Smart Directional Data Aggregation in VANETs

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.

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

Commercial floating car data application in Sub-Saharan African transport planning contexts: A critical review and research agenda

Urban road travel time estimation is of great importance for intelligent transportation applications, such as the vehicle navigation. With the ubiquitous sensing data, e.g., the mobile sensing of vehicle trajectories, real-time urban road travel time estimation is feasible. Though many road travel time estimation models have been developed by incorporating the road traffic status as an important influencing factor, including the tensor decomposition-based models and neural network-based models, there have been no studies exploring how road travel time is correlated to the corresponding road traffic status. In this paper, we propose to study the coherence of these two traffic indicators by utilizing a probability function. Specifically, we model the road travel time and traffic status with a Gaussian distribution by considering them as two time series signals. Further, we propose to estimate the road travel time with only the road traffic status as observations via the conditional Gaussian distribution. Experiments on real-world datasets demonstrate that road travel time can be estimated with quite a good accuracy by the conditional Gaussian distribution, which further indicates that road travel time and the corresponding traffic status can be captured by a nonparametric Gaussian distribution.

Establishing a highly sophisticated large-scale Traffic Information System (TIS) requires the creation and deployment of link travel time prediction models for large road networks. Due to the dimension of typical road networks and low coverage with Floating Cars (FC), data sets that can be used for prediction contain a large number of missing observations. Additionally, specifying prediction models for each link separately is impossible due to restrictions of both computational as well as modeling resources. This paper aims to improve the scalability of link travel time predictions by combining information from roads with similar characteristics. The Functional Road Class (FRC) is a widely accepted indicator for road similarity mainly based on static information from infrastructure planning. The coherence between the clustering introduced by the FRC and road dynamics measured by Floating Car Data (FCD) in the city of Vienna is discussed and analyzed. Clustering approaches that are based on indices characterizing speed measurement distributions are proposed as alternatives to the FRC system. It is demonstrated by way of examples that the new clustering is much more appropriate to provide predictions of link travel times.

The road traffic state is usually analyzed from a temporal and macroscopic perspective; however, traffic flow parameters, such as density and spacing, can explain the evolution of traffic states from the microscopic perspective and the spatial distribution of vehicles in lanes. In this paper, we attempt to take both temporal and spatial characteristics into consideration simultaneously, and a parameter is defined as the traffic spatiotemporal state of urban road sections to represent the operational status of road traffic, using advanced prediction techniques to estimate its short-term trends. An estimation method is constructed for the traffic spatiotemporal state considering travel times, speeds, and queuing situations from temporal and spatial perspectives. Then, based on Takens’ theorem and the single variable phase space, the phase space of multiple traffic parameters is reconstructed and the chaotic characteristics are analyzed. Next, an LSTM prediction model is constructed based on the phase space reconstruction of multiple variables, and the traffic parameters are predicted by empirical analysis. The results show the proposed estimation method has a significantly improved accuracy. Finally, combined with RFID data, the traffic spatiotemporal state of the case section is calculated, which provides a theoretical basis and practical reference for road traffic state evaluations.

With the gradual maturity of geographical multimedia information system, applying geographical multimedia information system to urban intelligent traffic management information system and urban traffic sharing information platform can realize unified management and real-time update of traffic information. Through the urban traffic sharing information platform, it can increase the value of information. The multimedia information of the entire intelligent traffic management system can be stored, organized and processed by the shared multimedia information platform. It can more effectively ensure the correctness, comprehensibility and avoid redundancy of data relationships. Then it improves the utilization and transmission speed of multimedia information in the system. The urban traffic multimedia information management system has improved the use efficiency of urban road construction in China. And it also made the traffic multimedia information system more scientific and intelligent. It can also improve its management level and play an important role in urban traffic management. This paper first briefly analyzes the current traffic situation and the development of intelligent traffic multimedia information system in China. The basic concepts of GIS and urban intelligent transportation system based on GIS are introduced secondly. At last, the possibility about GIS as urban intelligent traffic management system sharing multimedia information platform is analyzed. The system framework and the basic function of GIS platform are put forward, and the application of GIS in urban traffic common information platform using the key technical support was discussed. It expounds the application of GIS in urban traffic common multimedia information platform.

The paper develops a non-parametric method for route travel time estimation using low-frequency floating car data (FCD). While most previous work has focused on link travel time estimation, the method uses FCD observations directly for estimating the travel time distribution on a defined route. A list of potential biases associated with FCD is presented and discussed. For each source of bias, a correction method for the observations is proposed. The estimation method is implemented using FCD data from taxis in Stockholm, Sweden. Estimates are compared to observed travel times for two routes equipped with automatic number plate recognition (ANPR) cameras. The mean travel time estimates incorporating all bias corrections perform equally well or better than the link-based approach in terms of RMSE, and estimated percentiles show a good match to ANPR.

With advances in information technologies, floating car data (FCD) (e.g. trajectories of taxis) becomes an important data source for collecting traffic information in urban road networks. Due to the low sampling frequency of FCD, it is a challenge to obtain accurate and reliable travel time estimation based on FCD. This paper presents a location-speed method for accurately estimating travel times in signalized road networks based on FCD. The proposed model utilizes trajectories of probe vehicles on the upstream and downstream of signalized intersections in order to estimate delays caused by traffic signals. A large-scale case study using real-world FCD in Wuhan is carried to validate the proposed method. The results of case study show that the proposed method can obtain satisfied estimation of travel times in signalized road networks.

Assessing the Accuracy of Average Travel Speeds from Floating Car Data on Urban Street Facilities

Analysis of Road Safety Speed from Floating Car Data

Simulation studies of information propagation in a self-organizing distributed traffic information system

<div class="htmlview paragraph">Considerable public and private resources are devoted to the collection and dissemination of real-time traffic information in the Chicago area. Such information is intended to help drivers make more informed trip decisions, yet its effect on driver behavior remains largely unexplored. This study was conducted to evaluate the effect of traffic reports on route and departure time changes.</div> <div class="htmlview paragraph">Downtown Chicago automobile commuters were surveyed during the AM peak period by giving them mail-back questionnaires. Drivers indicated whether or not they had changed their route and departure time decisions based on traffic reports. Further, drivers indicated how they perceived the traffic information system in terms of key attributes. Respondents also evaluated suggested improvements to the traffic information system.</div> <div class="htmlview paragraph">A majority (more than 60%) of drivers reported that they used traffic reports for modifying their trip decisions. Drivers perceived radio traffic reports positively in terms of information system attributes. Commuters were more likely to use traffic reports for their route changes if they perceived traffic reports to be accurate and timely, frequently listened to traffic reports, perceived their regular route as congested, knew multiple alternate routes, were male and higher income. Drivers were more likely to change departure times if they perceived traffic reports to be accurate and relevant, frequently listened to traffic reports and perceived their regular route as congested.</div> <div class="htmlview paragraph">Drivers indicated a relatively lower level of overall satisfaction with traffic reports indicating the existence of opportunities for improvement. One promising improvement is near-term <i>prediction</i> of traffic conditions on congested and unreliable routes (where conditions may change rapidly). This is because:</div> <div class="htmlview paragraph"> <ul class="list disc"> <li class="list-item"> <div class="htmlview paragraph">Drivers generally want to know the conditions at any given point (or link) along their route when they reach that point.</div> </li> <li class="list-item"> <div class="htmlview paragraph">Drivers would be able to make better and more informed judgments regarding their trip decisions due to reduced uncertainty of traffic conditions.</div> </li> <li class="list-item"> <div class="htmlview paragraph">The possibility that drivers may find traffic conditions significantly better or worse would be reduced. This would improve the credibility of radio traffic reports.</div> </li> </ul> </div>

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.

With the widespread adoption of automated traffic sensors, the increase of computer processing power, and the evolution and growth of the global Internet, our ability to monitor traffic and convey traffic information in real-time has been dramatically advanced. To the authors' knowledge, most of the current traffic information systems mainly provide general traffic flow information. Vehicle classification data, such as truck volumes, that should be key inputs for good transportation planning, freight mobility analysis, roadway geometric and structural design and traffic control and operation, are often excluded or only roughly estimated. To address this problem, this paper describes the design and implementation of a traffic monitoring and information system recently developed at the University of Washington that visually conveys speed and vehicle classification information, obtained by processing real-time single-loop measurements, to the general public through the client/server computer architecture. The development of this system makes real-time monitoring of truck volume data on a freeway network possible.