Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction

A K-nearest neighbor （K-NN） based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data （FCD） system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.

Freight Vehicle Routing Optimization for Sporadic Orders Using Floating Car Data

The increasing popularity of e-commerce brings large volumes of sporadic orders from different customers,which have to be handled by freight trucks and distribution centers. To improve the level of service and reduce the total shipping cost as well as traffic congestions in urban area, flexible methods and optimal vehicle routing strategies should be adopted to improve the efficiency of distribution effort. An optimization solution for vehicle routing and scheduling problem with time window for sporadic orders (VRPTW- S) was provided based on time-dependent travel time extracted from floating car data (FCD) with ArcGIS platform. A VRPTW-S model derived from the traditional vehicle routing problem was proposed, in which uncertainty of customer orders and travel time were considered. Based on this model, an advanced vehicle routing algorithm was designed to solve the problem. A case study of Shenzhen, Guangdong province, China, was conducted to demonstrate the vehicle operation flow,in which process of FCD and efficiency of delivery systems under different situations were discussed. The final results demonstrated a good performance of application of time-dependent travel time information using FCD in solving vehicle routing problems.

Evaluation of floating car technologies for travel time estimation

Travel times have been traditionally estimated from data collected by roadway sensors. Recently, new tech- nologies, such as cell phone tracking, license plate matching, automatic vehicle identifications and video detection, are employed for this purpose. In this study, the data collected by TRANSMIT readers, Bluetooth sensors, and INRIX are assessed by comparing each to the ＂ground truth＂ travel times collected by probe vehicles carrying GPS-based naviga- tion devices. Travel times of probe vehicles traveling on the study segment of 1-287 in New Jersey were collected in 2009. Statistical measures, such as standard deviation, average absolute speed error, and speed error bias, were used to make an in-depth analysis. The accuracy of each travel time estimation method is analyzed. The data collected by Bluetooth sensors and the TRANSMIT readers seem more consistent with the ground true data, and slightly outperform the data reported by 1NRIX. This study established a procedure for analyzing the accuracy of floating car data （FCD） collected by different technologies.

A Comprehensive Taxi Assessment Index Using Floating Car Data

With the expansion of urban area and development of taxi system,problems arise,such as low operation efficiency,high taxi idling rate,and long passenger waiting-time. Although various studies have been conducted,only limited overview of the factors towards urban taxi system has been provided. Consequently,a comprehensive evaluation of taxi system is essential for the urban planner to analyze the current situation and take effective measures. This paper,by using Floating Car Data( FCD),proposes a Comprehensive Taxi Assessment Index( CTAI) to quantify the quality of existing urban taxi system with the assistance of Geographic Information System( GIS) technology. The proposed index system extracts and classifies key factors,reflecting the taxi system from the perspectives of operation efficiency,customer and taxi-driver satisfaction. The system contributes to improving the organization and operation of urban taxi system. Based on the data obtained from the city of Shenzhen,Guangdong Province,China,for both weekday and weekends( Dec.,2011),the proposed CTAI was illustrated by using the Principal Component Analysis( PCA) with ArcGIS 10. 0 platform. The results indicate that the system provides a good multi-dimensional view to delve into the existing urban taxi operation, thus to point out the most sensitive indices towards the entire system,which consequently provides guidelines for future improvement and management of urban taxi system.

The Relationship between Road Characteristics and Speed Collected from Floating Car Data

Speed is of great importance to the safety level of a road. Speed choice is strongly influenced by the road environment and the drivers＇ assessment of safe speed level at a specific location. This paper presents an analysis of the relationships between speed and road characteristics and speed and driver characteristics. The analysis is based on big data on speed and driver characteristics combined with data on road characteristics on 49 secondary rural two-lane roads in Denmark. Data is modelled using multivariate linear regression. The results show a primarily influence from road and shoulder width, the extent of road markings and the section lengths on speed. Secondly, they also show the presence of woodland and intersections influencing speed as gender, age of vehicle and time of day do.

A Comparison of Floating Car vs.Loop Detector Estimated Freeway Travel Time Delay

Floating car-and loop detector-based methods are two different types of methods frequently used to collect travel time delay information across a freeway network.Sometimes,it is necessary to use them jointly to achieve the necessary freeway network coverage,due to the high labor costs for the floating car-based method and the indispensability of sufficient network instrumentation for the loop detector-based method.For example,both floating car-and loop detector-based methods were once used in the Highway Congestion Monitoring Program in the California Department of Transportation.It is therefore necessary to evaluate whether these two types of methods estimate similarly in terms of total travel time delay.To this end,corresponding delay information estimated using both types of methods from 37 freeway segments in the Greater Sacramento Area were collected and compared.It was found that these two types of methods do not estimate similarly in terms of total segment travel time delay.The mean absolute relative difference(MARD)can be as high as 78%,especially when delay is defined using a lower reference speed,such as 56 km/h.However,in terms of total segment travel time,the loop detector and the modified floating car method estimated similarly.The MARD is 19%.It was also found that the estimation from the different methods did correlate fairly well,which provides a means of conversion when different methods are used to monitor the total delay across a freeway network.As a spin-off,it was also found that a 1.5 km spacing of loop detectors is sufficient to achieve the 19%MARD as compared with the modified floating car method in terms of total travel time estimation.

Intersection delay estimation from floating car data via principal curves： a case study on Beijing＇s road network

It is a pressing task to estimate the real-time travel time on road networks reliably in big cities, even though floating car data has been widely used to reflect the real traffic. Currently floating car data are mainly used to estimate the real-time traffic conditions on road segments, and has done little for turn delay estimation. However, turn delays on road intersections contribute significantly to the overall travel time on road networks in modem cities. In this paper, we present a technical framework to calculate the turn delays on road networks with float car data. First, the original floating car data collected with GPS equipped taxies was cleaned and matched to a street map with a distributed system based on Hadoop and MongoDB. Secondly, the refined trajectory data set was distributed among 96 time intervals （from 0：00 to 23： 59）. All of the intersections where the trajectories passed were connected with the trajectory segments, and constituted an experiment sample, while the intersections on arterial streets were specially selected to form another experiment sample. Thirdly, a principal curve-based algorithm was presented to estimate the turn delays at the given intersections. The algorithm argued is not only statistically fitted the real traffic conditions, but also is insensitive to data sparseness and missing data problems, which currently are almost inevitable with the widely used floating car data collecting technology. We adopted the floating car data collected from March to June in Beijing city in 2011, which contains more than 2.6 million trajectories generated from about 20000 GPS-equipped taxicabs and accounts for about 600 GB in data volume. The result shows the principal curve based algorithm we presented takes precedence over traditional methods, such as mean and median based approaches, and holds a higher estimation accuracy （about 10%-15% higher in RMSE）, as well as reflecting the changing trend of traffic congestion. With the estimation result for the travel delay at intersections, we analyzed the spatio-temporal distribution of turn delays in three time scenarios （0： 00-0： 15, 8： 15-8：30 and 12： 00-12： 15）. It indicates that during one＇s single trip in Beijing, average 60% of the travel time on the road networks is wasted on the intersections, and this situation is even worse in daytime. Although the 400 main intersections take only 2.7% of all the intersections, they occupy about 18% travel time.

A novel method for road network mining from floating car data

Vehicles have been increasingly equipped with GPS receivers to record their trajectories,which we call floating car data.Compared with other data sources,these data are characterized by low cost,wide coverage,and rapid updating.The data have become an important source for road network extraction.In this paper,we propose a novel approach for mining road networks from floating car data.First,a Gaussian model is used to transform the data into bitmap,and the Otsu algorithm is utilized to detect road intersections.Then,a clothoid-based method is used to resample the GPS points to improve the clustering accuracy,and the data are clustered based on a distance-direction algorithm.Last,road centerlines are extracted with a weighted least squares algorithm.We report on experiments that were conducted on floating car data from Wuhan,China.To conclude,existing methods are compared with our method to prove that the proposed method is practical and effective.

Evaluating spatial accessibility to healthcare services from the lens of emergency hospital visits based on floating car data

Public medical facilities that are closely related to the health of residents have been recognised as one of the most crucial elements in sustainable urban planning.For the sake of social equality of medical services(especially for emergency medical conditions),the spatial distributions of medical resources need to be accurately measured and continuously optimized.This study presents an effective method to examine night emergency hospital visit and analyse its spatiotemporal characteristics using float car data(FCD).By extracting the hospital service areas,the two-step floating catchment area(2SFCA)methodology was improved to calculate hospital accessibility.Then,the balance between hospital accessibility and population density was analysed.In addition,we investigated the relationship between individual hospital choice preferences and hospital level and analysed several factors that affect individual choices.These results help us understand the special requirements and need of emergency hospital travel in cities and identify areas where medical resources are scarce.They can be used as guidance for urban hospital planning and construction.And the approach of hospital access behaviour investigation and the improved 2SFCA method can also provide insights for other activity-based travel behaviour research.

Analyzing Behavior Differences of Occupied and Non-Occupied Taxi Drivers Using Floating Car Data

As the travel purpose of non-occupied taxies is to find new passengers rather than to arrive at the destination, large differences exist in the route choice behavior between the occupied and non-occupied taxies.With the assistance of geographic information system(GIS) and taxi-based floating car data(FCD), this paper investigates the behavior differences between occupied and non-occupied taxi drivers with the same origin and destination. Descriptive statistical indexes from the FCD in Shenzhen, China are explored to identify the route choice characteristics of occupied and non-occupied taxies. Then, a conditional logit model is proposed to model the quantitative relationship between drivers' route choice and the related significant variables. Attributes of the variables related to non-occupied taxies' observed routes are compared with the case of occupied ones. The results indicate that, compared with their counterparts, non-occupied taxi drivers generally pay more attention to choosing arterial roads and avoiding congested segments. Additionally, they are also found less sensitive to fewer traffic lights and shorter travel time. Findings from this research can assist to improve urban road network planning and traffic management.

How many probe vehicles are enough for identifying traffic congestion？--a study from a streaming data perspective

Many studies have been carried out using vehicle trajectory to analyze traffic conditions, for instance, identifying traffic congestion. However, there is a lack of a systematic study on the appropriate number of probe vehicles and their sampling interval in order to identify traffic congestion accurately. Moreover, most of related studies ignore the streaming feature of trajectory data. This paper first represents a novel method of identifying traffic congestion considering the stream feature of vehicle trajectories. Instead of processing the whole data stream, a series of snapshots are extracted. Congested road segments can be identified by analyzing the clusters＇ evolution among a series of adjacent snapshots. We then calculated a series of parameters and their corresponding congestion identification accuracy. The results have implications for related probe vehicle deployment and traffic analysis; for example, when 5% of probe vehicles are available, 85% identification accuracy can be reached if the sampling time interval is 10 s.

Traffic services for vehicles: the process from receiving raw probe data to space-time diagrams and the resulting traffic service

Today, large quantities of vehicle data(FCD:floating car data) are widely used by traffic service providers to create and broadcast traffic states in road networks. As a first processing step, all raw position data received from Global Positioning Systems(GPS) have to be map matched in a digital road map. The technical aspects of such a matching process for GPS data are described in this report. After the matching process, spacetime-diagrams are created of the probe data showing traffic situation details over space and time. Various examples illustrate how traffic service quality depends on the number of matched GPS raw data; it will be stated that when 2% of connected vehicles in the total traffic flow are sending their GPS data in shorter time intervals, a high quality and precise reconstruction of the current traffic phases is achieved. Traffic reconstruction is followed by a translation into traffic information messages, which can be sent and used in vehicle navigation systems for driver information and dynamic route guidance.