With the rapid development of geolocation technology, the volume of spatio-temporal trajectory data has surged. This data is widely used in fields such as geographic information systems and mobile computing, but its storage and query processing present significant challenges. Current methods of offline indexing are inefficient and cannot be updated in real-time. To address this issue, this paper proposes a concept of the online index that supports real-time storage and indexing of trajectory data and significantly reduces indexing time and storage space requirements. Based on this concept, two vector-based online trajectory indexing methods are proposed in this paper. The first is an online trajectory indexing method based on vector extraction (VBIndex), which offers the advantages of high efficiency and less storage space. The second is an online trajectory indexing method based on road-network matching (RAIndex), which further improves the vector-based indexing efficiency when road network involved. Through experiments with real datasets, the proposed algorithms were evaluated, confirming their superiority in terms of indexing construction time and storage space. Furthermore, we have theoretically proven that queries based on this index are accurate, and statistical analysis is feasible. Both algorithms have a time complexity of$O(N)$in indexing construction, demonstrating good performance.

With the vigorous development of transportation infrastructure in various countries, the traffic network within the city is becoming more and more complex, and when an emergency occurs in one or more areas of the city, it will inevitably cause traffic congestion in the area and keep spreading. There are still many challenges to solve the urban emergency route planning problem. In this paper, we have employed a double layer search structure, where we have empowered the traditional A* model with a neural network, to construct a region-level dynamic path planning model known as “Double Layer A*”. The model divides the road network into two layers, and implements the outer layer and inner layer search. In the outer layer search, we use the historical cab travel data for training to achieve the general direction planning; in the inner layer search, we update the original planning according to the changes of the road condition characteristics of the regional nodes, and perform the re-planning in real time. We conducted experimental evaluations using the road network data of Beijing, and the results showed that compared to a single-layer search structure path planning model, our Double layer A* model planned paths with higher similarity in land characteristics, connectivity, and average connectivity between adjacent nodes, which demonstrates the effectiveness and reasonableness of the Double layer A* model in emergency path planning.

With the widespread use of the Global Positioning System (GPS) in the fields such as traffic monitoring, sports navigation, and track recording, the trajectory data recording users’ spatial and temporal information has grown dramatically. The huge volume of trajectory data causes high cost and poses a great challenge to data storage, network transmission, query and analysis. Therefore, the compression of trajectory data becomes a crucial issue. This paper proposes an online trajectory compression algorithm based on vector extraction (VOLTCom), which aims to achieve efficient data compression while retaining more effective information, and is mainly applied to trajectory recording and analysis in the traffic field. VOLTCom first generates vectors for trajectory data according to customized vector features, and then performs real-time vector extraction to achieve online trajectory compression. The vector extraction of the trajectory data ensures the stability of the compression time per unit and achieves efficient compression. Experiments on real datasets show that VOLTCom can retain the information of object velocity variation by vector density and outperforms traditional algorithms in terms of error, compression rate, and execution time. The algorithm is$O(1)$in compression time complexity and has better compression performance.

Nowadays, wireless sensor networks have played an important role in many applications. In these applications, a large number of wireless sensors are deployed in an environment to collect information and form network to transmit collected information to the base station or sink. Because wireless sensors have to work for a long period of time without maintenance, the energy consumption of wireless sensors has a great impact on the lifetime of wireless sensor network. To reduce and balance the energy consumption of wireless sensors, many information transmission routing approaches have been proposed. However, most of them do not consider all energy consumption factors of wireless sensors. To this end, an innovative information transmission routing approach based on Q-learning is proposed in this paper, which enables wireless sensors to adaptively select suitable neighboring sensors to achieve energy efficient information transmission in a decentralized manner. Based on the proposed approach, a wireless sensor first collects state and action information of its neighboring sensors, which includes information transmission direction and distance, the remaining energy, the energy consumption for information transmission and information transmission action. Then, all this information is used to update the Q-values of neighboring sensors, so as to enable the wireless sensor to select suitable neighboring sensor to transmit information according to Q-values. From simulation experiments, it can be seen that the proposed approach enables wireless sensors to reduce and balance the energy consumption of wireless sensors and extend the lifetime of the entire wireless sensor network.

The skyline query is one of the most important supporting technologies for the location-based query services in the road network. Usually, when a user queries the skyline points in the road network, the query area is a user-centered circle or rectangle area, without considering the impact of the current movement speed and direction of the user on the formation of the query area. In this context, a speed and direction aware skyline query method is proposed, which can provide the skyline query area for the users by considering their moving speed and direction. Since the efficiency to directly obtain points of interest from speed and direction aware query area is not high, a Voronoi based speed and direction query area generation algorithm is proposed to approximate the query area, so as to improve the obtaining efficiency of points of interest in the area. The experiments on road networks and points of interest data of Beijing show the performance of the proposed method in terms of query efficiency and quality.

With the development of location-based services and smart terminals, skyline query technique has been used widely in intelligent transportation systems. In skyline queries, the areas and keywords queried by users have a great impact on the quality of the query results and users may only be interested in the closer results of the skyline query. However, current approaches for the continuous skyline query limit the area of the skyline query to a specific area in the road network, which leads to that many useful query results cannot be retrieved. To this end, an innovative continuous skyline query approach in city range is proposed in this paper, where a multi-scale area divisions of the urban road network are provided to find the optimize query scale and area. In our approach, first, the dominant area of each intersection node in the road network is established based on the Voronoi. Then, all Points of Interest ($POI\text{s}$) are divided into the dominant area of each intersection node. After that, the intersection node aggregation algorithm ($INAA$), link remolding algorithm ($LMA$) and link fitting algorithm ($LFA$) are proposed to reduce the number of intersection nodes in the road network, so as to increase the dominant area of the remaining intersection nodes and the number of POIs in these nodes. Finally, a better query scale by considering the efficiency and quality of the query is given through the studies.

Urban rail transit stations are the key nodes of urban rail transit network. Identifying and analyzing land use characteristics around urban rail transit stations can significantly contribute to urban rail transportation operation and management. Therefore, a visualization method of land use characteristics around urban rail transit stations based on POI is proposed in this paper. In the proposed method, first, the Voronoi diagram is used to determine coverage of urban rail transit stations and each POI is put in a coverage area based on their physical location. Then, topic-oriented hierarchical POIs of each urban rail transit station are extracted based on skyline idea. Finally, the land use characteristics around an urban rail transit station are visualized based on the extracted hierarchical POIs. We carried out two case studies and a quality evaluation. By using realistic data from Beijing rail transit in order to validate the method proposed in this paper. Results show that our method can clarify various situations of land use of urban rail transit stations and may provide support for the application of transportation model technology.

Multi-agent technologies have been widely applied to many applications, such as in e-markets, cloud computing, service-oriented environments, etc. In real applications, service-oriented environments are open and dynamic, where loosely coupled agents interact to consume and provide services. How to accurately evaluate the potential performance (i.e., QoS) of service providers on the service requested by a service consumer in such open and dynamic environments is a challenging issue in both theory and practice. In this paper, an innovative approach is proposed to evaluate the QoS of service providers in service-oriented environments. The proposed approach first borrows the reference report mechanism from the certified reputation model, so as to efficiently collect reference reports (i.e., historical performance) of service providers in open and dynamic environments. Then, a tensor-based QoS model is proposed to construct multi-dimensional relationships between QoS evaluation factors and the QoS values of service providers based on the collected reference reports. The QoS evaluation factors include the type of services, the performance of service providers, the subjectivity of service consumers, the time slot of reference reports. Finally, a CANDECOMP/PARAFAC decomposition and gradient descent-based mechanism is used to evaluate the QoS values of service providers through completing the missing entry values in the constructed tensor. The uniform random simulation experiments indicate that the proposed approach can achieve efficient and accurate QoS evaluation in service-oriented environments with only limited collected reference reports, especially when some service providers do not have reference reports.

With the development of society and economy, the urban rail transit has become one of the important components of urban transportation system, while the construction of the urban rail greatly improves the public transportation environments. Currently, there are many research focus on the passenger flow predictions according to their corresponding historical data, however, it is hard to assist transport models vary such volumes for a new station planning or being constructed. In view of this limitation, we provide a novel method for urban rail station characteristics analysis in intelligent transportation considering city land usages. Initially, point of interest (POIs) are divided by the proposed RC-tree (Colored R-tree)-based algorithm into the bounded areas for each station. Second, the Diversity and Proportion approaches are proposed to extract the top-k POIs from bounded areas based on their semantic and spatial characteristics. Then, classify the stations based on the similarity of the extracted top-k POIs. Moreover, we made a case study on real dataset, including a large volume of Automatic Fare Collection system (AFC) records for the experimental evaluations, and the results show that the proposed method can verify the rationality of land use and provide support for the application of transportation model technology.

We are witnessing increasing interests in developing “smart cities” which helps improve the efficiency, reliability, and security of a traditional city. An important aspect of developing smart cities is to enable “smart transportation,” which improves the efficiency, safety, and environmental sustainability of city transportation means. Meanwhile, the increasing use of GPS devices has led to the emergence of big trajectory data that consists of large amounts of historical trajectories and real-time GPS data streams that reflect how the transportation networks are used or being used by moving objects, e.g., vehicles, cyclists, and pedestrians. Such big trajectory data provides a solid data foundation for developing various smart transportation applications, such as congestion avoidance, reducing greenhouse gas emissions, and effective traffic accident response, etc. Instead of proposing yet another specific smart transportation application, we propose the parallel-distributed network-constrained moving objects database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner, which provides an infrastructure that is able to support a wide variety of smart transportation applications and thus benefiting the smart city vision as a whole. The PD-NMOD manages both transportation networks and trajectories in a distributed manner. In addition, the PD-NMOD is designed to support general SQL queries over moving objects and to efficiently process the SQL queries on big trajectory data in parallel. Such design facilitates smart transportation applications to retrieve relevant trajectory data and to conduct statistical analyses. Empirical studies on a large trajectory data set collected from 3,500 taxis in Beijing offer insight into the design properties of the PD-NMOD and offer evidence that the PD-NMOD is efficient and scalable.

Location update strategy is one of the most important factors that affect the performance of moving objects databases. However, current motion vector based location tracking methods are designed for regular movements and are thus not suitable for transportation networks with changeful traffic conditions. To solve this problem, we propose a new location update mechanism, Adaptive Network-constrained moving object Location Update Mechanism (ANLUM), in this paper. In ANLUM, the moving object can switch between different location tracking policies according to difference traffic conditions, so that the overall performance can be improved. To evaluate the performance of the proposed method, an experimental system is implemented and the results show that ANLUM can effectively reduce the communication costs with location tracking accuracy guaranteed in traffic jammed transportation networks.

MOIR is a Web-based prototype to support a number of novel applications with network-constrained moving object management. Technical aspects of MOIR range from data acquisition and trajectory smoothing, trajectory data management and query processing, movement predications, and location based Web page recommendation. The demo is in the context of detailed digital road maps with 38,0000 road segments and 55,000 road intersection points, and real spatiotemporal data of over ten thousand taxis in Beijing.