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This paper studies the dynamic performance of subway transportation systems. A three-layer model, consisting of a rail layer, a train layer, and a passenger layer, is proposed to describe the structure and operation of a metro system. Two parameters, namely, time efficiency and maximum load ratio, are proposed to assess the transport efficiency and the train utilization rate, respectively. Case studies of the metro networks in Beijing, Tokyo, and Hong Kong show that the time efficiency decreases nonlinearly with the increase of vehicle resource and passenger demand, whereas maximum load ratio varies in an opposite manner. For the three metro networks under study, the Tokyo metro system has the highest time efficiency when passengers adopt a shortest-path (SP) routing strategy, while the Hong Kong system’s highest time efficiency exceeds the others’ when passengers adopt a minimum-transfer-path (MTP) strategy. In general, the maximum time efficiency is higher when passengers adopt SP routing rather than MTP routing. Moreover, the Beijing metro system has the highest maximum load ratio, regardless of the passengers’ routing behavior. This paper can be applied to a metro network to optimize the train departure interval under a certain passenger entrance rate, with the aim to maximize the time efficiency and maximum load ratio. Our model permits assessment of the operational effectiveness of metro systems, which helps the metro operators to improve the performance and reduce the cost. [ABSTRACT FROM AUTHOR]

Understanding intra-urban human mobility patterns and their potential driving forces are vital to city planning and commercial site selection. In this paper, we first investigate the functions of urban regions and how different region types dynamically influence people’s trip decisions. Furthermore, we characterize urban circadian rhythms by time-vary inter-regional transition probabilities between these regions with different functions, and integrate them into intervening opportunity model to predict human mobility. Public transportation card data in Shanghai are used to demonstrate the effectiveness of the model in terms of station passenger flows, travel time and trip flux. By taking regional function into consideration, the proposed model significantly improved the prediction accuracy. Quantitative analysis ulteriorly indicates that trip intentions and regional features are critical elements in trip flux prediction, especially in the afternoon and evening when people have an abundance of opportunities to travel by their own volition. When the function of a certain region changes, our model is able to make reasonable predictions accordingly. The results indicate the importance of considering individual travel motivation and regional function in modeling human mobility. The proposed model could serve as a guide for popularity and trip flux prediction in urban planning and reconstruction. [ABSTRACT FROM AUTHOR]

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a $k$ k -nearest neighbor ($k$ k -NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has attracted extensive attention in the research field. To deeply study the IoV and build a vehicle spatiotemporal interaction network, it is necessary to use the trajectory data of private cars. However, due to privacy and security protection policies and other reasons, the data set of private cars cannot be obtained, which hinders the research on the social attributes of vehicles in the IoV. Most of the previous work generated the same type of data, and how to generate private car data sets from various existing data sets is a huge challenge. In this paper, we propose a tri-layer framework to solve this problem. First, we propose a novel region division scheme that considers detailed inter-region relations connected by traffic flux. Second, a new spatial-temporal interaction model is developed to estimate the traffic flow between two regions. Third, we devise an evaluation pipeline to validate generation results from microscopic and macroscopic perspectives. Qualitative and quantitative results demonstrate that the data generated in heavy density scenarios can provide strong data support for downstream IoV and mobility research tasks. [ABSTRACT FROM AUTHOR]

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram is able to discover fine-grained semantic patterns effectively and accurately. [ABSTRACT FROM AUTHOR]

In this paper, a cooperative model free adaptive iterative learning control (CMFAILC) scheme is proposed for multiple subway trains subjected to actuator faults and actuator saturation. The controller design includes: the dynamic linearization technique is adopted to transform the subway train system into an equivalent dynamic linearization data model along the iteration axis, the CMFAILC controller is designed by optimizing the cost function, the projection algorithm is applied to deal with the unknown nonlinear terms, the RBFNN is used to compensate the effects of unknown actuator faults, and the anti-windup compensator is introduced to tackle actuator saturation. Theoretical analysis shows that the speed tracking errors of multiple subway trains are bounded, and the distance of adjacent subway trains is stabilized in a safe range. Finally, the validity of the proposed CMFAILC scheme is verified through multiple subway train simulations. [ABSTRACT FROM AUTHOR]

Multimodal path queries over transportation networks are receiving increasing attention due to their widespread applications. A multimodal path query consists of finding multimodal journeys from source to destination in transportation networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. Transportation networks are generally continent-sized. This characteristic highlights the need for parallel computing to accelerate multimodal path queries. Meanwhile, transportation networks are often fragmented and distributively stored on different machines. This situation calls for exploiting parallel computing power for these distributed systems. Therefore, in this paper, we study distributed multimodal path (DMP) queries over large transportation networks. We develop algorithms to explore parallel computation. When evaluating a DMP query $Q$ Q on a distributed multimodal graph $Gmult$ G m u l t , we show that the algorithms possess the following performance guarantees, irrespective of how $Gmult$ G m u l t is fragmented and distributed: (1) each machine is visited only once; (2) the total network traffic is determined by the size of $Q$ Q and the fragmentation of $Gmult$ G m u l t ; (3) the response time is decided by the largest fragment of $Gmult$ G m u l t ; and (4) the algorithm is parallel scalable. Using real-life and synthetic data, we experimentally verify that the algorithms are scalable on large graphs. [ABSTRACT FROM AUTHOR]

Ride-sourcing services are now reshaping the way people travel by effectively connecting drivers and passengers through mobile internets. Online matching between idle drivers and waiting passengers is one of the most key components in a ride-sourcing system. The average pickup distance or time is an important measurement of system efficiency since it affects both passengers’ waiting time and drivers’ utilization rate. It is naturally expected that a more effective bipartite matching (with smaller average pickup time) can be implemented if the platform accumulates more idle drivers and waiting passengers in the matching pool. A specific passenger request can also benefit from a delayed matching since he/she may be matched with closer idle drivers after waiting for a few seconds. Motivated by the potential benefits of delayed matching, this paper establishes a two-stage framework which incorporates a combinatorial optimization and multi-agent deep reinforcement learning methods. The multi-agent reinforcement learning methods are used to dynamically determine the delayed time for each passenger request (or the time at which each request enters the matching pool), while the combinatorial optimization conducts an optimal bipartite matching between idle drivers and waiting passengers in the matching pool. Four tailored reinforcement learning methods, delayed multi-agent deep Q learning (Delayed-M-DQN), delayed multi-agent actor-critic (Delayed-M-A2C), delayed multi-agent Proximal Policy Optimization (Delayed-M-PPO), and delayed multi-agent actor-critic with experience replay (Delayed-M-ACER), are developed. Through extensive empirical experiments with a well-designed simulator, we show that the proposed framework is able to remarkably improve system performances, by well balancing the trade-off among pick-up time, matching time, successful matching rate. [ABSTRACT FROM AUTHOR]

In this paper, a battery charging and swapping optimization model is established for electric vehicles (EVs) and battery charging/swapping stations (BCSS). The EVs are categorized into private electric vehicles (PrEVs) and electric taxis (ETs), where the charging behaviors of PrEVs are modeled based on the Monte Carlo (MC) method, and the battery swapping (BS) strategies of ETs are optimized by bi-level dynamic game. Moreover, the voltage deviation of the power grid is considered in the load regulation process of ETs. A “path-location” model is established combining with the Floyd algorithm in the simulation and the IEEE 14-Bus system is used to derive the node voltage. Numerical results show that the proposed strategy can simultaneously increase the revenue of BCSS and ETs and reduce the voltage deviation. [ABSTRACT FROM AUTHOR]

The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient. [ABSTRACT FROM AUTHOR]

In subway systems, increasing attention has been paid to improve energy efficiency due to remarkable energy consumption for rail transportation operation. The optimization of single-train driving control and multi-train operation synergy are two critical and closely related aspects for energy saving, but they are rarely solved in an integrative manner. The comprehensive study on these two aspects is a complex, large-scale, multi-variable and constrained task, which involves nested, coupled and mutual-input optimization processes. This paper proposes an energy-efficient integrated model, which includes optimizing single-train operation control in an inner model and multi-train operation synergy, committing to the utilization of regenerative braking energy (RBE) in an outer model. A parallelized particle swarm optimization based on Map-Reduce (PPSO-MR) algorithm is proposed to solve the integrated model to reduce the execution time. Two concepts of temporal-spatial record and time shift are introduced to derive the movement of a train at inter-stations for the operation adjustment of multiple trains on a subway line. Experimental results show that the proposed control model solved by the holistic optimization approach can achieve lower energy consumption with fast execution time. This study provides the novel model and algorithm to obtain energy-saving schedules serving the multi-train operation coordination. [ABSTRACT FROM AUTHOR]

Objective: Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection. Methods: The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG. Results: SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5% for pre-ejection period (PEP), 67.2% for left ventricular ejection time (LVET), and 57.7% for PEP/LVET—a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage. Conclusion: These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation. Significance: Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings. [ABSTRACT FROM AUTHOR]

Real-time taxi–passenger matching plays a critical role in modern taxi dispatch systems. Currently, the greedy strategy is widely adopted, which limits the quality of the service (QoS) and the profit of the entire system. There are two crucial tasks in this system: 1) the pairwise prioritization and 2) the matching of taxi–passenger pairs. In this paper, we develop a two-stage taxi–passenger matching system to deal with these two tasks. In the first stage, we design a fuzzy controller to assign a priority score to each taxi–passenger pair in real time. To ensure its performance on providing good QoS and profit, the fuzzy controller is optimized by an offline differential evolution algorithm. New individual representation is designed to optimize the membership functions and fuzzy rule base simultaneously. To accelerate the optimization process, the algorithm is implemented in a parallel way. Then, in the second stage, considering the priority scores as weights in the bipartite graph of taxi and passenger sets, we further apply a polynomial Kuhn–Munkres algorithm to find the maximum weight perfect matching in the bipartite graph. Simulated results validate the effectiveness of the proposed algorithm, which is able to enhance the QoS provided by the taxi system and improve the profit gained by the taxi service company. [ABSTRACT FROM AUTHOR]

The accumulated large amount of mobility data and the ability to track moving people or objects have enabled us to develop advanced mobile recommendations, which are essential to recommend a sequence of locations to an individual user on the move. In this paper, we study a particular case of mobile recommendations, route recommendations to drivers, by utilizing vehicle GPS data. Specifically, we formulate a new Route Recommendation with Relaxed Assumptions (RR-RA) problem, the goal of which is to recommend a sequence of locations to a driver based on his current location in order to maximize his business success. To make our recommendation practical and scalable for real practice, we need to produce recommendation results in a timely fashion once a request emerges. Therefore, we propose an efficient algorithm to efficiently generate recommendations. Furthermore, we identify and address a destination-oriented route recommendation (DORR) problem. Without solving DORR problem, RR-RA alone does not work well in practice because drivers may encounter the destination constraint on a daily basis. We develop a dedicated and efficient algorithm for solving DORR problem. The package of solutions for both RR-RA and DORR problems provide a comprehensive approach for route recommendations to drivers. We evaluate our methods using both real-world GPS data and synthetic data, and demonstrate the effectiveness and efficiency of proposed methods with different evaluation metrics. [ABSTRACT FROM AUTHOR]

Individuals driving private cars in the urban environments to fulll their travel needs have become one of the major daily activities. In particular, unlike the travel with floating cars such as taxis or ride-hailing vehicles, the travel behaviors of private cars exhibit a certain degree of regularity based on daily travel demands. Understanding such travel behavior facilitates many applications, e.g., intelligent transportation, smart city planning, and location-based services (LBS). In this paper, we focus on extracting the regular travel behavior of private cars based on trajectory data analysis. Specifically, first, we construct a trajectory similarity matrix since the similarity of trajectories reflects regular travel behavior. To achieve this, we introduce the stay time and propose an Improved Edit distance with Real Penalty (IERP) to measure the temporal-spatial distance between trajectories. Then, we employ Kernel Principal Component Analysis (KPCA) to reduce the feature dimension of the similarity matrix. Finally, to identify the travel regularity from large set of unlabeled trajectory data, we propose a classification method based on transfer learning to migrate existing knowledge with the purpose of solving learning problems in the target domain with only a small amount of labeled trajectory data or even no data. Extensive experiments using large-scale real-world trajectory data demonstrate that the proposed method can effectively identify the regular travel pattern of private cars and obtain superior accuracy when compared with the existing methods. Our findings on discovering regular travel behaviors of private cars can be directly applied to applications including destination prediction, PoI recommendation and route planning. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to regulate the collective charging load of a large fleet of plug-in electric taxies (PETs) in a metropolis from the viewpoint of utility company. Because the charging load of PET is much higher and less predictable compared with common private electric vehicle, the unregulated charging load of PET fleet can potentially bring large and unpredictable peaks to the distribution system and cause severe damage. To address this problem, this paper proposes a real-time pricing mechanism that can successfully regulate the collective charging load to track a given load profile. The mechanism design consists of three steps. First, we propose two aggregated models for PET fleet to characterize, separately, the relationship between charging decisions of the fleet and real time prices, and the relationship between energy dynamics and charging decisions of the fleet. Then, an optimization problem is designed to calculate the optimal charging load profile that can be accomplished by the fleet. Finally, an efficient online method is launched to calculate proper real time prices, such that the collective charging load of fleet can track the desired value as the response to the prices. Simulation results validate the effectiveness of the proposed mechanism. [ABSTRACT FROM PUBLISHER]

Intelligent taxicab scheduling systems on smartphones continue to gain popularity as they offer prominent conveniences for urban travelling as well as increase potential profits for taxicab drivers, hence inject prosperity and vitality into intelligent transportation and urban business. Existing scheduling approaches usually fall into biases and myopia due to their single target perspective of satisfying the immediate order acceptance, or maximizing the global business success rates.However, the highly complex spatiotemporal dependencies among multiple factors and the efficiency bottleneck of massive order flows make the taxicab scheduling issue still challenging. To this end, in our paper, we propose an integrated scheduling algorithm with both future-aware and context-aware mechanisms. In particular, we first present a generalized graph-based framework which aims to capture traffic dependencies, providing precise and quantified supply-demand prediction in taxicab scheduling. Then, we develop a measurement of regional supply-demand context to perform cooperative and distributed scheduling. To tackle the efficiency bottleneck in massive order flow scenario, we correspondingly design a model to learn business patterns with considering benefits from both drivers and passengers, and further promote service delivery rates via a novel bi-incentive strategy. Extensive numerical studies illustrate the remarkable significance of our method, in terms of both service delivery rates and global driver revenues. The promising results and brand-new perspectives enable our algorithm to be a paradigm in general spatiotemporal scheduling tasks. [ABSTRACT FROM AUTHOR]

Ultra-dense networking (UDN) is a promising technology to improve the network capacity in the next-generation mobile communication system. The virtualization paradigm is tightly integrated into UDN to address the problem of interference management. However, mobility management based on virtual cells meets significant challenges in UDN due to the frequent handovers and massive signaling overhead. These problems become severe for vehicles owing to their high-speed movement. In this paper, driven by trajectory prediction using a real-world vehicle mobility dataset, we propose a proactive mobility management solution based on virtual cells. Four modules are designed in the centralized Software-Defined Networking controller to support the proposed solution. The proposed LSTM-DR framework predicts the next locations of vehicles by integrating Long Short-Term Memory (LSTM) networks and Dead Reckoning (DR) method. The active gNBs selection algorithm selects the serving gNBs to form virtual cells according to predicted locations and mobility preferences. The corresponding signaling procedure is then carefully designed to further reduce the signaling overhead. Simulation results show that the proposed prediction framework can achieve higher accuracy and robustness in trajectory prediction. The proposed proactive solution reduces the handover frequency and handover failure rate and thereby saves the handover signaling overhead significantly. [ABSTRACT FROM AUTHOR]

Novel road-network applications often recommend a moving object (e.g., a vehicle) about interesting services or tasks on its way to a destination. A taxi-sharing system, for instance, suggests a new passenger to a taxi while it is serving another one. The traveling cost is then shared among these passengers. A fundamental query is: given two nodes $s$ s and $t$ t , and an area $\mathcal {A}$ A on road network graph $\mathcal {G}$ G , is there a “good” route (e.g., short enough path) $P$ P from $s$ s to $t$ t that crosses $\mathcal {A}$ A in $\mathcal {G}$ G ? In a taxi-sharing system, $s$ s and $t$ t can be a taxi's current and destined locations, and $\mathcal {A}$ A contains all the places to which a person waiting for a taxi is willing to walk. Answering this Route and Area Matching (ROAM) Query allows the application involved to recommend appropriate services to users efficiently. In this paper, we examine efficient ROAM query algorithms. Particularly, we develop solutions for finding a $\rho$ ρ -route, which is an $s$ s - $t$ t path that passes $\mathcal {A}$ A , with a length of at most $(1+\rho)$ (1 + ρ) times the shortest distance between $s$ s and $t$ t . The existence of a $\rho$ ρ -route implies that a service or task located at $\mathcal {A}$ A can be found for a given moving object $m$ m , and that $m$ m only deviates slightly from its current route. We present comprehensive studies on index-free and index-based algorithms for answering ROAM queries. Comprehensive experiments show that our algorithm runs up to 30 times faster than baseline algorithms. [ABSTRACT FROM AUTHOR]

Owing to environmental concerns, the energy-efficient subway train scheduling problem is necessary in subway operation management. This paper designs an approximate dynamic programming (DP) approach for energy-efficient subway train scheduling problem with time-dependent demand. The train traffic model is proposed with the dynamic equations for the evolution of train headway, train passenger loads, and the energy consumption along the subway line. For the dynamic changing of the onboard passengers with time, the total train energy usage is modeled as the sum of energy consumptions from the traction system and auxiliary facilities. A nonlinear DP problem is formulated to generate a near optimal timetable to realize the tradeoff among the utilization of trains, passenger waiting time, service levels, and energy consumption. To overcome the curse of dimensionality in this optimization problem, we construct an approximate DP framework, where the conceptions of states, policies, state transitions, and reward function are introduced. And this algorithm is able to converge to a good solution with a short time compared to the genetic algorithm and differential evolution algorithm. Finally, the numerical experiments are given to demonstrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Because both magnets and windings can be mounted in the mover, the primary permanent-magnet linear motor (PPMLM) has a low-cost advantage for subway applications. In this paper, a novel direct thrust force control (DTFC) with single dc-link current sensor is proposed for PPMLM traction system in subway applications. The first phase current is directly measured by the dc-link current sensor. The second phase current is sensed with a limited delay, which is strictly limited by the limited measurement delay principle. The third phase current is calculated based on the condition that the summation of the three phase currents is zero. During the current reconstruction, no motor parameters are required. Because PPMLM may sink in the same phase for a long time and affect the current reconstruction accuracy, the single optimal target is proposed to re-select the original voltage vector. Compared with the conventional DTFC scheme, the proposed one has nearly the same performances; especially, the maximum speed and response time remain nearly unchanged. The effectiveness of the proposed DTFC scheme is verified by experimental results. [ABSTRACT FROM AUTHOR]

An Online Ride Hailing (ORH) system enables a rider to request a taxi through a smartphone app on a short notice. To enjoy ORH services, users have to submit their location information to ORH servers. There are serious privacy concerns for users to reveal location information to ORH servers. In this paper, we propose a lightweight and privacy-preserving ride matching scheme for ORH systems, named lpRide, which can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location privacy of both riders and taxis against the ORH server. In lpRide, we propose an efficient shortest road distance computation approach over encrypted data, which computes approximate road distance by using road network embedding and the modified Paillier cryptosystem. Moreover, we design a secure comparison method to compare two distances over the corresponding blinded ciphertexts, without revealing the actual distances. Theoretical analysis and experimental evaluations show that lpRide is private, accurate and efficient. [ABSTRACT FROM AUTHOR]

Because both magnets and windings can be mounted in the mover, the primary permanent-magnet linear motor (PPMLM) has a low-cost advantage for subway applications. In this paper, a fault-tolerant control (FTC) with single phase current sensor is proposed for PPMLM traction system in subway applications. In the proposed FTC, d- and q-axis currents are estimated from the reference synchronous currents and the surviving measured phase current. The estimation is robust since no machine parameters are required. In steady-state operation, the estimated current errors are half the actual ones. As a result, the current response is slowed but the current command can be achieved. However, the achievement of control target depends on the current tracking accuracy, which is enhanced by the voltage decoupler. Compared with existed FTC schemes, the proposed one is robust and has better transient-state performances. The effectiveness of the proposed FTC is verified by theoretical analysis and experimental results. [ABSTRACT FROM AUTHOR]

Recently chaos theory has emerged as a powerful tool to address forecasting problems of nonlinear time series, since it is able to meet the dynamical and geometrical structures of very complex systems, reaching higher accuracy on the prediction values than the classical approaches. This paper aims at applying the chaos theory principles to different problems, in order to pursue high levels of accuracy on the predicted results. After the verification of the chaotic behavior of the datasets taken into analysis through the largest Lyapunov exponent research, the detection of the suitable embedding dimension and time delay has been carried out, in order to reconstruct the phase space of the underlying dynamical systems. Three different predictive methods have been proposed for different datasets. Finally, the performance comparison with the moving average model, a deep neural network based strategy, and a chaos theory based algorithm recently proposed in literature has been provided. [ABSTRACT FROM AUTHOR]

Roadside unit (RSU) is a communication device for vehicular networks that provides vehicle-to-infrastructure (V2I) connectivity to nearby vehicles. It allows broadcasting of traffic and safety information, Internet connectivity, shared storage, advertisement, as well as supporting and enhancing vehicle-to-vehicle (V2V) communication for future autonomous vehicles. However, high cost of its deployment and management has so far prevented RSUs from being used widely in practice despite its utility and importance. In this paper, we investigate the performance-cost tradeoff and viability of using buses as mobile RSUs (mRSUs). Although there have been some prior works that suggest the use of cars, public transportation, and controlled vehicles as mRSUs to address the cost problem of static RSUs (sRSUs), their assumptions were mostly rather idealistic. We aim to provide a more realistic view of the problem. Through real-world measurements, experiments, analysis, and simulation, we show how mRSUs can replace sRSUs while maintaining the same level of throughput, contact time, and inter-contact time. Our results provide a basis for judging whether it is beneficial to complement sRSUs with mRSUs depending on the deployment environment and cost-performance tradeoff. [ABSTRACT FROM AUTHOR]

Operation optimization for modern subway trains usually requires the speed curve optimization and speed curve tracking simultaneously. For the speed curve optimization, a multi-objective seeking issue should be addressed by considering the requirements of energy saving, punctuality, accurate parking, and comfortableness at the same time. But most traditional searching methods lack in efficiency or tend to fall into the local optimum. For the speed curve tracking, the widely applied proportional integral differential (PID) and fuzzy controllers rely on complicated parameter tuning, whereas robust adaptive methods can hardly ensure the finite-time convergence strictly, and thus are not suitable for applications in fixed time intervals of trains. To address the above-mentioned two problems, this paper presents a novel approach for speed curve seeking and tracking control. First, we present the random reinforcement genetic algorithm (GA) algorithm to avoid the local optimum efficiently. Then, a sliding mode controller is developed for speed curve tracking with bounded disturbance. The Lyapunov theory is adopted to prove that the system can be stabilized in the finite time. Finally, using the real datasets of Yizhuang Line in Beijing Subway, the proposed approach is validated, demonstrating its effectiveness and superiorities for the operation optimization. [ABSTRACT FROM AUTHOR]

The GPS trajectories are rich with potential information that could be used to explore the regulation of traffic to serve the public. While that past approaches for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. In this paper, we propose a novel neural network, named L-CNN based on CNN and LSTM, and develop an effective real-time prediction model to forecast the most likely potential passenger for taxi drivers. It is noteworthy that our model can be easily extended to other real-time traffic prediction problems, such as road traffic and flow prediction. Finally, we test our method based on GPS trajectories generated by Cheng Du taxi. The method presented provides passenger prediction over 15-min intervals for up to 1 h in advance and the results prove the efficiency of our predicting system. [ABSTRACT FROM AUTHOR]

In the Paris subway system, stations represent about one-third of the overall energy consumption. Within stations, ventilation is among the top consuming devices; it is operated at maximum airflow all day long, for air quality reasons. In this paper, we present a concept of energy system that displays comparable air quality while consuming much less energy. The system comprises a battery that makes it possible to recover the trains braking energy, arriving under the form of erratic and strong peaks. We propose an energy management system that, at short time scale, controls energy flows and ventilation airflow. By using proper optimization algorithms, we manage to match supply with demand, while minimizing energy daily costs. For this purpose, we have designed algorithms that take into account the braking variability. They are based on the so-called stochastic dynamic programming (SDP) mathematical framework. We fairly compare SDP-based algorithms with the widespread model predictive control (MPC) ones. First, both SDP and MPC yield energy/money operating savings of the order of one-third, compared to the current management without battery. Second, depending on the specific design, we observe that SDP outperforms MPC by a few percent, with an easier online numerical implementation. [ABSTRACT FROM AUTHOR]

Human mobility data refers to records of human movements, such as cellphone traces, vehicle GPS trajectories, geo-tagged posts, and photos. While successfully mining human mobility data can benefit many applications such as city planning, transportation, urban economics, and public safety, it is very challenging to model large-scale Heterogeneous Human Mobility Data (HHMD) that are generated from different resources. In this paper, we develop a general collective learning approach to model HHMD at an individual level towards identifying and quantifying the urban forms of residential communities. Specifically, our proposed method exploits two geographic regularities among HHMD. First, we jointly capture the correlations among residential communities, urban functions, temporal effects, and user mobility patterns by analogizing communities as documents and mobility patterns as words. Also, we further combine explicit LASSO analysis and significant testing into latent representation learning as a regularization term by analogizing compatible Point-of-Interests (POIs) as the meta-data of communities. In this way, we can learn the urban forms, including a mix of functions and corresponding portfolios, of residential communities from HHDM and POIs. We further leverage these learned results to address two application problems: real estate ranking and restaurant popularity prediction. Finally, we conduct intensive evaluations with a variety of real-world data, where experimental results demonstrate the effectiveness of our proposed modeling method and its successful applications for other problems. [ABSTRACT FROM AUTHOR]

Massive data collected by automated fare collection (AFC) systems provide opportunities for studying both personal traveling behaviors and collective mobility patterns in urban areas. Existing studies on AFC data have primarily focused on identifying passengers’ movement patterns. However, we creatively leveraged such data for identifying pickpocket suspects. Stopping pickpockets in the public transit system has been crucial for improving passenger satisfaction and public safety. Nonetheless, in practice, it is challenging to discern thieves from regular passengers. In this paper, we developed a suspect detection and surveillance system, which can identify pickpocket suspects based on their daily transit records. Specifically, we first extracted a number of useful features from each passenger's daily activities in the transit system. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who typically exhibit abnormal traveling behaviors. Experimental results demonstrated the effectiveness of our method. We also developed a prototype system for potential uses by security personnel. [ABSTRACT FROM AUTHOR]

Safety applications based on vehicle-to-vehicle (V2V) communication have become a major goal toward the intelligent transportation. The performance of the applications is mainly affected by the communication links, which in turn are governed by the topology characteristics of vehicular ad hoc networks (VANETs). To analyze the performance of V2V communication for VANETs, accurate spatial modeling is of great importance. There is an absence of a widely accepted two-dimensional (2D) model that well characterizes the random vehicle locations; the widely used Poisson point process (PPP) model is simple but not close to reality. In this paper, we concentrate on spatial point process modeling for random vehicle locations in large and small cities, performing empirical experiments with real location data of mobile taxi trajectories recorded by the global positioning system in Beijing city of China and Porto city of Portugal. We find that the empirical probability mass functions (PMFs) of the number of taxis in test sets in different regions of Beijing or in Porto all follow a negative binomial (NB) distribution. The spatial correlations of the points are established by comparing the results of different sampling methods. Based on the above, we show that the Log Gaussian Cox Process (LGCP) model, whose empirical PMF nicely fits the NB distribution, accurately characterizes diverse spatial point patterns of random vehicle location in both large and small cities. This is verified by the minimum contrast method. Then, we study the node degree as an important metric for the communication performance of the networks. It is shown that the connectivity of the LGCP model closely represents the connectivity found in the actual data, for both representative cities. The LGCP model is far more accurate than the widely used one-dimensional models and the 2D PPP for modeling the vehicle distribution, which is significant in V2V communication. [ABSTRACT FROM AUTHOR]

Balise is a popular wayside device to provide accurate location information for subway station parking by sending telegrams to passing trains. By craftily disturbing wireless signals of balise telegrams, this paper proposes three attacks that may make passengers fall and even cause injury. Concretely, the first attack is to jam telegrams such that balises cannot be detected by a passing train; the second attack changes the location of transmitting telegrams by jamming and replaying; and the third attack is to change the total time of transmitting telegrams. All the attacks exploit the train localization mechanism such that a passing train localizes its position inaccurately and then takes improper control actions. Furthermore, since these attacks are independent, they can be launched at the same time to achieve advanced attacks. As the attacks do not require to tamper with the balises, they can be launched easily. Our simulations demonstrate the effectiveness of the proposed attacks. To defeat these attacks, the received telegrams need be verified by a train based on fidelity of telegram data. [ABSTRACT FROM AUTHOR]

Vehicular content centric networks (VCCNs) emerge as a strong candidate to be deployed in information-rich applications of vehicular communications. Due to vehicles’ mobility, it becomes rather inefficient to establish end-to-end connections in VCCNs. Consequently, content packets are usually sent back to the requesting node via different paths in VCCNs. To improve network performance of VCCNs, node mobility should be exploited for vehicles to serve as relays and to carry data for delivery. In this paper, we propose a scheme called cooperative caching based on mobility prediction (CCMP) for VCCNs. The main idea of CCMP is to cache popular contents at a set of mobile nodes that may visit the same hot spot areas repeatedly. In our CCMP scheme, we use prediction based on partial matching to predict mobile nodes’ probability of reaching different hot spot regions based on their past trajectories. Vehicles with longer sojourn time in a hot region can provide more services and should be preferred as caching nodes. To solve the problem of limited buffer at each node, we design a cache replacement based on content popularity to guarantee that only popular contents are cached. We evaluate CCMP through the opportunistic network environment simulator for its salient features in success ratio and content access delay compared to other state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Simulation is a valuable tool to evaluate energy consumptions and design of railway systems. The system limitations have an important influence on the system behavior and must be taken into account in the simulation. Especially, the electromechanical limitations have a major impact on the kinematical behavior. However, such limitations are often neglected in classical studies. This paper deals with the development of an adaptive electrokinematical simulation to adapt the kinematical behavior of the system according to its limitations. A flexible simulation tool is thus obtained by taking into account electrical, mechanical, as well as kinematical limitations. This tool is validated by experimental results on a real railway track. It is then used to study different solutions to increase the transport capacity of a subway system, even when limitations occur. [ABSTRACT FROM AUTHOR]

GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. In this paper, we study a new kind of query—Reverse k Nearest Neighbor Search over Trajectories (\mathbf{R}{k}\mathbf{NNT} ), which can be used for route planning and capacity estimation. Given a set of existing routes \mathcal{D}_{\mathcal{R}} , a set of passenger transitions \mathcalD\mathcal{T} , and a query route Q , an \mathbfRk\mathbfNNT  query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an \mathbfRk\mathbfNNT query. Then we introduce a filter refinement framework for processing \mathbfRk\mathbfNNT queries using the proposed indexes. Next, we show how to use \mathbfRk\mathbfNNT to solve the optimal route planning problem \mathbfMaxRk\mathbfNNT ( \mathbfMinRk\mathbfNNT ), which is to search for the optimal route from a start location to an end location that could attract the maximum (or minimum) number of passengers based on a predefined travel distance threshold. Experiments on real datasets demonstrate the efficiency and scalability of our approaches. To the best of our knowledge, this is the first work to study the \mathbfRk\mathbfNNT problem for route planning. [ABSTRACT FROM AUTHOR]

Utilizing large-scale GPS data to improve taxi services has become a popular research problem in the areas of data mining, intelligent transportation, geographical information systems, and the Internet of Things. In this paper, we utilize a large-scale GPS data set generated by over 7,000 taxis in a period of one month in Nanjing, China, and propose TaxiRec: a framework for evaluating and discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to seek passengers. In TaxiRec, the underlying road network is first segmented into a number of road clusters, a set of features for each road cluster is extracted from real-life data sets, and then a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster. In addition, TaxiRec can use this model with a training cluster selection algorithm to provide road cluster recommendations when taxi trajectory data is incomplete or unavailable. Experimental results demonstrate the feasibility and effectiveness of TaxiRec. [ABSTRACT FROM AUTHOR]

Providing an adequate quality of service in subway areas is a challenging task in cellular networks, due to the complexity of the indoor propagation and the effects of group mobility. Thus, it is common that traffic congestion appears in subway cells. In this paper, a self-tuning method for uplink power control (ULPC) parameters is proposed to solve congestion problems in long-term evolution (LTE) cells located in a subway network. First, a thorough analysis of congestion phenomena in LTE subway cells is presented. The aim of the analysis is to show how uplink performance is severely degraded with group mobility in subway cells. Then, a heuristic algorithm is proposed to tune the nominal power parameter in ULPC on a cell basis to solve localized congestion problems. The aim of the algorithm is to exploit cell isolation by increasing user transmit power to improve system spectral efficiency and thus reduce the average cell load. Field trial results show that the algorithm significantly improves network accessibility and retainability in subway scenarios without any hardware upgrade. [ABSTRACT FROM PUBLISHER]

Multitrain systems are difficult to study due to the size of the system and its specificities. To tackle these difficulties, this paper develops a reduced-scale power Hardware-In-the-Loop (HIL) simulation dedicated to subway line emulations. Energetic macroscopic representation (EMR) is used to organize the HIL simulation. A first mono-train HIL simulation is developed and validated by experimental results. The HIL simulation is then extended to a two-train study to analyze the different power flows between subsystems. [ABSTRACT FROM PUBLISHER]

Vehicle recognition plays an important role in traffic surveillance systems, advanced driver-assistance systems, and autonomous vehicles. This paper presents a novel approach for multivehicle recognition that considers vehicle space location and classification as a coupled optimization problem. It can speed up the detection process with more accurate vehicle region proposals and can recognize multiple vehicles using a single model. The proposed detector is implemented in three stages: 1) obtaining candidate vehicle locations with prior objectness measure; 2) classifying vehicle region proposals to distinguish the three common types of vehicles (i.e., car, taxi, and bus) by a single convolutional neural network (CNN); and 3) coupling classification results with the detection process, which leads to fewer false positives. In experiments on high-resolution traffic images, our method achieves unique characteristics: 1) It matches the state-of-the-art detection accuracy; 2) it is more efficient in generating a smaller set of high-quality vehicle windows; 3) its searching time is decreased by about 30 times compared with the other two popular detection schemes; and 4) it recognizes different vehicles in each image using a single CNN model with eight layers. [ABSTRACT FROM PUBLISHER]

In this paper, we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes. Existing approaches for abnormal behavior detection do either use trajectory-based or pixel-based methods. Unlike these approaches, we propose an integrated pipeline that incorporates the output of object trajectory analysis and pixel-based analysis for abnormal behavior inference. This enables to detect abnormal behaviors related to speed and direction of object trajectories, as well as complex behaviors related to finer motion of each object. By applying our approach on three different data sets, we show that our approach is able to detect several types of abnormal group behaviors with less number of false alarms compared with existing approaches. [ABSTRACT FROM PUBLISHER]

A consistency/latency tradeoff arises as soon as a distributed storage system replicates data. For low latency, distributed storage systems often settle for weak consistency conditions, providing little guarantee on data consistency. In this paper, we propose the notion of almost strong consistency as an option for the consistency/latency tradeoff. It provides both deterministically bounded staleness of data versions for reads and probabilistic quantification on the rate of “reading stale data”, while achieving low latency. We then investigate almost strong consistency in terms of probabilistically-atomic 2-atomicity. Our PA2AM algorithm for the single-writer model completes each read in one communication round-trip, and guarantees that each read obtains the value of within the latest two versions. To quantify the rate of “reading the stale version”, we decompose the so-called “old-new inversion” anomaly into long-lived-write concurrency patterns and non-monotonic read-write patterns, and propose a queueing model and a timed balls-into-bins model to analyze them, respectively. The probabilistic analysis not only demonstrates that old-new inversions rarely occur, but also reveals that the read-write pattern dominates in preventing them from occurring. These are then supported by our experiments. To further demonstrate the benefits of probabilistically-atomic 2-atomicity, we also compare it to weak consistency conditions. [ABSTRACT FROM AUTHOR]

With the advance of various location-acquisition technologies, a myriad of GPS trajectories can be collected every day. However, the raw coordinate data captured by sensors often cannot reflect real positions due to many physical constraints and some rules of law. How to accurately match GPS trajectories to roads on a digital map is an important issue. The problem of map-matching is fundamental for many applications. Unfortunately, many existing methods still cannot meet stringent performance requirements in engineering. In particular, low/unstable sampling rate and noisy/lost data are usually big challenges. Information fusion of different data sources is becoming increasingly promising nowadays. As in practice, some other measurements such as speed and moving direction are collected together with the spatial locations acquired, we can make use of not only location coordinates but all data collected. In this paper, we propose a novel model using the related meta-information to describe a moving object, and present an algorithm called IF-Matching for map-matching. It can handle many ambiguous cases which cannot be correctly matched by existing methods. We run our algorithm with taxi trajectory data on a city-wide road network. Compared with two state-of-the-art algorithms of ST-Matching and the winner of GIS Cup 2012, our approach achieves more accurate results. [ABSTRACT FROM PUBLISHER]

We consider a promising application for electric taxis (eTaxis) in transportation cyberphysical systems. The new rideshare scheme introduced herein takes into consideration both the limited battery of eTaxis and the user requirements. In the proposed eTaxi-sharing system, a passenger may share a taxi with others to enjoy a reduced fare and can potentially transfer from one eTaxi to another before reaching her destination, as long as her total trip time is within the maximum she specifies to be tolerable. Transfers are restricted to only take place at the designated (safe and convenient) battery charging stations scattered around the city. When an eTaxi comes to a charging station to pick up/drop off a transfer passenger, the eTaxi's battery can be charged. In this paper, we address a new optimization problem called Transfer-Allowed Shared eTaxis (TASeT), whose goal is to schedule an eTaxis service and find optimal rideshare and transfer plans to maximize the system throughput in terms of the number of passengers served by the taxi service within a given time period. A mixed-integer programming (MIP) formulation is presented to solve TASeT, along with an efficient greedy heuristic. Aside from large-scale simulation, we also present a case study that utilizes real taxi traces collected from the city of Shanghai, China. Compared with the nontransferable taxi-sharing (NTT) case, our solution could improve the number of served passengers and shared travel distance by 22% and 37%, respectively, during rush hours. [ABSTRACT FROM PUBLISHER]

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicular traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts two greedy heuristics to select the most “centric” vehicles as seeds for mobile advertising. Extensive simulations based on three real data sets of taxi and bus traces have been carried out. The results show that POST can greatly improve the coverage and the intensity of advertising. For all the three involved data sets, it achieves an average gain of 64 percent comparing with the state-of-art schemes. [ABSTRACT FROM PUBLISHER]

The papers in this special section were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC) that was held in Coimbra, Portugal, in October 2014. Advanced traction and propulsion systems are developing in order to ensure better energy performance, reduced operating cost, and higher lifetime of future transportation systems like road vehicles, as well as more electric trains, subways, ships, and airplanes. Such a powertrain integrates several complex subsystems (including power or energy sources, electricmachines, power electronics,mechanical transmission), and it becomesmandatory to consider the whole system in order to reach the best performance. [ABSTRACT FROM AUTHOR]