A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data

Understanding the employment status of passengers in public transit systems is significant for transport operators in many real applications such as forecasting travel demand and providing personalized transportation service. This paper develops a deep learning approach to infer a passenger's employment status by using smart card data (SCD) with a household survey. This paper first extracts an individual passenger's weekly travel patterns in different travel modes from the raw SCD as a three-dimensional image. A deep learning architecture, called a thresholding multi-channel convolutional neural network, was developed to predict an individual's employment status. The approach proposed here solves two critical problems of using the SCD for employment status studies. First, it automatically incorporates learning temporal features in different travel modes without the need for handcrafted travel feature design. Second, it considers the class-imbalance problem by leveraging the ensemble of oversampling and thresholding techniques. By applying our approach to a real dataset collected from the metropolitan area of London, U.K., about 72% of passengers were correctly categorized into six types of employment statuses. The promising results show the tight correlation between temporal travel behavior, mode choice, and social-demographic roles. To the best of our knowledge, this is the first paper to infer employment status by using the SCD

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

Modern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous

Exploring the relationship between travel pattern and social-demographics using smart card data and household survey

Understanding social-demographics of passengers in public transit systems is significant for transportation operators and city planners in many real applications, such as forecasting travel demand and providing personalised transportation service. This paper develops an entire framework to analyse the relationship between passengers’ movement patterns and social-demographics by using smart card (SC) data with a household survey. The study first extracts various novel travel features of passengers from SC data, including spatial, temporal, travel mode and travel frequency features, to identify long-term travel patterns and their seasonality, for the in-depth understanding of ‘how’ people travel in cities. Leveraging household survey data, we then classify passengers into several groups based on their social-demographic characteristics, such as age, and working status, to identify the homogeneity of travellers for understanding ‘who’ travels using public transit. Finally, we explore the significant relationships between the travel patterns and demographic clusters. This research reveals explicit semantic explanations of ‘why’ passengers exhibit these travel patterns

You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data

A graph deep learning method for short-term traffic forecasting on large road networks

Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction