Clustering the trajectories of vehicles moving on road networks is a key data mining technique for understanding human mobility patterns, as well as their interactions with urban environments. The development of efficient and scalable trajectory clustering algorithms, however, still faces challenges because of the computational costs when measuring similarities among a large number of network-constrained trajectories. To address this problem, a novel trajectory clustering framework based on the well-developed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach is proposed. This proposed framework accurately quantifies similarities using a trajectory representation of continuous polylines in the space and time dimensions, and does not require trajectory discretization. Further, the proposed framework utilizes the space-time buffering concept to formulate ε-neighborhood queries that directly retrieve the ε-neighbors of trajectories and thus avoids computing a trajectory similarity matrix. State-of-the-art trajectory databases and index structures are incorporated to further improve trajectory clustering performance. A comprehensive case study was carried out using an open dataset of 20,161 trajectories. Results show that the proposed framework efficiently executed trajectory clustering on the large test dataset within 3 min. This was approximately 2,700 times faster than existing DBSCAN algorithms.

The availability of Spatiotemporal Big Data has provided a golden opportunity for time geographical studies that have long been constrained by the lack of individual-level data. However, how to store, manage, and query a huge number of time geographic entities effectively and efficiently with complex spatiotemporal characteristics and relationships poses a significant challenge to contemporary GIS platforms. In this article, a hierarchical compressed linear reference (CLR) model is proposed to transform network-constrained time geographic entities from three-dimensional (3D) (x, y, t) space into two-dimensional (2D) space. Accordingly, time geographic entities can be represented as 2D spatial entities and stored in a classical spatial database. The proposed CLR model supports a hierarchical linear reference system (LRS) including not only underlying a link-based LRS but also multiple higher-level route-based LRSs. In addition, an LRS-based spatiotemporal index structure is developed to index both time geographic entities and the corresponding hierarchical network. The results of computational experiments on large datasets of space–time paths and prisms show that the proposed hierarchical CLR model is effective at storing and managing time geographic entities in road networks. The developed index structure achieves satisfactory query performance in milliseconds on large datasets of time geographic entities.

Detecting network communities has recently attracted extensive studies in many fields. However, little attention has been paid to detection and analysis of dynamical communities. This study intends to propose a methodological framework to detect dynamical communities in multilayer spatial interaction networks and examine their spatiotemporal patterns. Random walks are used to merge network layers with different weights, the Leiden technique is used for deriving dynamical communities and exploratory analytic methods are adopted to examine spatiotemporal patterns. To verify our methods, experiments were conducted in Wuhan, China, where trajectory data were used to construct the time-dependent multilayer networks. (1) We derived a set of spatiotemporally cohesive and comparable dynamical communities on each day for one week; (2) They exhibit interesting clustering patterns according to the similarity of their growth curves; (3) They display distinct life courses of occurrence, expansion, stability, contract and disappearance, and their dynamical interactions are vividly depicted; (4) They manifest mixed land use patterns via transfers of human activities. Thus, our methods can enrich research on dynamical organization of urban space and may be applicable in other contexts, while experimental results can provide decision-making support for sustainable urban management.

It is important to assess environmental impact of intelligent transportation systems, and hence developing a vehicle emission model with high accuracy has been a long-standing topic in transportation research. However, current vehicle emission models are either overly simple using average speed, resulting in low estimation accuracy, or they are too complicated requiring excessive inputs, relying on too much prior knowledge. In this study, we develop and evaluate a deep learning-based vehicle emission model (DL-VEM) to estimate the instantaneous CO2 emissions of taxicabs. First, we examine the correlation between observed emissions and vehicle driving condition data collected in a PEMS experiment. Then, an end-to-end deep learning structure is developed to model patterns of vehicle emissions. Specifically, LSTM networks are used to learn temporal dependencies of historical driving patterns, and fully connected networks are employed to extract deep features of current driving behaviors and external environment. Our model aggregates the outputs of these networks using different learnable weights. Experiments were conducted in Wuhan, China, where our model was trained and validated using observed datasets. Compared with the state-of-the-art models, our model achieved higher accuracy in estimating CO2 emissions. Thereafter, it was applied to a taxicab trajectory dataset in one day, and spatiotemporal patterns of CO2 emissions were presented using different fuel types. Importantly, we find that an increment of 24.94% emissions can be expected if petrol instead of compressed natural gas was used by each taxicab in Wuhan.

Traffic flow forecasting has been a long-standing topic in intelligent transportation systems, and a renewed interest has been seen in recent years due to the development of artificial intelligence techniques. New deep neural networks have been developed to model traffic flow, but it is very challenging to predict citywide traffic flow at the road level in fine temporal scale owing to the influence of spatiotemporal dependencies and spatial sparsity. In this study, based on an in-depth analysis of traffic flow patterns, we propose a deep learning based spatiotemporal neural network model to predict citywide traffic flow for each road segment with high accuracy. Firstly, we examine the transformation of road network into its compact 2D image, where road segments correspond to pixels and their topological relationships are maintained in a large extent. Then, an end-to-end deep learning structure is designed to model traffic flow patterns. Specifically, recurrent convolutional network is employed to learn temporal dependencies and densely connected convolutional network is adopted to learn spatial dependencies and handle spatial sparsity. Our model attempts to aggregate the outputs of those hybrid networks using different weights, which is further enhanced by external information such as day of week. Experiments were conducted in Wuhan, China, where taxicab trajectory data were used to train and validate our model. When compared to current state of the art models, our model achieves higher accuracy in both single and multi-step traffic flow prediction tasks.

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Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.

This paper introduces a robust method for computing the optimal route with hierarchy. We convert a planar road network into its Voronoi-based counterpart with multiple levels of detail (LoDs), which is subsequently assigned travel times that are estimated for different times of day using taxicab trajectory data. On the basis of this network structure, we model the path-finding process in travel, as the optimal route with hierarchy is computed in a ‘coarse-to-fine’ manner. In other words, the route is iteratively constructed from roads in a low LoD network to roads in a high LoD network. To confirm the efficiency and effectiveness of our method, comparative experiments were conducted using randomly selected pairs of origins/destinations in Wuhan, China. The results indicate that our travel lengths are on average 12% longer than those computed by the Dijkstra algorithm and 15% shorter than those computed by the hierarchical algorithm (in ArcGIS). Our travel times are on average 29% longer than those computed by the Dijkstra algorithm and 31% shorter than those computed by the hierarchical algorithm (in ArcGIS). Hence, we argue that our method is situated in terms of performance between the Dijkstra algorithm and the hierarchical algorithm (in ArcGIS). Moreover, road usage patterns confirm that our algorithm is cognitively equivalent to the hierarchical algorithm (in ArcGIS) by favoring high-class roads and outperforms the Dijkstra algorithm by avoiding choosing low-class roads. Computationally, our method outperforms the Dijkstra algorithm but is on the same level as the hierarchical algorithm (in ArcGIS) in terms of efficiency. Therefore, it has the potential to be used in real-time routing applications or services.