Mobilization Research Articles

Tracing long-term commute mode choice shifts in Beijing: four years after the COVID-19 pandemic

AbstractThe COVID-19 pandemic has brought urban mobility into a new era. This study traces post-pandemic shifts in commute mode switch behaviors and their environmental effects based on an event study design and a mobile phone signaling dataset for Beijing from April 2018 to November 2023. The results show that in the outbreak stage, public transit mode share nosedived, with transit riders 5.11 and 3.75 times more likely to switch to private car and active modes, respectively; in the post-pandemic stage, public transit recovered rapidly and to a large extent, but the increase of private mode dependency persisted, as transit riders were 1.88 times more likely to switch to private cars, and transit riders and car drivers were 1.37 and 1.32 times more likely to adopt active modes, respectively; in the pandemic-free stage, signs of private car dominance began to emerge, as driving habits strengthened and more active travelers resorted to driving, indicating the closing of the window of opportunity to promote low-carbon private transport. This study presents the longest longitudinal tracking of post-pandemic travel mode choice so far. The challenges and opportunities faced by the transportation system are discussed, and policy implications and future research directions are provided.

Exploring Park and Ride: A Spatial Analysis of Transit Catchment in Outer Melbourne

Public transportation is essential for improving urban mobility, enhancing travel quality, reducing reliance on private vehicles, and alleviating traffic congestion. However, inadequate public transportation in outer Melbourne is a significant issue limiting urban development. While existing research primarily focuses on walking distance to define service catchments, commuters in transit-disadvantaged or outlying urban areas often drive to transit, noted as the park-and-ride mode. This research uniquely examines drive-distance catchments for park-and-ride transit accessibility in outer Melbourne, using spatial SQL and GIS techniques to provide a detailed, multi-dimensional analysis of population coverage, parking capacity, and accessibility gaps. This approach fills a gap in the existing literature by offering adaptable insights and approaches to other outer urban areas with transit disadvantages. The findings underscore the necessity for targeted enhancements in public transportation in outer Melbourne: Most of the outer residents concentrate near the train stations, though significant spatial gaps exist; The general accessibility status of residential mesh blocks is found; Parking capacity varies with high tension found at certain stations. This study contributes insights to create more equitable and sustainable transportation systems by providing a detailed spatial analysis of current transit coverage and identifying critical gaps.

Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists

This study develops a progressive navigation and guidance model for the route selection of cyclists executed in a designated area. The route selection of cyclists is modeled as a Pareto multi-objective optimization problem which is solved with the NSGA-II algorithm. The study aims to contribute to the ongoing efforts to create efficient and cyclist-friendly navigation tools to promote sustainable urban mobility. Data collection methods include GPS tracking, field measurements, and qualitative approaches to understand cyclists’ behavior and preferences. Nine objective functions are constructed based on criteria related to safety and comfort, incorporating decision variables related to cyclists riding on sidewalks, capturing the complexity of urban cycling infrastructure. Tests are performed in a defined area in the center of Athens, Greece. The NSGA-II algorithm is executed with modifications and the Pareto front is constructed, which consists of 28 alternative routes between two origin–destination points. The four routes that optimize the nine criteria of the objective functions are presented, with most routes passing through the Zappeion Gardens. The NSGA-II algorithm is proven to be a suitable approach for applications in networks with complex characteristics and for capturing cyclists’ choices when they face conflicting options. The study presents how a novel approach for the multi-objective optimization of cyclists’ route choice, which considers a wide range of cyclists’ needs and preferences, can be implemented in an urban environment with a lack of cycle infrastructure.

Interseção entre Forma Urbana e Mobilidade

This study analyzes the relationship between urban form and mobility in Brazil, considering its regional diversity. The study involved meta-analysis and structured literature review examining studies published between 2013 and 2023. The results highlight the importance of walkability and adequate infrastructure for the improvement of urban mobility, influencing quality of life and sustainability. Public policies adapted to local particularities are necessary to promote sustainable urban mobility. The study contributes with an interdisciplinary and quantitative approach in the analysis of the complex relationships between urban form and mobility, using bibliometric analysis to offer a new perspective on the subject.

Cidades Inteligentes e Sustentáveis

With the growth of the urban population, parallel to the increase in the urbanization of urban centers, challenges arise for which intelligent and sustainable solutions need to be found. It is by using technology as an ally in caring for the environment that conscious urban development will be achieved. Unfortunately, the model imposed on cities does not prioritize issues essential to human development, such as urban infrastructure focused on people and the environment. The justification for this research lies in new technologies, strong allies of equations aimed at urban development and infrastructure issues aimed at the social and environmental comfort of cities, aiming to analyze, within the context of the city of Sorriso, the paths to a city smart and sustainable, with a focus on urban mobility, given the high number of traffic accidents occurring in the urban core. To meet this end, the research methodology used field data collection, documentary and bibliographic material based on authors who address the theme of smart and sustainable cities and could be related to the city of Sorriso, which aims to become a smart and sustainable city.

Commoning as Social Struggle

The article provides an empirical insight into urban initiatives that advocate for better urban mobility infrastructures and outlines a theoretical perspective of commoning infrastructures as a terrain for political struggles. Rather than constructing commons as the interplay of methodologically presumed elements (resources, a community of commoners, and their institutions of commoning) it takes a relational perspective on commoning that asks how activists mobilize and relate heterogeneous elements to make urban mobility infrastructures a common political concern. Based on ethnographic fieldwork on mobility activism in Berlin, the second part of the article illustrates such a relational perspective and presents three modes of commoning. To achieve what is called “mobility transition” (i. e. more sustainable and equitable urban mobility infrastructures), activists rely on mobilizations of knowledge, space, and affect.

Identifying the Urban Development Pattern of Subway Cities Using Predictive Analysis

Starting from the hypothesis that there are structural differences in urban development between cities that have implemented underground transport and those that have not, the paper aims to provide a predictive analysis model to identify cities that show the pattern for the development of the subway network. In order to identify the predictive analysis model capable of classifying urban settlements, the predictive analysis part consisted of two stages. In the first stage, a Random Forest classification model will be estimated on a sample of the 27 capitals of the European Member States. Subsequently, based on this model, 9 cities in Romania will be classified from the perspective of the existence of the urban development pattern for the development of underground public transport. In order for the classification error of the predictive model to be as low as possible, variables from all areas specific to urban development (economic development, population density, circular economy, green city, digitalization, urban mobility) were used. The element of originality of the paper consists in calculating variables on the area of urban mobility, urban infrastructure and circular economy, using GIS coordinates. Thus, in addition to official international data sources, variables calculated by using geospatial technology (average distances, densities of green spaces, etc.) were included in this research. The main results of the research show that there are cities in Romania that present the same pattern of development similar to the capitals of the Member States of the European Union that have implemented underground public transport. Therefore, it can be said that for these cities it is necessary to implement this type of transport, the depreciation of the investment representing an easy process, which can be achieved in a medium or short term.

Analysing macroscopic traffic rhythms and city size in affluent cities: insights from a global panel data of 25 cities.

The world is undergoing a rapid process of urbanization. Currently, it is estimated that over 55% of the global population resides in urban areas, a figure projected to reach nearly 70% by 2050. This trend is accompanied by a spatial reorganization of human activities on a global scale, bringing about significant changes in mobility patterns and urban traffic management capabilities. Consequently, it is imperative to evaluate, on a broad scale, how city size influences traffic capacity. This study aims to analyse on-road traffic patterns using a diverse dataset comprising cities of varying population sizes, geographical extents and global locations. Specifically, we conduct an analysis encompassing 25 cities primarily situated in several European countries (France, Italy, Germany, Spain, Switzerland, and the United Kingdom), as well as in North America (Canada) and East Asia (Japan and Taiwan). Our findings shed light on how physical aspects related to urban form influence mobility patterns, offering insights for the implementation of more effective and sustainable traffic management policies.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

Evaluating the Dynamic Comprehensive Resilience of Urban Road Network: A Case Study of Rainstorm in Xi’an, China

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management.

Formalizing Sustainable Urban Mobility Management: An Innovative Approach with Digital Twin and Integrated Modeling

Background: Urban mobility management faces growing challenges that require the analysis and optimization of sustainable solutions. Digital twins (DTs) have emerged as innovative tools for this assessment, but their implementation requires standardized procedures and languages; Methods: As part of a broader methodology for continuous DT validation, this study focuses on the conceptual validation phase, presenting a conceptualization approach through formalization using Specification and Description Language (SDL), agnostic to simulation tools. The conceptual validation was achieved through stakeholder engagement in the Bolzano context, producing 41 SDL diagrams that define both elements common to different urban realities and specific local data collection procedures; Results: The feasibility of implementing this stakeholder-validated conceptualization was demonstrated using Simulation of Urban MObility (SUMO) for traffic simulation and optimization criteria calculation, and its framework SUMO Activity GenerAtion (SAGA) for generating an Activity-Based Modeling (ABM) mobility demand that can be improved through real sensor data; Conclusions: The SDL approach, through its graphical representation (SDL/GR), enables conceptual validation by enhancing stakeholder communication while defining a framework that, while adapting to the monitoring specificities of different urban realities, maintains a common and rigorous structure, independent of the chosen implementation tools and programming languages.

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

Mamba-VNPS: A Visual Navigation and Positioning System with State-Selection Space

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

Smart Parking System

As urbanization progresses, the surge in vehicle numbers has amplified the need for efficient parking management in cities. Traditional parking methods frequently lead to traffic congestion, poor space usage, and user dissatisfaction. This study introduces a smart parking solution that operates independently of Internet of Things (IoT) technology, instead leveraging software-based strategies with minimal hardware dependency. Key Words: Traffic Congestion,Urban Mobility,Space Optimization,Real-Time Availability.

Predictive Modeling for Bike Rental Demand: Enhancing Operational Efficiency and User Satisfaction in Urban Mobility

This project focuses on developing a predictive model to forecast bike rental demand by analyzing historical rental data, weather information, and temporal variables. The model will employ machine learning techniques, such as regression analysis, time series forecasting, and ensemble methods, to capture the complex relationships between these factors and rental demand.

Resilience through Mobility: Urban Residents’ Adaptation Strategies in Conflict-Affected Bamenda, Cameroon

The global phenomenon of human population displacement during environmental stressors and socio-political crises has far-reaching impacts. In urban areas, residents' mobility is often driven by various factors. This study investigates urban residents' mobility as a resilience strategy in response to the Anglophone socio-political crisis in Bamenda, Cameroon. Employing a case study design and purposive sampling, the research selected five neighborhoods based on the extent of residential changes as a result of the ongoing socio-political crisis in the region. Data were collected through semi-structured interviews with 105 respondents and focus group discussions to explore residential mobility as a resilience mechanism. The findings revealed that five specific population groups were affected by the socio-political crisis, prompting them to relocate within Bamenda. Among those displaced, 60% were households led by individuals aged 26-32. Key reasons for displacement included ongoing violence, loss of life, family disconnection, property destruction, disrupted economic activities, halted education, and increased internally displaced persons in the region. The study also found that residents with higher financial status could relocate to safer areas, while vulnerable individuals often fled or temporarily sought refuge in forests. The prolonged crisis, over seven years now, has severely impacted Bamenda residents, escalating forced displacement rates. Consequently, the study recommends that the government implement targeted urban planning policies to enhance resilience strategies, focusing on affordable social housing, resettlement structures, and improved access to basic services for displaced residents, while fostering peace and stability in Cameroon’s Northwest and Southwest regions.

Smart Park: Innovating the Future of Vehicle Parking

Effective parking solutions become increasingly important as urban areas increase because of wasted space, time-consuming searches, and traffic congestion. By utilizing cutting edge technologies to improve parking management and user experience, the Smart Vehicle Parking System (SVPS) tackles these issues. To deliver dynamic solutions, SVPS combines sophisticated algorithms, sensor networks, and real-time data. Parking space sensors track occupancy and transmit data to a central system, where machine learning maximizes space distribution and directs vehicles to open spaces. This smart parking system is software and hardware based. Different algorithms are used to find vacant slots using machine learning and deep learning. IR sensors, RFID, Servomotors and ESP 32 microcontrollers and Internet of things are useful to find vacant slots in real time signal processing. In traditional method it is difficult to search free space, and this method is time consuming, Smart parking system overcome this problem with new technology and improve parking management system. The SVPS not only improves urban mobility but also contributes to reducing vehicle emissions and enhanced overall efficiency. Keywords— smart parking, integration, secure payment,advanced technologies, convenient.

Historical Insights into CO2 Emission Dynamics in Urban Daily Mobility: A Case Study of Lyon’s Agglomeration

CO2 emissions from urban daily mobility play a major role in both environmental degradation, rising economic costs, and sustainability. Reducing these emissions not only advances environmental sustainability but also fosters economic development by enhancing public health, lowering energy consumption, and alleviating the financial strain caused by climate change. Understanding the dynamics of CO2 emissions from urban daily mobility provides valuable historical insights into environmental impacts and economic costs tied to urban development. This study takes a historical perspective, examining changes in CO2 emissions associated with daily mobility in the Lyon agglomeration across two decades, drawing on data from the 1995 and 2006 household travel surveys. Our findings reveal that individual factors such as gender, age, employment status, and income significantly influence CO2 emissions, with males and full-time workers exhibiting higher emissions. Furthermore, household characteristics, including size and vehicle ownership, are critical in shaping emission levels, while urban form variables such as population density and mixed land use demonstrate a negative correlation with emissions, highlighting the importance of urban planning in mitigating CO2 output. The analysis emphasizes that greater accessibility and proximity to essential services are vital in reducing individual emissions. Based on these insights, we discuss the implications for policy design, suggesting targeted strategies to enhance urban mobility, improve public transport accessibility, and promote sustainable urban development. Finally, we outline directions for future research to further explore the intricate relationship between urban characteristics and emissions, ultimately aiming to contribute to the development of effective climate policies.

A Study on Bike Share Analysis

This study explores the role of bike-sharing programs within urban mobility and the potential impacts of autonomous vehicles (AVs) on cyclists' safety perceptions and behaviour. The research focuses on analysing factors that influence the adoption of bike-sharing services, including safety perceptions, AV technology familiarity, and demographic characteristics. Using tools like MS Excel and statistical models such as multiple regression and logistic regression, the study examines patterns in bike-sharing usage, public attitudes toward AVs, and the implications of AV testing on public streets. Findings suggest that factors like age, perceived safety, and AV familiarity significantly influence bike-sharing behaviour. The study recommends enhanced infrastructure, integration with public transit, and data-driven insights to improve bike-sharing systems and promote safer interactions with AVs

Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.

Bicycle Simulator Use to Evaluate Safety Risks and Perceptions for Enhanced Sustainable Urban Mobility

(1) Background: As cycling gains popularity as a mode of transportation, the frequency of accidents involving cyclists also rises. This has become a major concern for traffic safety, sustainability, and city planning. Identifying the risk factors that contribute to bicycle road accidents remains a significant challenge. This study aims to figure out which risk factors make some road segments more dangerous for cyclists than others. (2) Methods: This study introduces the use of a bicycle simulator to test different road segments involving thirty-nine participants. The impact of demographics and some risk factors related to infrastructure were analyzed in terms of their influence on the perceived level of risk through pre- and post-surveys. (3) Results: The findings showed that the bicycle facility type affects the perceived level of risk. Shared-use roads were ranked as riskiest, while separated bike lanes were least risky. Bicycle roads with no separated safety barriers had higher risks. Heavy traffic jams increased danger among cyclists. Women gave higher risk ratings than men. The perceived levels of risk were then compared with the previously developed risk index and they correlated well. (4) Conclusions: This confirms that the risk index can reliably evaluate the degree of risk of each road segment.