Taxi Trajectory Research Articles

Resident Travel Analysis based on GPS Trajectory

Taxi is an important means of transportation, and its trajectory data contains a wealth of travel information. Taxi trajectory data processed by trajectory data mining technology can reflect residents' activity rules and behavior patterns, so as to provide a reference for urban planning decisions. with its wide coverage, high sampling rate, good location accuracy, large data scale and rich information, taxi trajectory data has been widely used in traffic management, urban planning, user behavior analysis and intelligent transportation. Analyze the characteristics of residents' travel demand from two dimensions: time and space. Analysis of travel demand time characteristics. firstly, analyze the overall demand characteristics of residents, and secondly, analyze the demand characteristics of residents on workdays and rest days; In the analysis of spatial characteristics of travel demand, the static and dynamic spatial characteristics of residents are analyzed separately.

Vehicle Trajectory Prediction via Urban Network Modeling.

Taxis are an important component of the transportation system, and empty taxis represent a significant waste of transportation resources. To alleviate the imbalance between supply and demand and relieve traffic congestion, real-time prediction of taxi trajectories is necessary. Most existing trajectory prediction studies focus on extracting time-series information but do not capture spatial information sufficiently. In this paper, we focus on the construction of an urban network and propose an urban topology-encoding spatiotemporal attention network (UTA) to address destination prediction problems. Firstly, this model discretizes the production and attraction units of transportation, combining them with key nodes in the road network to form an urban topological network. Secondly, GPS records are matched to the urban topological map to construct a topological trajectory, which significantly improves trajectory consistency and endpoint certainty, helping to model destination prediction problems. Thirdly, semantic information concerning surrounding space is attached to effectively mine the spatial dependencies of trajectories. Finally, after the topological encoding of city space and trajectories, this algorithm proposes a topological graph neural network to model the attention calculation with the trajectory context, comprehensively considering the spatiotemporal characteristics of the trajectories and improving prediction accuracy. We solve the prediction problems with the UTA model and also compare it with some other classical models, such as the HMM, RNN, LSTM, and transformer. The results suggest that all the models work well in combination with the proposed urban model (with a rough increase of 2%), while the UTA model is less affected by data sparsity.

Vehicular Crowdsensing with High-Mileage Vehicles: Investigating Spatiotemporal Coverage Dynamics in Historical Cities with Complex Urban Road Networks

Background. Vehicular crowdsensing (VCS) can be a cost-effective solution to gather data in urban environments, leveraging the onboard sensors of modern vehicles moving around the city. Many experimental studies have proven that high-mileage vehicles, such as taxis, can be effectively used for VCS. However, these studies have been mostly carried out in cities with regular, grid-based, road networks. Conversely, little work has been conducted to assess the suitability of VCS in cities with more complex urban road networks, such as historical ones. Goal. As a step towards filling this gap, the present study investigates the feasibility of using different-sized fleets of taxis to crowdsense information in the urban areas of the historical cities of Porto (Portugal) and Rome (Italy), whose road networks evolved over the centuries and feature a complex topology. Data and Methodology. This work leverages massive real-world datasets of taxi trajectories collected over three contiguous weeks in the cities of Porto and Rome to estimate the spatiotemporal coverage achievable by different-sized fleets of taxis if they were used for VCS. Indeed, using these trajectories, several simulations were conducted, considering four sizes of taxi fleets, ranging from 50 to 400 vehicles, for both cities. The achievable spatiotemporal road network coverage metrics were computed at a fine-grained scale of single road segments. Results. Results show that the achievable coverage in both historical cities exhibits very similar trends, with as few as 50 vehicles being capable of visiting a relevant part of the road network at least once in the considered time frame. As expected, increasing the number of involved vehicles improves spatial and temporal coverage. Still, time gaps between subsequent visits can be possibly inadequate for some VCS use cases. As a consequence, recruiting more vehicles and/or devising specialized routing/incentivization mechanisms might be necessary to achieve more comprehensive coverage of the urban road network.

An A2-Gurobi algorithm for route recommendation with big taxi trajectory data

An A2-Gurobi algorithm for route recommendation with big taxi trajectory data

LibCity-Dataset: a standardized and comprehensive dataset for urban spatial–temporal data mining

Abstract The LibCity-Dataset represents a significant contribution to the field of urban spatial-temporal data mining. This dataset uniquely integrates macro traffic state data with micro trajectory data, providing researchers with comprehensive and diverse urban spatial-temporal data. Specifically, we begin by collecting and processing existing open-source spatial-temporal data. Subsequently, we independently collected Beijing taxi trajectory data through third-party interfaces. This data bridges the gap in the scarcity of current open-source vehicle trajectory data. The distinctive aspect of the LibCity-Dataset lies in its innovative approach of standardizing the storage format, achieved through the implementation of atomic files. By adopting this standardized format, diverse data sources are harmonized, enabling effortless application of spatial-temporal prediction models across various datasets. The uniform storage format not only simplifies experimentation but also expedites the advancement of spatial-temporal prediction research, acting as a catalyst for further innovation. This Data Note provides a comprehensive insight into the creation methodology of the LibCity-Dataset, including data collection and processing methodology, data description, data validation, and usage notes. By facilitating open-source collaboration and setting a benchmark for standardization within the spatial-temporal prediction domain, this dataset aims to foster increased research cooperation and knowledge sharing. The open-source link of our dataset is https://drive.google.com/drive/folders/1g5v2Gq1tkOq8XO0HDCZ9nOTtRpB6-gPe.

HGST: A Hilbert-GeoSOT Spatio-Temporal Meshing and Coding Method for Efficient Spatio-Temporal Range Query on Massive Trajectory Data

In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of location-based services (LBS). Spatio-temporal range query, as the basis of many services, remains a challenge in supporting efficient analysis and calculation of data, especially when large volumes of trajectory data have been accumulated. We propose a Hilbert-GeoSOT spatio-temporal meshing and coding method called HGST to improve the efficiency of spatio-temporal range queries on massive trajectory data. First, the method uses Hilbert to encode the grids obtained based on the GeoSOT space division model, and then constructs a unified time division standard to generate the space–time location identification of trajectory data. Second, this paper builds a novel spatio-temporal index to organize trajectory data, and designs an adaptive spatio-temporal scaling and coding method based on HGST to improve the query performance on indexed records. Finally, we implement a prototype system based on HBase and Spark, and develop a Spark-based algorithm to accelerate the spatio-temporal range query for huge trajectory data. Extensive experiments on a real taxi trajectory dataset demonstrate that HGST improves query efficiency levels by approximately 14.77% and 34.93% compared with GeoSOT-ST and GeoMesa at various spatial scales, respectively, and has better scalability under different data volumes.

How do taxi usage patterns vary and why? A dynamic spatiotemporal analysis in Beijing

Existing studies lack attention to taxi usage dynamics, considering its trip proportion over other travel modes and its influencing factors at fine spatiotemporal resolutions. To fill these gaps, we propose a method for examining taxi usage in a grid of 1 km × 1 km cells per hour during a one-day cycle in Beijing. This method measures the differences between taxi trips from taxi trajectory data and mobile signaling data in the same week in January 2017. To explain the spatiotemporal variation in taxi usage, multiple linear models were used to investigate taxi usage dynamics with alternative transport modes, socioeconomic factors, and built environments. In summary, this study proposes to develop an indicator to measure taxi usage using multiple data sources. We confirm that taxi usage dynamics exist in both temporal and spatial dimensions. In addition, the effects of taxi usage factors vary over each hour in a one-day cycle. These findings are useful for urban planning and transport management, in which the dynamic interactions between taxi demand and distribution of facilities should be included.

KGCN‐LSTM: A graph convolutional network considering knowledge fusion of point of interest for vehicle trajectory prediction

AbstractUrban vehicle trajectory prediction positively alleviates traffic congestion, avoids traffic accidents, and optimizes the urban transportation system. Since taxi trajectories are influenced by the driving intention, it is significant to consider the Points of Interest (POI) as the spatial features for trajectory prediction. A Knowledge Graph Convolutional Network Long Short‐Term Memory (KGCN‐LSTM) model is proposed here to improve the accuracy and robustness of trajectory prediction. POI information is considered as the prior‐knowledge of the trajectory by the Graph Convolutional Network (GCN). Under multiple comparison experiments, Shopping POI gains the highest positive effect weight of 15% in holidays, and Hospital POI gains the highest weight of 16% in working days. In holidays, higher accuracy and robustness are achieved compared with benchmarks when performing the KGCN‐LSTM model with POI of shopping, food, life service, scenic spots, and entertainment classes, while the performance is not improved with the rest of the POI classes. In working days, higher accuracy and stronger robustness are achieved compared with benchmarks when performing the KGCN‐LSTM model with POI of hospital, life service, and exercise. While the performance is not improved with the rest of the POI classes.

Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen.

Urban vitality is the comprehensive form of regional development quality, sustainability, and attractiveness. Urban vitality of various regions within the cities has difference, and the quantitative evaluation of urban vitality within the cities can help guide to future city constructions. Evaluation of urban vitality needs the combination of multi-source data. Existing studies have developed index method and estimation models mainly based on geographic big data to evaluate urban vitality. This study aims to combine remote sensing data with geographic big data to evaluate urban vitality of Shenzhen at street block scale and build the estimation model by random forest method. Indexes and random forest model were built, and some further analyses were conducted. The results were: (1) urban vitality in Shenzhen was high in the coastal areas, business areas, and new towns; (2) compared to indexes, the estimation model had advantages of more accurate results, combination of various data, and the ability to analyze feature contributions; and (3) taxi trajectory, nighttime light, and housing rental data had the strongest influence on urban vitality.

Construct Trip Graphs by Using Taxi Trajectory Data

The trip graph, which can model the residents’ taxi demands, consists of vertices used to indicate trips and edges used to indicate the follow-up relationships between trips. A trip v_j is a tight follow-up trip of another trip v_i, if a taxi can arrive at the departure location of v_j within a time threshold delta after it finishes v_i. However, for big cities, there are a large amount of trips every day and it is time consuming to construct a trip graph. In this paper, we propose efficient algorithms to construct trip graphs for big cities. When constructing a trip graph, the most expensive step is to connect the vertices if the tight follow-up relationships exist. To find out the tight follow-up trips fast, we design an index considering both spatial and temporal constraints. To designate appropriate search areas, we propose efficient methods to determine the distance-based search areas and the traffic-based search areas. We conduct experiments on real datasets, i.e., the taxi trajectories of Shanghai in 2015. The experimental results show that our algorithm can construct the trip graph about 40 times faster than the straightforward method. We also demonstrate the usages of the trip graph in green transportation applications, i.e., the minimum fleet analysis and the minimum total “idle” mileage analysis.

Revealing the Impact of COVID-19 on Urban Residential Travel Structure Based on Floating Car Trajectory Data: A Case Study of Nantong, China

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics of urban mobility affected by the pandemic but has rarely examined the impact of COVID-19 on the travel conditions and psychological needs of residents. To quantitatively assess travel characteristics during COVID-19, this paper proposed a method by which to determine the purpose of residential travel by combining urban functional areas (UFAs) based on machine learning. Then, the residential travel structure, which includes origin–destination (OD) points, residential travel flow, and the proportion of flows for different purposes, was established. Based on taxi trajectory data obtained during the epidemic in Nantong, China, the case study explores changes in travel flow characteristics under the framework of the residential travel structure. Through comparison of the number and spatial distribution of OD points in the residential travel structure, it is found that residential travel hotspots decreased significantly. The ratios of commuting and medical travel increased from 43.8% to 45.7% and 7.1% to 8.1%, respectively. Conversely, the ratios of other travel types all decreased sharply. Moreover, under Maslow’s hierarchy of needs model, further insights into the impacts of COVID-19 on changes in residential psychological needs are discussed in this paper. This work can provide a reference for decision makers to cope with the change in urban traffic during a public health emergency, which is beneficial to the sustainable healthy development of cities.

Towards Moving Objects Behavior Analysis: Region Speed Limit Rate Measure

In this paper, a measure is proposed that, based on the trajectories of moving objects, computes the speed limit rate in each of the cells in which a region is segmented (the space where the objects move). The time is also segmented into intervals. In this way, the behavior of moving objects can be analyzed with regard to their speed in a cell for a given time interval. An implementation of the corresponding algorithm for this measure and several experiments were conducted with the trajectories of taxis in Porto (Portugal). The results showed that the speed limit rate measure can be helpful for detecting patterns of movement, e.g., in a day (morning hours vs. night hours) or on different days of the week (weekdays vs. weekends). This measure might also serve as a rough estimate for congestion in a (sub)region. This may be useful for traffic analysis, including traffic prediction.

Abnormal-Trajectory Detection Method Based on Variable Grid Partitioning

Abnormal-trajectory detection can be used to detect fraudulent behavior by taxi drivers when carrying passengers. Existing methods usually detect abnormal trajectories based on the characteristics of “few and different”, which require large data sets and, therefore, may identify “few and near” trajectories chosen by drivers according to their driving experience as abnormal situations. This study proposed an abnormal-trajectory detection method based on a variable grid to address this problem. First, the urban road network was divided into three regions: high-, medium-, and low-density road network regions using a kernel density analysis method. Second, grids with different sizes were set for different types of road network regions; trajectory tuples were obtained based on the grid division results, and the abnormality rate of the trajectory was calculated. Finally, a trajectory-abnormality probability function was developed to calculate the deviation of each trajectory from the benchmark trajectory to detect abnormal trajectories. Experimental results on a real taxi trajectory dataset demonstrated that the proposed method achieved a higher accuracy in detecting abnormal trajectories than similar methods.

Forecasting fine-grained sensing coverage in opportunistic vehicular sensing

Vehicles equipped with portable sensors can be used to collect data for large-scale urban sensing. However, the independent movement and non-uniform distribution of vehicles in opportunistic vehicular sensing raises questions about the quality of the sensing coverage. Appropriate vehicles are typically selected based on their historic trajectories, but this means that forecasting the sensor coverage for new vehicles is challenging. In this paper, we propose a method for tailored vehicle selection based on the forecast fine-grained sensing coverage without trajectory data. First, we propose a model, which is able to forecast fine-grained sensing coverage by coarse-grained information of candidate vehicles instead of trajectories. Then, by integrating the forecast sensing coverage with the genetic algorithm, a vehicle selection algorithm is proposed to select the appropriate vehicle fleet from the candidate vehicles to maximize the sensing quality. The method is assessed using taxi trajectory data in the evaluation experiments. The results demonstrate that the selected vehicles based on our method can achieve a higher sensing quality than two other baselines. This research provides fundamental guidelines for coverage estimation and vehicle selection in urban vehicular sensing applications. The demo is available at https://github.com/WenyanClaraHu/FiSC.

Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing.

Efficient Data Dissemination Strategy for UAV in UAV-Assisted VANETs

With development of emerging intelligent transportation systems (ITSs), unmanned aerial vehicles (UVAs) are gaining growing attention to achieve UAV-assisted VANETs for improving performance of data dissemination. However, in UAV-assisted VANETs, the performance of data dissemination can be significantly compromised by the limited bandwidth resource of UAVs. In this paper, we propose a scheduling strategy to provide efficient data dissemination. In particular, first, we take into account a scenario with either unavailable or overloaded infrastructure, where a UAV is employed to provide data dissemination. Furthermore, considering the limited bandwidth resource of the UAV, we formulate a coding-based cooperative caching data dissemination (C3D2) problem which makes use of the synergy between network coding and vehicular caching with the objective of maximizing the bandwidth efficiency of UAVs. Meanwhile, the NP-hardness of the C3D2 problem is proved by constructing a polynomial time reduction from the simultaneous matrix completion problem. Based on the effectiveness of the binary particle swarm optimization (BPSO) framework in solving the complex problems, we propose a BPSO-based cooperative caching scheduling (BPSO_C2S) algorithm consisting of a series of strategies, such as particle representation, particle evaluation, particle update and infeasible solution amendment. In addition, we analyze the computational complexity of the BPSO_C2S. Finally, we build the simulation model and give a comprehensive performance evaluation through the real-world map and taxi trajectories. Simulation results demonstrate the effectiveness and superiority of the proposed solution.

Dynamic community detection considering daily rhythms of human mobility

Detecting human mobility community structures is an important means to reveal urban spatial structures. Due to the circadian clock, human mobility has a daily rhythm, representing dynamic interactions among urban functional areas at different times of the day. However, few community detection methods have been developed by explicitly considering such daily rhythms of human mobility. To fill the gap, this study proposes a novel method for detecting dynamic community structures derived from the daily rhythms of human mobility. The proposed method employs a consensus clustering technique to detect consensus structures of dynamic communities throughout the day. The time series characteristics of the detected dynamic communities are further introduced. The hierarchical clustering technique is used to uncover evolving patterns of detected dynamic communities. A case study was carried out using taxi trajectories in New York City to verify the proposed method. Results indicated that the proposed method could well detect a robust structure of dynamic communities even when human mobility patterns changed dramatically at certain time periods. Results also suggested that the proposed method could identify underlying evolving patterns of detected dynamic communities.

Discovering Key Sub-Trajectories to Explain Traffic Prediction.

Flow prediction has attracted extensive research attention; however, achieving reliable efficiency and interpretability from a unified model remains a challenging problem. In the literature, the Shapley method offers interpretable and explanatory insights for a unified framework for interpreting predictions. Nevertheless, using the Shapley value directly in traffic prediction results in certain issues. On the one hand, the correlation of positive and negative regions of fine-grained interpretation areas is difficult to understand. On the other hand, the Shapley method is an NP-hard problem with numerous possibilities for grid-based interpretation. Therefore, in this paper, we propose Trajectory Shapley, an approximate Shapley approach that functions by decomposing a flow tensor input with a multitude of trajectories and outputting the trajectories' Shapley values in a specific region. However, the appearance of the trajectory is often random, leading to instability in interpreting results. Therefore, we propose a feature-based submodular algorithm to summarize the representative Shapley patterns. The summarization method can quickly generate the summary of Shapley distributions on overall trajectories so that users can understand the mechanisms of the deep model. Experimental results show that our algorithm can find multiple traffic trends from the different arterial roads and their Shapley distributions. Our approach was tested on real-world taxi trajectory datasets and exceeded explainable baseline models.

The timescape of the city: Example of spatial interactions based on big data

The paper develops the concept of a timescape of a city based on empirical analysis of geospatial big data. We understand the timescape as the temporal shape of a city, which reflects temporal dynamics of a city structure and rhythms based on aggregated time-space behaviour of individuals. The paper uses the concepts of rhythmanalysis and spatial interactions as theoretical basis. We analyse two data types: mobile phone location data and taxi trajectory data, and the city of Prague is taken as the example. The data capture the population mobility peak before the Covid-19 pandemic has burst out. We examine both the temporal (weekly and daily rhythms) and spatial (intra urban and ‘extra’ urban flows) hierarchies of the city time-space. We have confirmed the existence of distinct differences between workdays and weekend days. It has also been shown that the mobility based on taxi trajectories has specific temporal distribution. Based on ‘extra’ urban flows, rhythms and thus on the present population of the city we have identified two hierarchically different time waves, long (weekly) and short (daily), when the latter has opposite amplitude and span during workdays and during weekend days.

Exploring the topological characteristics of urban trip networks based on taxi trajectory data

As an essential mode of travel for city residents, taxis play a significant role in meeting travel demands in an urban city. Understanding the modal characteristics of taxis is vital to addressing many difficulties regarding urban sustainability. The movement trajectory of taxis reflects not only the operating features of taxis themselves but also urban structure and human mobility. In this work, the taxi trajectory data of Chengdu and New York City is processed, and the corresponding urban trip networks are constructed based on geographic information systems. We empirically and systematically analyze these urban trip networks according to the network hierarchy based on complex network theory. First, we studied the low-order organization of the urban trip networks (i.e., degree distribution, cluster-degree coefficient, rich-club coefficient, and so on.). We uncover the nontrivial relationship between network density and trip distance and find that the urban trip network in Chengdu is more heterogeneous than that in New York City. Second, we investigate the meso-order organization of the urban trip networks by using community detection. The community detection results show that the community boundaries are more or less mismatched with the administrative boundaries. Finally, we detect the higher-order organizations of the urban trip networks and find some critical nodes and regions. These empirical results from the perspective of complex networks provide insight to better understand the urban structure and human mobility, and potentially amend urban planning.