Transportation Big Data Research Articles

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.

Roadway Policy Linkage Based on Prediction of COVID-19 Spread Using Public and Transport Big Data

Roadway Policy Linkage Based on Prediction of COVID-19 Spread Using Public and Transport Big Data

On the Evaluation Framework of Comprehensive Trust for Data Interaction in Intermodal Transport

Due to the necessity to realize “building a strong transportation nation”, the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the effect of data application, the status of entities and data flow in the network should be supervised throughout. Therefore, an evaluation framework of comprehensive trust is proposed in this paper. With feature analysis of transportation big data, a quality assessment is conducted by three-dimensional metric sets, which is considered as a significant factor of trust measurement. Furthermore, a hierarchical trust structure is put forward to assess the trust of entities in different levels, in terms of the static and dynamic evidence. Furthermore, the visualization of a dynamic global information security state is discussed, based on temporal knowledge graphs. As shown in practical application and simulation analysis, this framework can meet the requirements of data security supervision and lay the foundation of further intelligent management. This research is of great significance to improve the data security level in intermodal transport, and to promote the utilization and sharing of public information resources.

Special Issue on Transportation Big Data and Its Applications

Large-scale traffic sensors are strategically deployed across various infrastructures and modes of transportation (e [...]

Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

Traffic Target Location Estimation Based on Tensor Decomposition in Intelligent Transportation System

As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.

Heter-Train: A Distributed Training Framework Based on Semi-Asynchronous Parallel Mechanism for Heterogeneous Intelligent Transportation Systems

Transportation big data (TBD) are increasingly combined with artificial intelligence to mine novel patterns and information due to the powerful representational capabilities of deep neural networks (DNNs), especially for anti-COVID19 applications. The distributed cloud-edge-vehicle training architecture has been applied to accelerate DNNs training while ensuring low latency and high privacy for TBD processing. However, multiple intelligent devices (e.g., intelligent vehicles, edge computing chips at base stations) and different networks in intelligent transportation systems lead to computing power and communication heterogeneity among distributed nodes. Existing parallel training mechanisms perform poorly on heterogeneous cloud-edge-vehicle clusters. The synchronous parallel mechanism may force fast workers to wait for the slowest worker for synchronization, thus wasting their computing power. The asynchronous mechanism has communication bottlenecks and can exacerbate the straggler problem, causing increased training iterations and even incorrect convergence. In this paper, we introduce a distributed training framework, Heter-Train. First, a communication-efficient semi-asynchronous parallel mechanism (SAP-SGD) is proposed, which can take full advantage of acceleration effect of asynchronous strategy on heterogeneous training and constrain the straggler problem by using global interval synchronization. Second, Considering the difference in node bandwidth, we design a solution for heterogeneous communication. Moreover, a novel weighted aggregation strategy is proposed to aggregate the model parameters with different versions. Finally, experimental results show that our proposed strategy can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.74 \times$</tex-math> </inline-formula> speedups on training time, with almost no accuracy decrease.

Federated Learning Enabled Credit Priority Task Processing for Transportation Big Data

Due to the epidemic COVID-19 spread and Intelligent Transportation System (ITS) development, investigators are now to conduct their research over the generated Transportation Big Data (TBD) in many critical areas, such as medical supplies, food supplies, as well as logistics supplies. At present, Vehicular Edge Computing (VEC) is an emerging paradigm to integrate resources from vehicles, road-site units, base stations, and cloud center to promote the performance of TBD tasks scheduling and running. In this paper, we design a three-layered TBD task processing architecture with a federated learning mechanism for credit priority-based task scheduling and running. In our design, we consider the efficiency of task offloading and misbehavior attack problems simultaneously. We propose a vehicular federated learning framework combined with Multi-Layer Perceptron (MLP) credit measurement, which can preserve the privacy of vehicles and obtain the related features for vehicular credit prediction. We also propose a task offloading algorithm to solve the optimization problem for credit priority task offloading between edge computing servers and vehicles. The proposed solution can prioritize tasks and assign sufficient resources for reliable and active task requesters. Experimental results expose that the proposed mechanism outperforms the state-of-the-art solutions when considering efficiency and attack simultaneously for TBD tasks scheduling and running.

Guest Editorial Introduction to the Special Issue on Advanced Signal Processing and AI Technologies for Transportation Big Data and Their Applications in COVID-19 Scenario and Beyond

Guest Editorial Introduction to the Special Issue on Advanced Signal Processing and AI Technologies for Transportation Big Data and Their Applications in COVID-19 Scenario and Beyond

Federated Learning-based Route Choice Modeling for Preserving Driver’s Privacy in Transportation Big Data Application

Federated Learning-based Route Choice Modeling for Preserving Driver’s Privacy in Transportation Big Data Application

Passenger Flow Prediction and Risk Early Warning for Metro Cross-section based on Transportation Big Data

With the increasing pace of urbanization and the diversification of urban transportation modes, the demand for urban transportation in people's life is also increasing. For the purpose of relieving urban traffic, cities have made great efforts to develop public transport, especially metro transport. This paper will use the subway swipe card data of Changsha city from March 1-15, 2023, and optimize the LSTM model based on particle swarm algorithm, to predict the metro cross-sectional passenger volume. Based on the data and methods, this paper investigates the short-time cross-sectional passenger flow forecasting of the metro using the variables of initial passenger flow, inbound passenger volume and outbound passenger volume as references. It is found that the combined short-time passenger flow prediction model (PSO-LSTM) has good prediction effect. Based on the standard LSTM model, the PSO algorithm optimizes the learning rate, the number of iterations and the number of hidden neurons in the LSTM parameters to construct the PSO-LSTM model. The video recognition technology more accurately reflects the traffic congestion and makes early warning. The short-time cross-sectional passenger flow prediction of subway will help to improve people's living standard and save transportation time cost, which is of practical significance to urban rail operating companies.

Optimizing Road Transportation Big Data: A Novel Approach for Feature Selection through Optimization Techniques

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this research work, the model involves two main phases (i) Feature Selection and (ii) Classification. Since the length of feature vector tends to high, optimal feature selection technology is included, from which the most relevant features are selected by the Lion-based Firefly Algorithm which is referred as Optimization based Feature Selection Method (OFSM). The main objective of this paper is projected on minimizing the correlation between the selected features, which results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called Neural Network (NN) is adopted, which can classify the data in an effective manner with the selected features.

A non-local grouping tensor train decomposition model for travel demand analysis concerning categorical independent variables

With the booming of transportation big data, plenty of spatial and temporal features become available for developing travel demand analysis models. However, existing machine learning based travel demand fitting models could face significant model complexity issues once there are multiple discrete/categorical independent variables. This paper embeds a non-local grouping (clustering) process into the tensor train decomposition (NLG-TTD) to characterize the higher-order and nonlinear relationship between categorical variables and travel demand. That is, the transportation dataset with multiple discrete/categorical independent variables and travel demand is treated as a multi-mode tensor, wherein levels of a discrete/categorical variable are regarded as indexes of a mode in the tensor, leading to a “regression on index”. The proposed NLG-TTD model divides the full tensor into non-local sub-tensors, clusters sub-tensors with similarity, conducts tensor train decomposition (TTD) within each cluster, and aggregates cluster-specific learning results. Compared with a conventional TTD that generally works on image completions, NLG-TTD can reveal non-Euclidean and non-linear patterns between categorical/discrete levels in independent variables. It is expected to discover similar travel demand patterns among categorical levels and provide high-level goodness-of-fit. For numerical studies, we use bike-sharing usage data and metro demand data in New York City (NYC), in which several widely-used machine learning models are trained for comparison. The results demonstrate the good performance of the proposed model. Moreover, we apply the NLG-TTD model to analyze and predict the impact of land use shifts on travel demand.

An overview of Hadoop applications in transportation big data

An overview of Hadoop applications in transportation big data

Retracted: Higher Education Management Analysis of Smart Transportation Big Data Application under the Background of Internet +

Retracted: Higher Education Management Analysis of Smart Transportation Big Data Application under the Background of Internet +

Modeling and Analysis of Proof-Based Strategies for Distributed Consensus in Blockchain-Based Peer-to-Peer Networks

Blockchain technology has a wide range of applicability in the fields of transportation infrastructure construction and maintenance, transportation big data analysis and application, expressway toll collection, and logistics. The core technology lies in the distributed, decentralized, immutable, and programmable features brought about by consensus. This paper studies the dynamic analytical modeling of Proof-Based Consensus (PBC) strategies in blockchain systems, focusing on basic strategies, including Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Luck (PoL), which can be extended to other PBC models. We focus on modeling these typical strategies and discuss their solution characteristics in terms of algorithmic mechanisms and principles. The relevant results can be used for quantitative analysis and evaluation of distributed consensus based on the model.

Can shared micromobility programs reduce greenhouse gas emissions: Evidence from urban transportation big data

Shared micromobillity has been extensively developed globally in the past few decades, but its impact on the environment remains unclear. This study quantitatively estimates the effects of global shared micromobillity programs on greenhouse gas (GHG) emissions using a life cycle assessment (LCA) perspective. Specifically, it takes major countries and cities around the world as examples to empirically analyze the impact of station-based bike-sharing (SBBS), free-floating bike-sharing (FFBS), free-floating e-bike sharing (FFEBS), and free-floating e-scooter sharing (FFESS) programs on the GHG emissions of urban transportation. The results show that, with the exception of SBBS, the other shared micromobillity programs have not achieved desirable GHG emissions reduction benefits. Contrarily to subjective expectations, although the rapid progress of technology in recent years has promoted the vigorous development of shared micromobility, it has brought negative impacts on the GHG emissions rather than the positive benefits claimed by related promoters and operators. The overcommercialization and low utilization rate makes shared micromobility more likely to be an environmentally-unfriendly mode of transportation. In addition, the regional differences in mode choice, operational efficiency, fleet scale, and market potential of shared micromobility and the corresponding impacts on GHG emissions vary greatly. Therefore, authorities should formulate appropriate shared micromobility plans based on the current conditions and goals of the region. This empirical study helps to better understand the environmental impact of the global shared micromobility program and offers valuable references for improving urban sustainability.

Inertial Sensing Meets Machine Learning: Opportunity or Challenge?

The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, machine learning (ML) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using ML technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes the state of the art, advantages, and challenges on each aspect, and points out future research directions.

Optimizing City-Scale Traffic Through Modeling Observations of Vehicle Movements

The capability of traffic-information systems to sense the movement of millions of users and offer trip plans through mobile phones has enabled a new way of optimizing city traffic dynamics, turning transportation big data into insights and actions in a closed-loop and evaluating this approach in the real world. Existing research has applied dynamic Bayesian networks and deep neural networks to make traffic predictions from floating car data, utilized dynamic programming and simulation approaches to identify how people normally travel with dynamic traffic assignment for policy research, and introduced Markov decision processes and reinforcement learning to optimally control traffic signals. However, none of these works utilized floating car data to suggest departure times and route choices in order to optimize city traffic dynamics. In this paper, we present a study showing that floating car data can lead to lower average trip time, higher on-time arrival ratio, and higher Charypar-Nagel score compared with how people normally travel. The study is based on optimizing a partially observable discrete-time decision process and is evaluated in one synthesized scenario, one partly synthesized scenario, and three real-world scenarios. This study points to the potential of a “living lab” approach where we learn, predict, and optimize behaviors in the real world.

Economic impact of road transportation infrastructure projects: the case of Egnatia Odos Motorway

ABSTRACT Non-cyclical and high-leverage infrastructure projects, such as road transportation networks, have been considered as critical policy instruments to promote growth and social development. Yet, their growth-generating effect on the economic welfare of countries and regions has come under scrutiny in recent years: several studies have nuanced the implicit positive associations between investments in transportation and economic benefits. Building upon a set of traffic flow characteristics as potential correlates to the regional development indicator, this research focuses on the economic impacts of Egnatia Odos Motorway (EOM) on Greece's Northern region. To specify, by employing a large-scale longitudinal dataset which includes over 230 million entry records of various vehicle types along EOM, generated from toll collection systems between 2010 and 2019, we investigate the associations between traffic data and potential variations in regional GDP per capita annual growth rate. Our results suggest positive associations between regional economic development and the EOM operation and, more specifically, with passengers' transportation and inland freight. We conclude that transportation big data provide essential input for the appraisal of a road transport investment project, reveal the status of regional welfare, and may contain valuable information for spatial management and planning.