Analysis Of Urban Mobility Research Articles

Long-term validation of inner-urban mobility metrics derived from Twitter/X

Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the. metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal and spatial scales, employing over 1.6 × 1011 mobile phone records of around three million users during the non-stationary mobility period from April 2020 to. June 2022, which coincided with the COVID-19 pandemic. The results highlight the need for caution when using Twitter for short-term modeling of urban mobility flows. Short-term inference can be influenced by Twitter policy changes and the availability of publicly accessible tweets. On the other hand, this long-term study demonstrates that employing multiple mobility metrics simultaneously, analyzing dynamic and static mobility changes concurrently, and employing robust preprocessing techniques such as rolling window downsampling can enhance the inference capabilities of Twitter data. These novel insights gained from a long-term perspective are vital, as Twitter - rebranded to X in 2023 - is extensively used by researchers worldwide to infer human movement patterns. Since conclusions drawn from studies using Twitter could be used to inform public policy, emergency response, and urban planning, evaluating the reliability of this data is of utmost importance.

Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data

Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model’s adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis.

Design, Analysis and Detection of Urban Mobility Patterns in Guayaquil through Supervised Learning Algorithms

This work explores the use of GPS trajectory data (Git Hub) and supervised learning algorithms to analyze mobility patterns in the city of Guayaquil-Ecuador. The analysis reveals that identifying mobility patterns is crucial to improve urban planning and optimize public transport in terms of its innovations from 2024. The methodology has included data collection from various open access sources, data preprocessing and the use of a Random Forest Classifier to detect mobility patterns. The results indicate a model accuracy of 50%, opening the possibilities for future specialized research to optimize the model and collect more data to improve its performance and propose new StartUPS for the city. The data compiled supports the implementation of the Single Card for Public Transport (TUT), which modernizes and unifies the payment system, facilitating the collection of essential data for analysis. The research concludes that the integration of data from applications such as Waze and Moovit is crucial for efficient and sustainable urban planning and that supervised learning algorithms are effective tools for urban mobility analysis and leaving open the possibility of exploring additional data and other algorithms to improve the accuracy of models and the generation of new mobility routes.

BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data.

While extremely useful (e.g., for COVID-19 forecasting and policy-making, urban mobility analysis and marketing, and obtaining business insights), location data collected from mobile devices often contain data from a biased population subset, with some communities over or underrepresented in the collected datasets. As a result, aggregate statistics calculated from such datasets (as is done by various companies including Safegraph, Google, and Facebook), while ignoring the bias, leads to an inaccurate representation of population statistics. Such statistics will not only be generally inaccurate, but the error will disproportionately impact different population subgroups (e.g., because they ignore the underrepresented communities). This has dire consequences, as these datasets are used for sensitive decision-making such as COVID-19 policymaking. This paper tackles the problem of providing accurate population statistics using such biased datasets. We show that statistical debiasing, although in some cases useful, often fails to improve accuracy. We then propose BiasBuster, a neural network approach that utilizes the correlations between population statistics and location characteristics to provide accurate estimates of population statistics. Extensive experiments on real-world data show that BiasBuster improves accuracy by up to 2 times in general and up to 3 times for underrepresented populations.

Urban Mobility Analysis Using Geospatial Analysis

This project proposes a method to analyze the traffic and urban mobility of a locality with advanced visualization and understanding mechanisms. urban areas worldwide face escalating challenges in managing transportation systems efficiently. This research delves into the realm of urban mobility analysis, leveraging geospatial data to extract insightful patterns and trends. The project aims to contribute a comprehensive understanding of the dynamics governing urban transportation, enabling informed decision- making for city planning and infrastructure development. The methodology encompasses the collection of extensive geospatial datasets, including traffic flow, public transportation routes, and demographic information. Utilizing advanced geospatial analysis techniques, such as Geographic Information Systems(GIS) and spatial analytics, the study explores spatial-temporal patterns of traffic congestion, public transit usage, and pedestrian movement. Machine learning algorithms are employed to predict future mobility trends and assess the impact of potential interventions. The findings of this research offers valuable insights for urban planners, policymakers, and transportation authorities. By identifying key bottlenecks, optimizing transit routes, and understanding the interplay between land use and growing populations and foster sustainable urban development. This project not only contributes to academic understanding of urban mobility but also provides actionable recommendations for creating smarter and resilient cities. The integration of geospatial analysis techniques in urban planning emerges as a powerful tool to address the complexities of modern urban transportation systems. Keywords: informed decision-making, geospatial data, Geographic Information System(GIS), spatial analysis, sustainable urbandevelopment.

Connectivity of Urban Spaces Typology and Urban Mobility Topology

In the study, the connectivity of the ontological and epistemological knowledge of the space in intervening in urban transportation and mobility problems is questioned through the sample area. The purpose of the questioning; is to understand whether potential urban mobility analysis and original application principles can be obtained by trying to reflect the epistemological knowledge of the space to the ontological knowledge. Urban mobility studies; it is seen as sustainable and up-to-date approaches that are handled with original preparation, plan, implementation and monitoring processes, that bring together land use regulations, development plans and transportation plans in the solution of urban transportation problems, and combine spatial and other relational dimensions that develop accordingly. As a result of the inquiry, it was determined that the connectivity between epistemological and ontological knowledge of the space effectively solved urban transportation and mobility problems. It has been seen that it is effective in directing planning and design, especially in urban mobility planning, at the lower scale, during plan implementations, or before plan making. The first stage of the study consists of understanding the ontological and epistemological layers of urban space. In the second stage, the connectivities of these layers in intervening in urban transportation and mobility problems are examined. In the study, while producing urban mobility resolutions, improvements were made to the ontological and epistemological layers of the space (topographic, typological, topological, and hodological), and these were tested on the sample area. Thus, it will be understood whether the method to be followed will be valid in other cities.

Methodological Proposal for the Analysis of Urban Mobility Using Wi-Fi Data and Artificial Intelligence Techniques: The Case of Palma

Knowing and modeling mobility in smart city spaces is important for both planning and managing city resources. The optimization of public resources and the improvement of their management are some of the main concerns in the development of sustainable urban development policies. This study proposes the application of several artificial intelligence methodologies to support mobility planning based on data provided by public Wi-Fi infrastructures in the city. Considering that Wi-Fi networks provide high-frequency data about the devices under their coverage radius, three classification techniques are proposed: by frequency of occurrence of the devices, by estimation of the mode of transport, and by estimation of the most common travel routes. As a case study, the city of Palma (Mallorca, Spain), an international tourist destination where mobility is of singular importance, is selected. This study shows the results obtained from a Wi-Fi network with wide coverage that is integrated into the urban space. It provides novel and updatable information on the mobility model of the city by taking advantage of public high-frequency monitoring resources.

Revisiting ‘Sabil’ buildings as nuclei of sustainable urban mobility in Fatimid Cairo: An urban-touristic approach considering urban and environmental qualities

This study adopts two levels of comparative urban analysis to investigate the current role played by Sabil buildings as nuclei for sustainable urban rehabilitation in Al-Muiz Street, Fatimid Cairo, Egypt. The study is tackled from an urban touristic approach to promote linear and diffused mobility. Hence, Linear Urban Mobility Analysis divided the linear spine into nine sectors based on Sabil locations. These varied in densities, activities, services, social behaviour and opportunities. They were compared based on a matrix including quantitative and qualitative parameters reflecting the existing urban conditions, and were added to an ArcGIS ModelBuilder to develop a spatial assessment model. Then, for a Diffused Urban Mobility Analysis, three proposed itineraries were compared using validated software programs to scrutinise their urban and environmental qualities. The results present a pilot study for promoting sustainable urban mobility in historic cities based on Sabil buildings contextual settings.

A mobility-based deployment strategy for edge data centers

The main objective of Multi-access Edge Computing (MEC) is to bring computational capabilities at the edge of the network to better support low-latency applications. Such capabilities are typically offered by Edge Data Centers (EDC). The MEC paradigm is not tied to a single radio technology, rather it embraces both cellular and other radio access technologies such as WiFi. Distributed intelligence at the edge for AI purposes requires careful spatial planning of computing and storage resources. The problem of EDC deployment in urban environments is challenging and, to the best of our knowledge, it has been explored only for cellular connectivity so far. In this paper, we study the possibility of deploying EDC without analyzing the expected data traffic load of the cellular network, a kind of information rarely shared by network operators. To this purpose, we propose in this work CLUB, CLUstering-Based strategy tailored on the analysis of urban mobility. We analyze two experimental mobility data sets, and we analyze some mobility features in order to characterize their properties. Finally, we compare the performance of CLUB against state-of-the-art techniques in terms of the outage probability, namely the probability an EDC is not able to serve a request. Our results show that the CLUB strategy is always comparable with respect to our benchmarks, but without using any information related to network traffic.

Using bundling to visualize multivariate urban mobility structure patterns in the S\xe3o Paulo Metropolitan Area

Internet-based technologies such as IoT, GPS-based systems, and cellular networks enable the collection of geolocated mobility data of millions of people in large metropolitan areas. In addition, large, public datasets are made available on the Internet by open government programs, providing ways for citizens, NGOs, scientists, and public managers to perform a multitude of data analysis with the goal of better understanding the city dynamics to provide means for evidence-based public policymaking. However, it is challenging to visualize huge amounts of data from mobility datasets. Plotting raw trajectories on a map often causes data occlusion, impairing the visual analysis. Displaying the multiple attributes that these trajectories come with is an even larger challenge. One approach to solve this problem is trail bundling, which groups motion trails that are spatially close in a simplified representation. In this paper, we augment a recent bundling technique to support multi-attribute trail datasets for the visual analysis of urban mobility. Our case study is based on the travel survey from the São Paulo Metropolitan Area, which is one of the most intense traffic areas in the world. The results show that bundling helps the identification and analysis of various mobility patterns for different data attributes, such as peak hours, social strata, and transportation modes.

Clustered embedding using deep learning to analyze urban mobility based on complex transportation data.

Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage. In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. However, as urban mobility becomes increasingly complex, it becomes difficult to embed patterns into a single vector because of its limited capacity. In this paper, we propose a novel method for analyzing urban mobility based on deep learning. The proposed method involves clustering mobility patterns and embedding them to capture their implicit meaning. Clustering groups mobility patterns based on their spatiotemporal characteristics, and embedding provides meaningful information regarding both individual residents (i.e., personalized mobility) and all residents as a whole, enabling a more effective analysis of mobility patterns. Experiments were performed to predict the successive points of interest (POIs) based on transportation data collected from 1.5 million citizens in a large metropolitan city; the results demonstrate that the proposed method achieves top-1, 3, and 5 accuracies of 73.64%, 88.65%, and 91.54%, respectively, which are much higher than those of the conventional method (59.48%, 75.85%, and 80.1%, respectively). We also demonstrate that the proposed method facilitates the analysis of urban mobility through arithmetic operations between POI vectors.

Challenges and Strategies for Post-COVID-19 Gender Equity and Sustainable Mobility

The COVID-19 pandemic has forced countries around the world to take unprecedented measures to contain it. In particular, its impact on mobility has been enormous, causing a sudden decrease in the number of trips and changes in the choice and use of modes of transport. Furthermore, the effects of this health crisis on the social and economic spheres have aggravated inequalities between population groups, with women being one of the most affected groups, which may accentuate the already known gender gap in mobility. Based on these premises, we carried out a strategic analysis of urban mobility from the perspective of sustainability and gender equity in the context of the pandemic, identifying a set of effective strategies to address the post-COVID-19 urban mobility scenario. To this end, based on the review of the literature and expert opinions, we carried out a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis that allowed us to formulate 16 strategies after careful consideration of the weaknesses, strengths, threats and opportunities encountered. Our results provide useful recommendations for making successful policy decisions on post-COVID-19 mobility planning towards a more sustainable, equitable and safe model.

Sustainable urban mobility analysis for elderly and disabled people in S\xe3o Paulo

The Brazilian Urban Mobility Policy integrates urban transport with traffic planning, establishing appropriate public policies that indicate the need for a safe and accessible public transport system. The major challenge is the inclusion of the elderly and people with disabilities. In this paper, we quantify the impact of rainfall on the number of people with disabilities and elderly people who use the public bus transportation system for accessibility in the first and last miles in the city of São Paulo. The proposed methodology is used to evaluate the co-movements between the time series of free-fare users and rainfall in São Paulo. The findings confirm the hypothesis that significant rainfall causes a reduction in the number of daily free-fare passengers who use the public bus system in São Paulo.

Analysis of urban mobility in South Florida using Uber Movement

Ridesharing services linked to mobile devices are increasingly playing a major role in human mobility in urban areas. With the recent release of Uber Movement data for certain large cities it is now possible to analyze urban mobility at finer spatial scales. In the present study, we used aggregate Uber trip data from 2016 to 2019 for the Miami metropolitan area. The peak hour of Uber trips and mean travel times were calculated using harmonic analysis. The total number of trips were expectedly higher on the weekdays compared to weekends for both sources and destinations. In addition, there was a distinct spatial clustering along major thoroughfares connecting the entire study area from north to south, and the two main airports of the study area, Miami International Airport and Fort Lauderdale Hollywood International Airport. The main thoroughfares along which the Uber trips were clustered also overlap with the two modes of mass transit, metro rail and Tri Rail. This is indicative of most riders using mass transit to the closest station of their destination, and then using Uber as a connection to the location of interest. The results of our study will be useful for urban planning and better understanding of human mobility across the metropolitan area.

Smart Mobility, Transport Poverty, and the Right to Inclusive Mobility

Smart mobility aims to provide efficient, sustainable, and connected mobility solutions to congested urban centers. It is often assumed that smart mobility will benefit cities’ residents and improve overall accessibility. Nevertheless, smart-mobility strategies presuppose that transport users are digitally literate, autonomous, and can afford either public or private transportation. For the millions of citizens throughout the world who are affected by transport poverty, this is not the case. Transport poverty is a multidimensional phenomenon which is often overlooked in the legal literature. Yet, this specific type of poverty can limit individuals’ upward mobility and impede them from exercising social and economic rights. This paper explores the phenomenon of transport poverty and delves into the possibility of designing smart-mobility policies that include those affected by this type of poverty. It discusses existing or proposed policies of free public transit and explains why inclusive mobility should be regarded as a concretization of the right to equal treatment. This paper contributes to existing scholarship on smart cities, smart mobility, and fundamental rights with a novel interdisciplinary analysis of urban mobility. Furthermore, it draws attention to the need to design inclusive connected transport systems.

Nonnegative tensor decomposition for urban mobility analysis and applications with mobile phone data

This paper reveals the spatial-temporal patterns of urban mobility by exploring massive mobile phone data based on the nonnegative tensor decomposition method. First, human mobility data with the trip origin, destination, and timestamp are formulated to a three-way tensor. Second, the nonnegative Tucker decomposition model is used to reconstruct the core tensor and the factor matrix to extract hidden structures. Third, the model is efficiently estimated using the hierarchical alternating least square nonnegative tensor decomposition (NTD) algorithm with the nonnegative matrix factorization (NMF) initialization. Using the one-week data of over 4 million cell phone users in Hangzhou, China, we evaluate the performance of the proposed method and explore how different initialization strategies affect tensor decomposition performance. The results show that the NMF initialization strategy can speed up the convergence process and achieve a better fit and more stable results than random initialization in tensor decomposition.

Comparing bicycling and pedestrian mobility: Patterns of non-motorized human mobility in Greater Boston

During the past 100 years, many large cities around the world prioritized individual transportation in cars over more sustainable and healthier modes of transportation. As a result, traffic jams, air pollution, and fatal accidents are a daily reality in most metropolis, in both developed and developing countries. On the other hand, walking and bicycling are effective means of transportation for short to medium distances that offer advantages to both the city environment and the health of its citizens. While there is a large body of research in modeling and analysis of urban mobility based on motorized vehicles, there is much less research focusing on non-motorized vehicles, and almost no research on comparing pedestrian and cyclist behavior. In this paper, we present a detailed quantitative analysis of two datasets, for the same period and location, covering pedestrian and bike sharing mobility. We contrast the mobility patterns in the two modes and discuss their implications. We show how pedestrian and bike mobility are affected by temperature, precipitation and time of day. We also analyze the spatial distribution of non-motorized trips in Greater Boston and characterize the associated network of mobility flows with respect to multiple metrics. This work contributes to a better understanding of the characteristics of non-motorized urban mobility with respect to distance, duration, time of day, spatial distribution, as well as sensitivity to the weather.

Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy

Urban mobility is known to have a relevant impact on work related car accidents especially during commuting. It is characterized by highly dynamic spatial–temporal variability. There are open questions about the size of this phenomenon; its spatial, temporal, and demographic characteristics; and driving mechanisms. A case study is here presented for the city of Rome, Italy. High-resolution population presence and demographic data, derived from mobile phone traffic, were used. Hourly profiles of a defined mobility factor (NPM) were calculated for a gridded domain during working days and cluster analyzed to obtain mean diurnal NPM mobility patterns. Age distributions of the population were calculated from demographic data to get insight in the type of population involved in mobility, and spatially linked with the mobility patterns. Census data about production units and their employees were related with the classified NPM mobility patterns. Seven different NPM mobility patterns were identified and mapped over the study area. The mobility slightly deviates from the census-based demography (0.15 on average, in a range of 0 to 1). The number of employees per 100 inhabitants was found to be the main driving mechanism of mobility. Finally, contributions of people employed in different economic macrocategories were assigned to each mobility time-pattern. Results provide a deeper knowledge of urban dynamics and their driving mechanisms in Rome.

The study of urban climate and traffic: Considerations from the Araraquara case, SP

The article discusses the interface between the influence of the urban climate and the traffic accident, there are as reference the results of the research carried out for the Araraquara case, SP, Brazil. Initially presented a brief analysis of urban mobility, geoprocessing (GIS) and the climatic importance in relation to urban sustainability. The article analyzes the urban climate of Araraquara, especially with regard to solar radiation on city streets and green areas. It was based on the premise that there is a correlation between the urban microclimates and the variables related to the traffic accident. Thus, a georeferenced mapping of the urban geometry was carried out by associating the climatic variables such as solar radiation, temperature and humidity of the air, to three parameters related to the traffic accident were chosen from the a) traffic accident with victim, b) without victim and c) motorcycle. At the end, the article proposes how the results found can reduce traffic accident and contribute to urban planning

Anticipating Constraints on Upscaling from Urban Innovation Experiments

The upscaling of innovations from urban experiments is often assumed to be relatively easy, as if they can simply be ‘rolled out’. In practice, however, upscaling is usually constrained by a range of factors in the wider context of the innovation, typically a context of interconnected and ‘obdurate’ urban socio-technical networks and institutions. Innovation studies have used the notion of upscaling from experiments most explicitly in studies of transitions, especially of strategic niche management (SNM) and transition management (TM). However, these studies have focused more on niche internal dynamics and future visions, respectively, and much less on constraints in the present socio-institutional context. This paper offers a conceptual contribution on ‘constraints on upscaling’, elaborating on how upscaling can be more effective when constraints on upscaling are first identified in retrospective systems analysis, and then anticipated in the design of urban experiments. Our focus is on innovation in urban mobility systems. After a conceptualization of ‘constraints on upscaling’, based on a review of the literature of transition, social innovation, geography and science & technology studies, we present a retrospective analysis of urban mobility in Maastricht (NL) in which these interrelated constraints can be recognized. Further, we analyze a pilot on electric bus mobility which was relatively successful in anticipating future constraints. Based on this, we offer some guidelines on how to anticipate upscaling in the design of urban experiments with socio-technical innovations.