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Currently, spatio-temporal fusion strategy is a key direction in traffic flow prediction. Current work employs a higher degree of spatio-temporal self-attention in order to capture spatio-temporal dependencies. However, the features to which this approach is applied are more targeted, increasing the computational complexity of the process and making it more difficult to capture long-range dependencies. This paper proposes a new framework for predicting traffic flow that enhances spatio-temporal features at multiple levels. The framework includes a periodic embedding module that captures temporal periodicity and encodes input data into more representative feature vectors for model training. Also, a component for fusing parallel channel attention has been designed to adaptively weigh the aggregation of features from global, local, and aggregated channels. This enhances the attention given to important feature information in the model. In addition, a multilevel sequential feature fusion enhancer has been designed that ensures feature processing at different levels. Experimental results on four public transportation datasets demonstrate that the innovative approach enhances the MAE metrics by an average of 2.50%, respectively, over all metrics in the baseline models. Notably, it reduces training time by approximately 50%. This paper also discusses ablation experiments to evaluate the performance of each module. [ABSTRACT FROM AUTHOR]

• We review the state of the art clustering methods. • We survey the modern techniques and models related to privacy-preserving clustering. • We propose a privacy-preserving tensor-based multiple clustering framework. • We discuss some open issues of privacy-preserving clustering especially for CPSS big data. Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized recommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing methods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because of the high cost of computation and storage. With the powerful cloud computing, this challenge can be effectively addressed, but it brings enormous threat to individual or company's privacy. Currently, privacy preserving data mining has attracted widespread attention in academia. Compared to other reviews, this paper focuses on privacy preserving clustering technique, guiding a detailed overview and discussion. Specifically, we introduce a novel privacy-preserving tensor-based multiple clustering, propose a privacy-preserving tensor-based multiple clustering analytic and service framework, and give an illustrated case study on the public transportation dataset. Furthermore, we indicate the remaining challenges of privacy preserving clustering and discuss the future significant research in this area. [ABSTRACT FROM AUTHOR]

First-mile transportation provides convenient transit service for passengers to travel from their homes, workplaces, or public institutions to a public transit station that is located beyond comfortable walking distance. This paper studies the Passenger-Centric Vehicle Routing for First-Mile Transportation (PCVR-FMT) problem to plan optimal vehicle routes that provide a high quality of service (QoS) to enhance passenger experience. We consider a practical scenario with 1) deterministic requests, consisting of travel requests that are known in advance (e.g., submitted by passengers through a mobile application), and 2) uncertain requests (e.g., new travel requests generated during service operation). We formally formulate the PCVR-FMT problem to maximize the QoS in terms of the passenger waiting and riding time (for both deterministic and uncertain requests) while jointly considering the traditional constraints on the pickup time window as well as vehicle capacity. We developed an Ant-Colony Optimization algorithm based on a novel Dynamic Request Driven scheme for vehicle route construction, denoted as DRD-ACO, to efficiently solve PCVR-FMT. DRD-ACO relies on a novel request-location graph that models both deterministic and uncertain requests, which enable the ants to share information across different generations via pheromone for dealing with uncertain requests. A dynamic seat reservation mechanism is devised to determine a suitable number of reserved seats to deal with uncertain requests. A time window expansion mechanism is developed to selectively expand passengers' pickup time window if the number of vehicles is insufficient. The effectiveness of the proposed methods is evaluated using Singapore's road network and synthetic travel requests generated from real bus travel demands. Some of the travel requests are treated as uncertain requests. The results show that on relatively small size instances, our methods obtain solutions that are close to the optimal ones computed by CPLEX, with deviations ranging from only 2.55% to 8.79%. In comparison to other baselines, our methods achieve better results in the objective function, ratio of served uncertain passengers, as well as the expansion in waiting time due to request uncertainty, for both small-scale and large-scale problem instances. [ABSTRACT FROM AUTHOR]

High-precision short-term inbound passenger flow prediction is of great significance to the daily crowd management and line rescheduling in urban rail systems. Although current models have been applied to prediction, most methods need optimization to meet refined passenger flow management demand. In order to better predict the passenger flow, a novel Wave-LSTM model, based on long short-term memory network (LSTM) and wavelet, is introduced in this paper. In an empirical study with practical passenger flow data of Dongzhimen Station in the Beijing Subway system, the hybrid model exhibited more effective performance in terms of prediction accuracy than the existing algorithms, e.g., autoregressive integrated moving average (ARIMA), nonlinear regression (NAR), and traditional LSTM model. The study illustrates that our newly adopted model is a promising approach for predicting high-precision short-term inbound passenger flow. [ABSTRACT FROM AUTHOR]

• GPS and IC card data are used to simulate the time-dependent travel time and passenger demand. • An improved passenger selection model is proposed by introducing a preference coefficient. • A working hours constraint is first formulated in bus timetabling problem. • The model is transformed into an integer linear program, followed by a model contraction approach. • A two-stage solution method is proposed for large-scale bus timetabling problems. Bus lines are suffering from serious decline in passenger volume due to the rapid development of urban rail transit and shared transport, and big data intelligence may help them change the status quo. However, the tremendous amount of travel data collected in recent years have not got effectively utilization. In order to improve passenger volume for bus lines, this paper devotes to develop a data-driven bus timetable to substitute the existing experience-based bus timetable, which is now widely used by bus lines. Driven by the bus GPS data and IC card data, a timetable optimization model with time-dependent passenger demand and travel time among stops is proposed. The objective of maximizing passenger volume is based on a new preference-based passenger selection model. The working hours constraint is initially formulated, and the headway constraint and departure time constraints are also taken into account. For handling the step functions in both objective and constraints, we introduce a set of 0–1 variables to transform the proposed model into an integer linear programming. A model contraction approach is provided for solving the medium-scale problems and a two-stage solution method is proposed for the large-scale problems. The proposed model and methodology are tested on a real-world bus line in Beijing. The results show that it is able to produce a satisfactory timetable that outperforms the previously used experience-based one in terms of raising the average passenger volume by 8.2%. [ABSTRACT FROM AUTHOR]

Nowadays, every public transportation company uses Automatic Vehicle Location (AVL) systems to track the services provided by each vehicle. Such information can be used to improve operational planning. This paper describes an AVL-based evaluation framework to test whether the actual Schedule Plan fits, in terms of days covered by each schedule, the network’s operational conditions. Firstly, clustering is employed to group days with similar profiles in terms of travel times (this is done for each different route). Secondly, consensus clustering is used to obtain a unique set of clusters for all routes. Finally, a set of rules about the groups content is drawn based on appropriate decision variables. Each group will correspond to a different schedule and the rules identify the days covered by each schedule. This methodology is simultaneously an evaluator of the schedules that are offered by the company (regarding its coverage) and an advisor on possible changes to such offer. It was tested by using data collected for one year in a company running in Porto, Portugal. The results are sound. The main contribution of this paper is that it proposes a way to combine Machine Learning techniques to add a novel dimension to the Schedule Plan evaluation methods: the day coverage. Such approach meets no parallel in the current literature. [ABSTRACT FROM AUTHOR]

Automatic Vehicle Monitoring (AVM) systems are exploited by public transport companies to manage and control their fleet of vehicles. However, these systems are usually based on the background knowledge of the transport network which can change during the time and in some cases can be missing or erroneous. GPS data and other information captured by the vehicles during their work can be exploited to update the network knowledge. This paper presents a novel approach, namely DeCoClu (Density Consensus Clustering), that aims at mining the topology of a public transport network by means of a consensus clustering density-based approach. In particular, the method exploits static information from time series of positioning signals (i.e., GPS data) to infer geographical locations of stops by means of a consensus clustering strategy based on a new distance function. Moreover, the logical pathway of a route (i.e., stops sequence) is defined by an Hamiltonian cycle. Experiments performed on real-data collections provided by a public transport company demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]