A Data-driven Inertial Navigation/Bluetooth Fusion Algorithm for Indoor Localization

Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems

A Data-Driven Approach to Trip Generation Modeling for Urban Residents and Non-local Travelers

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.

Intersection Traffic Signal Optimization Considering Lane-Changing Behavior Caused Nearby Bus Bay Stop Upstream

Ranking contributors to traffic crashes on mountainous freeways from an incomplete dataset: A sequential approach of multivariate imputation by chained equations and random forest classifier

Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data

Bus Travel Time Prediction Based on Ensemble Learning Methods

Automated traffic incident detection with a smaller dataset based on generative adversarial networks

A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction

Missing data estimation method for time series data in structure health monitoring systems by probability principal component analysis

A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems

Missing data, especially a block of missing data, inevitably occur in structural health monitoring systems. Because of their severe negative effects, many methods that use measured data to infer missing data have been proposed in previous research to solve the problem. However, capturing complex correlations from raw measured signal data remains a challenge. In this study, empirical mode decomposition is combined with a long short-term memory deep learning network for the recovery of the measured signal data. The proposed hybrid method converts the missing data imputation task as a time series prediction task, which is then solved by a “divide and conquer” strategy. The core concept of this strategy is the prediction of the subsequences of the raw measured signal data, which are decomposed by empirical mode decomposition rather than directly predicted, as the decomposition can assist in the modeling of the irregular periodic changes of the measured signal data. In addition, the long short-term memory network in the hybrid model can remember more long-range correlations of subsequences than can the traditional artificial neural network. Three widely used prediction models, namely, the autoregressive integrated moving average, support vector regression, and artificial neural network models, are also implemented as benchmark models. Raw acceleration data collected from a cable-stayed bridge are used to evaluate the performance of the proposed method for missing measured signal data imputation. The recovery results of the measured signal data demonstrate that the proposed hybrid method exhibits excellent performance from two perspectives. First, the decomposition by empirical mode decomposition can improve the accuracy of the core long short-term memory prediction model. Second, the long short-term memory model outperforms other benchmark models because it can fit more microscopic changes of measured values. The experiments conducted in this study also suggest that the change patterns of raw measured signal data are complex, and it is therefore important to extract the features before modeling.

Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model

Coupled application of deep learning model and quantile regression for travel time and its interval estimation using data in different dimensions

Real-time traffic incident detection based on a hybrid deep learning model

Self-reports of workloads and aberrant driving behaviors as predictors of crash rate among taxi drivers: A cross-sectional study in China

Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method

The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China

Revealing the Varying Impact of Urban Built Environment on Online Car-Hailing Travel in Spatio-Temporal Dimension: An Exploratory Analysis in Chengdu, China

Online car-hailing travel is an increasingly popular mode of urban transport. A fundamental understanding of the relationship between the urban built environment and online car-hailing travel is essential for developing the corresponding traffic strategy and addressing sustainable urban planning and design. However, the varying impact of the urban built environment on online car-hailing travel in the spatial dimension has not been sufficiently investigated. This paper aims to fill this gap by using geographically weighted regression (GWR) to check the spatial heterogeneity of the likely influence. The result shows that the GWR model is superior to the global model (OLS) from the perspective of goodness of fit. The study finds that the recreation and entertainment Point of Interest (POI) and the residential district POI are the most influential factors on night online car-hailing travel. Land-use mix is found to have a positive effect on online car-hailing travel, and online car-hailing services can be a complementary mode for public transport, especially in suburban areas.

A New Solution for Freeway Congestion: Cooperative Speed Limit Control Using Distributed Reinforcement Learning

Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm

Traffic speed prediction for intelligent transportation system based on a deep feature fusion model

Analyzing Passenger Travel Demand Related to the Transportation Hub inside a City Area using Mobile Phone Data

The passenger transportation hub plays a crucial role in the urban transportation system. Analyzing transportation hub related travel demand is necessary to support urban transportation planning and management. However, it is difficult to use the traditional travel survey methods to study travel demand because tracking passenger travel trajectories is a near impossible task. The location information from the cellular system provides a feasible way to solve the problem. This paper concentrates on applying mobile phone data to study passenger travel demand related to the Hongqiao transportation hub in Shanghai, China. First, a method is introduced to collect passenger travel information related to the hub from mobile phone data. Then, travel demand indexes are presented to characterize the travel demand in a visual way. Finally, transportation corridors, which connect the hub and other urban areas, are identified to analyze the distribution of travel demand more thoroughly. The results illustrate that the passenger travel demand shows an obvious tide pattern in the city area with the Hongqiao transportation hub as the center. Moreover, there are two identified transportation corridors which reveal the major distribution directions of the passengers, that is, the city center and the Zizhu industrial development zone. The approach in this study testifies that mobile phone data has great potential for transportation planning and management related to transportation hubs.

Robust and flexible strategy for missing data imputation in intelligent transportation system

An Improved Single-Lane Cellular Automaton Model considering Driver’s Radical Feature

Traffic flow models are of vital significance to study the traffic system and reproduce typical traffic phenomena. In the process of establishing traffic flow models, human factors need to be considered particularly to enhance the performance of the models. Accordingly, a series of car-following models and cellular automaton models were proposed based on comprehensive consideration of various driving behaviors. Based on the comfortable driving (CD) model, this paper innovatively proposed an improved cellular automaton model incorporating impaired driver’s radical feature (RF). The impaired driver’s radical feature was added to the model with respect to three aspects, that is, desired speed, car-following behavior, and braking behavior. Empirical data obtained from a highway segment was used to initialize impaired driver’s radical feature distribution and calibrate the proposed model. Then, numerical simulations validated that the proposed improved model can well reproduce the traffic phenomena, as shown by the fundamental diagram and space-time diagram. Also, in low-density state, it can be found that the RF model is superior to the CD model in simulating the speed difference characteristics, where the average speed difference of adjacent vehicles for RF model is more consistent with reality. The result also discussed the potential impact of impaired drivers on rear-end collisions. It should be noted that this study is an early stage work to evaluate the existence of impaired driving behavior.

Processing Inductance Loop Detector Data on an Urban Expressways

Autonomous and Connected Vehicles: The Capacity of Mixed Traffic Flow at Signalized Intersection with the ACDA-MTD Model

Travel time prediction for highway network based on the ensemble empirical mode decomposition and random vector functional link network

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

Due to the increase of congestion on highways, providing real-time information about the traffic state has become a crucial issue. Hence, it is the aim of this research to build an accurate traffic speed prediction model using symbolic regression to generate significant information for travellers. It is built based on genetic programming using Pareto front technique. With real world data from microwave sensor, the performance of the proposed model is compared with two other widely used models. The results indicate that the symbolic regression is the most accurate among these models. Especially, after an incident occurs, the performance of the proposed model is still the best which means it is robust and suitable to predict traffic state of highway under different conditions.

Real-Time Traffic Incident Detection with Classification Methods

Multiple imputation for incomplete traffic accident data using chained equations

Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model

Bagging-SVMs Algorithm-Based Traffic Incident Detection

Analysis of Factors Influencing the Vehicle Damage Level in Fatal Truck-Related Accidents and Differences in Rural and Urban Areas

Accidents involving large trucks very often end up with deadly consequences. Innocent people getting killed are acknowledged globally as one of the traffic safety greatest problems and challenges. While risk factors on truck-related accidents have been researched extensively, the impact on fatalities has received little or no attention, especially considering rural and urban areas, respectively. In this study, the generalized ordered logit model was used in Stata 11.0 to explore the complex mechanism of truck-related accidents in different areas. Data were obtained from The Trucks in Fatal Accidents database (TIFA). The Akaike Information Criterion (AIC) indicates that the model used in this paper is superior to traditional ordered logit model. The results showed that 9 variables affect the vehicle damage level in a fatal crash in both areas but with different directions. Furthermore, 23 indicators significantly affect the disabling damage in the same manner. Also, there are factors that are significant solely in one area and not in the other: 12 in rural and 2 in urban areas.

Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information

Professional drivers’ views on risky driving behaviors and accident liability: a questionnaire survey in Xining, China