Urban Traffic Data Research Articles

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Nowadays air quality is the main issue in urban areas that have been affecting human health, the environment, and the ecosystem. So, governmental authorities, environmental and health agencies usually need the prediction of daily air pollutants. This prediction is often based on statistical relations between various conditions and air pollution. This study aims to compare the performance of Multiple Linear Regression (MLR) and Multi-layer perceptron (MLP) for predicting SO2 concentration in the air of the Tehran. Different parameters namely meteorological parameters, urban traffic data, urban green space information, and time parameters were chosen for the prediction of SO2 daily concentration. Considering result, the correlation coefficient (R2), and root means square error (RMSE) of the MLR model are 0.708, and 6.025, respectively while these values for the MLP equal 0.9 and 0.42. According to the result of sensitivity analysis, the value of the one-day time delay, park indicator, season/year, and the total area parks were the main factors influencing SO2 concentration. MLP model suggested in this research could be applied to support, analysis, and improve predicting air pollution and air quality management. This study shows the importance of modeling and application of ANN in presenting management strategies to reduce urban pollution.

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem, a top-down approach is used to construct a knowledge graph of urban traffic system in this paper. First, the model layer of the knowledge graph was used to realize the reuse and sharing of knowledge. Furthermore, the model layer then was stored in the graph database Neo4j. Second, the representation learning based knowledge reasoning model was adopted to implement knowledge completion and improve the knowledge graph. Finally, the proposed method was validated with an urban traffic data set and the results showed that the model could be used to mine the implicit relationship between traffic entities and discover traffic knowledge effectively.

Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation

Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance. However, like any sensory data, urban traffic data are prone to imperfections leading to missing measurements. In this paper, we focus on inter-region traffic data completion. We model the inter-region traffic as a spatiotemporal tensor that suffers from missing measurements. To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic. To derive the urban characteristics, we divide the area of study into regions. Then, for each region, we compute urban feature vectors inspired from biodiversity which are used to compute the urban similarity matrix. To mine the temporal aspect, we first conduct an entropy analysis to determine the most regular time-series. Then, we conduct a joint Fourier and correlation analysis to compute its periodicity and construct the temporal matrix. Both urban and temporal matrices are fed into a modified CP-completion objective function. To solve this objective, we propose an alternating least square approach that operates on the vectorized version of the inputs. We conduct comprehensive comparative study with two evaluation scenarios. In the first one, we simulate random missing values. In the second scenario, we simulate missing values at a given area and time duration. Our results demonstrate that our approach provides effective recovering performance reaching 26% improvement compared to state-of-art CP approaches and 35% compared to state-of-art generative model-based approaches.

Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.

Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics.

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such aggregation processes can lead to perturbations in the network inputs. This issue can significantly destabilize the feature embeddings and the predictions - rendering deep networks much less useful for the experts. This paper approaches this challenge by leveraging unit visualization techniques that enable the investigation of many-to-many relationships between dynamically varied multi-scalar aggregations of urban traffic data and neural network predictions. Through regular exchanges with a domain expert, we design and develop a visual analytics solution that integrates 1) a Bivariate Map equipped with an advanced bivariate colormap to simultaneously depict input traffic and prediction errors across space, 2) a Moran's I Scatterplot that provides local indicators of spatial association analysis, and 3) a Multi-scale Attribution View that arranges non-linear dot plots in a tree layout to promote model analysis and comparison across scales. We evaluate our approach through a series of case studies involving a real-world dataset of Shenzhen taxi trips, and through interviews with domain experts. We observe that geographical scale variations have important impact on prediction performances, and interactive visual exploration of dynamically varying inputs and outputs benefit experts in the development of deep traffic prediction models.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

Urban Traffic Detectors Data Mining for Determination of Variations in Traffic Volumes

This paper analyses road traffic volumes in the urban environment for the purpose of traffic planning and creation of traffic models. For modelling traffic in a certain area, the initial information about transport demand and distribution in given area is required. The demand for transport is further re-distributed to the transport network and measured against the current road traffic volumes / intensity of traffic. Traffic volumes over time are characterized by various periodic and non-periodic influences (variations). By studying these variations, the tools can be specified for making the final estimate of traffic volumes for a specific time period, a specific type of road or specific vehicle category, and for improving the traffic models for a specific area. In this paper, the authors study time variations in traffic volumes using the data obtained from vehicle detectors for monitoring traffic located on roads in the city of Ceske Budejovice, the Czech Republic.

SS-ITS: secure scalable intelligent transportation systems

This paper introduces a secure and scalable intelligent transportation and human behavior system to accurately discover knowledge from urban traffic data. The data are secured using blockchain learning technology, where the scalability is ensured by a threaded GPU. In addition, different optimizations are provided to efficiently process data on the GPU. A reinforcement deep learning algorithm is also established to merge local knowledge discovered on each site into global knowledge. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known intelligent transportation and human behavior data. Our results show that our proposed framework outperforms the baseline solutions for the outlier detection use case.

A novel optimal road layout model based on urban cloud data

The model in traditional methods is poor in stability, efficiency and accuracy in urban road layout. In order to solve those problems, an optimal road layout model based on urban cloud data is proposed in this paper. In this model, the hardware is supplemented by GIS application software and Arc GIS technology is adopted for integrated development mode; geographic information is processed according to urban cloud data. Starting from road layout, simulation software such as VISSIM and VISSUM is combined on the designed model for traffic data sharing and simulation decision; software is designed based on calibration processing of 3S optimal urban road layout model parameters and traffic data are collected for road layout design, and the optimal layout is extracted through the genetic algorithm. The experimental results show that the method has high stability and design efficiency.

Review and comparison of prediction algorithms for the estimated time of arrival using geospatial transportation data

Considering the increasing number of vehicles used for a variety of purposes, the demand for accurate traffic services is also rising. Most drivers use traffic services to find a path through the city and get an estimation of how long the trip will take. Due to a large number of factors affecting driving in urban areas the task of predicting the estimated time of arrival is not trivial. In this paper we discuss different methods used for trip duration prediction and compare their performances. We use a real urban traffic data set for testing, and also make this data set public to stimulate future research.

Analysis of driver’s choice behavior based on the combined model of utility-regret

In the context of increasing urban traffic trajectory data, based on large-scale urban traffic trajectory data, in the field of intelligent transportation, it has become an urgent need to analyze travelers’ routing behaviors and establish effective routing models to provide travelers with efficient and reasonable driving route recommendations. It is often the case that urban taxi groups has better route choice behavior than other travel group under the cost-benefit constraints. Therefore, providing route recommendations for other travelers according to the route choice habits of urban taxi group is a feasible solution to the above need. This paper starts with the utility maximization model and the stochastic regret minimization model, for the problem that the traveler cannot satisfy the premise of the complete rationality of the utility maximization model when choosing routes and the random regret minimization model ignores the traveler’s sensitivity to the original value of the alternative attribute, and improve the calculation formula of the regret value in the random regret minimization model. A route choice model under the utility-regret combination rule is proposed based on the combination of the utility model and the improved regret model, and then, based on the historical trajectory data of taxis in Xi’an, the RP data required by the model is extracted, and the parameters of the model are estimated and verified. The experimental results show that the route choice probability calculated by the model is closer to the route choice probability in the real scene than the simple utility maximization model or the simple random regret minimization model.

Real-Time QoS Optimization for Vehicular Edge Computing With Off-Grid Roadside Units

To sustainably provide low-latency communication and edge computing for connected vehicles, a promising solution is using Solar-powered Roadside Units (SRSUs), which consist of small cell base stations and Mobile Edge Computing servers. However, due to the intermittent nature of solar power, SRSUs may suffer from a high risk of power deficiency, which will lead to severe disruption of vehicular edge computing applications. In this paper, we aim to address this challenge of Quality of Service (QoS) loss (i.e., edge computing service outage for vehicle users (VUs)). We formulate a QoS optimization problem for VUs and solve it in two phases: an offline solar energy scheduling phase, and an online user association and SRSU resource allocation phase. We simulate our proposed technique in a dense SRSU network environment with real-world urban vehicular traffic data and solar generation profile. The simulation results show that our proposed approach can significantly reduce QoS loss of vehicular edge computing applications using SRSUs, compared to existing techniques. Further, the results are beneficial to service providers and city planners to identify adequate SRSU configurations for expected solar energy generation and edge computing service demands.

Research on the Integration of Urban Traffic and Big Data

The powerful data processing ability of big data technology can allocate traffic resources more efficiently, and can deal with various sudden traffic problems flexibly. It is an unprecedented opportunity and challenge for urban transportation and smart cities to effectively collect and utilize traffic big data to meet the application requirements of high timeliness traffic administrative supervision, traffic enterprise management and traffic citizen service. This article expounds the concept and characteristics of big data, discusses the application research of big data in urban transportation at home and abroad in recent years, summarizes its application research scope and trend, points out that intelligent transportation is the focus of the application of big data in urban transportation, and finally looks forward to the future research direction.

Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities

This paper provides a short overview of space–time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping clustering. The first hierarchical category is to identify hierarchies in space–time series data. The second partitioning-based category focuses on determining disjoint partitions among the space–time series data, whereas the third overlapping category explores fuzzy logic to determine the different correlations between the space–time series clusters. We also further describe solutions for each category in this paper. Furthermore, we show the applications of these solutions in an urban traffic data captured on two urban smart cities (e.g., Odense in Denmark and Beijing in China). The perspectives on open questions and research challenges are also mentioned and discussed that allow to obtain a better understanding of the intuition, limitations, and benefits for the various space–time series clustering methods. This work can thus provide the guidances to practitioners for selecting the most suitable methods for their used cases, domains, and applications.

Multistep-Ahead Prediction of Urban Traffic Flow Using GaTS Model

The mathematical models for traffic flow have been widely investigated for a lot of application, like planning transportation and easing traffic pressure by using statistics and machine learning methods. However, there remains a lot of challenging problems for various reasons. In this research, we mainly focused on three issues: (a) the data of traffic flow are nonnegative, and hereby, finding a proper probability distribution is essential; (b) the complex stochastic property of the traffic flow leads to the nonstationary variance, i.e., heteroscedasticity; and (c) the multistep-ahead prediction of the traffic flow is often of poor performance. To this end, we developed a Gamma distribution-based time series (GaTS) model. First, we transformed the original traffic flow observations into nonnegative real-valued data by using the Box-Cox transformation. Then, by specifying the generalized linear model with the Gamma distribution, the mean and variance of the distribution are regressed by the past data and homochronous terms, respectively. A Bayesian information criterion is used to select the proper Box-Cox transformation coefficients and the optimal model structures. Finally, the proposed model is applied to the urban traffic flow data achieved from Dalian city in China. The results show that the proposed GaTS has an excellent prediction performance and can represent the nonstationary stochastic property well.

Dissecting GeoSparkSim: a scalable microscopic road network traffic simulator in Apache Spark

Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install global positioning system(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) Granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. This paper proposed GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 300 thousand vehicles over a very large road network (250 thousand road junctions and 300 thousand road segments) and outperform the existing competitors.

Exploring the robustness of public transportation for sustainable cities: A double-layered network perspective

The reliable and safe operation of an urban public transportation (UPT) system is of great significance to the sustainable development of a city. In-depth analyses of UPT network by exploring the topological structure and passenger travel features can help explain the mechanisms driving reliable UPT operations. Based on complex network theory, we proposed a robust model for a Bus-Subway double-layered network (B–S DLN) from the structural and functional perspectives. The following indicators were used to quantify the structural robustness of B–S DLN: average path length (APL), relative size of giant component (RSGC) and global network efficiency (GNE). The functional robustness was measured using the ratio of passenger flow loss (R). Using urban traffic data from the Xicheng District of Beijing, we analyzed the cascading failure of B–S DLN based on a nonlinear load-capacity model with two capacity control parameters: α and β. The simulation results show that α and β significantly impact the cascading failure process of B–S DLN. An increase in β or decrease in α can enhance the network’s ability to resist cascading failure. The parameter control method revealed that the B–S DLN achieves strong robustness when α = 0.2 and β = 0.5. Comparing the network robustness under a random attack and intentional attack, the B–S DLN is more robust under an intentional attack than in the random attack mode. This indicates that the robustness of B–S DLN can be significantly improved by rationally increasing station capacities.

Design and implementation of ITS Architecture based on Big Data

With the progress of social economy and the rapid development of transportation industry, in the face of the rapid growth of urban road traffic data, this paper puts forward the design scheme of intelligent traffic analysis system based on Hadoop. In this paper, HBase distributed database is used to store urban road static RDF data, hive data warehouse is used to store urban road traffic data, and MapReduce programming model is used to store massive and heterogeneous urban road traffic data The urban road traffic data are analyzed, and the whole scheme is verified by the prototype system.

Urban Traffic Data Imputation With Detrending and Tensor Decomposition

Due to various uncontrollable factors (such as random faulty acquisition equipment and data distortion), urban traffic flow data inevitably suffers from some form of data loss. Finding an effective filling method to estimate the missing data is of great help to the study of transportation networks. Traffic flow during a day are likely to have its regular peak period and off-peak period. For most regions of the urban road network, normally there is a certain trend in the traffic flow data. In this paper, we propose a data imputation method that employs a tensor decomposition approach, which fully considers the characteristics of the traffic flow in both time and space. The proposed method is based on high order singular value decomposition with soft thresholding core. In this method, traffic data are divided into its main trend part and the residual part, which is called detrending. And tensor decomposition is performed on these two parts separately. For each part, dynamic rank method is used to adjust the rank of tensor decomposition. With the actual 214 anonymous road segments with 10 minutes interval data in Guangdong, China, the highway data with 15 minutes interval in Madrid, Spain, and the traffic flow data from PeMS with 5 minutes interval in California, USA. The results of the different models are discussed in the case of continuous data missing and random data missing by different time intervals. In addition, by comparing with other data imputation methods, our method can fill the missing data with better performance.