Beijing Taxi Research Articles

Exploring multi-relational spatial interaction imputation with distance-decay effects

ABSTRACT Spatial interaction imputation aims to compensate for missing stable connections in geographical space, bolstering interaction network integrity and accuracy. Graph neural networks excel in graph-structured interaction data. However, existing research often focuses on homogeneous networks, neglecting the impact of heterogeneous interaction relationships influenced by distance decay on interaction imputation. Neglecting edge heterogeneity constrains the ability to effectively model the network structure, consequently leading to suboptimal performance in interaction imputation. This study introduces an interaction imputation graph convolutional network model. It constructs a heterogeneous interaction network with multi-distance relationships, considering distance decay. The model performs graph embedding based on interaction relationships between nodes. It comprehensively incorporates multiple interaction modes, topological structures, and node attributes to enhance spatial interaction imputation accuracy. Empirically validated using Beijing taxi travel data, our model outperforms existing models, improving imputation accuracy by approximately 8.70%. Our model consistently maintains superior accuracy in interaction networks of various sizes, demonstrating the stable superiority of our model. We also demonstrated that the variation in the number of interaction relationships affects imputation accuracy. A reasonable number of relationships and a larger feature dimension of geographical units yield better interaction imputation results.

A kriging interpolation model for geographical flows

The kriging model can accommodate various spatial supports and has been extensively applied in hydrology, meteorology, soil science, and other domains. With the expansion of applications, it is essential to extend the kriging model for new spatial support of high-dimensional data. Geographical flows can depict the movements of geographical objects and imply the underlying mobility patterns in geographical phenomena. However, due to the bias, sparsity, and uneven quality of flow data in the real world, research about flows remains hindered by the lack of complete flow data and effective flow interpolation methods. In this study, we design a kriging interpolation model for flows based on several flow-related concepts and the autocorrelation of flows. We also analyze the second-order stationarity and anisotropy in the flow spatial random field. To illustrate the effectiveness and applicability of our method, we conduct two case studies. The former case study compares several experiments of flow density interpolation using Beijing mobile signaling data and illustrates the conditions of applicable areas. The latter case study extends our model to other flow attributes, such as travel time uncertainty, using Beijing taxi origin-destination flow data. The results of these cases demonstrate the effectiveness and high accuracy of our model.

A Cost-Effective Sequential Route Recommender System for Taxi Drivers

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0112 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Comparative analysis of comprehensive benefits of Beijing's taxi electrification paths

Comparative analysis of comprehensive benefits of Beijing's taxi electrification paths

Density-based clustering for bivariate-flow data

Geographical flows reflect the movements, spatial interactions or connections among locations and are generally abstracted as origin-destination (OD) flows. In this context, clustering is a spatial pattern describing a group of flows with adjacent O and D points. For data composed of two types of flows (bivariate-flow data), a bivariate-flow cluster is a cluster comprising two types of flows, at least one of which exhibits a clustering pattern. In a bivariate-flow cluster, varying flow density combinations imply different meanings. For instance, a cluster with high-density travel flows on both weekdays (type A) and weekends (type B) may be associated with entertainment, whereas high-density flows on weekdays and sparse flows on weekends may reveal work-related travel. However, identifying bivariate-flow clusters with different flow density combinations is still an unsolved problem. To this end, we extend a bivariate-point clustering method and propose a density-based clustering method for bivariate flows. The simulation experiments verify model robustness. In a case study, we apply this method to extract clusters of bivariate-flow data comprising Beijing taxi OD flows of different periods, and identify clusters of work-related, entertainment, tourism, or egress and return travels. These results demonstrate the capability of our method in detecting bivariate-flow clusters.

Query the trajectory based on the precise track: a Bloom filter-based approach.

Fast and precise querying in a given set of trajectory points is an important issue of trajectory query. Typically, there are massive trajectory data in the database, yet the query sets only have a few points, which is a challenge for the superior performance of trajectory querying. The current trajectory query methods commonly use the tree-based index structure and the signature-based method to classify, simplify, and filter the trajectory to improve the performance. However, the unstructured essence and the spatiotemporal heterogeneity of the trajectory-sequence lead these methods to a high degree of spatial overlap, frequent I/O, and high memory occupation. Thus, they are not suitable for the time-critical tasks of trajectory big data. In this paper, a query method of trajectory is developed on the Bloom Filter. Based on the gridded space and geocoding, the spatial trajectory sequences (tracks) query is transformed into the query of the text string. The geospace was regularly divided by the geographic grid, and each cell was assigned an independent geocode, converting the high-dimensional irregular space trajectory query into a one-dimensional string query. The point in each cell is regarded as a signature, which forms a mapping to the bit-array of the Bloom Filter. This conversion effectively eliminates the high degree of overlap and instability of query performance. Meanwhile, the independent coding ensures the uniqueness of the whole tracks. In this method, there is no need for additional I/O on the raw trajectory data when the track is queried. Compared to the original data, the memory occupied by this method is negligible. Based on Beijing Taxi and Shenzhen bus trajectory data, an experiment using this method was constructed, and random queries under a variety of conditions boundaries were constructed. The results verified that the performance and stability of our method, compared to R*tree index, have been improved by 2000 to 4000 times, based on one million to tens of millions of trajectory data. And the Bloom Filter-based query method is hardly affected by grid size, original data size, and length of tracks. With such a time advantage, our method is suitable for time-critical spatial computation tasks, such as anti-terrorism, public safety, epidemic prevention, and control, etc.

A Map Inference Approach Using Signal Processing from Crowd-sourced GPS Data

The amount of GPS data that can be collected is increasing tremendously, thanks to the increased popularity of Global Position System (GPS) devices (e.g., smartphones). This article aims to develop novel methods of converting crowd-sourced GPS traces into road topology maps. We explore map inference using a three-stage approach, which incorporates a novel Multi-source Variable Rate (MSVR) signal reconstruction mechanism. Unlike conventional map inference methods based on map graph theory, our approach, to the best of our knowledge, is the first use of estimation theory for map inference. In particular, our approach addresses the unique challenges of vehicular GPS data. This data is plentiful but suffers from noise in location and variable coverage of regions. This makes it difficult to differentiate between noise and sparsely covered regions when increasing coverage and reducing noise. Due to the asynchronous, variable sampling rate, and often under-sampled nature of the data, our MSVR approach can better handle inherent GPS errors, reconstruct road shapes more accurately, and better deal with variable GPS data density in empirical environments. We evaluated our method for map inference by comparing to Open Street Map maps as ground truth. We use the F-Measure, Precision, and Recall metrics to evaluate our method on Tsinghua University’s Beijing Taxi Dataset and Shanghai Jiao Tong University’s SUVnet Dataset. On these datasets, we obtained a mean<?brk?> F-Measure, Precision, and Recall of 0.7212, 0.9165, and 0.6021, respectively, outperforming a well-known method based on Kernel Density Estimation in terms of these evaluation metrics.

Modeling Dynamic Spatio-Temporal Correlations for Urban Traffic Flows Prediction

Prediction of traffic crowd movement is one of the most important component in many applications ' domains ranging from urban management to transportation schedule. The key challenge of citywide crowd flows prediction is how to model spatial and dynamic temporal correlation. However, in recent years several studies have been done, but they lack the ability to effectively and simultaneously model spatial and temporal dependencies among traffic crowd flows. To address this issue, in this article a novel spatio-temporal deep hybrid neural network proposed termed STD-Net to forecast citywide crowd traffic flows. More specifically, STD-Net contains four major branches, i.e., closeness, period volume, weekly volume, and external branches, respectively. We design a residual neural network unit for each property to depict the spatio-temporal features of traffic flows. For various branches, STD-Net provides distinct weights and then combines the outputs of four branches together. Extensive experiments on two large-scale datasets from New York bike and Beijing taxi have demonstrated that STD-Net achieves competitive performances the existing state-of-the-art prediction baselines.

Visualizing spatial interaction characteristics with direction-based pattern maps

In the era of big data, large amounts of detailed spatial interaction data are readily available due to the increasing pervasiveness of location-aware devices and techniques. Such data are often applied in the research of spatial structures, land use characteristics, and human activity regularities. However, visualizing such data on maps is a great challenge. Existing approaches either encounter overlapping and intersection problems, which may cause information loss or distortion, or are unable to present interaction characteristics. Consequently, the functional and positional features of places and human mobility trends are not fully demonstrated. We propose direction-based pattern maps, a new visualization method to display the spatial interaction pattern of every place by aggregating spatial interaction data in cardinal directions. This approach can well represent local interaction characteristics and is applicable to different spatial scales. Owing to regular hexagonal tessellations, it can avoid cluttering problems and maintain a geographical context. A case study using Beijing taxi trip data is conducted to validate the usefulness of our approach.

A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends

Massive flows that represent the individual level of movements and communications can be easily obtained in the age of big data. Generalizing spatial and temporal flow patterns from such data is essential to demonstrate spatial connections and mobility trends. Clustering approaches provide effective methods to handle data sets that contain massive individual-level flows. However, existing flow clustering studies obscure the geometric properties of flow data, such as direction and length, which significantly indicate movement trends. In addition, temporal information is often ignored because previous approaches have mainly focused on the perspective of spatial clusters of flow data, resulting in a loss of temporal patterns. In this paper, we introduce new spatial and temporal similarity measurements between flows and propose a new clustering approach of flow data based on a stepwise strategy. This method can identify clusters from distinct flow distributions and discover significant spatio-temporal trends from large flow data. Simulated experiments with synthetic flows and a case study using Beijing taxi trip data are conducted to validate the usefulness of the proposed method.

Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data

Quantitative research of urban geography has benefited greatly from the rapid development of big geo-data. Spatial assembly is an essential analytical step to summarize and perceive geographical environment from individual behaviours. Most research focuses on the methodology of how to utilize the big data, while the adopted spatial units for data aggregation remain areal in nature. This article conceptually proposes an idea of sensing cities from a street perspective, emphasizes the significance of street units in quantitative urban studies. Using a three-month taxi trajectory dataset and the major streets in Beijing, we explore the spatio-temporal patterns of urban mobility on streets, cluster streets into nine types based on their dynamic functions and capacities. Additionally, we discuss the differences and connections between the linear street unit and traditional areal units, investigate the possibility of uncovering urban communities using streets, and point out the complexity of streets. We conclude that street unit as a supplement to areal units, is able to effectively minify the modifiable areal unit problem (MAUP), sense urban dynamics, depict urban functions, and understand urban structures.

Outlier Detection over Massive-Scale Trajectory Streams

The detection of abnormal moving objects over high-volume trajectory streams is critical for real-time applications ranging from military surveillance to transportation management. Yet this outlier detection problem, especially along both the spatial and temporal dimensions, remains largely unexplored. In this work, we propose a rich taxonomy of novel classes of neighbor-based trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real-time applications. Our theoretical analysis and empirical study on two real-world datasets—the Beijing Taxi trajectory data and the Ground Moving Target Indicator data stream—and one generated Moving Objects dataset demonstrate the effectiveness of our taxonomy in effectively capturing different types of abnormal moving objects. Furthermore, we propose a general strategy for efficiently detecting these new outlier classes called the <underline>m</underline>inimal <underline>ex</underline>amination (MEX) framework. The MEX framework features three core optimization principles, which leverage spatiotemporal as well as the predictability properties of the neighbor evidence to minimize the detection costs. Based on this foundation, we design algorithms that detect the outliers based on these classes of new outlier semantics that successfully leverage our optimization principles. Our comprehensive experimental study demonstrates that our proposed MEX strategy drives the detection costs 100-fold down into the practical realm for applications that analyze high-volume trajectory streams in near real time.

A hybrid life cycle and multi-criteria decision analysis approach for identifying sustainable development strategies of Beijing's taxi fleet

To identify and evaluate sustainable strategies of taxi fleet in Beijing in terms of economic, policy, and environmental implications, a hybrid approach was developed through incorporating multi-criteria decision analysis (MCDA) methods within a general life-cycle analysis (LCA) framework. The approach can (a) help comprehensive evaluate environmental impacts of multiple types of vehicles, (b) facilitate analysis of environmental, economic and policy features of such vehicles, and (c) identify desirable taxi fleet development strategies for the city. The developed approach represented an improvement of the decision-making capability for taxi implementation based on multiple available technologies and their performance that can be specifically tailored to Beijing. The results demonstrated that the proposed approach could comprehensively reflect multiple implications of strategies for the taxi fleet in Beijing to reduce air pollution in the city. The results also indicated that the electric vehicle powered with the year 2020 electricity projections would be the ideal solution, outranking the other alternatives. The conventional vehicle ranked the lowest among the alternatives. The plug-in hybrid vehicle powered by 2020 electricity projects ranked the third, followed by the plug-in hybrid vehicle ranking the fourth, and the hybrid vehicle ranking the fifth.

The Study on Development Countermeasures of Beijing Taxi

The taxi is indispensable to be a part of city traffic. With the people's living level improve and city scale process much faster, the taxi is developing rapidly in large city in our country, it is not only related to the vital interests of the public, but also related to the planning and layout of the city, so attracted the attention of all society. This paper mainly analyzes the significance of taxi industry management, the purpose of the subject, especially the taxi of Beijing. Investigate the situation of taxi management like Beijing taxi. Beside, according to the investigation and analysis the problems of big city such as Beijing taxi, discussing the measures and key point to improve the Beijing taxi management, putting forward some reformation and suggestions.