Mobility Patterns Of People Research Articles

Still prefer, but not that much: the premium of subway access on house prices during the COVID-19 pandemic period

ABSTRACT Rail access has traditionally been considered a factor that enhances the amenity value of urban areas, resulting in a price premium for properties located near these stations. However, the Covid-19 pandemic has exerted a profound influence on people's mobility patterns and travel behavior within urban areas. One of the most conspicuous changes is a substantial decrease in subway ridership. This study investigates whether the decline in public transit ridership has led to shifts in housing location preferences driven by proximity to rail access during the Covid-19 pandemic. This study focuses on assessing the impact of the pandemic on the premium associated with rail access in relation to house prices. Using approximately 180,000 apartment transaction records from Seoul, South Korea, the empirical findings, based on a difference-in-differences approach, reveal an immediate and significant depreciation effect resulting from this exogenous shock during the 25-month pandemic period. The premium decreases significantly within a 1.2-kilometer radius from a subway station, and this reduction exhibits a distance-decay pattern within the designated area. Highlights Traditional amenity value of rail access challenged by Covid-19. Profound decline in subway ridership during pandemic. Investigation into pandemic's impact on housing location preferences. Assessment of pandemic's effect on rail access premium. Immediate and significant depreciation effect within 1.2 km from subway stations.

Exploring the relationship between public transport use and COVID-19 infection: A survey data analysis in Madrid Region

The COVID-19 pandemic has changed people's mobility patterns, increasing the preference for private modes and reducing the public transportation demand. Most scientific contributions have studied the role of mobility levels in the spread of the virus and the influence of public transport on COVID-19 infections, but ignoring the importance of individual-level variables potentially affecting COVID-19 infection, such as daily habits. This paper analyses the relationship between the probability of being infected by COVID-19 and using public transport through a survey data analysis, taking Madrid (Spain) as the case study. This research uses a survey campaign with more than 15,000 responses, capturing socio-demographic aspects, COVID-19 infections, daily habits, and mobility patterns with high risk of COVID-19 infection. Through a multilevel probit model, this paper explores the extent to which a higher use of public transport is related to a greater likelihood of COVID-19 infection. The results suggest a relationship, although not very strong, between the probability of infection and the conjunction of higher frequency of use of metro services and level of crowding during the trip, whereas the use of bus services and travel time within the vehicle do not appear to affect.

Associations between socio‐demographic factors and change in mobility due to COVID‐19 restrictions in Ontario, Canada using geographically weighted regression

AbstractTransportation research has shown that socio‐demographic factors impact people's mobility patterns. During the COVID‐19 pandemic, some of these effects have changed in accordance with changing mobility needs adapting to the pandemic, including restrictions on in‐person gatherings, closure of in‐person businesses, and working from home. We investigate two gaps in current knowledge in this area of transportation research: to what extent the associations between socio‐demographic factors and mobility metrics have changed, and how these associations vary across geographic space. We used aggregate deidentified cell tower location data to measure two mobility metrics—movement time and radius of gyration—and socio‐demographic data from the 2016 Canadian Census to model these associations across Ontario, Canada in 2020 using a linear model and a geographically weighted regression model. We find that certain associations between socio‐demographics and mobility have changed from what we previously observed before the pandemic, and we can see the variation of these associations across space. These findings will improve our understanding of how socio‐demographic factors affect mobility patterns in different communities and demonstrate the importance of measuring these associations at a more fine‐grained level using models that consider spatial variation to best reflect the nature of these associations.

MOBILITY EMBEDDING FROM CALL DATA RECORD USING WORD2VEC TO SUPPORT NETWORK WITH UNMANNED AERIAL VEHICLE

Call Detail Records (CDRs) are records that provide information about phone conversations and text messages. CDR data has been proved in several studies to give useful information on people's mobility patterns and associations with fine-grained temporal and geographical characteristics. This paper proposes to embed the traces recorded in the CDRs to extract meaningful information. These latter provide insights about the location that may need support to cover or recover the network. After embedding the users' trajectories step, we use the embedding results to recommend the antennas with coordinates and support demand needed to a fleet of Unmanned Aerial Vehicle. Finally, we ended up with a capacitated vehicle routing problem that we solved using a Google open-source software named OR-Tools.

Mobility Patterns of People with Disabilities in Public Transport: A Case Study in Serbia

Mobility Patterns of People with Disabilities in Public Transport: A Case Study in Serbia

Effective Optimization of Billboard Ads Based on CDR Data Leverage

Call Detail Records (CDRs) provide metadata about phone calls and text message usage. Many studies have shown these CDR data to provide gainful information on people's mobility patterns and relationships with fine-grained aspects, both temporal and spatial elements. This information allows tracking population levels in each country region, individual movements, seasonal locations, population changes, and migration. This paper introduces a method for analyzing and exploiting CDR data to recommend billboard ads. We usher by clustering the locations based on the recorded activities' pattern regarding users' mobility. The key idea is to rate sites by performing a thorough cluster analysis over the achieved data, having no prior ground-truth information, to assess and optimize the ads' placements and timing for more efficiency at the billboards.

Aggravated social segregation during the COVID-19 pandemic: Evidence from crowdsourced mobility data in twelve most populated U.S. metropolitan areas.

The notion of social segregation refers to the degrees of separation between socially different population groups. Many studies have examined spatial and residential separations among different socioeconomic or racial populations. However, with the advancement of transportation and communication technologies, people's activities and social interactions are no longer limited to their residential areas. Therefore, there is a growing necessity to investigate social segregation from a mobility perspective by analyzing people's mobility patterns. Taking advantage of crowdsourced mobility data derived from 45 million mobile devices, we innovatively quantify social segregation for the twelve most populated U.S. metropolitan statistical areas (MSAs). We analyze the mobility patterns between different communities within each MSA to assess their separations for two years. Meanwhile, we particularly explore the dynamics of social segregation impacted by the COVID-19 pandemic. The results demonstrate that New York and Washington D.C. are the most and least segregated MSA respectively among the twelve MSAs. Since the COVID-19 began, six of the twelve MSAs experienced a statistically significant increase in segregation. This study also shows that, within each MSA, the most and least vulnerable groups of communities are prone to interacting with their similar communities, indicating a higher degree of social segregation.

Towards Lifestyle Understanding: Predicting Home and Vacation Locations from User's Online Photo Collections

Semantic place labeling has been actively studied in the past few years due to its importance in understanding human mobility and lifestyle patterns. In the last decade, the rapid growth of geotagged multimedia data from online social networks provides a valuable opportunity to predict people's POI locations from temporal, spatial and visual cues. Among the massive amount of social media data, one important type of data is the geotagged web images from image-sharing websites. In this paper, we develop a reliable photo classifier based on the Convolutional Neutral Networks to classify the photo-taking scene of real-life photos. We then present a novel approach to home location and vacation locations prediction by fusing together the visual content of photos and the spatiotemporal features of people's mobility patterns. Using a well-trained classifier, we showed that the robust fusion of visual and spatiotemporal features achieves significant accuracy improvement over each of the features alone for both home and vacation detection.

Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs

Analyzing social data as a participatory sensing system (PSS) provides a deep understanding of city dynamics, such as people's mobility patterns, social patterns, and events detection. In a PSS, individuals with mobile devices sense their environment, collect, and share data. For smart cities, intelligent city dynamics analysis has many applications such as for urban planning, transportation systems, city environment, energy consumption, public safety, and city economy. This study aimed to develop an intelligent application to predict the potential number of visitors for specific venues based on the analysis of mobility patterns of individuals. The ability to accurately predict the number of visitors to a venue allows authorities to better understand the behavior of the people and allocate recourses accordingly. We formulated the venue-popularity problem as a sequence-based regression and classification problem. We employed three recurrent neural network (RNN)-based models to predict the locations of popular venues on a city map. The proposed models include basic RNN, long short-term memory (LSTM), and gated recurrent unit (GRU). We constructed several social datasets for Riyadh city using Twitter and Foursquare as the PSS. Our results revealed that modeling venue-popularity prediction as a sequence regression problem yields better results than modeling it as a sequence classification problem. For the city-popularity map prediction problem, the vector autoregression baseline model achieved better performance than the RNN-family models.

Multiuser Multivariate Multiorder Markov-Based Multimodal User Mobility Pattern Prediction

Excavating human's temporal and spatial regularities hidden in trajectory data and predicting users' mobility patterns are conducive to providing proactive smart services for people. Combining Markov transition and tensor theories to improve the prediction performance has proved to be effective. However, the existing state-of-the-art multivariate multiorder Markov model neglects the mutual influence among different users. In a practical trajectory system, people's mobility patterns are influenced by their social relationships. Therefore, this article focuses on proposing a novel multiuser multivariate multiorder Markov model and a multimodal user mobility pattern prediction approach. First, we construct two concrete Markov trajectory transition models based on the single-user multivariate multiorder Markov model. Then, we propose a multiuser multivariate multiorder Markov model, including the influence model of multiple users and the multiuser Markov trajectory transition model. Afterward, two unified product-based power methods are developed to calculate the stationary joint eigentensor (SJE) for single-user and multiuser multivariate multiorder Markov models. Furthermore, an SJE-based multimodal prediction approach is proposed to realize precise mobility pattern prediction. Finally, we conduct a series of experiments based on real-world GPS trajectory data set to verify the performance of the proposed approaches. Experimental results demonstrate that the proposed multiuser multivariate multiorder Markov-based multimodal prediction approach can improve the trajectory prediction accuracy by highest up to 31.10% points compared with the Z-eigen-based approach.

Urban groups: behavior and dynamics of social groups in urban space

The tendency of people to form socially cohesive groups that get together in urban spaces is a fundamental process that drives the formation of the social structure of cities. However, the challenge of collecting and mining large-scale data able to unveil both the social and the mobility patterns of people has left many questions about urban social groups largely unresolved. We leverage an anonymized mobile phone dataset, based on Call Detail Records (CDRs), which integrates the usual voice call data with text message and Internet activity information of one million mobile subscribers in the metropolitan area of Milan to investigate how the members of social groups interact and meet onto the urban space. We unveil the nature of these groups through an extensive analysis, along with proposing a methodology for their identification. The findings of this study concern the social group behavior, their structure (size and membership) and their root in the territory (locations and visit patterns). Specifically, the footprint of urban groups is made up by a few visited locations only; which are regularly visited by the groups. Moreover, the analysis of the interaction patterns shows that urban groups need to combine frequent on-phone interactions with gatherings in such locations. Finally, we investigate how their preferences impact the city of Milan telling us which areas encourage group get-togethers best.

MPE: a mobility pattern embedding model for predicting next locations

The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people's mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of ``phantom transitions'' arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.

Planners and the Pride Factor: The Case of the Electric Escalator in Medellín

The outdoor electric escalator in Medellín's Comuna 13, part of an Integrated Urban Project aimed at bridging the city's socio‐spatial divide, did not fully accomplish the project's goal of increasing the mobility of 12,000 people living in the area, nor did it make a significant contribution to diminishing social inequalities. The escalator project can be considered a prestigious solution to an ill‐defined problem that did not take people's mobility patterns into account. Although the project succeeded in increasing civic pride and improving Medellín's image of modernity and progress, it also led to new social inequalities.

A computational cognitive modeling approach to understand and design mobile crowdsourcing for campus safety reporting

The under-reporting of public safety incidents is a long-standing issue. In this paper, we propose a computational cognitive modeling approach to understand and design a mobile crowdsourcing system for improving campus safety reporting. In particular, we adopt drift-diffusion models (DDMs) from cognitive psychology to investigate the effect of various factors on users reporting tendency for public safety. Our lab experiment and online study show consistent results on how location context impacts people's reporting decisions. This finding informs the design of a novel location-based nudge mechanism, which is tested in another lab experiment with 84 participants and proved to be effective in changing users reporting decisions. Our follow-up interview study further suggests that the influence of people's mobility patterns (e.g., expected walking distance) could explain why the nudge design is effective. Our work not only informs the design of mobile crowdsourcing for public safety reporting but also demonstrates the value of applying a computational cognitive modeling approach to address HCI research questions more broadly. A computational cognitive modeling approach is used to study crowdsourcing decisions.Location context and crime severity impact people's safety reporting decisions.A novel location-based nudge mechanism is created and proved to be effective.

Developing and Evaluating Intermodal E-Sharing Services–A Multi-method Approach

Different studies assume that travel behavior and mobility patterns of people may change within the next years: multimodal and intermodal usage of transport modes are getting more and more important. We expect a great potential for sharing services especially on intermodal trips. We aim at developing and evaluating intermodal electric mobility management concepts from the customer perspective. Since conventional approaches and singular methods are not appropriate, we adopted a multi-method approach consisting of five parts: (1) supply concepts are developed, (2) vehicle requirements for intermodal sharing are identified, (3) intermodal trip information is collected, (4) an agent based model and a macroscopic demand model are developed further in order to represent intermodal trips and e-vehicles and to evaluate several supply concepts, and (5) the impact and acceptance of modern and flexible mobility services like carsharing, bikesharing or new electric vehicle concepts (e.g. segways or light cars) is assessed and evaluated. The proposed methodology can be used for the development of customer oriented and attractive intermodal sharing services. Hence, the model results are essential for the evaluation and economic appraisal of e-sharing services from the supplier perspective. The proposed methodology can be applied to other cities and regions.

A New Generation

Like other industrialized countries, Germany and Great Britain have experienced increasing motorization over the past five decades. However, results from national travel surveys, vehicle registration statistics, and driver's licensing databases suggest that young Germans and Britons today are less automobile oriented than their parents' generation. The paper sheds light on this trend, with a focus on the group of 20- to 29-year-olds. The analysis finds decreasing car availability, a significant reduction of automobile mileage, increases in the use of other modes, and growing multimodal behavior of the young, with men reducing their automobile travel more than women. Even though the development is more pronounced in Germany, the similarity of the changes in young people's mobility patterns in the two countries is striking. This similarity suggests that the observed changes in travel behavior are not an idiosyncratic development in one country. Instead, the similarity may indicate a structural change in travel behavior that may be found in other Western countries. The paper substantiates findings of changes in trends in mobility patterns of young adults and identifies important storylines of this development on the basis of a harmonized international comparison. Finally, the paper intends to stimulate a discussion and research about reasons for these changes in mobility trends.