A novel algorithm to identifying vehicle travel path in elevated road area based on GPS trajectory data

In recent years, the increasing development of traffic information collection technology based on floating car data has been recognized, which gives rise to the establishment of real-time traffic information dissemina- tion system in many cities. However, the recent massive construction of urban elevated roads hinders the processing of corresponding GPS data and further extraction of traffic information （e.g., identifying the real travel path）, as a result of the frequent transfer of vehicles between ground and elevated road travel. Consequently, an algorithm for identifying the travel road type （i.e., elevated or ground road） of vehicles is designed based on the vehicle traveling features, geometric and topological characteristics of the elevated road network, and a trajectory model proposed in the present study. To be specific, the proposed algorithm can detect the places where a vehicle enters, leaves or crosses under elevated roads. An experiment of 10 sample taxis in Shanghai, China was conducted, and the comparison of our results and results that are obtained from visual interpretation validates the proposed algo- rithm.

Investigating distance halo effect of fixed automated speed camera based on taxi GPS trajectory data

Background:The deterrence effect of automated speed camera(ASC)is still inconclusive.Moreover,it is pointed out that ASC may have varying deterrence effects on different types of road users(e.g.,taxis).Objective:This study intends to investigate the distance halo effect of fixed ASC(hereafter called ASC)on taxis.Method:More than 1.34 million taxis’GPS trajectory data were collected.A novel indicator,the delta speed(defined as the difference between the traveling speed and the speed limit),was proposed to continuously describe the variations in traveling speeds.The upstream and downstream critical delta speeds during each time period on weekdays and weekends were obtained by using K-means clustering method,respectively.Based on the critical delta speeds,the ranges of upstream and downstream distance halo effects of ASC during different time periods on weekdays and weekends were determined separately and compared.Results:The downstream critical delta speed is smaller than the upstream one.The upstream and downstream distance halo effects of ASC on taxis are within a range of 8-2180 m and an area of 10-580 m to the ASC location,respectively.There are no obvious difference in the ranges of upstream and downstream distance halo effects of ASC on taxis between different time periods or between weekdays and weekends.Conclusion:The present study confirms that the upstream and downstream distance halo effects of ASC on taxis have different ranges and the stabilities of time-of-day and day-of-week.Practical application:The findings of this study can provide a basic reference for reasonably deploying ASCs within a region.

Extracting Campus’Road Network from Walking GPS Trajectories

Road network extraction is vital to both vehicle navigation and road planning.Existing approaches focus on mining urban trunk roads from GPS trajectories of floating cars.However,path extraction,which plays an important role in earthquake relief and village tour,is always ignored.Addressing this issue,we propose a novel approach of extracting campus’road network from walking GPS trajectories.It consists of data preprocessing and road centerline generation.The patrolling GPS trajectories,collected at Hunan University of Science and Technology,were used as the experimental data.The experimental evaluation results show that our approach is able to effectively and accurately extract both campus’trunk roads and paths.The coverage rate is 96.21%while the error rate is 3.26%.

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

Travelling is a critical component of daily life.With new technology,personalized travel route recommendations are possible and have become a new research area.A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations,based on the road networks and users’travel preferences.In this paper,we define users’travel behaviours from their historical Global Positioning System(GPS)trajectories and propose two personalized travel route recommendation methods–collaborative travel route recommendation(CTRR)and an extended version of CTRR(CTRR+).Both methods consider users’personal travel preferences based on their historical GPS trajectories.In this paper,we first estimate users’travel behaviour frequencies by using collaborative filtering technique.A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model.The CTRR+method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability.This paper also conducts some case studies based on a real GPS trajectory data set from Beijing,China.The experimental results show that the proposed CTRR and CTRR+methods achieve better results for travel route recommendations compared with the shortest distance path method.

Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories

Smart cities have given a significant impetus to manage traffic and use transport networks in an intelligent way. For the above reason, intelligent transportation systems （ITSs） and location-based services （LBSs） have become an interesting research area over the last years. Due to the rapid increase of data volume within the transportation domain, cloud environment is of paramount importance for storing, accessing, handling, and processing such huge amounts of data. A large part of data within the transportation domain is produced in the form of Global Positioning System （GPS） data. Such a kind of data is usually infrequent and noisy and achieving the quality of real-time transport applications based on GPS is a difficult task. The map-matching process, which is responsible for the accurate alignment of observed GPS positions onto a road network, plays a pivotal role in many ITS applications. Regarding accuracy, the performance of a map-matching strategy is based on the shortest path between two consecutive observed GPS positions. On the other extreme, processing shortest path queries （SPQs） incurs high computational cost. Current map-matching techniques are approached with a fixed number of parameters, i.e., the number of candidate points （NCP） and error circle radius （ECR）, which may lead to uncertainty when identifying road segments and either low-accurate results or a large number of SPQs. Moreover, due to the sampling error, GPS data with a high-sampling period （i.e., less than 10 s） typically contains extraneous datum, which also incurs an extra number of SPQs. Due to the high computation cost incurred by SPQs, current map-matching strategies are not suitable for real-time processing. In this paper, we propose real-time map-matching （called RT-MM）, which is a fully adaptive map-matching strategy based on cloud to address the key challenge of SPQs in a map-matching process for real-time GPS trajectories. The evaluation of our approach against state-of-the-art approaches is performed through simulations based on both synthetic and real-word datasets.

Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

The data collected from taxi vehicles using the global positioning system(GPS)traces provides abundant temporal-spatial information,as well as information on the activity of drivers.Using taxi vehicles as mobile sensors in road networks to collect traffic information is an important emerging approach in efforts to relieve congestion.In this paper,we present a hybrid model for estimating driving paths using a density-based spatial clustering of applications with noise(DBSCAN)algorithm and a Gaussian mixture model(GMM).The first step in our approach is to extract the locations from pick-up and drop-off records(PDR)in taxi GPS equipment.Second,the locations are classified into different clusters using DBSCAN.Two parameters(density threshold and radius)are optimized using real trace data recorded from 1100 drivers.A GMM is also utilized to estimate a significant number of locations;the parameters of the GMM are optimized using an expectation-maximum(EM)likelihood algorithm.Finally,applications are used to test the effectiveness of the proposed model.In these applications,locations distributed in two regions(a residential district and a railway station)are clustered and estimated automatically.

Trajectory analysis at intersections for traffic rule identification

In this paper,we focus on trajectories at intersections regulated by various regulation types such as traffic lights,priority/yield signs,and right-of-way rules.We test some methods to detect and recognize movement patterns from GPS trajectories,in terms of their geometrical and spatio-temporal components.In particular,we first find out the main paths that vehicles follow at such locations.We then investigate the way that vehicles follow these geometric paths(how do they move along them).For these scopes,machine learning methods are used and the performance of some known methods for trajectory similarity measurement(DTW,Hausdorff,and Fréchet distance)and clustering(Affinity propagation and Agglomerative clustering)are compared based on clustering accuracy.Afterward,the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed-and time-profiles of trajectories.We show that depending on the regulation type,different movement patterns are observed at intersections.This finding can be useful for intersection categorization according to traffic regulations.The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information(traffic signs,traffic lights,etc.).

APFD:an effective approach to taxi route recommendation with mobile trajectory big data

With the rapid development of data-driven intelligent transportation systems,an efficient route recommendation method for taxis has become a hot topic in smart cities.We present an effective taxi route recommendation approach(called APFD)based on the artificial potential field(APF)method and Dijkstra method with mobile trajectory big data.Specifically,to improve the efficiency of route recommendation,we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates.Then,based on the APF method,we put forward an effective approach for removing redundant nodes.Finally,we employ the Dijkstra method to determine the optimal route recommendation.In particular,the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing.On the map,we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony(AC)algorithm,greedy algorithm(A*),APF,rapid-exploration random tree(RRT),non-dominated sorting genetic algorithm-II(NSGA-II),particle swarm optimization(PSO),and Dijkstra for the shortest route recommendation.Compared with AC,A*,APF,RRT,NSGA-II,and PSO,concerning shortest route planning,APFD improves route planning capability by 1.45%–39.56%,4.64%–54.75%,8.59%–37.25%,5.06%–45.34%,0.94%–20.40%,and 2.43%–38.31%,respectively.Compared with Dijkstra,the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency.In addition,in the real-world road network,on the Fourth Ring Road in Beijing,the ability of APFD to recommend the shortest route is better than those of AC,A*,APF,RRT,NSGA-II,and PSO,and the execution efficiency of APFD is higher than that of the Dijkstra method.

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.

A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

To address the imbalance problem between supply and demand for taxis and passengers,this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit(EEMDN-SABiGRU)model on Spark for accurate passenger hotspot prediction.It focuses on reducing blind cruising costs,improving carrying efficiency,and maximizing incomes.Specifically,the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences,while dealing with the eigenmodal EMD.Next,a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid,taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid.Furthermore,the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information,to improve the accuracy of feature extraction.Finally,the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing framework.The experimental results demonstrate that based on the four datasets in the 00-grid,compared with LSTM,EMDLSTM,EEMD-LSTM,GRU,EMD-GRU,EEMD-GRU,EMDN-GRU,CNN,and BP,the mean absolute percentage error,mean absolute error,root mean square error,and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%,44.91%,55.04%,and 39.33%,respectively.

Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.