A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Ding, Chenxi; Wang, Wuhong; Wang, Xiao; Baumann, Martin

doi:10.1155/2013/967358

Cited by 55 publications

(42 citation statements)

References 20 publications

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“…However, the trajectory reconstruction is an extremely complex and non‐linear problem due to the complicated spatiotemporal variation of the trajectories. Backpropagation (BP) neural networks have been used widely to solve such problems (Chen, Chi, Wang, Pang, & Xiao, ; Ding, Wang, Wang, & Baumann, ; Partsinevelos, Agouris, & Stefanidis, ; Xu, Li, & Claramunt, ). It was demonstrated in previous studies that BP neural networks have the ability to capture non‐linearity, and good prediction capability and flexibility.…”

Section: Related Workmentioning

confidence: 99%

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Liu

et al. 2018

Transactions in GIS

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Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time-series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log-linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub-district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.

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Section: Related Workmentioning

confidence: 99%

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Liu

et al. 2018

Transactions in GIS

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“…(iv) once the winning neuron and its string are determined, (a) a weight update matrix ( ) is computed for that string using the position information according to (7), (8), and (10), (b) the weight update matrix is computed for that string for updating the orientation information according to the new position as in Figure 2;…”

Section: Adaptive Process In Sofmat For a Given Input Patternmentioning

confidence: 99%

“…Especially in studies dealing with complex data, ANN is very useful and preferable. The use of ANN has a wide range, such as analyzing seismic signals [6], wind speed forecasting [7], feature prediction in urban traffic flow [8], in sludge bulking [9], and in founding of reference voltage of maximum power point under different atmospheric conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Organizing Artificial Neural Networks on Simulated Diffusion Tensor Images

Göksel-Duru

Ozkan

2013

Mathematical Problems in Engineering

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Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determination of fiber connections which leads to brain mapping. The success of DTMRI is very much algorithm dependent, and its verification is of great importance due to limited availability of a gold standard in the literature. In this study, unsupervised artificial neural network class, namely, self-organizing maps, is employed to discover the underlying fiber tracts. A common artificial diffusion tensor resource, named “phantom images for simulating tractography errors” (PISTE), is used for the accuracy verification and acceptability of the proposed approach. Four different tract geometries with varying SNRs and fractional anisotropy are investigated. The proposed method, SOFMAT, is able to define the predetermined fiber paths successfully with a standard deviation of (0.8–1.9) × 10−3depending on the trajectory and the SNR value selected. The results illustrate the capability of SOFMAT to reconstruct complex fiber tract configurations. The ability of SOFMAT to detect fiber paths in low anisotropy regions, which physiologically may correspond to either grey matter or pathology (abnormality) and uncertainty areas in real data, is an advantage of the method for future studies.

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“…H Zhou et al 11 proposed a method to infer a truck driver's intention of lane change before the maneuver initiation using the information of driver's gaze behavior. C Ding et al 12 provided a method to predict the lane-change trajectory of host vehicle with regard to the vehicle position, speed, acceleration, and the time headway using BP neural network with two hidden layers. Furthermore, host vehicle trajectory can be predicted with the positional relationship between the host vehicle and adjacent vehicles.…”

Section: Introductionmentioning

confidence: 99%

Lane-change prediction method for adaptive cruise control system with hidden Markov model

Yuan

Wang

2018

Advances in Mechanical Engineering

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A test platform with a millimeter-wave radar sensor, lane-line sensor, gyroscope, and controller area network was established to improve safety in using adaptive cruise control systems in vehicles. The motion-state characterization data of the host vehicle and surrounding vehicles in a real traffic environment were captured. The prediction method for the lane-changing maneuver of the vehicle ahead was developed using a hidden Markov model based on the distance between the host vehicle and the front vehicle, as well as the lateral and longitudinal velocities of the vehicle in front. The adaptive cruise control system control algorithm for assessing the target vehicle was optimized. The model was tested, and its predictions were compared with measured data. Result shows that the lane-changing and lane-keeping behaviors of the vehicle ahead can be predicted efficiently and accurately by the model. The maximum prediction accuracy rate for straight roads was 97% with the time window length of 4.5 s, whereas that for curved roads was 96% with the time window length of 3.5 s.

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A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Cited by 55 publications

References 20 publications

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Application of Self-Organizing Artificial Neural Networks on Simulated Diffusion Tensor Images

Lane-change prediction method for adaptive cruise control system with hidden Markov model

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