Query-based trip recommendation is an important task in location-based services (LBS), which aims to provide users with a sequence of points of interest (POIs) based on their queries. In trip recommendation, the effect of the visited POIs on the following decisions of users, called a forward effect, is mined by the existing studies. However, the effect of the following POIs on the previously visited ones, called a reverse effect, receives no attention. Therefore, this work proposes a graph-based double-layer bidirectional model for trip recommendation (GDB-TR), which is designed to mine both forward and reverse effects through bidirectional computation. Forward computation explores the influence of user’s history preferences on the next choice; reverse computation explores the influence of future goals on the current decision. Specifically, the model uses a heterogeneous graph to model users’ check-in trajectories with spatial and temporal information. Subgraphs are extracted from the heterogeneous graph, and an adjacency matrix is built for each subgraph. Vector representations of POIs and POI categories are obtained by fusing matrices based on a neural network. The double-layer bidirectional neural network is used to recommend a trip based on the user query, with one layer mining users’ preferences for POIs and the other layer mining the preferences for POI categories. Bidirectional computation is performed between the initial and destination nodes in each layer, capturing both forward and reverse effects. Specifically, a forward computation mines the influence of preceding POIs or POI categories on following ones, while a reverse computation does that reversely. Finally, experiments are conducted on five popular real data sets. The results show that GDB-TR outperforms all baseline models onF1and pairs-F1values, which validates the effectiveness of the proposed approach.

A share-a-ride problem (SARP), which integrates the transportation of both passengers and parcels by the ride-hailing platforms such as Uber and Lyft, has drawn considerable attention. This work introduces a novel share-a-ride problem with flexible pick-up and drop-off points (SARP-FUO) with the objectives of maximizing the total revenue of the ride-hailing platforms and minimizing the total travel distance of vehicles. A mixed integer programming model is developed to formulate SARP-FUO. Then, a knowledge-based multi-objective brain storm optimization algorithm (KM-BSO) is proposed to solve it. Two knowledge-based local search operators are specifically designed to enhance the exploration capability of KM-BSO for identifying potential nondominated solutions. The first operator employs a dynamic programming algorithm to readjust pick-up and drop-off points, while the second modifies vehicle routes based on four derived properties. Extensive experiments are conducted to compare KM-BSO with nondominated sorting genetic algorithm II, multi-objective evolutionary algorithm based on decomposition, multi-objective artificial bee colony algorithm, and a mathematical programming solver CPLEX. The results and statistical analysis demonstrate the superiority of the proposed approach in solving the studied problem. Finally, a sensitivity analysis is performed with and without flexible pick-up and drop-off points, demonstrating the advantages of the proposed model in developing intelligent public transportation systems.

With the advancement of vehicular automation and communication technology, autonomous driving has emerged as a significant trend in future transportation. Robotaxis, an innovative mode of transportation that integrates robotics and artificial intelligence, are anticipated to become widely used, thereby revolutionizing urban mobility. This work proposes a multi-objective mixed integer programming model for robotaxi dispatch. Unlike previous approaches, it can recommend passengers’ pick-up points, drop-off points, and boarding time (BT) that may deviate from their initial origins, destinations, and BT, respectively. It encourages passengers to accept the recommended pick-up and drop-off (UO) points or to be picked up slightly earlier or later. The objectives are to maximize the profit per kilometer of robotaxis and to minimize the total travel expense of passengers. Subsequently, a nondominated sorting genetic algorithm with mass center (NSGA-MC) is proposed to solve the model. It outperforms nondominated sorting genetic algorithm II (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) across several metrics. Some instances provide detailed results that illustrate the effectiveness of the proposed algorithm. Additionally, a sensitivity analysis is performed, comparing scenarios with and without UO-point and boarding-time recommendations. An experiment is also conducted to examine various recommendation acceptance rates, demonstrating the advantages of the proposed model in developing intelligent public transportation systems.

Autonomous driving has been successfully implemented in such particular areas as logistics distribution centers, container terminals, and university campuses. Robotaxi represents one of its important applications. This work studies a robotaxi dispatch problem during the pandemic era. It aims to design a robotaxi dispatch approach according to a defined severity degree of the pandemic, which can decrease a virus infection rate by reducing contact among passengers. It develops a multi-objective optimization model to minimize travel cost of robotaxis, waiting time of both robotaxis and passengers, and contact among passengers. A two-stage nondominated sorting genetic algorithm (NSGA-TS) is proposed to solve the problem. Three operations are designed to generate new solutions, which can ensure its solution diversity and speed up its convergence. Its effectiveness is verified via its comparison with two popular multi-objective optimization algorithms, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D) and nondominated sorting genetic algorithm II (NSGA-II). Experimental results show that the proposed model can effectively reduce travel cost and waiting time. Besides, it can reduce the virus infection rate by decreasing contact among passengers at different severity degrees of the pandemic. This work is conducive for our society to building intelligent transportation systems in the post-pandemic era.

Electrocardiogram (ECG) signal classification is an important task in healthcare as it plays a vital role in early prevention and diagnosis of cardiovascular diseases. In this work, we propose an attention-based hybrid ResNet-Transformer network (ART-Net) for 12-lead ECG signal classification. It is comprised of a stacked multi-scale attention-based ResNet and self-attention-based Transformer. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by attention-based Resnet through signal segments, and attention mechanisms are used to adjust the weight of different channel features based on their importance. Next, these multi-scale features from a same ECG signal are integrated in chronological order as input to the Transformer network. In this end, extracting and fusing contextual information based on self-attention mechanism, and extracting the correlation between beats at different positions. The experimental results on CPSC2018 indicate that our model outperforms three state-of-the-art methods, and achieve 85.27% of accuracy, 86.01% of sensitivity and 85.59% of specificity, respectively.

Freeway bottlenecks caused by traffic incidents contribute significantly to large-scale traffic congestion. Traditional strategies, including variable speed limit (VSL) and ramp metering, are commonly used for freeway traffic congestion management. Recently, vehicle platooning has become a promising way to alleviate traffic bottlenecks. This work proposes a novel framework that combines VSL and vehicle platooning for freeway bottleneck, referred to as VSL-VP, in mixed traffic of connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). First, the upstream road of a bottleneck is divided into two segments, called the former and the latter. VSL limits vehicle speed at the former segment, thereby reducing inflow traffic to the latter one. Then, deep reinforcement learning is employed for CAV platooning at the latter segment, where low traffic flow density and large car-following distance create conditions for smooth lane change and platoon formulation of CAVs. Simulation results demonstrate that VSL-VP significantly enhances the bottleneck throughput and reduces traffic congestion at elevated levels of CAV penetration rates.

Trip recommendation aims to provide users with a sequence of points of interest (POIs) according to their interests and requirements when exploring unfamiliar cities. In contrast to prior research on trip recommendation, our research deals with such a problem: If a user is scheduled to attend an academic conference at 2:30 PM, how might he/she make a visit to the city's attractions while still managing to attend the conference? To address this problem, a trip recommendation method based on mixed graph representation learning is proposed. Firstly, a mixed graph is used to describe the spatial temporal, and transition knowledge in users' check-in data. Then, we employ the graph convolutional network to integrate knowledge matrices extracted from the mixed graph. Finally, a trip inference module, which incorporates a dual decoder, POI popularity knowledge, and positional encoding, is designed to generate a trip for a given query. Experiments are conducted on five real-world trip datasets. The results demonstrate that the proposed method outperforms several widely-used baselines when recommending a trip with an FP.

Point of interest (POI) recommendation is a hot research topic. Current researches mainly focus on the analysis of personal check-in trajectories to obtain user preferences. However, a user's check-in data is generally sparse, and it is difficult to make accurate recommendation by only using the user's local information. Additionally, the public's check-in behavior may exhibit common visiting patterns, and incorporating global check-in information is beneficial for enhancing the learning of individual user preferences. Therefore, we propose a GCN-based model with Fusion of Global and Local information (GFGL) for the next POI recommendation. The model obtains global information such as spatial distance, social relationships., and transition probabilities from all users' visit trajectories, and utilizes graph convolution network (GCN) for learning of multi-dimensional global information. Next, we fuse global information with user local information through the user context information embedding module. Besides., a long short-term memory (LSTM) model and transformer model are used to learn the relationship between the user's sequential preference and non-adjacent visits in trajectories. Extensive experiments on two real-world datasets demonstrate the superiority of GFGL against state-of-the-art methods in the next POI recommendation.

In location-based services (LBS), trip recommendation accuracy is challenged by diverse user preferences and complex transfer behaviors. Previous studies overlook the reverse effect of following POIs on previously visited ones. To address this, we propose a Graph-based Double-layer Bidirectional Trip Recommendation (GDB-TR) model. This model uses a heterogeneous graph to model user check-in trajectories with spatial and temporal information. Subgraphs are extracted from the heterogeneous graph, and an adjacency matrix is built for each subgraph. These matrices are fused through a neural network to obtain vector representations for POIs and POI categories. GDB-TR's core is a double-layer bidirectional neural network: one layer describes POIs, the other POI categories. Bidirectional computation captures the influence of preceding POIs on following ones and vice versa. Experiments on five real-world datasets demonstrate GDB-TR's superiority over baseline models, measured by${\boldsymbol{F}}_{\boldsymbol{{1}}}$and pairs-${\boldsymbol{F}}_{\boldsymbol{{1}}}$metrics.

With the fast progress in autonomous driving and communication technologies, robotaxis emerge as a novel mode of transport. Optimization of robotaxi dispatch with ride-sharing can enrich the travel choices of residents and improve the network capacity of transportation systems. This work proposes a multi-objective optimization model for robotaxi dispatch. Unlike previous approaches, this is the first attempt to adjust simultaneously unreasonable boarding time (BT) and pick-up/drop-off (UO) points for passengers during the dispatch. It encourages passengers to walk to the recommended UO points or to be picked up slightly earlier or later, which aims to maximize the profit per kilometer of robotaxis and to minimize the total travel expense of passengers. Consequently, a nondominated sorting genetic algorithm with mass center (NSGA-MC) is proposed to address the model. Experimental results show that the proposed algorithm outperforms its peers from multiple metrics, which highlight the advantage in advancing intelligent public transportation systems.

The development of Connected and Automated Trucks (CATs) provides a new opportunity for freight industry to enhance fuel efficiency, increase traffic flow, and improve safety through platooning. Particularly at highway on-ramps, how to effectively form CAT platoons is a key research topic. In the process of CAT platooning, the timing, location, and speed of CAT merging significantly impact safety and energy consumption. Thus, this study proposes a hierarchical merging strategy, aimed at achieving effective autonomous CAT platooning at highway on-ramps by considering the interference of human-driven vehicles (HDVs). Specifically, we employ a model-free deep reinforcement learning method that guides CAT merging process by exploring optimal driving behaviors. It ensures the safety and efficiency of the CAT merging process. In addition, we use the real vehicle dynamics model in simulation. The proposed strategy can handle the variation of the CATs' initial positions and speeds at on-ramps, as well as interference caused by HDVs at highway mainline. The effectiveness of the proposed strategy has been validated through simulations. The results show that the proposed strategy can effectively coordinate CAT platooning at highway on-ramps.

Traffic congestion frequently occurs on slope segments of highways, which is a typical bottleneck. With the development of connected and autonomous vehicle (CAV) technology, there will be a scene that CAVs and human driven vehicles (HDVs) co-exist. This work studies a slope bottleneck on highway in mixed traffic scenarios and proposes a traffic flow model for slope bottlenecks incorporating CAV platooning based on cellular automata. A novel traffic flow control strategy for slope bottlenecks is proposed based on variable speed limit (VSL) and vehicle platooning. Firstly, it divides the upstream section of the slope bottleneck into two zones for implementing VSL and vehicle platooning, respectively. Via speed restrictions within the VSL zone, the inflow of vehicles into the vehicle platooning zone is effectively mitigated to create low traffic density. In the vehicle platooning zone, a hybrid vehicle platooning method for mixed scenarios is proposed. The experimental results demonstrate that our strategy effectively enhances traffic flow of the slope bottleneck, thereby mitigating traffic congestion.

Two-Wheelers (TWs) such as bikes, e-bikes, and motorcycles often occupy lanes illegally and exceed speed limits, which leads to many traffic accidents. Therefore, we use deep reinforcement learning to design driving strategies for Autonomous Vehicles (AVs) to avoid collision with TWs and reduce injury of TW riders with irregular riding behaviors at un-signalized occluded intersections. First, the collision-avoidance behaviors of TWs are modeled, respectively. The state spaces integrate a safe avoidance range of AVs, a new position of AVs after taking deceleration and a steering angle, a predicted acceleration, and position, speed, and steering angle of AVs and other vehicles. At the same time, a reward function is designed based on the injury of TW riders and the driving safety and comfort of AVs. Secondly, a reinforcement learning model for autonomous driving strategy is constructed. Finally, Soft Actor-Critic is used to train the model, and the randomness policy is used to help AVs flexibly deal with the uncertain behaviors of TW riders and realize the balance between exploring unknown behaviors and using existing information. The simulation results show that compared with an autonomous emergency braking system, the injury of the riders using the driving strategy is reduced by 18.02% on average; compared with a risk-aware high-level decision strategy, the injury is reduced by 41.24% on average.

Autonomous driving has been successfully realized in particular areas such as logistics distribution centers, container terminals, and university campuses. Robotaxi could be another potential application in the near future. This work studies a robotaxi dispatch problem during the pandemic time. It proposes a multi-objective optimization model to minimize the number, waiting time, and driving distance of the robotaxis. Besides, a dispatch strategy is innovatively designed according to a defined severity degree of the pandemic. A virus infection rate can be decreased by reducing contact among passengers. A two-stage nondominated sorting genetic algorithm (NSGA-TS) is proposed to solve the problem. Three operations are used to generate offspring solutions, which can ensure the diversity of the population and speed up the convergence of the algorithm. The effectiveness of NSGA-TS is verified compared with two popular multi-objective optimization algorithms, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D) and nondominated sorting genetic algorithm II (NSGA-II). Experimental results show that the proposed model performs well on the studied problem. It can reduce the virus infection rate by decreasing contact among passengers at different risk levels of the pandemic while accomplishing passenger orders. This work is conducive to society building intelligent transportation in the post-pandemic era.

Emergency vehicles (EVs) are crucial in responding to time-critical events such as traffic accidents, medical emergencies, and fires in urban areas. Most traffic control approaches try to reduce the travel time of EVs by giving them the highest road-use priority, which may cause delays for other nearby traffic participants and reduce the smoothness of normal traffic. This work proposes a novel approach to reduce both the travel time of an EV and the negative impact on normal traffic by dynamically evacuating traffic adjacent to an emergency path. The approach periodically acquires a subnet for each road segment of the emergency path based on dynamic traffic conditions. Regular vehicles on the subnet are restricted from using the emergency path, which minimizes the time for emergency service delivery. The experimental results show that the proposed approach outperforms the existing approach in many metrics, such as the travel time of the EV and the additional delay of normal traffic. In addition, this work performs sensitivity analysis on regular vehicles' compliance rate to evacuation. The experimental results show the superiority of the proposed approach at different compliance rates.

Un-signalized occluded intersections are residential road intersections with narrow lanes and surrounding buildings, which are prone to traffic accidents. This work uses deep reinforcement learning to design driving strategies for Autonomous Vehicles (AVs) for avoiding collision and reducing damage to electric bicycles (e-bikes) with dangerous behaviors at un-signalized occluded intersections. The conflict-avoidance behavior of e-bikes is modeled. It adopts a multi-objective reward function that considers the injury severity of e-bike riders and the driving safety and comfort of AVs. A deep deterministic policy gradient method is used to train the model to control the acceleration and steering of AVs. The performance of the proposed method is compared with that of an autonomous emergency braking system and a risk-aware high-level decision strategy by simulation experiments. Experimental results show that the driving strategy can reduce the collision probability by 26.38% on average, and the injury can be reduced by 14.05% on average when the collision is unavoidable. To our knowledge, this is the first paper that employs reinforcement learning to model and design driving strategies for AVs conflicting with e-bikes. It can be used to improve the state of the art in AV control and safety at intersections.

Emergency vehicles (EVs), such as ambulances, police vehicles, and fire-fighting trucks, play an essential role in delivering emergency services in our society. To decrease the negative impact of EVs on normal traffic, a traffic guidance strategy is proposed for the evacuation of regular vehicles on the road of the EV. Queuing theory is used to provide equilibrium guidance for the evacuation. Furthermore, lane-changing and traffic light preemption strategies are used to prioritize the EV. A simulation experiment is conducted on a map of Huangdao District, Qingdao City, China with the platform of SUMO. The proposed method is validated on three types of traffic flow density. Compared with the existing state-of-the-art strategies, the superiority of our approach is verified from the aspects of EV’s average waiting time and the time loss of other vehicles.

Non-negative latent factor analysis models such as tensor factorization have achieved significant success in collaborative-filtering-based recommendation tasks because they can perform representation learning to high-dimensional and incomplete data efficiently. However, they also suffer from either slow computational speed or representation accuracy loss. To address these issues, this paper presents a Partition-based Localized Tensor Factorization (PLTF) approach for predicting the missing values in the user-item-time rating tensors. First, a large sparse tensor is constructed to model users’ rating behavior. Then, it is transformed into recursive bordered-block-diagonal form by using the graph partitioning technology. Smaller and denser sub-tensors are extracted and factorized by using CP decomposition algorithm. Experimental results on sparse tensors from real applications show the efficiency of the proposed PLTF approach.

Vehicle Ad-hoc Networks have laid an essential technical foundation for realizing intelligent transportation. Unexpected mobility change of a specific node often causes a communication link failure. Thus, this work proposes a node backup strategy for routing protocol in software-defined vehicular networks. The core of the strategy is to promote communication link stability through backup nodes. A node backup routing algorithm is designed to search for alternative nodes for each node in a communication link. A node can flexibly select the next-hop node during packet transmission based on actual conditions. When an unexpected mobility change of a specific node causes a communication link failure, we can restore the link by enabling the alternative nodes. The influence of various factors on packet reception rate and communication delay is studied through simulation experiments. By comparing with two existing routing protocols, the effectiveness of the proposed approach is verified.

In recent years, with the development of location-based services and widely-used social networks, people can easily share their activities and location with their friends on the social network. Meanwhile, large amounts of data generated by social networks provide an opportunity for mining user behaviors and realize accurate personalized service recommendations. Previous studies focus on a single Point of Interest (POI) recommendation while few consider recommending a POI sequence. This paper proposes a POI-sequence recommendation method based on an exploitation-exploration strategy. It utilizes the historical data from the social network, fully considers the public preference and user’s personalized preference. After obtaining the exploration score from the historical records, the POI-sequence with the highest overall preference score is recommended to the user. This method can ensure the diversity of recommended POIs. Besides, a breadth-first search method is adopted to improve the recommendation efficiency. Finally, we verify the effectiveness of the proposed method through experiments on real-world datasets.

With the rapid development of electronic commerce, the number of transactions by credit cards are increasing rapidly. As online shopping becomes the most popular transaction mode, cases of transaction fraud are also increasing. In this paper, we propose a novel fraud detection method that composes of four stages. To enrich a cardholder's behavioral patterns, we first utilize the cardholders' historical transaction data to divide all cardholders into different groups such that the transaction behaviors of the members in the same group are similar. We thus propose a window-sliding strategy to aggregate the transactions in each group. Next, we extract a collection of specific behavioral patterns for each cardholder based on the aggregated transactions and the cardholder's historical transactions. Then we train a set of classifiers for each group on the base of all behavioral patterns. Finally, we use the classifier set to detect fraud online and if a new transaction is fraudulent, a feedback mechanism is taken in the detection process in order to solve the problem of concept drift. The results of our experiments show that our approach is better than others.

Personalized trip recommendation has drawn much attention recently with the development of location-based services. How to utilize the data in the location-based social network to recommend a single Point of Interest (POI) or a sequence of POIs for users is an important question to answer. Recommending the latter is called trip recommendation that is a challenging study because of the diversity of trips and complexity of involved computation. This work proposes a maximal-marginal-relevance-based personalized trip recommendation method that considers both relevance and diversity of trips in trip planning. An ant-colony-optimization-based trip planning algorithm is developed to efficiently plan a trip. Finally, case studies and experiments illustrate the effectiveness of our method.

This work designs a two-level strategy at signalized intersections for preventing incident-based urban traffic congestion by adopting additional traffic warning lights. The first-level one is a ban signal strategy that is used to stop the traffic flow driving toward some directions, and the second-level one is a warning signal strategy that gives traffic flow a recommendation of not driving to some directions. As a visual and mathematical formalism for modeling discrete-event dynamic systems, timed Petri nets are utilized to describe the cooperation between traffic lights and warning lights, and then verify their correctness. A two-way rectangular grid network is modeled via a cell transmission model. The effectiveness of the proposed two-level strategy is evaluated through simulations in the grid network. The results reveal the influences of some major parameters, such as the route-changing rates of vehicles, operation time interval of the proposed strategy, and traffic density of the traffic network on a congestion dissipation process. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.

This paper proposes a methodology to categorize drivers' behaviors at a congested signalized intersection. As a discrete event system model, timed Petri nets (TPNs) are used in this paper to formally define two kinds of behaviors: non-jam-induced driving behavior and jam-induced one. In order to systematically assess the performances of both behaviors, a new urban traffic network model is built: a cell transmission model is used to depict the road link traffic that is consistent with the kinematic property of traffic flow, and TPNs are used to model the behaviors and the conflicting traffic flow at the intersection. Some simulation results are given to evaluate the impact of driving behavior on the traffic delay.

Petri nets (PNs) are well utilized as a visual and mathematical formalism to model discrete-event systems. This paper uses deterministic and stochastic PNs to design an emergency traffic-light control system for intersections providing emergency response to deal with accidents. According to blocked crossing sections, as depicted by dynamic PN models, the corresponding emergency traffic-light strategies are designed to ensure the safety of an intersection. The cooperation among traffic lights/facilities at those affected intersections and roads is illustrated. For the upstream neighboring intersections, a traffic-signal-based emergency control policy is designed to help prevent accident-induced large-scale congestion. Deadlock recovery, livelock prevention, and conflict resolution strategies are developed. We adopt a reachability analysis method to verify the constructed model. To our knowledge, this is the first paper that employs PNs to model and design a real-time traffic emergency system for intersections facing accidents. It can be used to improve the state of the art in real-time traffic accident management and traffic safety at intersections.