Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Big Data Analytics in Intelligent Transportation Systems: A Survey

Face-pose estimation aims at estimating the gazing direction with two-dimensional face images. It gives important communicative information and visual saliency. However, it is challenging because of lights, background, face orientations, and appearance visibility. Therefore, a descriptive representation of face images and mapping it to poses are critical. In this paper, we use multimodal data and propose a novel face-pose estimation framework named multitask manifold deep learning ( $\text{M}^2\text{DL}$ ). It is based on feature extraction with improved convolutional neural networks (CNNs) and multimodal mapping relationship with multitask learning. In the proposed CNNs, manifold regularized convolutional layers learn the relationship between outputs of neurons in a low-rank space. Besides, in the proposed mapping relationship learning method, different modals of face representations are naturally combined by applying multitask learning with incoherent sparse and low-rank learning with a least-squares loss. Experimental results on three challenging benchmark datasets demonstrate the performance of $\text{M}^2\text{DL}$ .

Multimodal Face-Pose Estimation With Multitask Manifold Deep Learning

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Disassembly sequencing is important for remanufacturing and recycling used or discarded products. AND / OR graphs (AOGs) have been applied to describe practical disassembly problems by using “ AND ” and “ OR ” nodes. An AOG-based disassembly sequence planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and “ AND ” relationship issues can be addressed, OR (exclusive OR ) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual-objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual-objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.

Modeling and Planning for Dual-Objective Selective Disassembly Using and / or Graph and Discrete Artificial Bee Colony

Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy.

Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics

This paper proposes an optimized prediction algorithm of radial basis function neural network based on an improved artificial bee colony (ABC) algorithm in the big data environment. The algorithm first uses crossover and mutation operators of the differential evolution algorithm to replace the search strategy of employed bees in the ABC algorithm, then improves the search strategy of onlookers in the ABC algorithm to produce an optimal candidate food source near the population. The algorithm can better balance local and global searching capability. To verify the efficiency of this algorithm in the big data environment, apply it to Lozi and Tent chaotic time series and measured traffic flow time series, and then compare it with K-nearest neighbor model, radial basis function (RBF) neural network model, improved back propagation neural network model, and RBF neural network based on a cloud genetic algorithm model. The experimental results indicate that the accuracy of prediction for Lozi and Tent chaotic time series and the measured traffic flow improves greatly in the big data environment using the proposed algorithm, which proves the effectiveness of the proposed algorithm in predicting traffic flow time series.

Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network

The increasing number of trucks has a negative impact pertinent to efficiency and safety concerns on the operation of mixed traffic flow along freeways, especially at freeway entrance and exit ramps. To address such issue, this study proposes a simulation-based method for truck-prohibit ramp placement along freeways. The method framework contains two parts: the first part is to generate a set of new truck restriction schemes using simulation experiments, and the second part is to evaluate the generated schemes and find out the current optimal location of truck-prohibit ramps based upon the AHP-TOPSIS method. Three patterns of evaluation indicators are utilized to estimate the performance of freeway service in terms of traffic efficiency, road safety, and accessibility. A case study of the Beijing-Hong Kong-Macao freeway within Henan Province, China, is conducted to verify the effectiveness of the proposed method. Results could provide beneficial insights on the optimal location setting of truck-prohibit ramps to enhance the entire performance of mixed traffic flow along freeways.

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A Simulation-Based Optimization Method for Truck-Prohibit Ramp Placement along Freeways

The main objective of this study is to design a method for evaluating the reasonability of ramp spacing of the expressway in a specific district. The study proposes an entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) estimation method, in which the entropy weight method determines the indicator weights, and TOPSIS is employed to compare different alternatives of ramp spacing. Four patterns of evaluation indicators are taken into account representing traffic efficiency, safety, traffic accessibility, and economy, respectively. Using the Beijing–Hong Kong–Macao Expressway in Henan Province as a case study, the validity of the method is verified, and the optimal ramp spacing is obtained as 14 km for the given scenario. The results of the study show: (1) extreme spacing values are not conducive to the overall benefits of the expressway; (2) ramp spacing settings that allow for coordinated sharing of traffic demand along the route (TDAR) are a prerequisite for an expressway to have great overall benefits; and 3) appropriately shortening ramp spacing will allow the expressway to effectively respond to increased TDAR. The estimation method proposed in this study provides a theoretical reference for the local authority to plan ramp spacing that can satisfy regional traffic demand and ensure the overall benefits of expressways in a sustainable urban context.

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Ramp Spacing Evaluation of Expressway Based on Entropy-Weighted TOPSIS Estimation Method

Electric vehicles (EVs) are one of the most promising applications that are reshaping the future urban mobility market and benefitting the urban environment. Analyzing the adoption of EVs helps both vehicle sales market management and urban transportation-related environmental cost estimation. Previous studies have shown that EV adoption is mostly affected by the economic environment and users’ psychological factors; however, both factors vary among specific urban transportation networks. This paper thus proposes network-related vehicle operating cost functions and a logit-based choice model, which considers both the economic environment and users’ psychological factors at a network level. The model can thus estimate the vehicle adoption for specific networks. Numerical experiments and sensitivity analyses were conducted to illustrate the proposed method and provide practical insights in estimating EV adoption, respectively. The results suggest that EV adoption greatly varies among different cities.

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Dawei Chen

Papers

Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network

A Simulation-Based Optimization Method for Truck-Prohibit Ramp Placement along Freeways

Ramp Spacing Evaluation of Expressway Based on Entropy-Weighted TOPSIS Estimation Method

Analysis of Urban Electric Vehicle Adoption Based on Operating Costs in Urban Transportation Network