Examining the potential of floating car data for dynamic traffic management

Traditional traffic monitoring systems are mostly based on road side equipment (RSE) measuring traffic conditions throughout the day. With more and more GPS-enabled connected devices, floating car data (FCD) has become an interesting source of traffic information, requiring only a fraction of the RSE infrastructure investment. While FCD is commonly used to derive historic travel times on individual roads and to evaluate other traffic data and algorithms, it could also be used in traffic management systems directly. However, as live systems only capture a small percentage of all traffic, its use in live operating systems needs to be examined. Here, the authors investigate the potential of FCD to be used as input data for live automated traffic management systems. The FCD in this study is collected by a live country-wide FCD system in the Netherlands covering 6-8% of all vehicles. The (anonymised) data is first compared to available road side measurements to show the current quality of FCD. It is then used in a dynamic speed management system and compared to the installed system on the studied highway. Results indicate the FCD set-up can approximate the installed system, showing the feasibility of a live system

Road Infrastructure Safety Management. Proactive Safety Tools to Evaluate Potential Conditions of Risk

The identification of highly critical sections in a road network is possible by examining the network operation, with the goal of determining the risk factors and observe the critical issues, in order to better plan possible improvements. Therefore, this study proposes a method to evaluate the coherence of existing road layouts, through the analysis of the geometric characteristics, theoretical speeds and drivers operating speeds, under different environmental and flow conditions. The analysis focuses on the road network managed by ANAS SpA in the Veneto Region, for which the reconstruction of the road axes geometry, the curvature graph and the theoretical design speed profile have been obtained, according to the indications of the Italian Ministerial Decree 05/11/2001. The theoretical design speed profile has then been compared with the information relating to the road users' mobility, in terms of the 85th percentile speeds, obtained from the extraction and analysis of the Floating Car Data (FCD). The data were processed by reconstructing the continuous profile of operating speeds with a specific regression function known as "smoothing cubic spline". The comparison with the theoretical design speeds allows to observe whether the users assume a behavior close to or distant from what is expected, based on the technical and geometrical characteristics of the road layout. The proposed methodology can contribute to the implementation of a proactive road safety check, aimed at recognizing and assessing the potential risk conditions for road traffic, with particular attention to the point of view of the road user

Road Condition Estimation Based on Heterogeneous Extended Floating Car Data

Road condition estimation based on Extended Floating Car Data (XFCD) from smart devices allows for determining given quality indicators like the international roughness index (IRI). Such approaches currently face the challenge to utilize measurements from heterogeneous sources. This paper investigates how a statistical learning based self-calibration overcomes individual sensor characteristics. We investigate how well the approach handles variations in the sensing frequency. Since the self-calibration approach requires the training of individual models for each participant, it is examined how a reduction of the amount of data sent to the backend system for training purposes affects the model performance. We show that reducing the amount of data by approximately 50 % does not reduce the models’ performance. Likewise, we observe that the approach can handle sensing frequencies up to 25 Hz without a performance reduction compared to the baseline scenario with 50 Hz

Analysis of Road Safety Speed from Floating Car Data

Intelligent Transportation Systems aims at improving efficiency and safety of the transportation system by acting either on vehicle performances or assisting the driver with information on vehicle and traffic status. Although digital road graphs are available to derive quantitative parameters that describe the road geometry, the information provided usually includes speed limits and repetition of road signs. On the other hand, a huge amount of data is available on individual vehicle speeds and trajectories collected as Floating Car Data (FCD) but they are not combined with road parameters to derive information on how drivers perceive the infrastructure and behave when traveling on it. In the paper, a methodology is presented to evaluate the consistency between drivers' behavior and a theoretical safety speed determined from road geometry. The azimuth profile is progressively built for a road layout, based on the geometry described by a digital graph. Consecutive elements with the constant azimuth variation are identified as circular curves and their radii are computed by circle fitting. The safety speed with respect to longitudinal stability is estimated. The obtained safety speed is then compared to the distribution of speeds observed from about 200 million FCD collected on the regional road network of Latium. The obtained results permit to individuate critical points of the network in terms of road safety

Video Surveillance for Road Traffic Monitoring

This project addresses the improvement of the current process of road traffic monitoring system being implemented in Malaysia. The current monitoring system implies video feeds from a particular road to a place where there will be personnel monitoring the traffic condition. The personnel will then manually update the traffic condition to various radio and television networks throughout the country to be broadcasted. FM radio is a famous channel for traffic updates since every vehicle is equipped with one. This project will provide a real-time update of the current traffic condition

Blending of Floating Car Data and Point-Based Sensor Data to Deduce Operating Speeds under Different Traffic Flow Conditions

Nowadays, smart mobility can rely on innovative tools for the knowledge of road system conditions, like operating speed data extracted from the so-called Floating Car Data (FCD). Probe vehicles in the traffic flow send to operation centres a large amount of travel information, collected through GPS detection systems, especially with regard to geolocation, date and time, direction and speed. As the sample deriving from these vehicles represents a tiny portion of the entire vehicular fleet, in this paper an analysis and a comparison with data obtained by point-based traffic sensors is proposed.Therefore, the study analyses data collected by inductive loop detectors and microwave radar sensors, that provide information on the entire traffic flow in the time domain, in particular with the aim to identify free flow speed time bands. Afterwards, by means of the fusion between the results obtained from the data coming from these point-based control units and the ones coming from the probe vehicles, a comparison of the operating speeds in the two conditions of constrained and unconstrained traffic flow is performed

DEVELOPMENT OF SPATIOTEMPORAL CONGESTION PATTERN OBSERVATION MODEL USING HISTORICAL AND NEAR REAL TIME DATA

Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at certain parts of highway networks, referred to as bottleneck locations. Nonrecurring congestion, on the other hand, can be caused by different reasons, including work zones, special events, accidents, inclement weather, poor signal timing, etc. The work presented here demonstrates an approach to effectively identifying spatiotemporal patterns of traffic congestion at a network level. The Metro Atlanta highway network was used as a case study. Real time traffic data was acquired from the Georgia Department of Transportation (GDOT) Navigator system. For a qualitative analysis, speed data was categorized into three levels: low, median, and high. Cluster analysis was performed with respect to the categorized speed data in the spatiotemporal domain to identify where and when congestion has occurred and for how long, which indicate the severity of congestion. This qualitative analysis was performed by day of week to identify potential variation in congestion over weekdays and weekend. For a quantitative analysis, actual speed data was used to construct daily spatiotemporal maps to reveal congestion patterns at a more granular level, where congestion is represented as “cloud” in the spatiotemporal domain. Future work will be focusing on deep learning of congestion patterns using Convolutional Long Short Term Memory (ConvLSTM) networks

The path inference filter: model-based low-latency map matching of probe vehicle data

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.Comment: Preprint, 23 pages and 23 figure