Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization

Xiao, Guangnian; Juan, Zhicai; Zhang, Chunqin

doi:10.1016/j.trc.2016.08.008

Cited by 94 publications

(75 citation statements)

References 24 publications

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“…Several studies have used LBS data from different sources to implement it in transportation engineering applications. Some of the applications include travel data collection (Greaves et al 2015;Safi et al 2015Safi et al , 2016Patterson and Fitzsimmons 2016;Xiao et al 2016), activity analysis (Xiao et al 2012;Zhou et al 2016), travel behaviour analysis (Vlassenroot et al 2015;Ferrer López and Ruiz Sánchez 2014;Deutsch et al 2012), and travel mode detection (Zhou et al 2016;Wu et al 2016;Shin et al 2015).…”

Section: Related Studies On Mobile Phone Data and Population Synthesismentioning

confidence: 99%

Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling

2020

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Traditional approaches to travel behaviour modelling primarily rely on household travel survey data, which is expensive to collect, resulting in small sample sizes and infrequent updates. Furthermore, such data is prone to reporting errors which can lead to biased parameter estimates and subsequently incorrect predictions. On the other hand, mobile phone call detail records (CDRs), which report the timestamped locations of mobile communication events, have been successfully used in the context of generating travel patterns. However, due to their anonymous nature, such records have not been widely used in developing mathematical models establishing the relationship between the observed travel behaviour and influencing factors such as the attributes of the alternatives and the decision makers. In this paper, we propose a joint modelling framework that utilises the advantages offered by both travel survey data and low-cost CDR data to optimise the prediction capacity of traditional trip generation models. In this regard, we develop a model that jointly explains the reported trips for each individual in the household survey data and ensures that the aggregated zonal trip productions are close to those derived from CDR data. This framework is tested using data from Dhaka. Bangladesh consisting of household survey data (65,419 persons in 16,750 households), mobile phone CDR data (over 600 million records generated by 6.9 million users), and aggregate census data. The model results show that the proposed framework improves the spatial and temporal transferability of the joint models over the base model which relies on household travel survey data alone. This serves as a proof-of-concept that augmenting travel survey data with mobile phone data holds significant promise for the travel behaviour modelling community, not only by saving the cost of data collection, but also improving the prediction capability of the models.

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Section: Related Studies On Mobile Phone Data and Population Synthesismentioning

confidence: 99%

Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling

2020

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“…Machine learning models have been widely applied to explore and test assumptions revealed by data in the transportation research community. For instance, Xiao et al applied artificial neural network to infer trip purpose from smartphone-based data [21]. Various learning algorithms have been proposed to model the non-linear spatiotemporal evolution to predict traffic flows [22]- [25].…”

Section: Related Workmentioning

confidence: 99%

Travel Mode Choice Prediction Using Deep Neural Networks With Entity Embeddings

Zhang

2020

IEEE Access

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The prediction of travel mode preference, like many other choice prediction problems, may depend on categorical features of the choice options or the choice makers. Such categorical features need to be meaningfully encoded for better modeling and understanding. Problem-invariant encoding representations of the categorical features, such as one-hot encoding or label encoding, can severely limit the power of prediction models. We propose deep neural networks with entity embeddings for travel mode choice prediction. We adopt the entity embedding technique to jointly learn meaningful representation of categorical variables and accurate travel mode predictions. Experiments using the London travel dataset show that deep neural networks with entity embedding technique outperform neural networks with other encoding techniques, as well as tree-based models. Besides, we found that the learned embeddings can boost the performances of tree-based models by substituting categorical features with the neural network learned features. Finally, we verify that entity embedding can learn meaningful representations of the categorical features using feature visualization at low dimensional space. INDEX TERMS Travel mode choice, entity embedding, deep neural network, machine learning.

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“…They estimate the location of each mobile phone user and classify them into their home, work, and other places depending on the frequency of observation, day of the week, and time of the day. Others use POI data [17][18][19][20], as they tend to offer information on specific trip purpose, land use and activity. For instance, Wang et al [18] infer subway station functions by applying the Doc2vec model [21] to smart card data and POI data.…”

Section: Inference Based On Individual Travel Patterns and Additionalmentioning

confidence: 99%

Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data

et al. 2019

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Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization

Cited by 94 publications

References 24 publications

Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling

Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling

Travel Mode Choice Prediction Using Deep Neural Networks With Entity Embeddings

Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data

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