Real-time Traffic Information System Research Articles

Delay Factors Modelling for Real-Time Traffic Information Systems

The paper is presenting an overview of the factors that lead to critical delays which may occur in multi-modal realtime traffic and travel information systems (MRTTI) employing cellular data network communications. The analysis is taking into account MRTTI systems that use mobile devices to detect position and speed of a vehicle and receive traffic information furthermore used to adjust a previously defined route from origin to destination points. Information transmission delays are also investigated for route guidance systems used in critical applications, such as emergency vehicles management. The identification of the delay factors and some models regarding the delays are presented along with measurements on a real system.

Data-Driven Multi-Agent Vehicle Routing in a Congested City

Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we can predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This paper studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. These data are made available to all users, such that they may be able to learn and predict the effects of congestion for building a route adaptively. This method is further enhanced by combining the traffic information system data with previous routing experiences to determine the fastest route with less exploration. We test our method using a multi-agent simulation, demonstrating that this method produces a lower total route time for all vehicles than when using either a centralized traffic information system or direct experience alone.

Freeway Travel Time Information From Input- Output Vehicle Counts: A Drift Correction Method Based on AVI Data

Input-output cumulative count curves are a powerful tool to forecast freeway travel times in the very short term, as they rely on the predictive information given by the vehicles' accumulation in the target section. Therefore, they could represent a very appealing method to feed real-time traffic information systems. However, traffic detectors' count drift implies very poor accuracy in the estimation of the vehicles' accumulation, leading to completely unreliable travel time predictions. A drift correction method is necessary. In contrast, several technologies allow accurate direct travel time measurements, like automatic vehicle identification (AVI) or vehicle tracking systems. In all cases, direct travel time measurements are obtained once the vehicle has crossed the target section. This means that the information is representative of the near past traffic conditions, while the objective of real-time information systems is to transmit information about traffic conditions in the near future. In this context, the present paper aims to fuse the information provided by input-output diagrams, obtained from loop detectors, with AVI direct travel time measurements. This fusion allows exploiting the accuracy of the direct measurements to correct the count drift in loop detectors. Then, corrected input-output curves can be used to obtain reliable short-term predictions of travel times. The proposed data fusion method has been applied to a test site in the AP7 freeway near Barcelona, obtaining significantly better results than with the common practices of simply disseminating direct measurements or using spot-speed methods.

Empirical evaluation of real-time traffic information for in-vehicle navigation and the variable speed limit system

Advanced traveler information systems (ATIS) present traffic information to travelers to assist them in planning their journeys and also provide guidance when on the road. Providing travelers with information affects travel behavior and can result in improved performance and reduced congestion and emissions. However, the success of ATIS depends on, among other factors, whether drivers consider it useful and their particular responses to the provided information. There are, currently, many sources and market players in the traffic information industry producing traffic reports for consumers. The main objective of this paper was to examine whether traffic information provided from different sources are consistent with one other. A variable speed limit system and real-time traffic information system for in-vehicle navigation systems are used as a case study. This comparative analysis was made by superimposing the space-time regions of ground truth measured traffic conditions (“reality”) over the space-time regions of the reported information broadcasts. A predictive buffer was also introduced in the assessment. The predictive buffer was included to account for traffic information which could be considered as advance warning messages or correctly predicting and giving congestion information prior to the onset of actual congestion, which otherwise may have been reported as false alarms. This was motivated by the fact that optimal control approaches include the prediction of congestion in their control strategies. The methods developed in this research work have been applied to a real world problem to prove its applicability.

Intelligent Traffic Information System a Real-Time Traffic Information System on the Shiraz Bypass

Real-time traffic information system is an Intelligent Transportation System (ITS) that allows commuters to make their traveling plan better. In this regard, an intelligent and real-time traffic information system was developed based on the video detection and an image processing algorithm was applied to measure traffic-flow according to the average speed of vehicles. Then, traffic status of each pass way is broadcasted to the electronic boards installed on all decision making entrance / exit. Different levels of congestion related to the routes ahead are shown on the boards with different colors in order to assist commuters. This system was implemented on the Shiraz Dry River’s bypasses which account as vital routes to moderate traffic of city center. Experimental results are promising due to the proximity of determined traffic status by the system compared to the detection done by traffic experts. Average speed improvement is another result of using this system. This intelligent system developed and implemented in Shiraz city for the first time in Iran.s.

Traffic Information Estimation Methods From Handover Events

Abstract Fast growth of the economy and technology upgrades have led to improvements in the quality of traditional transport systems. As such, the use of intelligent transportation systems (ITS) has become more and more popular. The implementation and improvement of real-time traffic information systems are an important parts of ITS. Compared with other traditional methods, traffic information estimations from cellular network data are now readily available, more cost-effective, and easier to deploy and maintain. This study assumed that nonvehicle calls could be filtered out and vehicles could be tracked on road segments. A novel ITS model was proposed to indicate the relationship between call arrival rate and traffic density. Moreover, the vehicle speed and traffic flow were estimated by using cellular floating vehicle data (CFVD) and the proposed novel ITS model. In experiments, this study used a VISSIM traffic simulator and adopted the average call inter-arrival time and call holding time to simulate communication behavior on road segments. The estimated traffic information was compared with the simulated traffic information from stationary vehicle detectors (VD). The results indicated that the average accuracies for vehicle speed estimation, traffic flow estimation, and traffic density estimation in the congested flow case were 97.63, 89.72, and 90.45 %, respectively. Therefore, this approach was feasible to estimate traffic information for ITS improvement.

Design and Implementation of Real-Time Vehicular Camera for Driver Assistance and Traffic Congestion Estimation.

As society has developed, the number of vehicles has increased and road conditions have become complicated, increasing the risk of crashes. Therefore, a service that provides safe vehicle control and various types of information to the driver is urgently needed. In this study, we designed and implemented a real-time traffic information system and a smart camera device for smart driver assistance systems. We selected a commercial device for the smart driver assistance systems, and applied a computer vision algorithm to perform image recognition. For application to the dynamic region of interest, dynamic frame skip methods were implemented to perform parallel processing in order to enable real-time operation. In addition, we designed and implemented a model to estimate congestion by analyzing traffic information. The performance of the proposed method was evaluated using images of a real road environment. We found that the processing time improved by 15.4 times when all the proposed methods were applied in the application. Further, we found experimentally that there was little or no change in the recognition accuracy when the proposed method was applied. Using the traffic congestion estimation model, we also found that the average error rate of the proposed model was 5.3%.

Penerapan Teknologi GPS Tracker Untuk Identifikasi Kondisi Traffik Jalan Raya

Real time tracking system technology has been made possible by integrating three technologies, namely global positioning system (GPS), database technologies such as geographic information system (GIS) and mobile telecommunications technologies such as general packet radio service (GPRS). This paper has proposed a vehicle tracking mechanism based on GPS tracker to build a real-time traffic information system. A GPS server is built to process data of position and speed of the vehicle for further processed into vehicle traffic information. The Server and GPS tracker is designed to communicate using GPRS services in real time. Furthermore, the server processes the data from the GPS tracker into traffic information such as traffic jam, dense, medium and smoothly. Test results showed that the GPS server is able to visualize the real position of the vehicle and is able to decide the category of traffic information in real time.

Survey on In-vehicle Technology Use: Results and Findings

ABSTRACT The use of advanced technology in automobiles has increased dramatically in the past couple of years. Driver-assisting gadgets such as navigation systems, advanced cruise control, collision avoidance systems, and other safety systems have moved down the ladder from luxury to more basic vehicles. Concurrently, auto manufacturers are also designing and testing driving algorithms that can assist with basic driving tasks, many of which are being continuously scrutinized by traffic safety agencies to ensure that these systems do not pose a safety hazard. The research presented in this paper brings a third perspective to in-vehicle technology by conducting a two-stage survey to collect public opinion on advanced in-vehicle technology. Approximately 64 percent of the respondents used a smartphone application to assist with their travel. The top-used applications were navigation and real-time traffic information systems. Among those who used smartphones during their commutes, the top-used applications were navigation and entertainment.

RoadRank Algorithm – A Novel Method to Calculate Traffic Index for an Area

Traffic index hasbeen used to provide accurate traffic information to users. Many models havebeen developed to calculate the index for a road, but how to define andcalculate the index for an area still needs more investigation. Here we proposea new model for area index, including a definition of area index itself and a methodto calculate it. But this model can’t be widely used, for some innatelimitations. So we put forward another method based on well-known algorithmPageRank to calculate area index. To test the effectiveness, we apply ouralgorithmto conduct several experiments. Our experiments using standard trafficstatistics provided by ShanghaiTraffic Information Center (STIC), show our method have values for real-time traffic information system.

Heterogeneous Lanes’ Saturation Flow Rates at Signalized Intersections

Correct saturation flow rates are very important for the calculation of delays and level of service at intersections. However, the Highway Capacity Manual (HCM) procedure and the related studies assumed ideal conditions and cannot be applicable under mixed traffic circumstances. A traffic survey on 36 signalized intersections inside the Beijing's 5th Ring was conducted and a large number of experimental data with different lane types were statistically analyzed in this paper. Results show that the saturation headways approximately follow the normal distributions and the following base saturation flow rates are appropriate for Beijing intersections: 1380 passenger cars per hour per lane (pc/h/ln) of the right-turn lane, 1520 pc/h/ln of the left-turn lane, 1535 pc/h/ln of the through lane, 1457 pc/h/ln of the through-right lane and 1411 pc/h/ln of the through-left lane. These results have been used and verified in the real-time traffic information systems of Beijing Traffic Management Bureau.

Energy Optimal Real-Time Navigation System

The rapid development of Mobile Internet and Smart Devices and advent of a new generation of Intelligent Transportation Systems (ITS) increase information about present driving conditions and make its prediction possible. Real time traffic information systems (TIS) like SYTADIN help in route to destination planning and traffic state prediction. Energy-optimal routing for electric vehicles creates novel algorithmic challenges where the computation complexity and the quality of information on traffic state are the main issues. This complexity is induced by the possible negative values of edge energy as well as the variability of route and vehicle variables which render the standard algorithms unsuitable. In this paper the authors present an Energy Optimal Real Time Navigation System (EORTNS), implemented on Samsung Galaxy Tab, capable of calculating the route to destination based on information flow obtained from SYTADIN. As an application example the authors propose a real time energy management for a Hybrid Electrical Vehicle (HEV) composed of batteries and Super-Capacitors (SC). The EORTNS is not only capable of energy optimal route to destination calculation with respect to traffic state but also operates the On-Board power splitting between batteries and Super-Capacitors.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

Traffic information condition is a very useful information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.

The Optimal Traffic Information Process System by Using MIMO Algorithm

This paper presented dynamic vehicle information for wireless communication and safe systems by integrating radio frequency identification (RFID) technology. The systems provied different particular and advantage between RFID and other recognition technology. The car black box is the best choice and innovates specific application. It is not only the system can guard car but differentiate the responsibility for car accident event. Furthermore, the car black box is integrated the RFID, general packet radio service (GPRS), Bluetooth, personal digital assistant (PDA) and global positioning system (GPS). The proposed system has been trending the merger goal of digitization. Therefore, it is created a tremendous amount of commercial opportunity. The car black box is also combined multiple-user with real-time navigation information system. The system is integrated the independent real-time traffic information database and Geographical Information System (GIS) through data fusion method. We adopted a traffic information system offering advanced personalized route planning, including added services for traffic jam alerting by means of Short Message Service (SMS) in Global System for Mobile (GSM) communication network. It is shown small and low-cost GSM modules for data process by using the Multiple Input Multiple Output (MIMO), Ant colony optimization (ACO) algorithm, which developed a real-time software solution to help users and evaluation of user preferences for car traffic information system.

Effects of real-time traffic information systems on traffic performance under different network structures

The effects of real-time traffic information system (RTTIS) on traffic performance under parallel, grid and ring networks were investigated. The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures. For traffic on a parallel network, the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%, and a proportion of RTTIS usage higher than 90% would actually deteriorate the performance. For both grid and ring networks, a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS. For all three network structures, vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.

Evaluation of traffic data accuracy using Korea detector testbed

This study describes how and where a traffic detector, mainly used for real-time traffic management and information systems, is evaluated in Korea. Traditionally, detector evaluation in Korea is based on video ground-truth data. However, the increasing demand for real-time traffic management and information has resulted in increased focus on rapid and high-throughput detector evaluation – impossible with the conventional video method. Hence, the Korean government established a national traffic detector testbed equipped with a laser-based reference standard, relational database, video acquisition system, communication infrastructure and so forth. The laser-based baseline data source is thoroughly verified using tape-switch sensors with an oscilloscope. The testbed is actively used in evaluating the vehicle level performance of many in-road and over-road traffic detectors through consensus-based data verification. A comprehensive discussion on detector performance tests using the testbed is presented. The testbed developed in this research is expected to make traffic detector evaluation much easier than before.

Estimation of the automatic vehicle identification based spatial travel time information collected in Stockholm

To support the implementation of real-time traffic information systems in the Stockholm city area using automatic vehicle identification (AVI) data, a preliminary travel time analysis tool has been developed. The program can manage and analyse travel time measurements in a distributed database server where both online and historical traffic information are saved. Meanwhile, several existing travel time estimation algorithms are implemented in the travel time analysis program, and are evaluated using four months of AVI data collected in the urban streets and arterials of and near the Stockholm downtown area. The advantages and disadvantages of those algorithms are also analysed using the highly noisy travel time measurements collected under the urban context. In addition, the authors have also evaluated a common statistical median filtering approach and suggested some modifications for AVI data estimation. In general, all these algorithms have the potential to be applied for real daily travel time estimation and the statistical median filter with modifications has been suggested for historical travel time estimation in real application. Finally, the authors point out an essential problem in travel time estimation and suggest a direction that may have the potential to improve the online traffic information quality.

The implementation of SOA within grid structure for disaster monitoring

Nowadays, the administration and execution of disaster monitoring in Taiwan are able to achieve the goal of overall monitoring with the assistance of various tools and technology distributed in different organizations. Examples of information and technology include the data collected by monitoring stations, existing database and spatial data warehouse, precipitation information provided by the Central Weather Bureau (CWB), results of site investigation, Remotely Piloted Vehicle (RPV) airscape images, aerial photos, satellite images, and etc. Therefore, in order to facilitate tasks of disaster monitoring, heterogeneous data needs to be integrated via a consistent communication interface. The use of grids was proposed to solve the problems in heterogeneity, distribution, and efficiency triggered by networking. Information worth sharing is uploaded to a platform which can publish and register data and control the flow of information; users then access the platform to search and fetch valuable information and value-added applications. On the platform, all of the heterogeneous and distributed data is encrypted, decrypted, monitored, and hence interchangeable according to international standards. Because the employment of grid computing does not require large-scale modifications of existing systems, grids designed for different purposes are being developed. Our research adopts the emerging grid technology to create a grid-based disaster management mechanism. Taking disaster relief distribution as an example, we optimize the stockpile distribution and delivery routes by utilizing Sensor GRID and Application GRID and by combining the interpretation given by real-time traffic information systems. Our study will further the government’s abilities to manage disasters, and substantially strengthen the cooperation and communication between the government and academic units.

Identification and Correction of Dual-Loop Sensitivity Problems

Freeway traffic speed and bin volumes for different vehicle categories are typically collected by dual-loop detectors. Good quality dual-loop detector data are crucial for effective real-time traffic management systems and traveler information systems. However, loop detectors are subject to various malfunctions that can result in erroneous measurements. Previous studies indicated that loop sensitivity-level discrepancies between two single loops forming a dual-loop detector and unsuitable sensitivity levels of the single loops are two major causes of quality degradation in dual-loop data. This paper presents an algorithm and its implementation for identifying and correcting such loop sensitivity problems. The algorithm identifies dual-loop sensitivity problems using individual vehicle data extracted from loop event data and corrects dual-loop sensitivities through a two-step procedure: (a) remove the sensitivity discrepancy between the two single loops and (b) adjust their sensitivities to the appropriate level. Elimination of dual-loop sensitivity problems significantly enhances the reliability of dual-loop detectors and improves the quality of traffic speed and bin volume data.

Identification and Correction of Dual-Loop Sensitivity Problems

Freeway traffic speed and bin volumes for different vehicle categories are typically collected by dual-loop detectors. Good quality dual-loop detector data are crucial for effective real-time traffic management systems and traveler information systems. However, loop detectors are subject to various malfunctions that can result in erroneous measurements. Previous studies indicated that loop sensitivity-level discrepancies between two single loops forming a dual-loop detector and unsuitable sensitivity levels of the single loops are two major causes of quality degradation in dual-loop data. This paper presents an algorithm and its implementation for identifying and correcting such loop sensitivity problems. The algorithm identifies dual-loop sensitivity problems using individual vehicle data extracted from loop event data and corrects dual-loop sensitivities through a two-step procedure: (a) remove the sensitivity discrepancy between the two single loops and (b) adjust their sensitivities to the appropriate...