Large-scale Trajectory Data Research Articles

Spatio-Temporal Reachable Area Calculation Based on Urban Traffic Data

The spatio-temporal reachable area that can be reached by driving vehicles from a specific starting location at starting time during a given time period is widely used in urban applications, such as the online ride-hailing service and the charging station seeking by an electric car. Existing studies on this issue usually utilize the static road network, which implies the spatial features, whereas the temporal characteristics of the dynamic urban traffic have not been explored in depth. In this article, we propose a spatio-temporal reachable area calculation scheme, named STRC, based on a tremendous amount of vehicle trajectory data. In STRC, the objective trajectory fragments are extracted according to the starting node and the query time period, and then mapped to reachable nodes to reduce the computational complexity. From those reachable road segments having reachable nodes on, a boundary segment is selected in each sector area by using the arrival-based one segment policy for time-sensitive applications or the arrival-and-distance-based k -segment policy for distance-related applications. The experiments based on large-scale trajectory data from 34 040 taxis in Beijing for one month show that STRC outputs more accurate reachable area, which covers the trajectory points with much smaller area, than compared schemes.

A Parallel Algorithm For Anonymizing Large-scale Trajectory Data

With the proliferation of location-based services enabled by a large number of mobile devices and applications, the quantity of location data, such as trajectories collected by service providers, is gigantic. If these datasets could be published, then they would be valuable assets to various service providers to explore business opportunities, to study commuter behavior for better transport management, which in turn benefits the general public for day-to-day commuting. However, there are two major concerns that considerably limit the availability and the usage of these trajectory datasets. The first is the threat to individual privacy, as users’ trajectories may be misused to discover sensitive information, such as home locations, their children’s school locations, or social information like habits or relationships. The other concern is the ability to analyze the exabytes of location data in a timely manner. Although there have been trajectory anonymization approaches proposed in the past to mitigate privacy concerns. None of these prior works address the scalability issue, since it is a newly occurring problem brought by the significantly increasing adoption of location-based services. In this article, we conquer these two challenges by designing a novel parallel trajectory anonymization algorithm that achieves scalability, strong privacy protection, and high utility rate of the anonymized trajectory datasets. We have conducted extensive experiments using MapReduce and Spark on real maps with different topologies, and our results prove both effectiveness and efficiency when compared with the centralized approaches.

Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data

We collect vehicle trajectory data from major transportation network companies (TNCs) in New York City (NYC) in 2017 and 2019, and we use the trajectory data to understand how the growth of TNCs has impacted traffic congestion and emission in urban areas. By mining the large-scale trajectory data and conduct the case study in NYC, we confirm that the rise of TNC is the major contributing factor that makes urban traffic congestion worse. From 2017 to 2019, the number of for-hire vehicles (FHV) has increased by over 48% and served 90% more daily trips. These resulted in an average citywide speed reduction of 22.5% on weekdays, and the average speed in Manhattan decreased from 11.76 km/h in April 2017 to 9.56 km/h in March 2019. The heavier traffic congestion may have led to 136% more NOx, 152% more CO and 157% more HC emission per kilometer traveled by the FHV sector. Our results show that the traffic condition is consistently worse across different times of the day and at different locations in NYC. And we build the connection between the number of available FHVs and the reduction in travel speed between the two years of data and explain how the rise of TNC may impact traffic congestion in terms of moving speed and congestion time. The findings in our study provide valuable insights for different stakeholders and decision-makers in framing regulation and operation policies towards more effective and sustainable urban mobility.

A survey of trajectory distance measures and performance evaluation

The proliferation of trajectory data in various application domains has inspired tremendous research efforts to analyze large-scale trajectory data from a variety of aspects. A fundamental ingredient of these trajectory analysis tasks and applications is distance measures for effectively determining how similar two trajectories are. We conduct a comprehensive survey of the trajectory distance measures. The trajectory distance measures are classified into four categories according to the trajectory data type and whether the temporal information is measured. In addition, the effectiveness and complexity of each distance measure are studied. The experimental study is also conducted on their effectiveness in the six different trajectory transformations.

Fast large-scale trajectory clustering

In this paper, we study the problem of large-scale trajectory data clustering,k-paths, which aims to efficiently identifyk"representative" paths in a road network. Unlike traditional clustering approaches that require multiple data-dependent hyperparameters,k-paths can be used for visual exploration in applications such as traffic monitoring, public transit planning, and site selection. By combining map matching with an efficient intermediate representation of trajectories and a noveledge-based distance(EBD) measure, we present a scalable clustering method to solvek-paths. Experiments verify that we can cluster millions of taxi trajectories in less than one minute, achieving improvements of up to two orders of magnitude over state-of-the-art solutions that solve similar trajectory clustering problems.

Modeling and Analysis of Daily Driving Patterns of Taxis in Reshuffled Ride-Hailing Service Market

This paper aims to model and analyze the changes in daily driving patterns of taxis in a disrupted market due to the boom in e-hailing services. This is accomplished by mining large-scale trajectory data sets obtained from a major taxi company in Shanghai. The taxi data set includes more than 0.8 billion trajectory points associated with over 12,000 taxis obtained in a period of 10 days (5 continuous weekdays in 2012 and 2016, respectively). The raw data were efficiently processed with the acceleration of high-performance computing. Creatively, the concept of information entropy together with principal component analysis were adopted to spatially delineate the gridded daily taxi driving trajectories. This helps describe the disordered taxi traces in comparable profiles across different spatial zones. Then, distinct patterns were extracted using the k-means clustering method. The proposed analysis pipeline has built a stable way of comparing driving patterns between different time periods after relaxing concerns about potential spreading of demand over time. By comparing statistical features associated with the identified clusters, the changes in daily taxi driving patterns in the context of the wide popularization of e-hailing services were quantitatively unveiled. This will be informative for taxi service providers revamping their business models when facing the opportunities brought by e-hailing apps and competition from other ride-sourcing vehicles in urban areas.

Road Network Construction with Complex Intersections Based on Sparsely Sampled Private Car Trajectory Data

A road network is a critical aspect of both urban planning and route recommendation. This article proposes an efficient approach to build a fine-grained road network based on sparsely sampled private car trajectory data under complex urban environment. In order to resolve difficulties introduced by low sampling rate trajectory data, we concentrate sample points around intersections by utilizing the turning characteristics from the large-scale trajectory data to ensure the accuracy of the detection of intersections and road segments. In front of complex road networks including many complex intersections, such as the overpasses and underpasses, we first layer intersections into major and minor one, and then propose a simplified representation of intersections and corresponding computable model based on the features of roads, which can significantly improve the accuracy of detected road networks, especially for the complex intersections. In order to construct fine-grained road networks, we distinguish various types of intersections using direction information and detected turning limit. To the best of our knowledge, our road network building method is the first time to give fine-grained road networks based on low-sampling rate private car trajectory data, especially able to infer the location of complex intersections and its connections to other intersections. Last but not the least, we propose an effective parameter selection process for the Density-Based Spatial Clustering of Applications with Noise based clustering algorithm, which is used to implement the reliable intersection detection. Extensive evaluations are conducted based on a real-world trajectory dataset from 1,345 private cars in Futian district, Shenzhen city of China. The results demonstrate the effectiveness of the proposed method. The constructed road network matches close to the one from a public editing map OpenStreetMap, especially the location of the road intersections and road segments, which achieves 92.2% intersections within 20m and 91.6% road segments within 8m.

An efficient method for privacy-preserving trajectory data publishing based on data partitioning

Since Osman Abul et al. first proposed the k-anonymity-based privacy protection for trajectory data, researchers have proposed a variety of trajectory privacy-preserving methods. These methods mainly adopt a static anonymity algorithm, which only focusing on the trajectories in a specific time span, directly anonymizes data and publishes them without considering dynamic nature of trajectory data as the new time slice arriving. Furthermore, due to its correlation with time and position, the trajectory data is produced in large scale and many sensitive attributes; the traditional k-anonymity-based privacy-preserving models need to recalculate the last released trajectory data, which will increase the computing cost and reduce the availability of the released trajectories, are not fit for privacy protection in large-scale trajectory data. Therefore, this paper presents a method to dynamically publish the large-scale vehicle trajectory data with privacy protection under $$(k,\delta )$$ security constraints. According to the spatial and temporal characteristics of vehicle trajectory data, this paper first proposes a method to partition the trajectory data for storage and computation. We choose the sample point $$(x_{i},y_{i})$$ at time $$t_{i}$$ as partition points and store the partitions of the trajectory data according to the time sequence and location of the running vehicle. This results in the efficient trajectory scanning, clustering and privacy protection. We use $$(x_{i},y_{i},t_{m}-t_{n})$$ to represent the identifier of trajectory data to publish, use the generalize function to cluster trajectory data under the $$(k,\delta )$$ security constraints. Through this way, we can effectively process the trajectory in every data partition as time goes on and need not to recalculate the released trajectories, effectively reduce the computing cost. Through experiments on real trajectory data and Oldenburg trajectory data, confirming the data partitioning method in privacy-preserving large-scale trajectory data publishing under the security constraint of $$(k,\delta )$$, and the l-diversity. By the experimental comparison, our method maintains a least level of computing cost and higher data availability.

A versatile computational framework for group pattern mining of pedestrian trajectories

Mining patterns of large-scale trajectory data streams has been of increase research interest. In this paper, we are interested in mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining task that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. In particular, we focus on trajectories of pedestrians coming from motion video analysis and we are interested in interactive analysis and exploration of group dynamics, including various definitions of group gathering and dispersion. Traditional approaches to solve the problem adhere to strict definition of group semantics. That restricts their application to specific problems and renders them inadequate for many real-world scenarios. To address this limitation, we propose a novel versatile method, timeWgroups, for efficient discovery of pedestrian groups that can adhere to different pattern semantics. First, the method efficiently discovers pairs of pedestrians that move together over time, under varying conditions of space and time. Subsequently, pairs of pedestrians are used as a building block for effectively discovering groups of pedestrians that can satisfy versatile group pattern semantics. As such, the proposed method can accommodate many different scenarios and application requirements. In addition, we introduce a new group pattern, individual perspective grouping that focuses on how individuals perceive groups. Based on the new group pattern we define the concept of dominant groups, a global metric for defining important groups that respects the individual perspective group pattern. Through experiments on real data, we demonstrate the effectiveness of our methods on discovering group patterns of pedestrian trajectories against sensible baselines, for a varying range of conditions. Furthermore, a query-based search method is provided that allows for interactive exploration and analysis of group dynamics over time and space. In addition, a visual testing is performed on real motion video to assert the group dynamics discovered by our methods.

An effective and versatile distance measure for spatiotemporal trajectories

The analysis of large-scale trajectory data has tremendous benefits for applications ranging from transportation planning to traffic management. A fundamental building block for the analysis of such data is the computation of similarity between trajectories. Existing work for similarity computation focuses mainly on the spatial aspects of trajectories, but more rarely takes into account time in conjunction with space. A key challenge when considering time is how to handle trajectories that are sampled asynchronously or at variable rates, which can lead to uncertainty. To tackle this problem, we quantify trajectory similarity as an interval, rather than a single value, to capture the uncertainty that can result from different sampling rates and asynchronous sampling. Based on this perspective, we develop a new trajectory similarity measure, Trajectory Interval Distance Estimation, which models similarity computation as a convex optimisation problem. Using two real datasets, we demonstrate that our proposed measure is extremely effective for assessing similarity in comparison to existing state of the art measures.

A Simple Baseline for Travel Time Estimation using Large-scale Trip Data

The increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi 8 Limousine Commission regularly releases source/destination information of taxi trips, where 173 million taxi trips released for Year 2013 [29]. Such a big dataset provides us potential new perspectives to address the traditional traffic problems. In this article, we study the travel time estimation problem. Instead of following the traditional route-based travel time estimation, we propose to simply use a large amount of taxi trips without using the intermediate trajectory points to estimate the travel time between source and destination. Our experiments show very promising results. The proposed big-data-driven approach significantly outperforms both state-of-the-art route-based method and online map services. Our study indicates that novel simple approaches could be empowered by big data and these approaches could serve as new baselines for some traditional computational problems.

A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.

Visual Analysis of Multiple Route Choices Based on General GPS Trajectories

There are often multiple routes between regions. Drivers choose different routes with different considerations. Such considerations, have always been a point of interest in the transportation area. Studies of route choice behaviour are usually based on small range experiments with a group of volunteers. However, the experiment data is quite limited in its spatial and temporal scale as well as the practical reliability. In this work, we explore the possibility of studying route choice behaviour based on general trajectory dataset, which is more realistic in a wider scale. We develop a visual analytic system to help users handle the large-scale trajectory data, compare different route choices, and explore the underlying reasons. Specifically, the system consists of: 1. the interactive trajectory filtering which supports graphical trajectory query; 2. the spatial visualization which gives an overview of all feasible routes extracted from filtered trajectories; 3. the factor visual analytics which provides the exploration and hypothesis construction of different factors’ impact on route choice behaviour, and the verification with an integrated route choice model. Applying to real taxi GPS dataset, we report the system’s performance and demonstrate its effectiveness with three cases.

Finding Abnormal Vessel Trajectories Using Feature Learning

Global Positioning System technology has been widely used in vehicle tracking and road planning applications. An enormous amount of data concerning the trajectories of vehicles has been collected and stored for tracking purposes. A trajectory contains not only the footprints of a moving object but also additional information, such as speed and stopping points. Therefore, the large-scale trajectory data sets provide rich information and are currently attracting considerable attention; there have been many successful studies of event detection based on trajectory data. However, most of these studies have focused only on vehicles traveling in a road network and have note considered maritime trajectories. A maritime trajectory also contains auxiliary data (e.g., speed and rotation) in addition to the movements of a ship. However, ships are not bound to road networks, and consequently, it is difficult to apply traditional mining algorithms based on road networks. In addition, even if the amount of maritime trajectory data is very large, these data are also spatially sparse, which will significantly reduce the effectiveness of most existing mining algorithms. In this paper, we propose a new method of abnormal trajectory detection to address this problem. This method can detect abnormal vessel trajectories from Automatic Identification System (AIS), records for vessels via our feature learning algorithm. To reduce the search space, we invoke reference points as well as the Piecewise Linear Segmentation (PLS), algorithm to compress the trajectories without losing important information. A time-aware and spatially correlated collaborative algorithm is proposed to increase the density of the trajectories to improve the accuracy of the detection algorithm, which is based on Dynamic Time Warping (DTW). Finally, we report experiments conducted on a real-world data set, which demonstrate that the proposed detection method can detect anomalous trajectories effectively.

Using taxis to collect citywide E-commerce reverse flows: a crowdsourcing solution

The popularity of round-the-clock online shopping urges the rapid growth of e-commerce, which substantially generates additional parcels for the distribution on the forward side. On the other hand, collecting the returned goods on the revere side is also increasingly becoming a preoccupation, particularly in the crowd and dense metropolitan areas. Inspired by the concepts of Crowdsourcing and Physical Internet, we propose an innovative solution to collect the e-commerce returned goods from final consumption points back to retailers. As an alternative to traditional ways, this solution delivers the returned goods and passengers in an integrated way by leveraging the extra loading capacity and constant mobility provided by taxis that are already reserved to transport passengers. Thus, it could simultaneously migrate the negative economical, environmental and social impacts of reverse flows management. To address the issue of the returned goods collection, we first conduct the qualitative and quantitative study, and further investigate the feasibility and viability of the solution based on three real-world datasets, which consist of locations of shops, a road network and a large-scale trajectory data generated by over 7000 taxis in a month in the city of Hangzhou, China. Three collection strategies are proposed and evaluated. Experimental results generate several useful insights into the implementability and managerial issues of the proposed solution.

Trajectory ontology inference considering domain and temporal dimensions—Application to marine mammals

Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. To accomplish reasoning over trajectories, the ontology must consider mobile object, domain and other knowledge. In our approach, we suggest expressing this knowledge in the form of rules. To annotate data with these rules, we need an inference mechanism over trajectory ontology. Experiments over our model using domain and temporal rules address an inference computation complexity. This complexity has two important factors: time computations and space storage. In order to reduce the inference complexity, we proposed optimizations, such as domain constraints and temporal neighbor refinements. In this paper, we define a refinement specifically for the application domain. Then, we evaluate our contribution over real trajectory data. Finally, the results show the positive impact of the last refinement on reducing the complexity of the inference mechanism. This refinement reduces half of the time computation and then allows considering larger data sets.

Crowddeliver: Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis

Despite the great demand on and attempts at package express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called crowddeliver for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin–Destination pairs. Using the paths and travel time as the reference, in the second phase we develop an online adaptive taxi scheduling algorithm to find the near-optimal delivery paths iteratively upon real-time requests and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world data sets, which consist of a point of interest, a road network, and the large-scale trajectory data, respectively, that are generated by 7614 taxis in a month in the city of Hangzhou, China. Results show that over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.

A Survey on Trajectory Data Mining: Techniques and Applications

Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects. A trajectory is typically represented by a sequence of timestamped geographical locations. A wide spectrum of applications can benefit from the trajectory data mining. Bringing unprecedented opportunities, large-scale trajectory data also pose great challenges. In this paper, we survey various applications of trajectory data mining, e.g., path discovery, location prediction, movement behavior analysis, and so on. Furthermore, this paper reviews an extensive collection of existing trajectory data mining techniques and discusses them in a framework of trajectory data mining. This framework and the survey can be used as a guideline for designing future trajectory data mining solutions.

A Hybrid Compression Framework for Large Scale Trajectory Data in Road Networks

A hybrid compression framework of trajectory data (HCFT) is proposed for effective compressionof trajectory data with road network limited. It’s differentfrom the present researches which mainly focus on compression of single trajectory, and further takes data redundancy raised by the similarity of movement pattern of moving objects into consideration. HCFT divides the redundancy of trajectory data into Single trajectory redundancy(STR) and Multiple trajectories redundancy (MTR) andcompresses them in a hybrid way (i.e. synchronous compression for STR at first and then asynchronous compression for MTR). We propose an asynchronous extractionalgorithm for MTR based on frequent Road track subsequence (RTS), which replaces similar movement route byRTS, with the complexity of calculation significantly reduced. HCFT can not only gain higher compression ratio,but also ensure effectiveness of compressed trajectory. Wealso verify effectiveness and superiority of the new methodaccording to the experiments of real trajectory dataset.

An efficient data processing framework for mining the massive trajectory of moving objects

Recently, there has been increasing development of positioning technology, which enables us to collect large scale trajectory data for moving objects. Efficient processing and analysis of massive trajectory data has thus become an emerging and challenging task for both researchers and practitioners. Therefore, in this paper, we propose an efficient data processing framework for mining massive trajectory data. This framework includes three modules: (1) a data distribution module, (2) a data transformation module, and (3) a high performance I/O module. Specifically, we first design a two-step consistent hashing algorithm, which takes into account load balancing, data locality, and scalability, for a data distribution module. In the data transformation module, we present a parallel strategy of a linear referencing algorithm with reduced subtask coupling, easy-implemented parallelization, and low communication cost. Moreover, we propose a compression-aware I/O module to improve the processing efficiency. Finally, we conduct a comprehensive performance evaluation on a synthetic dataset (1.114TB) and a real world taxi GPS dataset (578GB). The experimental results demonstrate the advantages of our proposed framework.