Error and Traffic State Analysis of Floating Car Data

Floating Car Data (FCD) is a kind of emerging data in the field of traffic engineering. There are three problems in its application of the traffic state discrimination: First, the existing traffic state discrimination model is only for road segment detection; Second, the road-segment based discrimination model is not conducive to the spatial-temporal evolution analysis of traffic state. Third, the existing road segment traffic state discrimination model directly adopts the prototypical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and the detection result is limited in accuracy. We proposed a multi-dimensional traffic state discrimination method. Firstly, the traffic state of the road segment is determined based on the improved DBSCAN algorithm. The dynamic segmentation technology is used to realize the visualization of the road traffic state. Then, the traffic incident point discrimination model is constructed according to the spatial-temporal evolution pattern of the road conditions under traffic incidents. The visualization results show that the proposed method can achieve relatively fine multidimensional traffic state discrimination.

Aiming at the problems of heavy workload of basic data collection, complicated manual parameters calibration and inaccurate calibration in the traditional microscopic traffic simulation model parameter calibration, an adaptive microscopic traffic simulation model parameter calibration method based on floating car data is proposed. First, the basic data of the simulation road network were obtained by using the floating car technology. Secondly, the parameters calibration process of the microscopic traffic simulation model was constructed, and the self- adaptive orthogonal genetic was used to achieve the model parameters calibration. Finally, using the actual data of the South Ring Road main line, District Changping, Beijing to simulate and verify. The results show that the proposed method in this paper can not only reduce the workload of manual calibration, but also the model parameter calibration is more accurate, which proves the feasibility and effectiveness of the method.

Research on the Real-time Traffic Status Identification of Signalized Intersections Based on Floating Car Data

Analysis of Road Safety Speed from Floating Car Data

Tensor-based models emerged only recently in modeling and analysis of the spatiotemporal road traffic data. They outperform other data models regarding the property of simultaneously capturing both spatial and temporal components of the observed traffic dataset. In this paper, the nonnegative tensor decomposition method is used to extract traffic patterns in the form of Speed Transition Matrix (STM). The STM is presented as the approach for modeling the large sparse Floating Car Data (FCD). The anomaly of the traffic pattern is estimated using Kullback–Leibler divergence between the observed traffic pattern and the average traffic pattern. Experiments were conducted on the large sparse FCD dataset for the most relevant road segments in the City of Zagreb, which is the capital and largest city in Croatia. Results show that the method was able to detect the most anomalous traffic road segments, and with analysis of the extracted spatial and temporal components, conclusions could be drawn about the causes of the anomalies. Results are validated by using the domain knowledge from the Highway Capacity Manual and achieved a precision score value of more than 90%. Therefore, such valuable traffic information can be used in routing applications and urban traffic planning.

Traffic performance have been measured based on degree of saturation, velocity, travel time, and delays. However, they weren’t enough to measure the traffic fluctuation that happen on normal days. This study aims to develop some modified analysis about traffic congestion based on the degree of saturation (DS) and travel time ratio (TTR). The travel time ratio is defined as a ratio between the travel time at peak hour and the travel time at off-peak hour in a certain road section. Traffic congestion conditions are classified into 4 categories: Peak-hour Congestion, Lengthy Congestion, Momentary Congestion, and Smooth Traffic. Based on survey results on road sections in Palembang, Bandung, Yogyakarta, and Surakarta, it can be concluded that peak-hour congestion occurs when TTR > 1,40 and DS > 0,75, lengthy congestion occurs when TTR < 1,40 and DS > 0,75, momentary congestion occurs when TTR > 1,40 and DS < 0,75, and smooth traffic when TTR < 1,40 and DS < 0,75.

Urban traffic situation information is the basis of effective traffic guidance and traffic control. In order to promote communication and practical applications of traffic information, then can provide full, accurate, real time and sufficient traffic information service to public, government and correlative enterprises, in this paper, the authors proposed the dynamic traffic information collection and processing methods of intelligent transportation systems (ITS) common information platform (CIP) based on floating car technology. According to the characteristics of the ITS CIP data collection, they were required large scale, accurate and dynamic data collecting. A complete set of dynamic traffic information processing methods based on floating car technology were studied, including road data storage by two queues, error data elimination by taximeter, map matching by region dividing method and boundary rectangle, and traffic condition calculation by circle queues and A* algorithm. The application of the methods in the ITS CIP of Guangzhou City proved that the methods were quite effective and valuable to be popularized.

As a means of transportation, the position and role of the road network in essence concern the livelihoods of many people to maintain the sustainability and service quality of these roads. If the road section is damaged, it will have a significant impact on the condition of the traffic flow. Many road damages occur due to the high use of the road which causes a high volume of vehicles, overloaded vehicles, rainwater runoff or poor drainage systems and others. The Sheet-Sekotong-Pelangan road section is a provincial road section located in West Lombok Regency, NTB. This road segment has been damaged in several road segments and disrupted traffic flow so it requires handling. In this regard, the purpose of this study is to determine the level of road damage using the Pavement Condition Index (PCI) method and to determine the effect on vehicle speed and to find recommendations that are appropriate to the level of road damage. This study includes a visual survey to determine the condition of road damage and vehicle speed. The analysis used to determine the effect of road damage on vehicle speed is Simple Linear Regression. The results of the analysis show that the R square value is 0.893, which means that road damage has an influence on vehicle speed of 89.3%. Regression model y (vehicle speed) = 9.012 + 0.581x (PCI), which means that if the PCI 9.012 variable increases by 1 unit or 1%, the vehicle speed will increase by 0.581 units or by 58.1%. The lower the PCI value, the slower the vehicle speed will be. Conversely, the greater the PCI value, the faster the vehicle speed.

Adaptive signal control is a method that dynamically adjusts signal phases based on real-time traffic conditions, aiming to improve the efficiency of intersection light control. However, in practical applications, obtaining complete traffic spatiotemporal states at intersections is challenging given the limited data sources. To address this issue, this paper presents a novel deep reinforcement learning model, namely 3DQN-PSTER, which combines Dueling Deep Q Network, Double Deep Q Network, and Priority SumTree Experience Replay technology. This model effectively extracts traffic states from sampled floating car data (FCD) and achieves optimal timing schemes. To evaluate its performance, the proposed model is tested in a simulated intersection environment using actual intersection data. The model interacts with the environment by adjusting signal phases, updating traffic states, and obtaining optimal timing schemes. Simulation results demonstrate that the proposed model exhibits higher convergence efficiency and outperforms benchmark models. When applied, the reinforcement learning single control (RLSC) based on the 3DQN-PSTER model outperforms fixed-time signal control schemes and actuated signal control (ASC) schemes. Even in scenarios with low penetration ratios of FCD, RLSC still significantly outperforms ASC. Therefore, the signal timing optimization method proposed in this paper could greatly enhance intersection operation efficiency in both ideal overall data environments and partial states.

The ultimate goal of a Traffic Information System (TIS) consists in properly informing vehicles about road traffic conditions in order to reduce traffic jams and consequently CO2 emission while increasing the user comfort. Therefore, the design of an efficient aggregation protocol that combines correlated traffic information like location, speed and direction known as Floating Car Data (FCD) is of paramount importance. In this paper, we introduce a new TIS data aggregation protocol called Smart Directional Data Aggregation (SDDA) able to decrease the network overload while obtaining high accurate information on traffic conditions for large road sections. To this end, we introduce three levels of messages filtering: (i) filtering all FCD messages before the aggregation process based on vehicle directions and road speed limitations, (ii) integrating a suppression technique in the phase of information gathering in order to eliminate the duplicate data, and (iii) aggregating the filtered FCD data and then disseminating it to other vehicles. The performed experiments show that the SDDA outperforms existing approaches in terms of effectiveness and efficiency.

Abstract— The positive impact of video and image processing in traffic monitoring, analysis, and traffic condition monitoring in various cities and urban vicinities cannot be overstated. This paper is another approach considering estimating vehicles' speed. Basically, traffic videos are collected from a stationary camera mounted on freeways. On the one hand, camera calibration with respect to accurate measure could mainly solve the geometrical equations conveniently supported directly using references. Camera calibration could yield precise measurements, but rather difficult in achieving the accurate speed. The system is also adaptable to extend into other traffic-related applications. The average errors in detected vehicle speed were ± 7 km/h, and the experiments were conducted under various resolutions and different video sequences. The accelerating advancement of computing power of commonplace computers has now enabled a broader reach in the integration of deep learning techniques in traffic surveillance video comparisons. It constitutes the core functions of traffic analysis, such as prediction of traffic flows, anomaly detection, vehicle re-identification, and vehicle tracking. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most serious research subjects during the recent years. It can be a good solution to this problem in preventing road accidents and enhancing road engineering by almost checking the traffic demand. Vehicle speed prediction is effectively proposed through integration of state-of-the-art deep learning models and classical computer vision techniques. Some present-and-state-of-the-art efforts concerning estimation of vehicle speeds, detection of vehicles, and tracking of objects are discussed in this article. Optical flow contains the speed and direction information of pixel displacements in an image. Our final approach deals with collecting multi-scale convolutional network. This extracts parameter data of vehicles. Keywords: Speed detection in vehicles, video sequences, computer vision, background modeling, traffic monitoring, OpenCV

PurposeIn order to explore the effect of curb parking on dynamic traffic and formulate some reasonable and effective traffic management measures, the effects on capacity and traffic safety are studied.MethodsFour road segments with curb parking were selected as the investigation location. Several traffic characteristics and parameters were then investigated and counted, including traffic flow volume, speed, headway and traffic conflict. Based on a reduction of lane effective width and the Gap Acceptance Theory, two effect models describing curb parking and road capacity were constructed. Considering the factors involved in motor vehicle conflicts (such as non-motor vehicle lane distribution and stopping sight distance), the effects of curb parking on both motor vehicle and non-motor vehicle traffic safety was analyzed, as well as pedestrian crossing safety. This case study analyzes both the capacity and traffic safety of all four investigation segments.ResultsResults show that the lane effective width of road segment with curb parking significantly influences the road capacity and impacts the traffic flow volumes of the parking lane and its adjacent lane.ConclusionsCurb parking significantly effects traffic operation safety; however, it can be improved by implementing traffic safety management measures.

Accurate on-road vehicle speed prediction is important for many intelligent vehicular and transportation applications. It is also challenging because the individual vehicle speed is affected by many factors, e.g., traffic speed, vehicle type, and driver's behavior, in either deterministic or stochastic ways. This paper proposes a novel vehicle speed prediction method in the context of vehicular networks, where the real-time traffic information is accessible. Traffic speeds of following road segments are first predicted by Neural Networks (NNs) based on historical traffic data. Hidden Markov models (HMMs) are trained by the Baum-Welch algorithm with historical traffic and vehicle data to present the statistical relationship between vehicle speed and traffic speed. The forward-backward algorithm is applied on HMMs to extract vehicle's speed on each road segment along the driving route. Simulation is set up on the SUMO microscopic traffic simulator with the application of a real Luxembourg highway network and traffic count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.

On-line in-vehicle traffic simulation has been proposed as a means to provide predictions of future states of a traffic network based on current traffic conditions. The area covered by an in-vehicle simulation may change dynamically during the vehicle's journey. This paper is concerned with the issue of initializing the state of new regions that are added to a running microscopic traffic simulation. A warm-up period is required where the new road segments must be populated with simulated vehicles. Techniques that convert vehicle flow rate and queue length information to vehicle positions and speeds are proposed to minimize this warm-up period. These techniques are evaluated and compared with respect to estimated travel times of vehicles traveling through different paths in the road network under different traffic conditions. Travel time predictions are compared to a microscopic simulation of the entire region.

Energy-optimal route planning for electric vehicle (EV) is highly required for the wide-spread use of EV, which is hindered by limited battery capacity and relative short cruising range. Obtaining the cost for each link (i.e., link energy consumption) in road networks plays a key role in energy-optimal route planning process. The link energy consumption depends mainly on energy consumption factor, which is related to not only vehicle speed but also road type. This study aims to analyze the difference of EV’s energy consumption factors for different road types. According to the floating car data (FCD) collected from the road network in Beijing, the vehicle specific power (VSP) distributions under different average travel speeds for different road types are analyzed firstly, and then the EV’s energy consumption rates under different VSP-Bins are calculated. By using VSP as an intermediate variable, EV’s energy consumption factor models for different road types are established and the difference of EV’s energy consumption factors is analyzed. The results show that road type-based energy consumption factor should be used in EV’s energy-optimal route planning process.