Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures (‘concurrent malfunction’) is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-001087.pdf

Catastrophic cascade of failures in interdependent networks

With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

Short-term traffic flow prediction plays a key role of Intelligent Transportation System (ITS), which supports traffic planning, traffic management and control, roadway safety evaluation, energy consumption estimation, etc. The widely deployed traffic sensors provide us numerous and continuous traffic flow data, which may contain outlier samples due to expected sensor failures. The primary objective of the study was to evaluate the use of various smoothing models for cleaning anomaly in traffic flow data, which were further processed to predict short term traffic flow evolution with artificial neural network. The wavelet filter, moving average model, and Butterworth filter were carefully tested to smooth the collected loop detector data. Then, the artificial neural network was introduced to predict traffic flow at different time spans, which were quantitatively analyzed with commonly-used evaluation metrics. The findings of the study provide us efficient and accurate denoising approaches for short term traffic flow prediction.

Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison

To mitigate congestion caused by the increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. With a better understanding of the travel patterns and regularity (the “magnitude” level of travel pattern) of transit riders, transit authorities can evaluate the current transit services to adjust marketing strategies, keep loyal customers and improve transit performance. However, it is fairly challenging to identify travel pattern for each individual transit rider in a large dataset. Therefore, this paper proposes an efficient and effective data-mining approach that models the travel patterns of transit riders using the smart card data collected in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of smart card transaction data. Based on the identified trip chains, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to detect each transit rider’s historical travel patterns. The K-Means++ clustering algorithm and the rough-set theory are jointly applied to clustering and classifying the travel pattern regularities. The rough-set-based algorithm is compared with other classification algorithms, including Naive Bayes Classifier, C4.5 Decision Tree, K-Nearest Neighbor (KNN) and three-hidden-layers Neural Network. The results indicate that the proposed rough-set-based algorithm outperforms other prevailing data-mining algorithms in terms of accuracy and efficiency.

Mining Smart Card Data for Transit Riders’ Travel Patterns

Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

/pdf/a-survey-of-traffic-prediction-from-spatio-temporal-data-to-1n98645nzb.pdf

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

In the Intelligent Transportation Systems (ITS), highly accurate traffic flow prediction is considered as key technology to evaluate traffic state of the urban road network. However, due to disturbing from environment, the original traffic flow data may be influenced by noise and finally cause the decline of prediction accuracy. This study design a hybrid prediction model combining Ensemble Empirical Mode Decomposition (EEMD) denoising schemes and classifying learning algorithm based on Fuzzy C-means Neural Network (FCMNN) to improve prediction accuracy. In the model training process, several key parameters in EEMD and FCMNN are determined according to prediction errors based on traffic volume detected from highway network in the Minneapolis city. In the model validation, three widely used indicators for error evaluation are applied to estimate the prediction accuracy of four candidate models under single and multi step, including Artificial Neural Network (ANN), EEMD+ANN, FCMNN and EEMD+FCMNN. The results shown in the case study indicate that the prediction models combined with denoising methods are superior to the models without adopting denoising algorithm. Furthermore, the model using classifying learning method FCMNN can produce higher prediction accuracy than traditional ANN model. In addition, the long-term prediction performance of FCMNN is also much better than that of ANN because that sub-NN system is trained according to each classifying patterns to obtain better optimization effect. Results summarized in this study could be helpful for administration to design managing and controlling strategies according to high prediction accuracy.

A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network

Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

Efficient parking tends to be challenging in most large cities in China. Drivers often spend substantial amounts of time looking for parking lots while driving at low speeds, thereby resulting in interference with road traffic. This paper focuses on efficiently allocating parking spaces to the demanders. A double-objective model is proposed that considers both the utilizing rate and the walking distance. First, managers want to utilize parking resources fully. Therefore, they tend to prioritize the efficient distribution of parking spaces in response to parking demands. However, demanders typically choose parking spaces according to convenience. The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. We collected parking demand and supply data in a central business district (CBD) of Harbin in China and evaluated the feasibility of the model. The results demonstrate that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walking distance. The shared use of parking spaces maximizes the utility and alleviates the shortage of parking spaces in downtown.

/pdf/a-distribution-model-for-shared-parking-in-residential-zones-ii4ekazc1e.pdf

A distribution model for shared parking in residential zones that considers the utilization rate and the walking distance

Public transport networks (PTNs) undertake large amount of passenger demand in the urban transport system. Particularly, stations in metro networks with high significance in structure and function are more likely to contribute to passenger transport. Once these stations are under functional failure, it is easy to lead to the collapse of network connectivity. Thus, identifying critical nodes in PTNs is of practical significance for the public transport planning and operation. This study proposes an identification method for the critical nodes in multiplex network by considering the interaction between metro and bus networks. First, metro and bus networks are constructed by L-space and P-space methods, respectively. Then, ridership is extracted to describe the connection between nodes as the weights of links, and the metro-bus multiplex network is then constructed. Furthermore, the identification method in Multiplex Network based on Dempster–Shafer evidence theory (MNDS) is proposed to fuse the significance of nodes in sub-networks, and the critical nodes in the multiplex network are identified. Finally, the PTNs in Shenzhen City, China, is used as a case to demonstrate the feasibility of MNDS. By attacking critical nodes, a comparison is conducted and compared with two traditional identification methods (Weighted Closeness Centrality and Technique for Order Preference by Similarity to Ideal Solution) using two indicators, global efficiency (GE) and the size of the largest connected component (LCC). The results indicate the critical nodes identified by MNDS are of greater significance than those identified by the other two methods. This study provides a feasible method for critical nodes identification in urban public transport system, which can be applied to public transport operation and planning.

Fan Gao

Papers

A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network

Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

A distribution model for shared parking in residential zones that considers the utilization rate and the walking distance

Identifying critical metro stations in multiplex network based on D-S evidence theory