Public Transport Research Articles

Concentrations and short-term health effects of VOCs in explored subway, bus and taxi in Beijing, China

The transportation environment is one we are exposed to every day. However, previous research on exposure to volatile organic compounds (VOCs) and their acute health effects in various public transportation is limited. To address this gap, we conducted a panel study involving 25 healthy adults, investigating the exposures and acute health effects of exposures to 24 VOC species during 40-min commutes in Beijing’s subways, buses and taxis during early spring 2023. Our findings revealed that exposure to multiple VOC species, including toluene, styrene, tetrachloroethylene, ethylacetate, resulted in a decrease in Heart Rate Variability (HRV). Specifically, SDNN decreased by 4.37 ms (95%CI: -8.52, -0.21) with per interquartile range (IQR) increase in ethylacetate; rMSSD decreased by 9.93 ms (95%CI: -19.01, -0.86), 4.75 ms (95%CI: -8.63, -0.86), and 7.00 ms (95%CI: -13.20, -0.81) with per IQR increase in toluene, styrene, and tetrachloroethylene, respectively; HF decreased by 154.07 ms2 (95%CI: -284.03, -24.11) with per IQR increase in carbon tetrachloride. For joint effect of these VOC species, we found significant associations with HRV only in taxis. Moreover, higher cyclohexane and methylcyclohexane exposures were linked to reports of sick car syndrome. Despite wearing masks during the COVID-19 period, comparative results from a supplementary experiment conducted post-COVID-19 without masks indicated that masks may not significantly reduce the adverse effects of VOC exposure. Our findings indicated the potential acute health risks of short-term VOC exposures in public transportation, particularly in taxis, highlighting the necessity to improve air quality and related standards in these micro-environments.

Enhancing Urban Resilience through Strategic Parking Pricing in Jakarta’s Bus Rapid Transit Corridors

Although Jakarta has invested in various mass transport systems, these efforts have not successfully reduced private vehicle use. Due to this, this study aims to analyze the impact of implementing TransJakarta bus rapid transit corridor-based high parking tariffs on travel mode choice, including road- and rail-based public transport, ride-hailing, taxi, car, and motorcycle. Involving 478 private vehicle users and implementing a nested logit model, some variables, including respondents’ income, travel time, egress time, parking costs, parking distance, travel cost, and parking surcharge, are considered to understand to what extent these variables influence the use of proposed travel mode in the future. The nested logit model shows that not all variables significantly influence travel mode use, specifically related to rail-based public transport choice among motorcyclists. Meanwhile,parking distance insignificantly influences the choice of all travel modes except cars among car users. The results also indicate that increasing parking tariffs insignificantly influences the likelihood of both motorcyclists and car users shifting to public transport. Motorcyclists and car users tend to continue using motorcycles but change parking locations with higher tariffs. Additionally, some shifts towards ride-hailing services and TransJakarta Bus Rapid Transit are found, meaning that there is potential for these alternatives to play a significant role in reducing private vehicle use. Based on the model results, additional push-based policies, such as the odd-even license plate rule, are necessary to effectively support the transition from private vehicle use to public transport. Implementing these policies is expected to significantly contribute to reducing traffic congestion and promoting a sustainable and resilient urban environment.

Exploring determining factors of MaaS app use and its potential effects on mobility behavior: Keys to gender-sensitive planning and management

Mobility as a Service (MaaS) has become an emerging trend in transportation planning and management due to its potential to overcome the challenges of urban mobility toward the achievement of sustainable mobility goals. This requires people to be willing to use MaaS apps. However, given their relative novelty, there is still a lack of research on the factors that influence the use of these apps, as well as the possible role of gender in such use. In this context, this study aims to explore the complex relationships between a set of relevant factors and their influence on the intention to adopt MaaS apps, with special attention to the role of gender as a moderating variable. For this purpose, a survey was conducted in the Madrid Metropolitan Area (Spain), comprising 8358 respondents (3627 men and 4731 women), and a four-step methodological procedure was adopted: (i) Exploratory Factor Analysis (EFA), (ii) Confirmatory Factor Analysis (CFA), (iii) Structural Equation Model (SEM) on the total sample (iv) Gender-sensitive analysis, including a Multi-group analysis – SEM. The results demonstrate that technophilia, perceived usefulness and perceived ease of use are determining factors. Green values and attitudes toward shared modes have a notable total positive effect. However, contrary to expected, attitudes toward public transport, car and bicycle hardly explain the intention to adopt MaaS apps. Our analysis also reveals significant gender differences in the causal relationships hypothesized in the model. These findings allow us to outline the policy initiatives that could favor the use of MaaS apps, with a gender-sensitive approach.

LOCAL RESIDENTS’ WILLINGNESS TO DONATE FOR LOW CARBON CITY INITIATIVES IN TAIPING, PERAK: PROPOSED E-BIKE-SHARING PROJECT

The transport sector in Malaysia ranked second in the country's total energy consumption for 2021, thereby contributing to greenhouse gas emissions. Numerous cities strive to attain low-carbon urban environments. E-bikes, as environmentally friendly vehicles, have the potential to play a significant role in urban transportation by contributing to the creation of low-carbon cities. Taiping, Perak, has been recognised as one of the world's top 3 most sustainable cities for 2019. This prestigious accolade positions it as an ideal location for an e-bike project, further solidifying its commitment to maintaining its status as a sustainable city. This study seeks to evaluate the awareness and attitudes of Taiping residents towards e-bikes, determine the factors that influence their willingness to donate, and ascertain the average amount they are willing to contribute to the e-bike-sharing development project in Taiping, Perak. A survey was conducted with 385 respondents using the Contingent Valuation Method, employing stratified random sampling. The findings indicated a notable level of awareness and favourable attitudes towards e-bikes. Factors such as bid, age, education, income, and awareness impact the willingness to donate. On average, each person contributes RM10.40 towards the proposed e-bike-sharing project in Taiping. The discovery offered valuable insights to the Taiping Municipal Council regarding securing financial support and better understanding residents' perspectives on e-bike sharing.

Surat’s sustainable future: analyzing compact urban development and population density trends

Rapid urbanization is a challenge for the cities of developing countries like India. The share of an urban area is rapidly increasing in dynamically growing cities. Due to this, the land use is altered, causing sprawl and unplanned development. The consumption of resources is greater, and the impact on the surroundings is negative. The compact city concept is always more efficient and cost effective compared to unplanned organic growth. The compact city optimizes available land resources with its maximum possible capacity in accordance with other available natural resources. The primary study objective is to assess the growth trend of Surat city using satellite images, geographic information system techniques, and census data for the last few years. The analysis considers population density for different city wards from 1991 to 2020. Based on the land use land cover change carried out using the maximum likelihood method from the satellite images, the rate of spreading out, its direction, and causes are also found. In this study, population density is a crucial attribute of the compact city concept. The study shows that wardwise density is increasing in a specific direction and making that portion of the city a dense, compact development. This is observed in ward numbers 48, 42, and 35 in the east, north, and southeast directions from Central Business District, respectively. The average population density in persons per sq. km is continuously increasing from 5006 to 20329 at city level which also shows the compact city development for Surat. The governing authorities must incorporate the compact city concept into the planning policy for sustainable urban development. By this, policymakers can understand housing needs to promote affordable housing, invest in infrastructure development and public services, and identify areas where sustainable transportation options, such as public transit, walking, or cycling, need improvement.

Towards a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec

ObjectivesThe primary objective of this project was to develop a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec (QCnPI-Index). The resulting database systematically categorizes, multiple non-pharmaceutical interventions implemented in the 17 administrative regions (AR) of the province of Québec to mitigate the spread of COVID-19 in the form of an index.Data descriptionData represent interventions and groups of interventions implemented during the COVID-19 period in Québec. They are a compilation of policies, guidelines, and governmental interventions related to COVID-19, considering temporal and geographical dimensions. Data were collected for all 17 AR of Québec using dates as unit of analysis, from March 2020 to April 2022. They were first collected and then coded by an interdisciplinary research team to form the foundation of the QCnPI-Index.ContributionThis quantitative instrument offers the necessary granularity for nuanced spatial and temporal studies within the province of Québec, using AR, for instance, as unit of analysis. With this database, pre-, during-, and post-COVID periods can thus be better analyzed. Additionally, the innovative methodologies employed for data collection, coding, and weighting offer valuable insights that may have broader applications in public health, epidemiology, and other research domains. The QCnPI-Index could be instrumental for public health, epidemiology, and transportation researchers investigating the multifaceted impacts of non-pharmaceutical interventions on various societal domains, such as road safety, alcohol and cannabis consumption, and/or mental health, in the province of Québec.

Reexamining exposure from truck emissions considering daily movement of individuals

With the growing demand for urban freight transportation, trucks emit a large amount of pollutants such as particulate matters and nitrogen oxides, increasingly affecting public health. This study establishes a modelized air dispersion structure to simulate pollutant concentration distribution. By integrating multiple data sources including mobile phone signals and satellite images, we reconstruct the daily trajectories of individuals and further incorporate simulated pollution concentrations in calculating dynamic and static exposure of individuals to truck emissions. Econometric models considering spatial dependence are developed to evaluate the influencing factors and elucidate the mechanisms of pollutant exposure. Results show factors including freight demand, road network, residential and employment locations, personal commuting distance, and population age structure matter in assessing truck emission exposure. As a result, a mixture of vehicular emission standards, urban traffic control, land planning, and industrial policies is proposed to reduce truck pollutant exposure and safeguard public health.

A trajectory optimization method for CAVs at intersections considering energy-efficiency balance

With the development of vehicle-to-everything (V2X) technology, connected automated vehicles (CAVs) will be an important part of urban road transportation. However, frequent acceleration, deceleration, and rapid stopping within intersections lead to increased energy consumption, reduced driving efficiency and balanced benefits. The study proposes an optimal control method for vehicle trajectories that considers energy-efficiency balance (OCM-EEB) based on the characteristics of V2X technology. The method first develops a vehicle trajectory control model based on the Intelligent Driver Model (IDM) by accounting for the features of V2X technology, and further, introduces the idea of balanced optimization, considering energy-efficiency balance, a vehicle trajectory optimal control model (OCM) is proposed. The proposed model was solved by design algorithms based on the non-dominated sorting genetic algorithm-II (NSGA-II). The experimental results showed that the vehicle trajectory optimization control method could increase the balanced driving efficiency of the vehicle within intersection, which is conducive to improving the robustness of the vehicle’s trajectory state, and then prove the effectiveness of the method proposed in this study. The method also provides an important technical basis for the vehicle trajectory optimization experiments under a real V2X environment in the future.

A geographic-semantic context-aware urban commuting flow prediction model using graph neural network

Urban commuting flow prediction is crucial for urban planning, transportation optimization, and supply chain management. Traditional models and machine learning solutions often fall short in capturing the complex dynamics of urban mobility, overlooking factors like spatial correlation and human mobility patterns. Recent deep learning advancements have shown promise by integrating spatial correlation through incorporating geographical adjacency. However, this approach may not fully capture spatial correlation, particularly the semantic adjacency effect of zones connected via public transportation lines, such as metro networks—a novel dimension overlooked in prior research. The metro station serves as a major transit hub, attracting passengers from various origins and destinations and correlating the flow between the connected zones and other zones within the metro network. Devising an optimized model to encapsulate these spatial correlations is another crucial area of research where scientists are actively seeking the most effective solutions. We propose a novel sequential hybrid approach leveraging graph-based learning, focusing on the underexplored impact of metro networks on commuting flow prediction. Our model integrates two types of adjacency—geographic and semantic—using Graph Convolutional Networks (GCNs) to embed geographic correlations among proximate zones and Graph Attention Networks (GATs) to incorporate semantic adjacency defined by metro line connectivity. Furthermore, our approach innovatively incorporates features directly from online maps, eliminating reliance on third-party resources and enhancing scalability and efficiency. We validate our model using real-world datasets from Montreal, Canada, comprising travel surveys and GPS trajectory types, totaling nearly 900,000 trip records. Through evaluation using established performance metrics such as MAE, RMSE, MAPE, and CPC, we demonstrate significant improvement in predictive accuracy compared to existing state-of-the-art models. Our research has practical implications for urban planning and infrastructure development, aiding policymakers and urban planners in analyzing the impact of new urban infrastructure on commuting flow dynamics and facilitating informed decisions regarding transportation and infrastructure planning.

Weathering the wait: Temperature impacts on school bus delays

Transportation systems are growingly subject to extreme weather events induced by climate change. Yet, delays in operating public transportation systems due to adverse weather conditions are not well understood. In this study, we quantify the impact of temperature shocks on school bus delays in New York City during 2015–2019. We find that low temperatures lead to significant school bus delays regarding the frequency and delay time. Our heterogeneity analysis further shows that increased school bus delay time in cold weather is primarily driven by Curb-to-School buses (for students with special education and needs) and school bus operations in the mornings. Our findings provide new evidence on how temperature shocks affect transportation systems and highlight the need for adaptive strategies and planning that can aid school buses in better responding to adverse weather conditions.

A planning framework for intra-city electric bus road transportation systems

Gasoline-based vehicle transportation plays a crucial role in environmental pollution, which eventually leads to serious health issues in urban areas. The scope of large-scale deployment of personalized electric vehicles in the near future can lean toward such issues but can overburden local distribution grids (LDGs), and the entities are producing green energy. In this context, an electric bus road transportation system (EBRTS) can be seen as an effective and promising way, although it is capital-intensive. EBRTs can be successfully deployed by developing a well-tailored planning model that encompasses amicable solutions while considering the conflicting interests of electric utilities and investors/transporters.This paper proposes a planning model for intra-city EBRTS, especially for metropolitan cities, to extract tangible benefits in favour of electric utilities and private players. The proposed framework envisages a bus fleet charging model and financial model within the spatial, technical, and financial constraints yet preserves the interests of electric utilities and investors by providing tentative solutions with all information necessary from the investor’s perspective. The methodology is free from optimization as the realistic constraints inherently employ search space reduction. The simulation results on a real public transportation system reveal the proposed methodology’s simplicity, rationality, applicability, and flexibility.

Improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

Home, but Homebound After Traumatic Brain Injury: Risk Factors and Associations With Nursing Home Entry and Death

ObjectiveTo examine risk factors associated with homeboundness 1-year after traumatic brain injury (TBI) and to explore associations between homebound status and risk of future mortality and nursing home entry. DesignSecondary analysis of a longitudinal prospective cohort study. SettingTBI Model Systems centers. ParticipantsCommunity-dwelling TBI Model Systems participants (n=6595) who sustained moderate-to-severe TBI between 2006 and 2016, and resided in a private residence 1-year postinjury. InterventionsNot applicable. Main Outcome MeasuresHomebound status (leaving home ≤1-2d per week), 5-year mortality, and 2- or 5-year nursing home entry. ResultsIn our sample, 14.2% of individuals were homebound 1-year postinjury, including 2% who never left home. Older age, having less than a bachelor's degree, Medicaid insurance, living in the Northeast or Midwest, dependence on others or special services for transportation, unemployment or retirement, and needing assistance for locomotion, bladder management, and social interactions at 1-year postinjury were associated with being homebound. After adjustment for potential confounders and an inverse probability weight for nonrandom attrition bias, being homebound was associated with a 1.69-times (95% confidence interval, 1.35-2.11) greater risk of 5-year mortality, and a nonsignificant but trending association with nursing home entry by 5 years postinjury (RR=1.90; 95% confidence interval, 0.94-3.87). Associations between homeboundness and mortality were consistent by age subgroup (±65y). ConclusionsThe negative long-term health outcomes among persons with TBI who rarely leave home warrants the need to re-evaluate home discharge as unequivocally positive. The identified risk factors for homebound status, and its associated negative long-term outcomes, should be considered when preparing patients and their families for discharge from acute and postacute rehabilitation care settings. Addressing modifiable risk factors for homeboundness, such as accessible public transportation options and home care to address mobility, could be targets for individual referrals and policy intervention.

Analysis Best Practice Policy Road Pricing to Overcome Negative Externalities of Motorized Vehicles in West Java

This research analyzes best practice policy road pricing in overcoming the negative externalities caused by motorized vehicles in West Java. Negative externalities, such as traffic congestion and air pollution, are major challenges for urban areas in West Java. Through implementation policy studies on road pricing in Singapore and London, this research analyzes the effectiveness of these policies in reducing congestion and improving air quality. The research methods used include literature reviews, policy analysis, and in-depth interviews. The research results show that road pricing can be an effective tool in reducing congestion and air pollution, if there is collaboration with the transportation department, police, and raharja services, adequate technology, effective outreach, and integration with improved public transportation. This policy must be designed holistically by taking into account related opportunities and issues. The role of communication, transparency and an inclusive approach is very important in increasing public acceptance of this policy.

Impact of COVID-19 led transition of work culture and travel to work patterns on society and environment in Delhi

Globally, COVID-19 has led to changes in daily lifestyle and travel behavior. There was a shift from in-person physical jobs to work-from-home culture, even for those jobs that required in-person presence at the job location. However, this was not true for everyone, and a majority continued to travel to work. The limited capacity of public transport services and the fear of exposure to the virus restrained people from practicing their conventional mobility choices. We explore the impact of COVID-19 on the nature of jobs and related changes in work travel patterns by population groups for Delhi using in-person surveys conducted during 2021 in six localities of Delhi. We also estimated the short- and long-term impacts of altered behavior on expenditures and equivalent CO2 emissions.As per the analysis, lower income groups, having no vehicles, with lesser qualifications, and employed as daily wagers, had limited capacity to change to work-from-home during COVID-19. We observed an increased dependency on personal motorized vehicles, leading to increased expenditures and reduced affordability. Adopting work-from-home during COVID-19 helped reduce equivalent CO2 emissions per working member by 39%. However, as the work at the site resumes, the emissions are expected to increase. The study shows that COVID-19 led to short-term benefits, but in the long term, the externalities will likely increase. Working from home during the pandemic provides short-term benefits; the social benefits are not equally distributed, and the transport emissions shall increase in the long term.

POLICY ON THE IMPLEMENTATION OF SMART MOBILITY IN THE SOUTH TANGERANG CITY, INDONESIA BASED ON PUBLIC TRANSPORTATION USING THE PROMETHEE METHOD

The increase in population density and mobility in certain urban areas has underscored the need for a smart mobility concept. As a component of the broader smart city framework, smart mobility aims to enhance transportation services, making them more accessible, safe, comfortable, efficient, and affordable for the public. This research assesses the readiness for implementing public transportation-based smart mobility in South Tangerang and develops a policy strategy model for its transportation planning and development. The study employed a mixed-method approach, incorporating multivariate analysis and Multi-Criteria Decision Analysis. The findings reveal that South Tangerang is currently unprepared to implement smart mobility, as indicated by low scores on its assessment indicators. Recommended policies include improving the availability, security, and comfort of public transportation, reorganizing transit routes, providing real-time information access, adjusting schedules, and promoting bicycle use by adding bicycle lanes. This research highlights the current state of transportation systems in developing countries and emphasizes the importance of implementing policies that benefit a wide segment of the population.

MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot.

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

Mobility challenges for women: A case study of bus rapid transit Peshawar

In Pakistan, women face significant barriers to urban mobility. This is due to limited access to safe, affordable, and sustainable public transportation, exacerbated by socio-cultural norms and experiences of harassment. Addressing these challenges is crucial for promoting gender equality and unlocking women's potential for growth. This research inspects women's mobility experiences and challenges and proposes potential improvements for bus rapid transit (BRT) Peshawar. Interviews were conducted for data collection, and statistical tools and methods were employed to analyze the results. The most common modes of travel to and from the BRT stations were walk (39%&45%) followed by Taxi/Rickshaw (32%each) and car/bike(15%&9%), respectively. Most users accessed the BRT system within a 3km radius (55%) and typically traveled during the daytime (56% from 6-10 am and 51%from 2-6 pm). The main challenges identified, mainly for the younger users, were harassment faced at stations (66%for 18-30 years and 52%for 31-45 years of age groups, respectively) while on the buses with 54%for <18 years and 56%for 18-30 years of age groups, respectively, with a visual type of harassment in major. The study highlights women's unique mobility challenges with Peshawar BRT. Addressing these issues can boost their education and economic prospects, as a major study sample was comprised of lower-income employees (80% having an income less than 40,000PKR*) and middle-aged students (48%). The findings provide valuable insights for BRT Peshawar and future BRT projects in developing countries. Despite separate seats, challenges persist during peak hours due to the absence of partitions between male and female sections within buses and other BRT amenities.

Longitudinal Relationship between Built Environment and Physical Fitness in Chinese Children and Adolescents: Findings from the COHERENCE Study

BACKGROUNDThe built environment is increasingly recognized as a significant factor in promoting physical fitness (PF). However, there is a lack of research examining the relationship between the built environment and PF, and this study aims to address this gap. METHODSThe study utilizes data from the Children's Growth Environment, Lifestyle, Physical, and Mental Health Development Project (COHERENCE) in Guangzhou, China. This project collects annual data on PF and demographic information of primary and high school students from 2016/17 to 2019/20 academic year. The built environment data around participants' residential neighborhoods is obtained based on the 5Ds framework. RESULTSAmong the 96,402 participants, aged 8.66 (1.88) years old, 51,363 (53.3%) were boys. The fitted restricted cubic spline (RCS) curve revealed a reverse N-shaped relationship between population density and PF. Threshold analysis discovered that when the distance from the residential address to the nearest public transport station was not more than 470 m, a longer distance was associated with better PF [β (Unstandardized coefficient) = 0.764, 95%CI (Confidence Interval) = 0.297, 1.230]. CONCLUSIONSThis large population-based study suggests that there is an urgent need for community-scale policies related to built environment in order to enhance the PF of Chinese children and adolescents.

The Role of Age-friendly City Indicators in Predicting Social Belonging in Older Adults Residing in Mohammadiyeh County, Qazvin, Iran

Objectives A strong sense of belonging is crucial for the physical and mental health of older adults and their social interactions. The age-friendly city offers various opportunities for social participation of older adults. The present study aims to determine the role of age friendly-city indicators in predicting social belonging of older adults residing in Mohammadiyeh County, Qazvin Province, Iran. Methods &amp; Materials This descriptive-analytical study was conducted on 300 older adults in 2022, who were selected by a cluster sampling method from six municipal districts of Mohammadiyeh County. Data were collected using a demographic checklist, an age-friendly city scale, and a social belonging scale. Data analysis was conducted in SPSS software version 23 using multivariate linear regression analysis. The significance level was set at 0.05. Results The mean age of participants was 67.55±6 years, ranging from 60 to 84 years. The majority of them were male (n=157, 52.33%), married (n=216, 72%), and illiterate (n=157, 52.33%). Among the age-friendly city indicators, the highest score was related to the safety/ease of transportation (16.86±4.51) and open public spaces (45.61±11.47) indicators. The results of multivariate regression analysis showed that marital status (β=-0.150, P=0.006), lower than high school education (β=0.170, P=0.003) and high school diploma (β=0.192, P=0.001), and the age-friendly city indicator of safety/ease of transportation (β=0.328, P&lt;0.001) were predictors of social belonging in older adults. Conclusion The majority of older adults in Mohammadiyeh County are dissatisfied with age-friendly indicators. Those who feel safe and have easy access to public transportation are more likely to have a stronger sense of social belonging. The results of this study can effectively help policymakers and city managers in proper planning in order to develop an age-friendly city.