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Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis. [ABSTRACT FROM AUTHOR]

A distributed firewall is a security application that monitors and controls traffic on an organization's network. While centralized firewalls are used against attacks coming from outside a network, distributed firewalls are considered for inside attacks from internal networks such as wireless access and VPN tunnel. Distributed firewalls use policies, which are stated by rules, to find anomalous packets. However, such static rules may be incomplete. In this case, by monitoring firewall logs, the anomalies can be detected. Such logs become big when networks have high traffic, but their hidden knowledge contains valuable information about existing anomalies. In this paper, to detect the anomalies, we extract patterns from big data logs of distributed firewalls using data mining and machine learning. The proposed method is applied to big logs from distributed firewalls in a real security environment, and results are analyzed. [ABSTRACT FROM AUTHOR]

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. [ABSTRACT FROM AUTHOR]

In the urban rail transportation industry, collecting various data, including the number of trips, the number of fleet breakdowns, the number of breakdowns of railway lines, etc., contributes significantly to future planning and optimization of resources and costs. We are faced with big data analysis due to the daily production of large volumes of data in the urban rail transport industry. This study aims to find a trip prediction model for Tehran Metro to do trip decision-making. This paper provides a brief introduction to knowledge discovery from failure databases and presents the data mining methodology. The relevancy of data mining for a trip prediction model will be depicted. In this sense, considering the relevance of data collected on the metro control center and intelligent trip prediction, this paper presents a functional architecture of a predictive trip model using data mining techniques. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in the daily operation. This research has investigated three situations, including delayed trips, incomplete trips, and canceled trips, using actual data recorded over the past four years, including non-programmed failures and their impact on trips. A model with more than 90% (90.48%) accuracy suggests a trip status forecast on this line. Also, the research has used Rapid Miner software and the Decision tree technique in data mining and its rules. Also, the kappa index of 0.792 is calculated for the validation model. Therefore, the proposed model enables the prediction of three types of incomplete traffic, delayed trips, and cancellation of trips due to emergency failure at the time of the operation of the Tehran Metro. [ABSTRACT FROM AUTHOR]

Recently, the traffic control infrastructure and public transport fare records in Tehran have produced massive data. Such data can be analyzed to monitor the city's transportation condition and also to improve its transportation network. Understanding the fundamental features of traffic, including flow, density, and velocity, on important roadways is vital for a reliable traffic control mechanism. Obtaining this data from traffic cameras scattered across the city was the goal of this research. The average speed of cars, as recorded by automated license plate readers (ALPRs), was also obtained, and a relationship for estimating the flow density is proposed that simulates cars' behaviors. The curve plotted by the proposed relationship has about an 25% average speed deviancy from that determined by the probe vehicles for all cameras. This error seems acceptable, considering the limitations of ALPRs and/or diversity of roads. [ABSTRACT FROM AUTHOR]

The traffic problem in Intelligent Transportation Systems has recently become a very important issue. Thanks to Intelligent Transportation Systems, the formation of large amounts of traffic data has led to the formation of data-oriented models. There is a growing interest in predicting traffic measures by modeling complex scenarios based on big data with data mining and machine learning methods. In this study, traffic events from Twitter traffic notifications and vehicle density from sensor data were obtained. Traffic density analysis and traffic incident analysis were performed with the machine learning method. In the analysis of traffic incidents, 36627 traffic incidents were digitized. This data was separated into categories including type of accident; day; month; year; season; left, right or middle lane; and vehicle failure, maintenance-repair work and accident notification. Between 2016 and 2020, 1400 daily vehicle data logs were obtained from the sensor data located at 59 points of the D100 highway. Traffic density and parameters affecting traffic incidents on the Anatolian and European sides of the D100 highway in Istanbul were determined. Traffic density and accident event models were designed with the Bayesian network approach. In the sensitivity analysis of the model, it was concluded that the parameter that has the strongest effect on traffic events and density formation on the D100 highway line is the strips. With these models, the infrastructure of the early warning system has been created for region-specific traffic density situations and possible traffic events. [ABSTRACT FROM AUTHOR]

The article gives an overview of the evolution trends and stages of rail operations control centres (ROCCs) and outlines their future development and challenges in the context of the digital transformation of railway transport. It addresses the key aspects of further evolution of ROCCs in terms of closer integration of various functional layers and systems, automation of control and supervision functionalities, application of new data processing methods based on artificial intelligence. [ABSTRACT FROM AUTHOR]

It is widely considered that unlicensed taxis pose a risk to public safety and interfere with the effective management of traffic. Significant human and material resources are expended by traffic control departments to locate these vehicles with limited success. This study suggests a smart, trajectory big data-based approach entitled Trajectory Graph Embedding-based Unlicensed Taxi Detection (TGE-UTD) to identify suspected unlicensed taxis and address this issue. The model implementation comprises three stages: first, the Automatic Number Plate Recognition (ANPR) data are transformed into a trajectory graph; second, a biased random walk is deployed to embed the trajectory graph; and finally, the set of vehicles similar to the known licensed taxis is obtained as the set of suspected unlicensed taxis using the cosine similarity of the vehicle embedding vector. Through precision evaluation and dimension reduction experiments, the performance of the walk model TGE-UTD is compared to that of the no-walk models Word2Vec and Doc2Vec in detecting large vehicles and taxis. TGE-UTD is observed to exhibit the best performance among the three models. This study pioneers the application of machine learning for feature extraction in detecting unlicensed taxis. The model proposed in the study can be deployed to detect unlicensed taxis; moreover, its application can be extended to detect other types of vehicles, providing traffic management departments with supporting vehicle detection information. [ABSTRACT FROM AUTHOR]

With advances in mobile devices and systems and the emergence of new ideas such as cloud computing and big data, as well as the tremendous growth in the number of network users, the need to modify the current network architectures has been very much in the foreground in recent years. One of the promising solutions to overcome these challenges is software-defined networking (SDN). SDN is a unique innovative architecture in which network control and traffic flows are independent of each other and planned directly. The SDN's focused view of networks is more comprehensive than other methods, which is why SDN is more efficient in coping with malicious attacks including amplification attacks. The response to amplification of distributed denial of service (DDoS) attacks is larger than the request. In an amplification attack, the attacker fakes the victim's address as the source address and the responses are forwarded to the victim instead of the attacker. This is why these attacks are more difficult to discover in traditional networks, while the focused method of SDN can contribute to the detection of such attacks. There are different methods for detecting these attacks, one of which is to use machine learning (ML) algorithms. In line with this, the present paper is aimed at the detection of distributed reflection denial of service (DRDoS) attacks using ML algorithms. Simulation was performed by the use of ML algorithms, and the findings suggest a significant improvement in the detection of DRDoS attacks in comparison with previous methods. [ABSTRACT FROM AUTHOR]

Driving cycle is commonly known as a series of speed-time profile. Research on this discipline aids vehicle manufacturing industries in vehicle manufacturing, environmentalists to study on environment quality and profile in accordance to vehicle emissions besides traffic engineers to further investigate the behavior of drivers and the conditions of roads in a certain area or cluster. This also assists automotive industries to innovate energy efficient vehicles which reduce vehicle emissions and energy wastages which lead to air pollution in which a major threat for human health according to Goal 3 of united nations (UN) sustainable development goals (SDG). To construct an accurate driving cycle, data based on real-world driving behavior is crucial and as the world is advancing in technology, the usage of internet of things (IoT) plays an important role in innovatietcons. IoT is an idea of computing every day physical object and information into computers, devices and software. These devices work by using sensors that transmit data to a computer or software allowing them to perform important tasks as needed. In this research, an idea of data collecting device, driving cycle tracking device (DC-TRAD) is constructed with implementation of IoT in which the collected data will be saved into my structured query language (MySQL) database instantly for data storing. [ABSTRACT FROM AUTHOR]

The paper presents a view on the possibilities of using big data in traffic planning and traffic engineering, which are used in the urban and spatial planning of cities. The question is whether the method of this design activity—especially in feasibility studies, environmental impact assessments, or documentation for land-use decisions—can be part of Building Information Modelling (BIM). The result of this engineering activity could at the same time be linked to the documentation for building permits, where a number of professional civil engineers are already making full use of the BIM management system. The problem of traffic engineering and traffic planning is related to the need for extensive data, which are usually not available and, if they are, are from different database sources. Very often, however, engineers have to carry these tasks out with traffic surveys. Most often, these data are from transport centres, or from spatial databases of Geographical Information System (GIS) of cities or regions. In addition, in developed cities, detailed data from specialised strategic materials are available from municipal databases at various levels, in addition to the land-use plan that is the guarantor of the city's development. Based on empirical experience from the creation of the new city centre of Bratislava, the authors present, from more than six years of engineering activity, what different data traffic engineers and planners need, which can be processed and unified to be part of the management system in the future and, thus, assist in the development of linear construction using BIM tools. [ABSTRACT FROM AUTHOR]

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Traffic flow forecasting (TFF) is critical for constructing intelligent transportation systems and offering real-time traffic applications, and especially accurate flow forecasting based on traffic big data can drive reliable strategies for traffic management and control. To consider the weight of the influence of the spatial correlation among the road segments and capture the nonlinear characteristics of traffic flow, this paper presents a parallel Normal Distribution and Attention Mechanism Weighted Deep Bidirectional Long Short-Term Memory (NAW-DBLSTM) algorithm on Spark. Specifically, we employ the resilient distributed data set (RDD) to preprocess large-scale mobile trajectory data (e.g., taxi GPS trajectory data), and then Kalman Filter (KF) is utilized to smooth the taxi trajectory big data. Next, the parallel NAW-DBLSTM algorithm is put forward on a Spark distributed computing platform to enhance the accuracy and scalability of TFF, combined with the attention mechanism and the normal distribution, and then the time window is used for TFF. Finally, the traffic flow is forecasted successfully on Spark by our NAW-DBLSTM algorithm with the real-world GPS trajectories of taxicabs. The experimental results demonstrate that, compared with LSTM, BiLSTM, DBLSTM, DNN, SVR, KNN, SAEs, BP, CNN, GRU, and ANNs, NAW-DBLSTM can produce better performance with the MAPE value that is 85.1%, 80.1%, 85.8%, 73.1%, 78.2%, 77.9%, 78.8%, 84.6%, 96.4%, 86.2%, and 73.2% lower than that of comparable algorithms, respectively. Particularly, the MAPE value of NAW-DBLSTM is 28.3%, 20.1%, 71.1%, and 79.1% lower than that of LSTM weighted with the normal distribution and the attention mechanism (NAW-LSTM), BiLSTM weighted with the normal distribution and the attention mechanism (NAW-BiLSTM), DBLSTM weighted with the normal distribution (NW-DBLSTM), and NAW-DBLSTM weighted without the time window (NT-NAW-DBLSTM), respectively. [ABSTRACT FROM AUTHOR]

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results. [ABSTRACT FROM AUTHOR]

Autonomous driving decision is a critical component of automatic driving system, which informs and updates the unmanned vehicle of object movements. However, end‐to‐end autonomous driving decision is still a great challenge due to the different scales of traffic target in the wild dynamic traffic scenes. To solve these problems, this paper proposes a novel model with attention mechanism and spatiotemporal features extraction. Specifically, for the important spatial information of traffic targets with scale differences, the spatial dimensions of height H, width W and channel C are independent of each other to build sparse spatial attention map. Moreover, different sparse masks are trained for spatial network by pruning elements of feature maps at the end of each block of backbone, which improves the accuracy of the two subnetworks of spatial network by 2.3% and 3.9%, respectively. Then the extracted spatial information is introduced jointly to the time sequence network with previous speed as input to obtain the vehicle steering angle and speed. Experiments on public virtual datasets show that the prediction accuracy of the model reaches 85.8%. Compared with other state‐of‐the‐art models, our model increased by 4.8% and 2.2%, respectively. [ABSTRACT FROM AUTHOR]

We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our proposed scheme is equivalent to running regular temporal difference learning with linear function approximation, albeit with samples picked uniformly from a given dataset. Our method results in an O(d) improvement in complexity in comparison to LSTD, where d is the dimension of the data. We provide non-asymptotic bounds for our proposed method, both in high probability and in expectation, under the assumption that the matrix underlying the LSTD solution is positive definite. The latter assumption can be easily satisfied for the pathwise LSTD variant proposed by Lazaric (J Mach Learn Res 13:3041–3074, 2012). Moreover, we also establish that using our method in place of LSTD does not impact the rate of convergence of the approximate value function to the true value function. These rate results coupled with the low computational complexity of our method make it attractive for implementation in big data settings, where d is large. A similar low-complexity alternative for least squares regression is well-known as the stochastic gradient descent (SGD) algorithm. We provide finite-time bounds for SGD. We demonstrate the practicality of our method as an efficient alternative for pathwise LSTD empirically by combining it with the least squares policy iteration algorithm in a traffic signal control application. We also conduct another set of experiments that combines the SA-based low-complexity variant for least squares regression with the LinUCB algorithm for contextual bandits, using the large scale news recommendation dataset from Yahoo. [ABSTRACT FROM AUTHOR]

The vigorous progress in artificial intelligence and the widespread availability of computing power and big data have resulted in remarkable achievements in applying deep learning methods to traffic engineering. However, the complexity of deep learning models poses a significant challenge to their interpretability, despite their scalability to big data. As a result, academia has shown a keen interest in interpreting these intricate models. To address these limitations, this study introduces the Gated Recurrent Convolution Network (GRCN) for predicting spatiotemporal crash risks. Moreover, to transform the opaque nature of the GRCN into a transparent framework, SHapley Additive Explanations (SHAP) was employed, a game theory-based approach. The application of SHAP analysis not only enhances the explainability of the GRCN model but also reveals complex causal relationships regarding crash risks, using extensive datasets encompassing taxi trips data, law enforcement, and weather conditions. The study incorporates both local and global interpretability analyses at the grid level. Additionally, it indicates that intersection red light violations tend to elevate crash risks. The findings further demonstrate that adverse weather conditions, such as low visibility and strong crosswinds, negatively affect driving behavior and correlate with higher crash risks. However, the presence of cloudy skies is associated with a decrease in crash risks. By shedding light on the intricate dynamics of crash risk factors, this study contributes to the field of traffic engineering and emphasizes the importance of interpretability in deep learning models. The insights gained from this research can inform the development of effective interventions and policies aimed at reducing crash risks and improving overall transportation safety. [Display omitted] • Introducing a novel model that advances the field of spatiotemporal crash risk prediction. • Proposing a method rooted in game theory to enhance the interpretability of the model. • Delving into the causality inference of traffic law enforcement violations on crash risk. • Investigating the causality inference related to the impact of weather conditions on crash risks. [ABSTRACT FROM AUTHOR]

The paper presents a view on the possibilities of using big data in traffic planning and traffic engineering, which are used in the urban and spatial planning of cities. The question is whether the method of this design activity—especially in feasibility studies, environmental impact assessments, or documentation for land-use decisions—can be part of Building Information Modelling (BIM). The result of this engineering activity could at the same time be linked to the documentation for building permits, where a number of professional civil engineers are already making full use of the BIM management system. The problem of traffic engineering and traffic planning is related to the need for extensive data, which are usually not available and, if they are, are from different database sources. Very often, however, engineers have to carry these tasks out with traffic surveys. Most often, these data are from transport centres, or from spatial databases of Geographical Information System (GIS) of cities or regions. In addition, in developed cities, detailed data from specialised strategic materials are available from municipal databases at various levels, in addition to the land-use plan that is the guarantor of the city’s development. Based on empirical experience from the creation of the new city centre of Bratislava, the authors present, from more than six years of engineering activity, what different data traffic engineers and planners need, which can be processed and unified to be part of the management system in the future and, thus, assist in the development of linear construction using BIM tools.

Intelligent transportation brings huge benefits to humans' life and Industrial production in terms of vehicle control and traffic management. Now, the development of edge-cloud computing has once again promoted intelligent transportation into a new era. However, the development of intelligent transportation inevitably produces a large amount of data, which brings new challenges to data privacy protection and security. In this paper, we propose to develop an improved trajectory prediction framework based on the self-adaptive trajectory prediction model (SATP), which could significantly enhance traffic safety in transportation systems. The proposed framework is capable of guaranteeing the accurate trajectory prediction of moving target under different application scenarios. In particular, to reduce the size of original trajectory point data collected by sensors, the angle change and minimum description length (MDL) principle are first combined to remove the redundant points in raw trajectories. The obtained points can then be reduced for model using the two-step clustering method. To further enhance the prediction performance, we add the "self-transfer" to the original model to solve the problems that the state of original SATP model may be discontinuous. Furthermore, we propose to develop a trajectory complementation method based on Bezier curve to improve the prediction accuracy. Finally, by comparing the two-step clustering method with the commonly-used SinglePass and density-based clustering method (DBCM) algorithms, the proposed two-step clustering policy greatly reduce the time cost of clustering. At the same time, by comparing the improved SATP model with the original model, the results show that the improved SATP method can greatly improve the speed of prediction model. [ABSTRACT FROM AUTHOR]

Most complex real systems are found to have multiple layers of connectivity and required to be modelled as multiplex networks. One of the extremely critical problems is to reduce the congestion and enhance the transfer capacity, especially in real communication networks with a big data environment. A novel and effective strategy to improve traffic and control congestion is proposed by adding edges according to their weights which are defined by the topology structural properties. Furthermore, which layer is more effective when our strategy is applied is discussed based on its topology structure. Adding edges between nodes whose product of multiplex network betweenness is the highest is confirmed to be more effective, particularly in the layer with stronger community structure. Simulation experiments on both computer-generated and real-world networks demonstrate that our strategy can enhance the transfer capacity of multiplex networks significantly, which is in good agreement with our analysis. [ABSTRACT FROM AUTHOR]

The current traffic control system (TCS) is not the most efficient system present for regulating traffic. Hoping to solve this we come up with dynamic data recording systems which encompasses of RFID tag and reader. The traffic density at each lane is calculated based on count of RFID's apprehended. Depending on the density, the TCS is assigned a value of 15 to 70 seconds in round robin method for control of vehicular congestion. This proposed model also uses image processing for the detection of ambulances and also an active RFID for tracking the real-time location of these assets or in high-speed environments such as that of tolling. This allows the passage of ambulances through dense traffic. This system hopes to achieve to reduce the needless wait of crowded side, to reduce the long traffic chains, and to allow emergencies (medical/vigilante) quickly through the traffic. The major merits of the system are it prevents unnecessary waiting time when no cars are present at the opposite route, gives the commuting passengers a better and more comfortable driving experience through their journey. [ABSTRACT FROM AUTHOR]

Persistent congestions which are varying in strength and duration in the dense traffic networks are the most prominent obstacle towards sustainable mobility. Those types of congestions cannot be adequately resolved by the traditional Adaptive Traffic Signal Control (ATSC). The introduction of Reinforcement Learning (RL) in ATSC as tackled those types of congestions by using on-line learning, which is based on the trial and error approach. Furthermore, RL is prone to the dimensionality curse related to the state–action space size based on which a non-linear quality function is derived. The Deep Reinforcement Learning (DRL) framework uses Deep Neural Networks (DNN) to digest raw traffic data to approximate the quality function of RL. This paper provides a comprehensive analysis of the most recent DRL approaches used for the ATSC algorithm design. Special emphasis is set to overview of the traffic state representation and multi-agent DRL frameworks applied for the large traffic networks. Best practices are provided for choosing the adequate DRL model, hyper-parameters tuning, and model architecture design. Finally, this paper provides a discussion about the importance of the open traffic data concept for the extensive application of DRL in the real world ATSC. [ABSTRACT FROM AUTHOR]

• This paper proposes a network-wide method to identify intersection traffic congestion in a road network. • The proposed method is solely based on low-frequency probe vehicle data, i.e., map-independent. • It is able to rapidly and approximately detect intersection congestion along all turning directions. • It is a computer-aided method in the era of big data can greatly decrease traffic engineers' workload. Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles' turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers' workloads by providing information regarding where and when to investigate and solve traffic congestion problems. [ABSTRACT FROM AUTHOR]

Traditional crash prediction models use roadway geometric features, traffic control types, and annual average daily traffic volumes, as inputs to predict the annual crashes of a roadway site. Developing such models requires careful sampling of the crash sites from different locations and advanced statistical techniques; using them requires knowledge of the site and local calibrations. This paper introduces a big data approach to predicting the annual crashes of a roadway facility based on predictive analytics. It predicts what will happen in the future by analysing the rich historical data, detecting the underlying patterns and trends, and using them to predict future events. A tool was developed that uses the multi-year comprehensive state wide crash history dataset as backend data access. The tool can predict the annual crashes around a user selected location anywhere in the state. This tool was developed based on the rational that a multi-year state wide crash dataset covers all the possible locations where any types of traffic crashes could happen on a regular basis. Also, the causation factors of all documented crashes and the information encapsulated in such a big dataset should contain enough information to allow prediction of annual crashes anywhere on the state's roadway network. This method requires ready access to the statewide crash dataset, but no necessary prior knowledge of the roadway facility of interest. An auto searching algorithm was developed to perform dynamic sampling around the user selected location, followed by data processing to detect patterns and trends. This method is area based, however, by properly adjusting the searching criteria, the result can converge to an intersection or a roadway segment. It inherently considers the influences of nearby facilities. [ABSTRACT FROM AUTHOR]

With the progress of society and the acceleration of urbanization, the problem of multimedia assisted urban traffic is becoming increasingly apparent. Intelligent transportation system arises at the historic moment, but the road traffic information data accumulation of intelligent transportation system is quite large, and the information analysis is complex. Usually, there are huge amounts of data stored in the database of the traffic system, thus we need to analyze and manage the data with scientific methods. As the visualized tools, the multimedia based analysis for the on-time data conditions should also be integrated, therefore, how to effectively model the scenario is challenging. In this paper, we introduce and analyze the structure and related model of recurrent neural network, and apply RNN to traffic big data mining model. According to the characteristics of traffic flow, this paper analyzes the causes of the error data in the process of traffic data collection, and puts forward the corresponding processing methods. The proposed model uses the TensorFlow for development, and applies the proposed deep learning model to implement traffic data mining, and we use charts to visually show the prediction results. Experimental results show that proposed algorithm can effectively mine large traffic multimedia data and has good robustness. At the same time, the prediction accuracy has reached 97.36%. [ABSTRACT FROM AUTHOR]

In order to solve the problem of traffic jams, intelligent traffic technology and car networking technology were applied. In the context of big data, data acquisition and mining algorithms for vehicular network were studied. First, the overall architecture of the system was introduced. Then, the data acquisition technology based on the car network and the data mining technology based on the cloud plat-form were introduced. Finally, simulation experiments of real-time traffic information collection and recognition algorithms were performed. The results showed that the proposed mining algorithm had better data repair effect and better clustering effect, and the probability of misjudgment was smaller. Therefore, the algorithm can obtain accurate road traffic conditions. [ABSTRACT FROM AUTHOR]

Abstract Since the 2000s, an era of big data has emerged. Since then, urban planners have increasingly applied the theory and methods of big data in planning practice. Recent decades illustrate a rapid increase of the application of big data approaches in transportation, bringing new opportunities for innovation in transport modeling. This article analyzes the theories and methods of big data in traffic demand forecasting. In view of theory, the new models and algorithms are proposed in order to adapt to new big data and response to the limitations of traditional disaggregated approaches. In such approaches, three traffic demand-forecasting methods, the full sample-demand distribution model, the traffic integration model, the model organism protein expression database model, are discussed. Undoubtedly, the development of big data also presents new challenges to travel-demand forecasting methods regarding data acquisition, data processing, data analysis, and application of results. In particular, identifying how to improve approaches to traffic-demand forecasting in the big data era in the Third World will be a challenge to the researchers in the field. Highlights • Big data brings new opportunities for the innovation of transport modeling • The new models and algorithms are proposed responding to the limitations of the traditional disaggregated approaches. • Full sample demand distribution model, Traffic integration model, and MoPeD model are discussed. • Improvement of traffic demand forecasting approaches in the big data era in the Third World will be a challenge. [ABSTRACT FROM AUTHOR]

Highlights • A new mobility model combines the advantages of mobile phone and transportation data. • The model can capture ordinary and anomalous human mobility in a real-time manner. • We apply the model to predict crowd gatherings that usually cause traffic jams. Abstract Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams. [ABSTRACT FROM AUTHOR]

Machine learning models used in traffic classification make the assumption that the training data and test data have independent identical distributions. However, this assumption might be violated in practical traffic classification due to changes of traffic features. The models trained by existing data will be ineffective in classifying new traffic. A transfer learning model without making the above assumption is proposed in the present study. The maximum entropy model (Maxent) was adopted as the base classifier in the transfer learning model. To examine the efficacy of the proposed method, the traffic dataset collected at the University of Cambridge was used in the condition that the training and test dataset were not identical. Experimental results showed that good classification performance was obtained based on the transfer learning model. [ABSTRACT FROM AUTHOR]

As cities increase in size, governments and councils face the problem of designing infrastructure and approaches to traffic management that alleviate congestion. The problem of objectively measuring congestion involves taking into account not only the volume of traffic moving throughout a network, but also the inequality or spread of this traffic over major and minor intersections. For modeling such data, we investigate the use of weighted congestion indices based on various aggregation and spread functions. We formulate the weight learning problem for comparison data and use real traffic data obtained from a medium-sized Australian city to evaluate their usefulness. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD. [ABSTRACT FROM AUTHOR]

Intelligent Traffic Systems (ITS), as integrated systems including control technologies, communication technologies, vehicle sensing and vehicle electronic technologies, have provided valuable solutions to the increasingly serious traffic problems. In the process of construction and operation of ITS, big data, especially multimedia data is produced at a rapid speed, which has made traffic information more and more complicated, causing traffic management facing new challenges. Hence, in order to achieve efficient management of all types of transportation resources and make better use of ITS, it is necessary and significant to study the architecture and performance of ITS in depth. Through dividing the system into different functional modules and assigning these modules to components in Performance Evaluation Process Algebra (PEPA), we can adopt a new method to realize the modeling and evaluating the working process of real-time positioning and route planning in ITS. Meanwhile, the fluid flow approximation is employed to conduct a performance analysis through PEPA models, guaranteeing that the response time, the maximum utilization and the throughput of the system can be achieved and analyzed. [ABSTRACT FROM AUTHOR]

The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission. [ABSTRACT FROM AUTHOR]

Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles' cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles' cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system's throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary. [ABSTRACT FROM AUTHOR]

Smart cities have been recognized as a promising research focus around the world. To realize smart cities, computation and utilization of big data are key factors. More specifically, exploring the patterns of human mobility based on large amounts of multi-source data plays an important role in analyzing the formation of social-economic phenomena in smart cities. However, our acquired knowledge is still very limited for smart cities. In this article, we propose an integrated computing method to rescale heterogeneous traffic trajectory data, which leverages MLE and BIC. Our analysis is based on two real datasets generated by subway smart card transactions and taxi GPS trajectories from Shanghai, China, which contain more than 451 million trading records by 14 subway lines and 34 billion GPS records by 13,695 taxis. Specifically, we quantitatively explore the patterns of human mobility on weekends and weekdays. Through logarithmic binning and data fitness, we calculate the Bayesian weights to select the best fitting distributions. In addition, we leverage three metrics to analyze the patterns of human mobility in two datasets: trip displacement, trip duration, and trip interval. We obtain several important human mobility patterns and discover quite a few interesting phenomena, which lay a solid foundation for future research. [ABSTRACT FROM AUTHOR]

In this paper, we present necessary premises for the deployment of the Internet of Vehicles (IoV) integrating Big Data analytics of road network traffic measurements of the city of Mohammedia, Morocco. Thus, we introduce an architecture based on three main layers such as IoV, Fog Computing and Cloud Computing Layer. We specifically put more focus on Fog Computing layer in which we develop a framework for a real-time collecting and processing events generated by intelligent vehicles as well as visualizing traffic state on each road section. Furthermore, we consider deployment and test of the proposed framework using events retrieved from a Vanets -type micro simulation. Finally, we present and discuss the first obtained results as well as the advantages and limitations of the proposed architecture. [ABSTRACT FROM AUTHOR]

With the improvement of the storage and big data processing technology, mobile operators are able to extract and store a large amount of mobile network generated user behavior data, in order to develop various intelligent applications. One interesting application based on these data is traffic sensing, which uses techniques of learning human mobility patterns from updated location information in network interaction log data. Mobile networks, under which a huge amount of frequently updated location information of mobile users are tracked,can provide complete coverage to estimate traffic condition on roads and highways. This paper studies potential challenges and opportunities in intelligent traffic sensing from the data science point of view with mobile network generated data. Firstly, we classify the data resources available in the commercial radio network according to different taxonomy criteria. Then we outline the broken-down problems that fit in the framework of traffic sensing based on mobile user network log data. We study the existing data processing and learning algorithms on extracting traffic condition information from a large amount of mobile network log data. Finally we make suggestion on potential future work for traffic sensing on data from mobile networks. We believe the techniques and insights provided here will inspire the research community in data science to develop the machine learning models of traffic sensing on the widely collected mobile user behavior data. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as today's most exciting computing paradigm for providing services using a shared framework, which opens a new door for solving the problems of the explosive growth of digital resource demands and their corresponding convenience. With the exponential growth of the number of data types and data size in so-called big data work, the backbone network is under great pressure due to its transmission capacity, which is lower than the growth of the data size and would seriously hinder the development of the network without an effective approach to solve this problem. In this paper, an Intelligent Aggregation based on a Content Routing (IACR) scheme for cloud computing, which could reduce the amount of data in the network effectively and play a basic supporting role in the development of cloud computing, is first put forward. All in all, the main innovations in this paper are: (1) A framework for intelligent aggregation based on content routing is proposed, which can support aggregation based content routing; (2) The proposed IACR scheme could effectively route the high aggregation ratio data to the data center through the same routing path so as to effectively reduce the amount of data that the network transmits. The theoretical analyses experiments and results show that, compared with the previous original routing scheme, the IACR scheme can balance the load of the whole network, reduce the amount of data transmitted in the network by 41.8%, and reduce the transmission time by 31.6% in the same network with a more balanced network load. [ABSTRACT FROM AUTHOR]

One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have more potential passengers (more profitable areas) by analyzing and querying taxi trip data. In this paper, we propose a query processing system, called Distributed Profitable-Area Query (DISPAQ) which efficiently identifies profitable areas by exploiting the Apache Software Foundation's Spark framework and a MongoDB database. DISPAQ first maintains a profitable-area query index (PQ-index) by extracting area summaries and route summaries from raw taxi trip data. It then identifies candidate profitable areas by searching the PQ-index during query processing. Then, it exploits a Z-Skyline algorithm, which is an extension of skyline processing with a Z-order space filling curve, to quickly refine the candidate profitable areas. To improve the performance of distributed query processing, we also propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node. Through extensive evaluation with real datasets, we demonstrate that our DISPAQ system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a graph-based decision making mechanism for smart transportation by gathering continuously the real-time city road traffic information. The data is harvested using the Internet of Things (IoT) based road sensors and vehicular network. The weight calculations are proposed using the current traffic parameters for various graph algorithms to make smart transportation decisions to facilitate the citizens as well as the metropolitan authorities. Furthermore, to process incoming Big Data from IOT devices by generating big graphs and respond to the users at real-time, we proposed an efficient architecture that uses the Apache GraphX tool with parallel processing servers of Hadoop ecosystem. Vehicular datasets representing the real road traffic are used for analysis and evaluation purpose. Moreover, the efficiency of the system is tested in terms of system throughput and processing time. [ABSTRACT FROM AUTHOR]

Traffic big data has brought many opportunities for traffic management applications. However several challenges like heterogeneity, storage, management, processing and analysis of traffic big data may hinder their efficient and real-time applications. All these challenges call for well-adapted distributed framework for smart traffic management that can efficiently handle big traffic data integration, indexing, query processing, mining and analysis. In this paper, we present a novel, distributed, scalable and efficient framework for traffic management applications. The proposed cloud computing based framework can answer technical challenges for efficient and real-time storage, management, process and analyse of traffic big data. For evaluation of the framework, we have used OpenStreetMap (OSM) real trajectories and road network on a distributed environment. Our evaluation results indicate that speed of data importing to this framework exceeds 8000 records per second when the size of datasets is near to 5 million. We also evaluate performance of data retrieval in our proposed framework. The data retrieval speed exceeds 15000 records per second when the size of datasets is near to 5 million. We have also evaluated scalability and performance of our proposed framework using parallelisation of a critical pre-analysis in transportation applications. The results show that proposed framework achieves considerable performance and efficiency in traffic management applications. [ABSTRACT FROM AUTHOR]

Traffic Prediction is critical as it is enhancing day by day leading to worst on road situations. Increased accidents and delays in critical applications is causing awful situations for the user. In order to resolve the problem Modified ARIMA is used. Modified ARIMA is implied over the dataset. The dataset for the implication is fetched from online source. The UCI website is used for traffic dataset time series analysis. Modified ARIMA is used to make stationery time series from dynamic series at AR phase. MI phase is used to predict number of previous values to be analyzed and MA phase is hybridized using KNN with Euclidean distance. The result of the proposed literature is presented in terms of accuracy and mean square error. Result shows significant improvement in terms of accuracy and means square error. [ABSTRACT FROM AUTHOR]