Link Travel Time Estimation at Signalized Road Segments with Floating Car Data

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.

Floating car data (FCD) is an important source of traffic data for the design and management of an intelligent transportation system, and the efficiency and accuracy of FCD map-matching influences the application of FCD. Most map-matching algorithms are very time-consuming because many links need to be evaluated as to whether they are the correct link for the travelling floating car, especially in urban areas where most FCD are collected. In fact, the highest degree of map-matching a link for an FCD is not always the correct link, so the use of candidate links is proposed here. In this study, candidate links are defined as those that fall within the FCD error. We propose a novel algorithm for quickly identifying accurate candidate links on the basis of a vector to raster road map conversion approach. First, a road buffer is constructed based on the size of the confidence region by taking into account the position error of the FCD and the width of the link. Second, all link buffers are converted to a raster map with resolution in metres, each pixel position of the raster map corresponding to a geographical coordinate. A pixel can have one or more values, which represent the link ID whether the link buffer covers the pixel; so a spatial index map between geographical coordinate and link ID is built in this way. Then, candidate links are calculated rapidly in the next three steps: (1) Selecting a link ID from the spatial index map that, according to the FCD positioning fix coordinate, must fall within the confidence region of the FCD. The number is very small and this step is very fast because it does not include a traverse process. (2) Getting data for selected link from the road networks vector map by the link ID. This step uses a binary search and is very fast. (3) Calculating the degree of map-matching of these links to identify candidate links of the FCD. This algorithm is far less time-consuming than earlier approaches. The algorithm was tested using more than 13 million real-world FCD collected in Wuhan City, China, and the performance was shown to be satisfactory.

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

Estimation of link travel times from measured values is a critical service of floating car information systems (probe car information systems), and the results of estimation are utilized to improve quality of vehicle navigation. Because actual link travel times are affected by various factors, such as time period, traffic volume, and weather, these factors should be considered to estimate the link travel time. Also, they should be considered to estimate the minimum amount of data needed to estimate link travel time while maintaining accuracy to optimize the floating car systems. In the systems currently being implemented, however, effects of the weather are not utilized to estimate the link travel time, and the minimum amount of necessary data has not been studied in detail. This paper reveals the effect of weather differences on estimated link travel time using data actually obtained by floating car systems. In particular, this paper pays attention to effects of snowfall. This paper also evaluates the amount of data required to obtain estimated link travel times considering effects of various factors.

Modern design concepts apply probability density functions to model the traffic flow and capacity. In the application travel times are calculated by drawing random realisations or by estimating an expected value. Generally it is assumed that the distributions of different infrastructure elements are independent and not correlated. In literature this property is so far not often tested. During a project in November 2003 at IVT data was collected from floating cars using a device to record GPS-data on approximately 30,000 kilometres within the canton of Zurich. These floating car data (FCD) was aggregated to gain link travel times on the network of that area. In this paper the correlation of the link travel times or more generally the experienced travel speed is computed, grouped within different road types. This can be done by matching the GPS data to a network model. Different network dependencies are investigated by calculating the correlation for travel speeds measured on paths for given numbers of intersections and distances. It can be seen that the correlation is high for pairs of speeds which are near to each other and decreasing with an increase in the distance. The FCD of three vehicles over many days is combined by establishing a realistic space-time envelope based on theoretically possible routes to the position of other measurements. From each link with an aggregated travel time the FCD the shortest path (based on the travel time) to each later link information was calculated. If these two measurements on the links could have been produced by a car travelling that route at the time this link pair was also used for the calculation of the correlation. With these theoretical routes sufficient data pairs can be compared to get significant values for the correlation.

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

Abstract. Traffic congestion is a dynamic spatial and temporal process and as such might not be possible to model with linear functions of various dependent variables. That leaves a lot of space for non-linear approximates, such as neutral networks and fuzzy logic. In this paper, the focus is on the fuzzy logic as a possible approach for dealing with the problems of measuring traffic congestion. We investigate the application of this framework on a selected case study, and use floating car data (FCD) collected in Augsburg, Germany. A fuzzy inference system is built to detect degrees of congestion on a federal highway B17. With FCD, it is possible to obtain local speed information on almost all parts of the network. This information, together with collected vehicle location, time and heading, can be further processed and transformed into valuable information in the form of trip routes, travel times, etc. Initial results are compared with traditional method of expressing levels of congestion on a road network e.g. Level of Service – LOS. The fuzzy model, with segmented mean speed and travel time parameters, performed well and showed to be promising approach to detect traffic congestions. This approach can be further improved by involving more input parameters, such as density or vehicle flow, which might reflect traffic congestion event even more realistically.

Due to the propagation of traffic congestion from upstream to downstream links and the uncertainty of path choice behaviours, travel times between different links, particularly adjacent links, are highly correlated in a typical period from day to day. To improve the estimation accuracy of both the mean and covariance of link travel times, a novel measurement is proposed to optimize the locations of multi-type traffic sensors for link travel time estimation. Multi-source data from different types of traffic sensors can be integrated to better estimate link travel time in an entire road network. In practice, the allocation of multi-type traffic sensors is constrained by the total financial budget and should be optimized in accordance with measurement errors and the cost ratio. Numerical examples of synthetic and real road networks are conducted to demonstrate the applications and merits of the proposed multi-type sensor location model with covariance effects.

This paper describes a study which gives insight into the size of improvement that is possible with individual in-car routing advice based on the actual traffic situation derived from floating car data (FCD). It also gives an idea about the required penetration rate of floating car data needed to achieve a certain degree of improvement. The study uses real loop detector data from the region of Amsterdam collected for over a year, a route generating algorithm for in-car routing advice, and emulated floating car data to generate the routing advice. The case with in-car routing advice has been compared to the base case, where drivers base their routing decisions on average knowledge of travel times in the network. The improvement in total delay using the in-vehicle system is dependent on penetration rate and accuracy of the floating car data and varies from 2.0% to 3.4% for 10% penetration rate. This leads to yearly savings of about 15 million euros if delay is monetarised using standard prices for value of time (VOT).

Urban link travel time estimation using large-scale taxi data with partial information

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.

On the Initiation of World Neurosurgery

Vehicles exchange Floating Car Data (FCD) to improve awareness beyond their local perception and thereby increase traffic safety and comfort. If the FCD is required at distant locations, FCD can be shared using the cellular network to notify vehicles early of upcoming road events. However, this monitoring of the roads congests the cellular network, which is already utilized by other applications. The available bandwidth for monitoring is expected to decrease further with the introduction of fully autonomous vehicles.In this paper, we propose a hybrid dissemination approach for the distribution of road events in vehicular networks. Our approach aims to utilize only a predefined bandwidth for information exchange, which is achieved by two mechanisms: (i) the offloading of information to Wifi-based Vehicle to Vehicle (V2V) communication and (ii) the filtering of low-impact information. We offload the information to Wifi-based communication using non-cooperative game-theory: Each vehicle chooses the minimum impact of information it wants to receive via the cellular network. Through cooperation, the vehicles in proximity might provide information the other vehicles cannot receive. In the evaluation, we show that our approach significantly improves the data quality at the vehicles compared to traditional offloading approaches while sticking to the predefined bandwidth constraints.

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