Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

Xu, Mengyun; Guo, Ke; Fang, Jie; Chen, Zeshan

doi:10.1109/access.2019.2926851

Cited by 18 publications

(9 citation statements)

References 38 publications

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“…A more sophisticated ANN approach that considers passenger flow was developed by Amita et al (2016); this approach outperforms the traditional method. Xu et al (2019) presented an ANN-based travel-time prediction method that incorporates spatialtemporal relevancy to infer the travel-time distribution; they achieve relatively high prediction accuracy with low expenditure. To improve the prediction accuracy, some revised algorithms, such as the Bayesian inference theory (Van Hinsbergen et al 2009) and particle swarm optimization (Ji et al 2016), are combined with the ANN.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Liu

2021

Transport

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To improve the service quality and convenience of bus travel services, this paper proposes the Intelligent Bus Travel Service Model (IBTSM). The IBTSM makes it possible to provide a travel strategy considering every aspect of bus travel, specifically, delay in the peak period arising from limited carrying-capacities of buses. A three-step approach was executed toward implementing the IBTSM. First, the bus travel-time was predicted using Long Short-Term Memory (LSTM). Next, the crowding level in the bus was evaluated using a fuzzy expert system, based on which a reasonable start-off time was planned, and the delay caused by large passenger flow was circumvented. The k-Nearest Neighbours (k-NN) algorithm was used to provide input data of passenger flow. In this study, the correlation between passenger flow variation and bus services was investigated to extend the provisions of the travel strategy to include start-off time scheduling and target bus selection, rather than only bus running-time estimation. The proposed model was evaluated using a bus in China as a case study, and its reliability and positive impact on promoting both the quality of bus services and development of intelligent travel were demonstrated.

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Section: Literature Reviewmentioning

confidence: 99%

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Liu

2021

Transport

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“…Most scholars tend to study the prediction of traffic flow, travel time and speed of segments, however, there is a lack of applied research on map‐matching base on GPS trajectory. Du et al [32] considered the characteristics of multi‐channel and irregularity of urban traffic passenger flow between different traffic lines, and proposed a deep irregular convolutional residual long‐term and short‐term memory (LSTM) network model, which is used for urban traffic passenger flow prediction; Xu et al [33] used artificial neural networks to allocate travel time between segments of the travel route based on GPS floating car data. Zang et al [34] proposed a multi‐scale spatio‐temporal feature learning network model to deal with the challenging task of long‐term traffic speed prediction on elevated roads; Liu et al [35] proposed a speed prediction model based on wavelet packet decomposition, convolutional neural network (CNN) and convolutional LSTM (CNNLSTM) network; Ma et al [36] proposed the LSTM neural network (LSTM NN), which can effectively capture the non‐linear traffic dynamic speed.…”

Section: Review Of the Existing Algorithmsmentioning

confidence: 99%

Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

Liu

Fang

Tong

et al. 2020

IET intell. transp. syst

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Global positioning system (GPS) trajectory map matching projects GPS coordinates to the road network. Most existing algorithms focus on the geometric and topological relationships of the road network, while did not make full use of the historical road network information and floating car data. In this study, the authors proposed a deep learning enabled vehicle trajectory map-matching method with advanced spatial-temporal analysis (DST-MM). The algorithm mainly focused on the following three aspects: (i) analyse the spatial relevancy from the prospective of geometric analysis, topology analysis and intersection analysis; (ii) to make full use of the historical and real-time data, a deep learning model was conducted to extract the road network and vehicle trajectory features and (iii) establish a speed prediction model and nest it in the temporal analysis structure. It narrows down the path search range through establishing the dynamic candidate graph. Experimental results show that the proposed DST-MM algorithm outperforms the existing algorithms in terms of matching accuracy for low-sampling frequencies GPS data, especially in the central urban area.

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“…However, it is shown with the proposed method that timestamps can be considered with data having a sampling rate of 1 Hz to calculate travel time. Travel time is chosen because it is "easy to understand and widely employed in representing traffic status" [18]. The following section details how the traffic model is built.…”

Section: Model Descriptionmentioning

confidence: 99%

From Massive Trajectory Data to Traffic Modeling for Better Behavior Prediction in a Usage-Based Insurance Context

Blais

Badard

Duchesne

et al. 2020

IJGI

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Usage-Based Insurance (UBI) is an insurance framework that has made its appearance in the last few years. It allows direct measurement of the traveling of policyholders, hence the growing interest of the industry to better understand driving behaviors. UBI generates large data volumes, from which events can be extracted, like harsh brakes or accelerations. Still, these events are measured without contextual information, which limits their explanatory power. Traffic is one of these types of contextual information that may have great potential, but access to such data remains an issue. Solutions exist, like traffic data from external providers, but for insurance companies that conduct business over large areas, this could result in very large costs. This paper demonstrates that data from insurance companies acquired via UBI can be used to model traffic. A method based on link travel time is proposed and tested on four Canadian cities. Then, routes created with the model are compared with those created using Google Maps. The results show that for 38 routes with an average length of around 5 km, the difference between the travel time of the routes of the proposed model and Google Maps is as small as one second on average.

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Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

Cited by 18 publications

References 38 publications

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Study on Dynamic Influence of Passenger Flow on Intelligent Bus Travel Service Model

Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

From Massive Trajectory Data to Traffic Modeling for Better Behavior Prediction in a Usage-Based Insurance Context

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