticket or warning by the police force members. In addition, concerning the security of sensitive information that being sent over a medium is vital in the communication [16], as well as the importance of cryptographic [17] ...
... Macroscopic traffic model deals with macro events along the road such as traffic jams, while microscopic deals with the process of changing gear or changing lane. The first sensor obtained its data via our mobile application which is embedded in public commuters' or volunteers' smartphone [1] [2]. Since Global Positioning System (GPS) module is enabled in the smart phone during the operation of the application while on the highway, our Virtual Detection Zone (VDZ) method [3] can collect various data such as speed, location coordinates and time. ...
Vehicle counting is an important parameter in building highway macroscopic model. This model ultimately will help highway designers, road planners and even common commuters, since it can give short term predictions of the road's behaviour which is influenced for example by its traffic flow, number of lanes, as well as off and on ramps. This research attempts to count vehicles from existing video cameras, which gives low-resolution 3 seconds video of 1 frame per second. For low-resolution, conventional methods, such as Back-subtraction, and Viola Jones are unable to give high counting accuracy. However, with the aid of another custom-made software tool, various parameters of Deep Learning method, such as pixel-frame distance thresholds, and two different counting models can be run repetitively, to obtain better accuracy. Early results have shown that by varying pixel distance threshold, the percentage of error can go down from 40.8% to as low as 0.8%.
... Short term prediction requires vehicle traffic data, such as traffic density, speed, or flow [4], and in turn it will provide useful data like: fundamental diagrams [5], speed-time-distance contour graphs, travel time [6] and distance travelled [7]. A proposed solution is by using nonintrusive traffic sensors only, namely smartphones-GPS enabled, with Virtual Detection Zone (VDZ) application and CCTV [8]. It has been proven through a few experiments that VDZ method combined with CCTV snap shots as a system, can potentially replace the traditional systems using inductive loop detector sensor. ...
... The following criteria has been given by [9][18], as its quantitative evaluation of machine method in vehicle detection & count, namely: manual or Ground Truth (GT) count (5), Machine Method (MM) Count (6), as well as, recall, precision, and F1-scores. Recall score is given in (7), precision score is defined by (8), and F1 score is given by (9). FN means the number of missed vehicles, which is an important parameter in this analysis, and FP is equal to the number of times a vehicle has been counted more than once by the same method, e.g. ...
In this paper, vehicle counting is investigated using various machine methods on four datasets. Vehicle counting is needed to complete the data required for short term predictions using highway traffic model, which is in turn, applicable for road design and usage planning. The goal of this research is to show that automatic car counting using machine methods, can be obtained from utilizing existing CCTV image data or from better cameras. Then by applying quantitative evaluation, F1 and precision scores are obtained, so that a few recommendations can be given. Through numerous simulations, F1 scores ranging from 0.32 to 0.75 have been successfully obtained for one low resolution dataset, using Background Subtraction and Viola Jones methods, on existing CCTV data. It has been found also that Viola Jones method can improve F1 score, by about 39% to 56%, over Back Subtraction method. Furthermore, the use of Deep Learning especially YOLO has provided good results, with F1 scores ranging from 0.94 to 1 and its precision ranges from 97.37% to 100% involving three datasets of higher resolution.
... This development is pursued in the efforts of obtaining empirical traffic data without using any intrusive sensor which has to be embedded under the surface of a highway. Our previous works [21][22] [23][17] [24] have shown that GPS enabled smart phones with VDZ, used among a number of commuters, and existing CCTV data can provide standard empirical traffic. The set up the experiment for an integrated Intelligent Transport System (ITS), is depicted in Fig. 1. ...
... The application, which is Android based, must be installed and activated by the smart phone owner. Security and privacy concerns were considered in [21]. Once activated, it checks for the availability of a GPS module in the phone and Internet connection. ...
... The Direction is to be determined by the agent when 3 consecutive VD zones are valid. This step is included, in order to avoid map mismatching phenomenon [21] [25]. These components of information are then sent back to the server (MySQL as database). ...
... These components of information are then sent back to the server. The radius is considered via an experiment in [7]. As the agent moves, it continues to monitor B. Hardjono is a graduating computer science PhD student at Universitas Indonesia. ...
... While from the existing CCTVs, which are already available in many Jakarta main roads, we can obtain number of cars along the same zones or density over a common distance (either 1 mile or 1 km), as well as time stamps. This paper is a follow up to our previous research [7][11] [12] [13]. In our first major experiment we investigate the viability of the VDZ system, its speed accuracy in comparison to CCTV method with Speedometer as ground truth and whether two or more sensors can provide better traffic condition estimation [12]. ...
... From our previous work [7], we have already proposed an integrated system (see Fig. 1) which enables smart phone carriers to act like traffic sensor, by having an agent or application which gathers and then filters GPS data, due to zones' data sent by the server. Time stamp, speed, and zone ID are among the parameters sent via the Cell phone Network back to the server. ...
This paper shows that our novel method, Virtual Detection Zone (VDZ) system with CCTV snap shots can provide the empirical data needed to construct Fundamental Diagrams and to calibrate a chosen model. VDZ system is better than the traditional system which uses loop detectors, as it is able to show zero speeds at totally jammed density, which is an essential parameter for macroscopic traffic model. Real data set have been obtained by using only seven VDZ agents who have carried GPS enabled smart phones, together with snapshots from fifteen existing CCTV, on Tangerang to Jakarta highway (a distance of 21 km). The test, carried out without the use of any intrusive sensors, has produced data which is in agreement with previous traditional method. By adopting and modifying Cell Transmission Model or CTM, a variant Macro model, it is shown that simulated speeds for AM cluster generated by CTM can be calibrated to follow real traffic data. Its Mean Absolute Percentage Error or MAPE can be improved from 93.8 to 48.8% and improved further by 5.1% using type test method.
... Prevailing challenges currently limiting the expansion of the insurance telematics industry include privacy considerations for end-users [15], [16], the implementation and design of attractive value added services [11], the storage of data [17], and the issue of correlating driving behavior with drivers' insurance claim history. Furthermore, the global navigation satellite system (GNSS) receivers commonly installed in smartphones are often of low quality, and the high presence of errors in the data demands a substantial use of data processing algorithms to increase the reliability of the data. ...
We propose a framework for the detection of dangerous vehicle cornering events, based on statistics related to the no-sliding and no-rollover conditions. The input variables are estimated using an unscented Kalman filter applied to global navigation satellite system (GNSS) measurements of position, speed, and bearing. The resulting test statistic is evaluated in a field study where three smartphones are used as measurement probes. A general framework for performance evaluation and estimator calibration is presented as depending on a generic loss function. Furthermore, we introduce loss functions designed for applications aiming to either minimize the number of missed detections and false alarms, or to estimate the risk level in each cornering event. Finally, the performance characteristics of the estimator are presented as depending on the detection threshold, as well as on design parameters describing the driving behavior. Since the estimation only uses GNSS measurements, the framework is particularly well suited for smartphone-based insurance telematics applications, aiming to avoid the logistic and monetary costs associated with, e.g., on-board-diagnostics or black-box dependent solutions. The design of the estimation algorithm allows for instant feedback to be given to the driver and, hence, supports the inclusion of real-time value-added services in usage-based insurance programs.
... However, the work in [5] does not address issues such as how to provide incentives for the participants. In general, the use of the sensors and processing capabilities of smartphones for vehicle applications is an emerging area with several recent publications [6]- [9]. The case study presented in this paper includes a smartphone probing of road traffic with a number of probes in the vicinity of the Berkeley Mobile Millennium Project, CA, USA, which is believed to have an interest in its own right. ...
A framework is presented to deploy a smartphone-based measurement system for road vehicle traffic monitoring and usage-based insurance (UBI). Through the aid of a hierarchical model to modularize the description, the functionality is described as spanning from sensor-level functionality and technical specification to the topmost business model. The designer of a complex measurement system has to consider the full picture from low-level sensing, actuating, and wireless data transfer to the topmost level, including enticements for the individual smartphone owners, i.e., the end users who are the actual measurement probes. The measurement system provides two data streams: a primary stream to support road vehicle traffic monitoring and a secondary stream to support the UBI program. The former activity has a clear value for a society and its inhabitants, as it may reduce congestion and environmental impacts. The latter data stream drives the business model and parts of the revenue streams, which ensure the funding of the total measurement system and create value for the end users, the service provider, and the insurance company. In addition to the presented framework, outcome from a measurement campaign is presented, including road vehicle traffic monitoring (primary data stream) and a commercial pilot of UBI based on the driver profiles (secondary data stream). The measurement system is believed to be sustainable due to the incitements offered to the individual end users, in terms of favorable pricing for the insurance premium. The measurement campaign itself is believed to have an interest in its own right, as it includes smartphone probing of road traffic with a number of probes in the vicinity of the current state of the art, given by the Berkeley Mobile Millennium Project. During the ten-month run of the project, some 4500 driving h/250 000 km of road vehicle traffic data were collected.
... Thank you for your attention And your knowledge-able feed back will be valuable for me 32 ...
Conventionally, Fundamental Diagrams, which consist of vehicle traffic flow and density pairs, are obtained from intrusive sensor such as inductive loop detectors. However these sensors are uncommon in developing countries as they are embedded in the roads, and consequently expensive to deploy and impractical to implement on busy roads. Our novel method, VDZ with CCTV snap shots can provide the data needed to construct Fundamental Diagrams and able to show zero speeds at jam density, which provide essential parameters for macroscopic traffic model. The results obtained, without the use of any intrusive sensor, have shown agreement with previous traditional method.
... These zones, together with the map of destination, are sent by a computer server, as requested by an activated application (or agent) made for this purpose, in to GPS enabled smart phones. The radius is considered via an experiment in [10]. As the agent moves, it continues to monitor its current longitude-latitude coordinates and compares them to the pre-set-zones sent by the server. ...
... Direction is to be determined by the agent when 3 consecutive VD zones are valid. These components of information are then sent back to the server [10]. After obtaining initial traffic data for a certain target road, VD zones can be placed more frequently in dense areas [11] rather than sparse areas. ...
... While from the existing CCTVs, which are already available in Jakarta (JKT) main roads, we can obtain number of cars along the same zones or density over a common distance (normalized to 1 km). This paper is a follow up to our previous research [10] [12][14] [15]. This time we propose not to care about penetration rate, simply because the CCTV along the road, which takes photos at about the same time, our agents pass by, will give enough evidence to support the speeds obtained from our agents. ...
Conventionally, Fundamental Diagrams, which
consist of vehicle traffic flow and density pairs, are obtained from
intrusive sensor such as inductive loop detectors. However these
sensors are uncommon in developing countries as they are
embedded in the roads, and consequently expensive to deploy
and impractical to implement on busy roads. Our novel method,
VDZ with CCTV snap shots can provide the data needed to construct Fundamental Diagrams and able to show zero speeds at jam density, which provide essential parameters for macroscopic traffic model. The results obtained, without the use of any intrusive sensor, have shown agreement with previous traditional method.
... These zones, together with the map of destination, are sent by a computer server, as requested by an activated application (or agent) made for this purpose, in to GPS enabled smart phones. The radius is considered via an experiment in [10]. As the agent moves, it continues to monitor its current longitude-latitude coordinates and compares them to the pre-set-zones sent by the server. ...
... Direction is to be determined by the agent when 3 consecutive VD zones are valid. These components of information are then sent back to the server [10]. After obtaining initial traffic data for a certain target road, VD zones can be placed more frequently in dense areas [11] rather than sparse areas. ...
... While from the existing CCTVs, which are already available in Jakarta (JKT) main roads, we can obtain number of cars along the same zones or density over a common distance (normalized to 1 km). This paper is a follow up to our previous research [10] [12][14] [15]. This time we propose not to care about penetration rate, simply because the CCTV along the road, which takes photos at about the same time, our agents pass by, will give enough evidence to support the speeds obtained from our agents. ...
Conventionally, Fundamental Diagrams, which consist of vehicle traffic flow and density pairs, are obtained from intrusive sensor such as inductive loop detectors. However these sensors are uncommon in developing countries as they are embedded in the roads, and consequently expensive to deploy and impractical to implement on busy roads. Our novel method, VDZ with CCTV snap shots can provide the data needed to construct Fundamental Diagrams and able to show zero speeds at jam density, which provide essential parameters for macroscopic traffic model. The results obtained, without the use of any intrusive sensor, have shown agreement with previous traditional method.
