Daily Commute Research Articles

The Walkability of Alvalade Neighbourhood for Young People: An Agent-based Model of Daily Commutes to School

The Alvalade neighbourhood in Lisbon, Portugal, was built in the mid-XX century as low-cost housing for workers, but it has become inhabited by the middle and upper classes. The neighbourhood is home to a large population of young people, including children and teenagers who attend the schools located in the area. We present an agent-based model which aims to investigate the walkability of the neighbourhood for these young people, focusing on the mobility patterns of children and teenagers as they navigate their daily routines of going to school. We simulate the pedestrian movement of these young people, considering factors such as the availability of sidewalks, crosswalks, distance to schools, and the presence of other amenities. Our research reveals the mobility patterns emerging in this area and compares them across the different schools in the area. These results inform both urban policies and interventions that promote safe and accessible routes to school.

Active mobility as an alternative for the daily commute. Issues and chances for the city of Varna

Active mobility as an alternative for the daily commute. Issues and chances for the city of Varna

Automatic pothole detection

Road irregularities, such as potholes and road cracks, are a common hazard of daily commute and transportation. Automobile owners everywhere have long worried about the high price of fixing damage caused by potholes. Many accidents caused by potholes can be avoided if road repair agencies can respond quickly. Thus, an automated pothole detection system may help improve this issue. In this work, automatic pothole detection is performed using Faster R-CNN, Sparse R-CNN, YOLOv3, YOLOv5, and YOLOv7 computer vision algorithms. For this purpose, the dataset of road damage images collected in the Slovak region was used. The use of country-specific data is of great importance since the accuracy of pothole detection is dependent on the country of origin of the data. In this work, YOLOv7 achieved the highest detection accuracy of 0.884 (mAP@0.5) followed by YOLOv5 with the accuracy of 0.854 (mAP@0.5) on the pothole dataset.

Design and Development of a User-Centered Mobile Application for Intermodal Public Transit in Bangkok: A Design Thinking Approach

With the constant increase in public transit coverage in Bangkok Metropolitan Area in Thailand, many people are still hesitant to switch from using private to public transit, with one potential cause being the unavailability and difficulty in accessing accurate and timely information about their itineraries, while they are commuting. To assess and tackle such issues, the researchers adopted the user-centric Design Thinking methodology to empathize with target users' pain points in this study. They proposed a solution in a user-centric manner by assessing the usability flaws of existing mobile navigation applications, such as Google Maps and ViaBus. By developing a holistic mobile application called 'Disgovery' that covers all modes of public transit in the Bangkok Metropolitan Area and provides relevant information about their trips, the researchers aim to help commuters in Bangkok easily access it in a timely fashion. Through the user-friendly interface, commuters can eliminate the difficulty of finding routes and prices suitable to their needs. By making public transit more accessible with the help of ubiquitous mobile computing, commuters are also encouraged to switch from using private vehicles to public transit, which also can reduce accidents and carbon emissions. The findings from the usability testing in this study suggest that 'Disgovery' is an effective and user-friendly application for daily commuters in Bangkok that can help them achieve their goals without difficulties. The findings also indicate the importance of user interface and user experience guidelines in designing such applications.

Modelling the spatial spread of COVID-19 in a German district using a diffusion model.

In this study, we focus on modeling the local spread of COVID-19 infections. As the pandemic continues and new variants or future pandemics can emerge, modelling the early stages of infection spread becomes crucial, especially as limited medical data might be available initially. Therefore, our aim is to gain a better understanding of the diffusion dynamics on smaller scales using partial differential equation (PDE) models. Previous works have already presented various methods to model the spatial spread of diseases, but, due to a lack of data on regional or even local scale, few actually applied their models on real disease courses in order to describe the behaviour of the disease or estimate parameters. We use medical data from both the Robert-Koch-Institute (RKI) and the Birkenfeld district government for parameter estimation within a single German district, Birkenfeld in Rhineland-Palatinate, during the second wave of the pandemic in autumn 2020 and winter 2020-21. This district can be seen as a typical middle-European region, characterized by its (mainly) rural nature and daily commuter movements towards metropolitan areas. A basic reaction-diffusion model used for spatial COVID spread, which includes compartments for susceptibles, exposed, infected, recovered, and the total population, is used to describe the spatio-temporal spread of infections. The transmission rate, recovery rate, initial infected values, detection rate, and diffusivity rate are considered as parameters to be estimated using the reported daily data and least square fit. This work also features an emphasis on numerical methods which will be used to describe the diffusion on arbitrary two-dimensional domains. Two numerical optimization techniques for parameter fitting are used: the Metropolis algorithm and the adjoint method. Two different methods, the Crank-Nicholson method and a finite element method, which are used according to the requirements of the respective optimization method are used to solve the PDE system. This way, the two methods are compared and validated and provide similar results with good approximation of the infected in both the district and the respective sub-districts.

Rail trails and housing values: A longitudinal study

Greenways are recognized as an essential tool to build sustainable communities. An example in the United States are the rail trails where decommissioned rail corridors are being improved to provide the benefits of non-motorized transportation systems for recreation and exercise, and the daily commute to school, work and shopping. Yet despite these benefits some still argue the presence of the rail trail as a NIMBY (Not In My Back Yard). To address this argument, we calculate the economic benefit to homeowners living in proximity to rail trails over a ten year period to observe the dramatic changes in value over time. It was hypothesized that there would be a change in housing values based on proximity to greenways. This article will begin with an overview and a literature review on research related to greenways and home values, describe the context of this particular case study in the Pioneer Valley of Western Massachusetts, United States, provide an overview of the methods employed for the calculations and analysis, and conclude with some reflections and the potential limitations that should be considered for undertaking future research on this topic.

Dynamic Analysis of School Mobility Using Geolocation Web Technologies

Pedestrian travel represents one of the most complex forms of mobility owing to the numerous parameters that influence its analysis and the difficulty of acquiring accurate travel information. In addition, the vulnerability of its protagonists, especially in urban environments, in coexistence with other types of transport, makes its study interesting. This paper proposes a web tool for use in geolocated surveys that allows the acquisition of georeferenced thematic information of interest for mobility studies. The analysis of different school routes from students’ homes to their respective schools has been proposed as a case study. This work covered a sample of 1883 students from 26 schools in Galicia (Spain), where population dispersion generates a particular type of mobility. We obtained relevant mobility data, such as the routes most traveled by students in their daily commute to school, the most efficient routes, the most used means of transport, or the exact location of various elements that hinder and dangerously affect students traveling these routes, such as sidewalks or crosswalks in poor condition, among others.

Do commuters intend to avail electric street cars as public transport? Evidence from urban India

PurposeElectric street car (ESC) is a globally popular clean and safe electric transport system for urban agglomeration. India envisions achieving “all-electric transport” by 2030, yet ESC as a modal transport alternative is not distinct in the policy discussion. The emerging market for electric transportation in urban spaces requires a detailed demand study at the service user level to remove behavioural barriers and design integrated energy planning in developing economies. This paper explores the probabilistic uptake intentions of the daily public transport commuters for ESCs over e-buses from the only Indian city with operational ESCs, Kolkata.Design/methodology/approachUsing a random utility model on primary survey data from daily commuters, the authors identify demographic, psychometric and socio-economic factors influencing probabilistic uptake of ESC over e-buses.FindingsIt estimates that 38% of the commuters demand ESC over e-buses, given the alternatives' comparative details. Factors like frequent availability and technological upgradation would increase the uptake of ESCs.Social implicationsThe study highlights that even though there are infrastructural challenges in the implementation of ESC, so does any other electric transport system; it is worth considering as a decarbonising transport alternative, given the high up-take intension of the users.Originality/valueThis is the first attempt to study the demand for ESC in developing economies, identifying the factors which may be considered in the sustainable urban transportation policy perspective.

An analysis of the harassments and challenges faced by the public transport users in a developing country of South Asia

Bangladesh—located in south Asia and home to almost 167 M people, is one of the most densely populated countries in the world. Despite high population density, the country is yet to have a well-coordinated mass transit system. However, most people rely on public transportation due to comparatively low motorization. Public transport users, especially vulnerable cohorts (e.g., women), face enormous challenges, including molestations, assaults, and rape during their daily commute. Few studies that analyzed the state of harassment exclusively focused on women, which might not be comprehensive enough to understand the state of the problem and devise effective policies. Therefore, the current study explores the state of harassment in the three cities of Bangladesh, namely, Dhaka—the capital; Rajshahi; and Mymensingh across all genders. The study also investigates people’s perception of women’s mobility, the experience of using public transport, and the desirable safety precautions to understand the gender differences and the variability across the three cities. A detailed questionnaire survey was conducted to collect data from the three cities. The study found considerable gender differences regarding the perception of women’s mobility hindrances, the experience of using public transportation, and desirable safety precautions. The differences across the three cities were also noticeable. Contrary to the general belief, the study found that people from smaller towns like Mymensingh were more likely to get harassed than those from the bigger cities like Dhaka and Rajshahi—insinuating the lack of reporting from the smaller towns of the country. The findings from the study could be helpful for the transit and city planners in creating a conducive transit ambiance in Bangladesh. Based on the desirable safety precautions, female-only rides, especially at night, would be beneficial. City planners could also plan small and medium business activities around transit stops to attract the crowd and reduce the possibility of getting harassed while walking alone to access transit.

Digital transformation and sustainability in healthcare and clinical laboratories.

Healthcare, and in particular, clinical laboratories, are major contributors to carbon emissions and waste. Sustainability in healthcare has shifted from an environmental concern towards a holistic definition that includes balancing socio-ecological and socio-technical systems, including health services effectiveness and cost efficiency. Digital transformation can reduce waste and thecost of services by enhancing effectiveness while maintaining quality. Digital health interventions can provide personalized patient-centered care on a global scale and include decision support systems that have the potential to improve the performance and quality of healthcare. The right interfaces must be used so that theadvantages of going digital are felt throughout the health system: a successful and sustainable implementation of digital innovation depends on its integration into a functional health ecosystem. Telehealth has the potential to reduce carbon emissions due to the reduced daily commute of health professionals, although research is limited. Recently, economic models have changed from the linear "take-make-dispose" to circular models based on recycling and upcycling that have the goal of keeping products, components, and materials at their highest utility and value. The previous linear models threaten human health and well-being and harm natural ecosystems.

Are citizens of Delhi willing to stop using private vehicles for their daily office commute in order to reduce air pollution and related ailments? A protocol for a contingent valuation study

Background: Increased vehicular traffic is associated with higher levels of air and noise pollution which in turn causes high morbidity and mortality. Governments, including Delhi have invested high amounts in setting up and running public transport systems in order to rationalize road usage and limit vehicles on road. This study proposes to assess the willingness of the citizens to give up their private vehicles in favor of public transport and will provide specific policy recommendations.Methods: This study will be conducted in Delhi/NCR along the Delhi metro route. A systematic review and cross-sectional community survey will be carried out to meet the study objectives. Data will be captured through computer aided personal interviews (CAPI) using contingent valuation methods (CVM). This study will provide information on the perceptions and attitudes of commuters towards the current public transport system and the economic value placed by them on private transportation and provide an estimate of the welfare gain associated with it. Specific objective wise outputs have been framed. This will be the first study using contingent valuation methods (CVM) in a similar setting in India.Conclusions: The result will provide an evaluation of the public transport system of Delhi NCR from a user perspective. The willingness to pay values will provide a monetary value to the health benefits arising from shift from private to public modes of transport along with workable models of interventions for policy makers.

IoT Based on Potholes and Speed Breakers Detection and Alert System

In a world where 90 percent of people travel by road, the safety of the same becomes of utmost importance. And two of the most major issues a road traveler faces are Potholes and speed breakers. The total number of road accident deaths due to potholes in 2018, 2019, and 2020 stood at 2015, 2140 and 1471, respectively. Although speed breakers are intended to prevent traffic accidents, 49.6 per cent of crashes still happen in areas where there are speed breakers.
 This paper aims to present a method for locating, identifying, and documenting speed breakers and potholes. The suggested method will employ driving information collected by various sensors, such as vibrations, jerks, bumps, changes in distance from the base of the vehicle, etc., to detect and then store the location of the object in an online database. Additionally, a stream of images of the object spotted will be simultaneously taken and uploaded to a server, to analyze using an Image Detection model. This model will facilitate the live detection of potholes and speed breakers more accurately than previous solutions.
 Once this data is added to a database, it will be effectively used by government agencies to repair the roads as well as by daily commuters to avoid the route with more obstacles, when the database is supplied to mapping services like Google Maps.

Improvisation of mental health: creating an interactive platform for the people during their daily commute in metropolitan cities

Working and Mental health have usually been focused on in many research areas. But in this research paper, the time period of travelling from place to place for work or some other reasons has been implemented in regards to the mental health of the people. With the help of an artistic approach, an open platform is aimed to be formed for the people to interact, share and listen to each other's stories concerning mental health challenges by ignoring the stigmatised notion of the same. The methodology used comprised extensive secondary and primary research. The study was conducted mainly in Indian metropolitan cities. Distribution of surveys was conducted remotely on ‌mental health and the various perceptions of it, continuing with one-on-one interviews with a total of 16 volunteers from the three major target audience groups - artists, mental health professionals and mental health challengers. After receiving the results from the methodology tools, a device was created for interaction among the people who commute by metro trains. Through this device, tasks were given out for the users/travellers for them to perform during their commute with an end incentive attached to it.

Working from home during COVID-19 lockdown: Changing competencies and work-home life boundaries

Crisis situations often introduces drastic lifestyle changes. This study is focused on the COVID-19 pandemic and aims to shed light on an unprecedented context of forcing employees to work from home with a short notice of companies and the government. The goal is three-fold: i) to understand the extent to which employees were indeed prepared to work from home; ii) to uncover the most important competencies that enabled employees to deal better with a crisis situation, such as the COVID-19 pandemic; and iii) to discuss the real impact that working from home had in the employees’ lives during the pandemic situation and the quarantine period.
 Using narrative inquiry, this study explores the experiences of 18 young adult consultants working in different business areas, in Portugal. Semi-structure interviews were conducting during the third wave (12/2020 – 03/2021) and thematic analysis was used to analysis the transcripts. The analysis revealed three main themes: ‘management competencies’, ‘work-life balance’ and ‘work flexibility’. Each theme consisted of several subthemes which illustrates how the participants perceived working from home and the factors that reflect their experiences and understanding.
 The research findings illustrate that interpersonal communication, anxiety and stress management, time management, and e-leadership are vital skills to cause a great impact on participants’ productivity and well-being at work. Participants all appeared to notice that working from home provides a better work-life balance (e.g. saving time on daily commute) and more flexibility with regards to the work schedule and home commitments. Nonetheless, their experiences of home working depend on the personal situation, personality and the perceived management support offered during the COVID-19 lockdown.
 The research highlights a need to train employees on soft skills and prepare them to future crisis situations. Theoretical implications suggest that academics should expand research and interventions to include not only the work environment but also other external factors that affect employees. The limitations of the study and recommendations for future research are suggested.

Modified State-Dependent Queuing Model for the Capacity Analysis of Metro Rail Transit Station Corridor during COVID-19

The COVID-19 pandemic policies have had a significant impact on the daily commuter flow at the metro rail transit stations. In this study, we propose a modified state-dependent M(n)/G(n)/C/C queuing model for the analysis of commuter flow in the corridor of metro rail transit stations in the COVID-19 situation in order to ensure safe social distance. The proposed model is a finite capacity queuing system with state-dependent commuter arrivals and state-dependent service rates based on the flow–density relationship. First, a mathematical queuing model is developed by using the birth–death process (BDP) and the expected number of commuters, and average area occupied per commuter and blocking probabilities are computed. Then, the accuracy of the proposed model is verified by a discrete-event simulation (DES) framework. (1) The proposed model’s results are compared to those of the existing M/G(n)/C/C model. The proposed modified model’s sensitivity analysis revealed that the anticipated number of commuters in the corridor remains smaller when the arrival rate is state-dependent. (2) In accordance with COVID-19 protocol, when the facility is congested, commuters are discouraged from entering and a safe social distance is maintained between them. (3) No commuters are impeded, and adequate throughput is ensured from the corridor. The proposed model will assist the metro rail transit station operators in making intelligent decisions regarding the operations in the COVID-19 situation.

Carbon Footprint of Patient and Staff Travel for Proton Therapy: How Many Trees do We Need to Offset It?

<h3>Purpose/Objective(s)</h3> There is an increasing concern about rising carbon dioxide (CO2) levels and its hazardous impact on human health and climate change. Patients travel sometimes significant distances for their daily radiation treatments, especially for rare modalities such as proton therapy; the carbon footprint of this commute is not well understood. This study quantifies the distances traveled by patients and staff, and estimates their corresponding carbon footprint. Additionally, the study evaluates possible ways to offset CO2 equivalent emissions. <h3>Materials/Methods</h3> Patients treated between 07/2020 and 06/2021 at our proton program were evaluated. Their distance traveled was evaluated from their address using Google Maps. Most of the patients commuted daily, while some stayed for some nights at nearby hotels. Calculations assume full daily commute, as a worst-case scenario. Patients treated were reviewed for disease, site treated, and number of fractions. Staff members commute distances were evaluated and prorated depending on their proton therapy workload. EPA web calculator was used to convert distance in miles to kg of CO2, and the number of new trees required to offset it. <h3>Results</h3> There were 185 patients treated (average 19.9 per day); 63% had full course with protons, 31% had photon boost, and 6% had protons as boost. Total of 5,176 fractions were delivered (average 28 fractions per treatment course, maximum 42 fractions). The average patient distance traveled was 39 miles one way; 19 patients lived >100 miles away. An average patient course required 2,158 miles of commute. Total distance driven was 399,271 miles. Patients with prostate cancer accounted for highest average miles (3,517) per course; patients with CNS tumors accounted for the highest cumulative miles (100,207). The corresponding CO2 equivalent emissions were 859 kg per patient treatment course (range 15 kg to 5,147 kg); cumulative patient travel footprint was 158 metric tonnes of CO2 equivalent. To offset this footprint would require on average 14 new trees planted per patient (range 0.2 trees to 85 trees); total patient travel offset would require 2,625 new trees planted annually. There were 14 staff members supporting protons therapy. Their average daily commute was 31 miles, for total of 215 miles per day. This translated to 82,134 miles driven, 32 metric tonnes of CO2 equivalent, and 540 new trees planted annually. Per patient course treated, the incremental contribution was additional 177 kg of CO2 and 2.9 additional trees. <h3>Conclusion</h3> Travel carbon footprint of patients undergoing proton therapy was 859 kg CO2 per treatment course, which would require planting 14 new trees to offset. Staff travel contributed further 177 kg CO2 and additional 3 trees to be planted per patient. Strategies to lower CO2 footprint may include hypofractionation and optimizing transportation options, and we hope other radiation oncology practices will consider patient travel in evaluating their carbon footprint.

Women-Only Transportation: How “Pink” Public Transportation Changes Public Perception of Women’s Mobility

It is well documented that women often face sexual harassment and violence during their daily commutes, particularly in countries with extreme levels of gender inequality. A popular reaction to this problem is to issue women-only transit services. Despite this growing trend, we know very little about it. Looking at the case of Mexico City, this study examines, analyzes, and evaluates women-only transportation, focusing on the roles of culture and public opinion. Drawing from both qualitative and quantitative data, it finds that the violence women face in public transit leads them to always opt for women-only services, encouraging local policy makers to increase their numbers. Local feminist groups have advanced this system by arguing that violence in regular public transit is gender discrimination. Consequently, they have positioned themselves as administrators of women-only transportation, using it as a campaign to defend women’s equal right to urban mobility.

How the COVID-19 pandemic changes daily commuting routines – Insights from the German Mobility Panel

The COVID-19 pandemic has had a major impact on everyday travel and, by extension, everyday commuting. During the pandemic, some people were able to work from home while others continued commuting. This study examines how commuting behavior changed between 2019 and 2020. In this study, we analyze panel data of the German Mobility Panel, a national household travel survey. We paint a broad picture of the characteristics and behavior of those who commuted during the pandemic. The analyses focus on the intra- and interpersonal differences and are presented in a mostly descriptive way. The results show that people with low income and a low level of education are primarily those who cannot work from home and do not have flexible working hours. The results further show that especially public transport has lost importance in daily commuting. However, those who commuted in 2019 and 2020 did not significantly change their commuting behavior regarding commuting time and commuting mode.

Psychological predictors of Sustainable Transportation Behaviour: Implications for Low Carbon Economy and Society

There’s been a consistent increase in carbon emissions globally in the recent decades, primarily because of a consistent increase in personal transportation needs. Thus sustainable transportation behaviour has become a pertinent variable for investigation. The current study thus aimed to study the impact of environmental consciousness, green identity, values (biospheric, altruistic, egoistic), and consideration of future consequences on sustainable transportation behaviour such as usage of public transport and carpooling. The study employed a quantitative survey design with 351 participants in total and (M=67% F=33%) who were daily commuters to offices or universities. Results indicated that environmental consciousness, green identity and consideration of future consequences to be positively correlated with sustainable transportation behaviour. Biospheric and altruistic values were also found to have a positive correlation with sustainable transportation behaviour while egoistic values had a negative correlation with sustainable transportation behaviour. These findings have implications for a low carbon economy and society since they highlight important psychological variables that can promote sustainable transportation behaviour at an individual level and reduce overall carbon emissions.

A holistic approach for modeling and predicting bike demand

Bike-sharing systems have enjoyed tremendous success in many major cities around the world today as a new means of urban public transportation offering a green and facile solution for daily commuters and tourists. One common problem featured in these systems is that the distribution of bikes among stations can be quite uneven, due to various factors including topography, location of dockings, hours of service, safety and security, weather, rush hours or even during the occurrence of major events around the city. Such imbalances often result in shortage of bikes or bike parking racks. An unbalanced bike system indicates an unreliable form of transportation and disappointed users. Current studies in the literature are limited as they are not designed to handle fluctuating, high or unpredictable demand during large city events that typically affect multiple stations and require real-time rebalancing, during the event, to ensure seamless operation. In this work we solve the bike rebalancing problem while considering fluctuating demand that leads to an imbalance between supply and demand. We present “SmartBIKER”, a holistic and cost-effective framework for bike sharing systems addressing both normal operation and operation during major city events. SmartBIKER models bike demand during both normal operation and major events, identifies bike stations with low or high demand using two different forecasting models and determines a relocation strategy that maximizes the utility of the stations while minimizing the relocation cost. Our experimental evaluation shows that our approach is practical, efficient and outperforms state-of-the-art relocation and prediction schemes.