What about people in pedestrian navigation?

Fang, Zhixiang; Li, Qingquan; Shaw, Shih‐Lung

doi:10.1080/10095020.2015.1126071

Cited by 29 publications

(32 citation statements)

References 128 publications

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“…The occurrences of trip origins during each hour every day can be easily obtained and represent the characteristics of human activities over time. Consistent with many previous studies, as depicted in Figure 3, there are strong daily rhythms and day-to-day trip similarities [33][34][35][36][37]. People take more trips during the day than at night, and the temporal patterns on weekends are significantly different from those on workdays.…”

Section: Distribution Of Tripssupporting

confidence: 77%

Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale

Zhang

Zhu

Guo

et al. 2016

IJGI

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Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi's trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to

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Section: Distribution Of Tripssupporting

confidence: 77%

Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale

Zhang

Zhu

Guo

et al. 2016

IJGI

View full text Add to dashboard Cite

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“…Existing research has shown that taxi trajectory trip patterns have weekly cycles [18,19]. The traffic flow of different direction is different and the same direction traffic flow between target and adjacent links has an important significance on each other.…”

Section: Extracting Influencing Factorsmentioning

confidence: 99%

Multiple-Factor Based Sparse Urban Travel Time Prediction

et al. 2018

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Abstract:The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

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“…The obtained statistical data includes link ID, entering endpoint ID, exiting endpoint ID, probe vehicle ID, the moment a probe vehicle entered the link, the travel time for a probe vehicle traversing the link, and the average speed of a probe vehicle traversing the link, as depicted in Table 4. Existing research has shown that probe vehicle trajectories display similar traffic patterns over a weekly cycle [50][51][52][53]. Therefore, we extracted characteristics between target and upstream links as historical characteristics according to the weekly cycle.…”

Section: Data Description and Preparationmentioning

confidence: 99%

Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

Zhang

Zhu

Hu³

et al. 2016

IJGI

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Abstract:The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient-boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient-boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient-boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.

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What about people in pedestrian navigation?

Cited by 29 publications

References 128 publications

Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale

Analyzing Urban Human Mobility Patterns through a Thematic Model at a Finer Scale

Multiple-Factor Based Sparse Urban Travel Time Prediction

Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

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