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Fan Ding
School of Transportation, Southeast University, Nanjing 211189, China

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Original research paper
Published: 10 July 2021 in IET Intelligent Transport Systems
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Urban rail transit has become an indispensable mode in major cities worldwide regarding the advantages of large capacity, high speed, punctuality, and environmental protection. Origin-destination (OD) matrix data is crucial to the organisation of rail train operation and management. Nevertheless, rail transit OD matrices are inevitably suffered from data loss problems due to the data transmission and acquisition failures. Tensor completion is a state-of-the-art method for missing data imputation. In this paper, a novel tensor completion method for OD- matrix completion is proposed. To this end, an OD-matrix tensor is established to represent OD information, and the similarity matrix of OD-matrix tensor for each dimension is extracted as a piece of auxiliary information expressing underlying multi-mode relationships of OD data. Finally, a manifold regularised tensor factorisation is applied to impute the missing OD data, in which the Graph Laplacians inferred from similarity weight matrices are used as regularisation priors on factorisation factors. The proposed model is applied to a case study of the metro line in Xi'an, China. The experimental results indicate that the proposed method outperforms baselines. It can accurately impute missing data within the OD matrices and work well even when the missing ratio is up to 80%.

ACS Style

Hanxuan Dong; Fan Ding; Huachun Tan; Yuankai Wu; Qin Li; Bin Ran. Rail transit OD‐matrix completion via manifold regularized tensor factorisation. IET Intelligent Transport Systems 2021, 1 .

AMA Style

Hanxuan Dong, Fan Ding, Huachun Tan, Yuankai Wu, Qin Li, Bin Ran. Rail transit OD‐matrix completion via manifold regularized tensor factorisation. IET Intelligent Transport Systems. 2021; ():1.

Chicago/Turabian Style

Hanxuan Dong; Fan Ding; Huachun Tan; Yuankai Wu; Qin Li; Bin Ran. 2021. "Rail transit OD‐matrix completion via manifold regularized tensor factorisation." IET Intelligent Transport Systems , no. : 1.

Journal article
Published: 25 June 2021 in Sustainability
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With the finishing of the construction of the main body of a freeway network, adequately monitoring the traffic status of the network has become an urgent need for both travelers and transportation operators. Various methods are proposed to collect traffic information for this purpose. In this article, a data-driven feature-based learning application is implemented to detect segment traffic status using mobile phone data, building on the practical success of deep learning models in other fields. The traffic status estimation is achieved via the application of a three-level long, short-term memory model. Two phone features are extracted from the raw mobile phone data. A large-scale field experiment was conducted using actual data in Jiangsu, China collected over the “National Holiday Golden Week” of 2014. To evaluate the performance, both precision and recall scores are given along with the overall accuracy. The final results of the large-scale experiment indicate that the proposed application performed well and can be an emerging solution for traffic state monitoring when only limited roadside sensing devices are installed.

ACS Style

Qiang Liu; Jianguang Xie; Fan Ding. A Data-Driven Feature Based Learning Application to Detect Freeway Segment Traffic Status Using Mobile Phone Data. Sustainability 2021, 13, 7131 .

AMA Style

Qiang Liu, Jianguang Xie, Fan Ding. A Data-Driven Feature Based Learning Application to Detect Freeway Segment Traffic Status Using Mobile Phone Data. Sustainability. 2021; 13 (13):7131.

Chicago/Turabian Style

Qiang Liu; Jianguang Xie; Fan Ding. 2021. "A Data-Driven Feature Based Learning Application to Detect Freeway Segment Traffic Status Using Mobile Phone Data." Sustainability 13, no. 13: 7131.

Journal article
Published: 08 February 2021 in Sustainability
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Highway system is experiencing increasing traffic congestion with fast-growing number of vehicles in metropolitan areas. Implementing traffic management strategies such as utilizing the hard shoulder as an extra lane could increase highway capacity without extra construction work. This paper presents a method of determining an optimal traffic condition and speed limit of opening hard shoulder. Firstly, the traffic states are clustered using K-Means, mean shift, agglomerative and spectral clustering methods, and the optimal clustering algorithm is selected using indexes including the silhouette score, Davies-Bouldin Index and Caliski-Harabaz Score. The results suggested that the clustering effect of using K-Means method with three categories is optimal. Then, cellular automata model is used to simulate traffic conditions before and after the hard shoulder running strategy is applied. The parameters of the model, including the probabilities of random deceleration, slow start and lane change, are calibrated using real traffic data. Four indicators including the traffic volume, the average speed, the variance of speed, and the travel time of emergency rescue vehicles during traffic accident obtained using the cellular automata model are used to evaluate various hard shoulder running strategies. By using factor analysis and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods, the optimal traffic condition and speed limit of opening hard shoulder could be determined. This method could be applied to highway segments of various number of lanes and different speed limits to optimize the hard shoulder running strategy for highway management

ACS Style

Fan Yang; Fan Wang; Fan Ding; Huachun Tan; Bin Ran. Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy. Sustainability 2021, 13, 1822 .

AMA Style

Fan Yang, Fan Wang, Fan Ding, Huachun Tan, Bin Ran. Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy. Sustainability. 2021; 13 (4):1822.

Chicago/Turabian Style

Fan Yang; Fan Wang; Fan Ding; Huachun Tan; Bin Ran. 2021. "Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy." Sustainability 13, no. 4: 1822.

Journal article
Published: 28 November 2020 in Sustainability
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With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

ACS Style

Fan Ding; Jiwan Jiang; Yang Zhou; Ran Yi; Huachun Tan. Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon. Sustainability 2020, 12, 9955 .

AMA Style

Fan Ding, Jiwan Jiang, Yang Zhou, Ran Yi, Huachun Tan. Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon. Sustainability. 2020; 12 (23):9955.

Chicago/Turabian Style

Fan Ding; Jiwan Jiang; Yang Zhou; Ran Yi; Huachun Tan. 2020. "Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon." Sustainability 12, no. 23: 9955.

Journal article
Published: 26 October 2020 in IEEE Transactions on Intelligent Transportation Systems
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Understanding the underlying patterns of the urban mobility dynamics is essential for both the traffic state estimation and management of urban facilities and services. Due to the coupling relationship of generative factors in spatial-temporal domain, it is challenging to model the citywide traffic dynamics under a structural pattern of critical features such as hours of days, days of weeks and weather conditions. To address this challenge, this article develops a disentangled representation learning framework to learn an interpretable factorized representation of the independent data generative factors. In order to make full use of the knowledge on generative factors, this article proposes spatial-temporal generative adversarial network (ST-GAN) to assign the generative factors of traffic flow to the feature vector in latent space and reconstructs the high-dimensional citywide traffic flow from the given factors. With the help of the disentangled representations, the decomposed feature vector in latent space discloses the relationship between underlying patterns and citywide traffic dynamics. Several comprehensively experiments show that ST-GAN not only effectively improves the prediction accuracy but also promisingly characterize structural properties of the traffic evolution process.

ACS Style

Hailong Zhang; Yuankai Wu; Huachun Tan; Hanxuan Dong; Fan Ding; Bin Ran. Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -11.

AMA Style

Hailong Zhang, Yuankai Wu, Huachun Tan, Hanxuan Dong, Fan Ding, Bin Ran. Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-11.

Chicago/Turabian Style

Hailong Zhang; Yuankai Wu; Huachun Tan; Hanxuan Dong; Fan Ding; Bin Ran. 2020. "Understanding and Modeling Urban Mobility Dynamics via Disentangled Representation Learning." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-11.

Journal article
Published: 29 September 2020 in IEEE Intelligent Transportation Systems Magazine
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ACS Style

Xiasen Wang; Yang Zhou; Don MacKenzie; Fan Ding. Predicted Network Equilibrium Model of Electric Vehicles with Stationary and Dynamic Charging Infrastructure on the Road Network. IEEE Intelligent Transportation Systems Magazine 2020, 1 .

AMA Style

Xiasen Wang, Yang Zhou, Don MacKenzie, Fan Ding. Predicted Network Equilibrium Model of Electric Vehicles with Stationary and Dynamic Charging Infrastructure on the Road Network. IEEE Intelligent Transportation Systems Magazine. 2020; (99):1.

Chicago/Turabian Style

Xiasen Wang; Yang Zhou; Don MacKenzie; Fan Ding. 2020. "Predicted Network Equilibrium Model of Electric Vehicles with Stationary and Dynamic Charging Infrastructure on the Road Network." IEEE Intelligent Transportation Systems Magazine , no. 99: 1.

Journal article
Published: 17 September 2020 in Sustainability
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Trip generation modeling is essential in transportation planning activities. Previous modeling methods that depend on traditional data collection methods are inefficient and expensive. This paper proposed a novel data-driven trip generation modeling method for urban residents and non-local travelers utilizing location-based social network (LBSN) data and cellular phone data and conducted a case study in Nanjing, China. First, the point of interest (POI) data of the LBSN were classified into various categories by the service type, then, four features of each category including the number of users, number of POIs, number of check-ins, and number of photos were aggregated by traffic analysis zones to be used as explanatory variables for the trip generation models. We used a random tree regression method to select the most important features as the model inputs, and the trip models were established based on the ordinary least square model. Then, an exploratory approach was used to test the performance of each combination of the variables with various test methods to identify the best model for residents’ and travelers’ trip generation functions. The results suggest land use compositions have significant impact on trip generations, and the trip generation patterns are different between urban residents and non-local travelers.

ACS Style

Fan Yang; Linchao Li; Fan Ding; Huachun Tan; Bin Ran. A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers. Sustainability 2020, 12, 7688 .

AMA Style

Fan Yang, Linchao Li, Fan Ding, Huachun Tan, Bin Ran. A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers. Sustainability. 2020; 12 (18):7688.

Chicago/Turabian Style

Fan Yang; Linchao Li; Fan Ding; Huachun Tan; Bin Ran. 2020. "A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers." Sustainability 12, no. 18: 7688.

Journal article
Published: 03 September 2020 in Sustainability
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A driving cycle is important to accomplish an accurate depiction of a vehicle’s driving characteristics as the traction motor’s flexible response to stop and start commands. In this paper, the driving cycle construction of an urban hybrid electric bus (HEB) in Zhengzhou, China is developed in which a measurement system integrating global positioning and inertial navigation function is used to acquire driving data. The collected data are then divided into acceleration, deceleration, uniform, and stop fragments. Meanwhile, the velocity fragments are classified into seven state clusters according to their average velocities. A transfer matrix applied to reveal the transfer relationship of velocity clusters can be obtained with statistical analysis. In the third stage, a three-part construction method of driving cycle is designed. Firstly, according to the theory of Markov chain, all the alternative parts that satisfy the construction’s precondition are selected based on the transfer matrix and Monte Carlo method. The Zhengzhou urban driving cycle (ZZUDC) could be determined by comparing the performance measure (PM) values subsequently. Eventually, the method and the cycle are validated by the high correlation coefficient (0.9972) with original data of ZZUDC than that of the other driving cycle (0.9746) constructed with traditional micro-trip and as well by comparing several statistical characteristics of ZZUDC and seven international cycles. Particularly, with around 20.5 L/100 km fuel and approximately 12.8 kwh/100 km electricity consumption, there is a narrow gap between the energy consumption of ZZUDC and WVUCITY, and their characteristics are similar.

ACS Style

Jiankun Peng; Jiwan Jiang; Fan Ding; Huachun Tan. Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China. Sustainability 2020, 12, 7188 .

AMA Style

Jiankun Peng, Jiwan Jiang, Fan Ding, Huachun Tan. Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China. Sustainability. 2020; 12 (17):7188.

Chicago/Turabian Style

Jiankun Peng; Jiwan Jiang; Fan Ding; Huachun Tan. 2020. "Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China." Sustainability 12, no. 17: 7188.

Journal article
Published: 10 August 2020 in IEEE Access
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This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers’ risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from the design car-following target. With the risk-sensitive parameter embedded in LEQG, the proposed method has the capability to characterize risk preference heterogeneity of each AV against uncertainties according to each human drivers’ preference. Further, the established control theory can achieve both expensive control mode and non-expensive control mode via changing the weighting matrix of the cost function in LEQG to reveal different treatments on input. Simulation tests validate the proposed approach can characterize different driving behaviors and its effectiveness in terms of reducing the deviation from equilibrium state. The ability to produce different trajectories and generate smooth control of the proposed algorithm is also verified.

ACS Style

Jiwan Jiang; Fan Ding; Yang Zhou; Jiaming Wu; Huachun Tan. A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control. IEEE Access 2020, 8, 145056 -145066.

AMA Style

Jiwan Jiang, Fan Ding, Yang Zhou, Jiaming Wu, Huachun Tan. A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control. IEEE Access. 2020; 8 ():145056-145066.

Chicago/Turabian Style

Jiwan Jiang; Fan Ding; Yang Zhou; Jiaming Wu; Huachun Tan. 2020. "A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control." IEEE Access 8, no. : 145056-145066.

Journal article
Published: 31 March 2020 in IEEE Access
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Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data imputation. Despite their superior imputation accuracies, the adoption of these imputed data is not uniform in modern ITSs applications. The primary goal of this paper is to explore how to use tensor completion methods to support ITSs. In particular, we study how to improve traffic flow prediction accuracy under different missing scenarios. Specifically, three common missing scenarios including element-wise random missing, time-structured missing, and space-structured missing are considered. Four classical tensor completion models including Smooth PARAFAC Decomposition based Completion (SPC), CP Decomposition-based (CP-WOPT) Completion, Tucker Decomposition-based Completion (TDI), and High-accuracy Low-rank Tensor Completion (HaLRTC) are used to impute the missing data. Four well-known prediction methods including Support Vector Regression (SVR), K-nearest Neighbor (KNN), Gradient Boost Regression Tree (GBRT), and Long Short-term Memory (LSTM) are tested. The simple mean value interpolation completed traffic data is regarded as the baseline data. The extensive experiments show that improvements of traffic flow prediction can be achieved by increasing missing traffic data imputation accuracy at most cases. Interestingly we find that prediction accuracy cannot be improved by an imputation model when the sparsely observed training datasets already provide sufficient training samples.

ACS Style

Qin Li; Huachun Tan; Yuankai Wu; Linhui Ye; Fan Ding. Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods. IEEE Access 2020, 8, 63188 -63201.

AMA Style

Qin Li, Huachun Tan, Yuankai Wu, Linhui Ye, Fan Ding. Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods. IEEE Access. 2020; 8 (99):63188-63201.

Chicago/Turabian Style

Qin Li; Huachun Tan; Yuankai Wu; Linhui Ye; Fan Ding. 2020. "Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods." IEEE Access 8, no. 99: 63188-63201.

Journal article
Published: 04 October 2019 in Sustainability
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The rapid development of urban metropolises has attracted a growing number of immigrants and travelers, increasing the burden on transportation systems. Previous research on urban mobility patterns have ignored the temporal variations and heterogeneity in divergent urban trip makers due to the limited data resolution and coverage. In this paper, we analyzed cellular phone data of more than five million travelers for one month in Nanjing, China and proposed a method to extract trip origin and destination information from cellular phone signal data. We found that mobility patterns are different for urban residents, short-term travelers, and transfer travelers, and that trip length distributions can best be described by gamma and exponential distributions. In addition to the daily trip length distribution models, we utilized the agglomerative hieratical clustering method in order to group similar hourly trip patterns and further proposed within-day trip length distribution models under different times of the day and days of the week.

ACS Style

Fan Yang; Zhenxing Yao; Fan Ding; Huachun Tan; Bin Ran. Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing. Sustainability 2019, 11, 5502 .

AMA Style

Fan Yang, Zhenxing Yao, Fan Ding, Huachun Tan, Bin Ran. Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing. Sustainability. 2019; 11 (19):5502.

Chicago/Turabian Style

Fan Yang; Zhenxing Yao; Fan Ding; Huachun Tan; Bin Ran. 2019. "Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing." Sustainability 11, no. 19: 5502.

Journal article
Published: 11 June 2019 in Sustainability
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Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

ACS Style

Fan Yang; Fan Ding; Xu Qu; Bin Ran. Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data. Sustainability 2019, 11, 3220 .

AMA Style

Fan Yang, Fan Ding, Xu Qu, Bin Ran. Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data. Sustainability. 2019; 11 (11):3220.

Chicago/Turabian Style

Fan Yang; Fan Ding; Xu Qu; Bin Ran. 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data." Sustainability 11, no. 11: 3220.

Research article
Published: 14 May 2019 in IET Intelligent Transport Systems
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Two traffic features [unique cellphone counts (UCC) and pseudo speed (PS)] could be extracted from cellphone activity (CA) data that contain all wireless communication records of a standby or active cellphone. PS has been validated to be close to the traffic speed in the previous works. Further investigation was conducted in this study on the relationship between UCC and the traffic volume. First, a dynamic time warping (DTW)-based method was proposed to analyse the UCC time-series from different freeway links, which consisted of a DTW-based hierarchical clustering step and a DTW-based classification step. Field data was applied in the case study and this proposed analysis method found that UCCs varied from link to link. Besides, this method efficiently classified the links into different groups based on the similarity between UCC and the traffic volume. Moreover, the analysis results were applied to formalising the relationship model between UCC and traffic volume. The case study indicated that a generic linear relationship model might be calibrated, if those links were classified in the same group and were neighbouring enough for a shared close traffic volume value. Besides, it was validated that this model has a good performance under the uncongested traffic condition.

ACS Style

Shanglu He; Fan Ding; Yang Zhou; Yang Cheng; Bin Ran. Investigating and modelling the relationship between traffic volume and extracts from cellphone activity data. IET Intelligent Transport Systems 2019, 13, 1299 -1308.

AMA Style

Shanglu He, Fan Ding, Yang Zhou, Yang Cheng, Bin Ran. Investigating and modelling the relationship between traffic volume and extracts from cellphone activity data. IET Intelligent Transport Systems. 2019; 13 (8):1299-1308.

Chicago/Turabian Style

Shanglu He; Fan Ding; Yang Zhou; Yang Cheng; Bin Ran. 2019. "Investigating and modelling the relationship between traffic volume and extracts from cellphone activity data." IET Intelligent Transport Systems 13, no. 8: 1299-1308.

Journal article
Published: 01 May 2019 in Journal of Transportation Engineering, Part A: Systems
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ACS Style

Fan Ding; Zhen Zhang; Yang Zhou; Xiaoxuan Chen; Bin Ran. Large-Scale Full-Coverage Traffic Speed Estimation under Extreme Traffic Conditions Using a Big Data and Deep Learning Approach: Case Study in China. Journal of Transportation Engineering, Part A: Systems 2019, 145, 05019001 .

AMA Style

Fan Ding, Zhen Zhang, Yang Zhou, Xiaoxuan Chen, Bin Ran. Large-Scale Full-Coverage Traffic Speed Estimation under Extreme Traffic Conditions Using a Big Data and Deep Learning Approach: Case Study in China. Journal of Transportation Engineering, Part A: Systems. 2019; 145 (5):05019001.

Chicago/Turabian Style

Fan Ding; Zhen Zhang; Yang Zhou; Xiaoxuan Chen; Bin Ran. 2019. "Large-Scale Full-Coverage Traffic Speed Estimation under Extreme Traffic Conditions Using a Big Data and Deep Learning Approach: Case Study in China." Journal of Transportation Engineering, Part A: Systems 145, no. 5: 05019001.

Journal article
Published: 20 January 2019 in Sensors
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Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis, a filtration and mining algorithm was developed to extract traffic state information (i.e., travel time, traffic volume, and speed). In addition, to evaluate the performance of the proposed system, a practical field test was conducted through the use of the system to monitor traffic states of a major corridor in China. The comparison results with loop data indicated that traffic speed obtained from the system was consistent with that collected from loop detectors. The mean absolute percentage error reached 3.55% in the best case. Furthermore, the preliminary analysis proved the existence of the highly correlated relationship between volumes obtained from the system and from loop detectors. The evaluation confirmed the feasibility of applying Wi-Fi signal data to acquisition of traffic information, indicating that Wi-Fi signal data could be used as a supplementary data source for monitoring real-time traffic states.

ACS Style

Fan Ding; Xiaoxuan Chen; Shanglu He; Guangming Shou; Zhen Zhang; Yang Zhou. Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test. Sensors 2019, 19, 409 .

AMA Style

Fan Ding, Xiaoxuan Chen, Shanglu He, Guangming Shou, Zhen Zhang, Yang Zhou. Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test. Sensors. 2019; 19 (2):409.

Chicago/Turabian Style

Fan Ding; Xiaoxuan Chen; Shanglu He; Guangming Shou; Zhen Zhang; Yang Zhou. 2019. "Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test." Sensors 19, no. 2: 409.

Research article
Published: 13 January 2019 in Journal of Advanced Transportation
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Cellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies predicted people-flow using cellular probe data in real-time. In addition, it is hard to validate the prediction method at a large scale. This paper proposed a data-driven method for dynamic people-flow prediction, which contains four models. The first model is a cellular probe data preprocessing module, which removes the inaccurate and duplicated records of cellular data. The second module is a grid-based data transformation and data integration module, which is proposed to integrate multiple data sources, including transportation network data, point-of-interest data, and people movement inferred from real-time cellular probe data. The third module is a trip-chain based human-daily-trajectory generation module, which provides the base dataset for data-driven model validation. The fourth module is for dynamic people-flow prediction, which is developed based on an online inferring machine-learning model (random forest). The feasibility of dynamic people-flow prediction using real-time cellular probe data is investigated. The experimental result shows that the proposed people-flow prediction system could provide prediction precision of 76.8% and 70% for outbound and inbound people, respectively. This is much higher than the single-feature model, which provides prediction precision around 50%.

ACS Style

Xiaoxuan Chen; Xia Wan; Fan Ding; Qing Li; Charlie McCarthy; Yang Cheng; Bin Ran. Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data. Journal of Advanced Transportation 2019, 2019, 1 -12.

AMA Style

Xiaoxuan Chen, Xia Wan, Fan Ding, Qing Li, Charlie McCarthy, Yang Cheng, Bin Ran. Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data. Journal of Advanced Transportation. 2019; 2019 ():1-12.

Chicago/Turabian Style

Xiaoxuan Chen; Xia Wan; Fan Ding; Qing Li; Charlie McCarthy; Yang Cheng; Bin Ran. 2019. "Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data." Journal of Advanced Transportation 2019, no. : 1-12.