Public Transport Research Articles

An 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.

Open E-Bus Blueprint

Buses form the backbone of public transportation in India, catering to 39.9 crore (399 million) passengers daily and accounting for over 90% of all public transport trips. As part of a broader goal to reduce transport emissions, India is looking to electrify up to 800,000 buses in the next seven years, including public, private and institutional fleet. Given the different dynamics of operating electric buses, a more connected and digital ecosystem can be a potential game changer in this transition.

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.

Sistem E-Tiket Bus Menggunakan QR-Code Berbasis Website

The land public transportation mode remains the main choice for the community in carrying out mobility, especially in traveling between cities and provinces. However, many users are worried because of the rampant fraud committed by irresponsible people, because of this unrest, the perpetrators of land public transportation service providers have begun to use technology in their transaction system, this aims to minimize the existence of passengers who do not have tickets and unauthorized ticket sellers. This research uses the SDLC (System Development Life Cycle) method because it can provide the approach and structure needed in system planning and development. This system was created using the PHP programming language and the CodeIgniter Framework in the development of the system. the utilization of QR Code technology on the system is as a way to encode the code on the ticket into an E-Ticket without using paper, besides aiming to increase security, the QR Code also provides efficiency in the ticket transaction process. The use of this website is also more structured because it prevents damage or loss of tickets, increases time efficiency and increases sales. The result of this research is the successful design of a bus E-Ticket system that uses a QR Code and is website-based.

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.

Forecasting CO2 Emissions from Libya’s Transport Sector

This paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.

Designing an E-Bike City: An automated process for network-wide multimodal road space reallocation

Effective and timely decarbonization of urban mobility requires systemic changes to transportation systems. High-quality cycling networks are seen as one of such measures and multiple scholars have developed automated approaches for a quick generation of such interventions. However, a common shortcoming is that they mostly ignore the tradeoffs and dependencies in allocating scarce road space to different modes. In this paper, we introduce an automated process for generating alternative multimodal transport networks within the boundaries of existing road space. Based on the user’s configuration, the resulting networks can prioritize separated cycling infrastructure, dedicated lanes for public transport, or a dense provision of on-street parking spaces. Also, the prioritization and the resulting designs can follow a variety of design principles, such as one-way streets, or impermeable superblocks. The outputs can be visualized on a map and used in common transport simulation toolkits. A case study in Zurich is used to demonstrate the process on a real-world network and discuss the results. The underlying software package SNMan (Street Network Manipulator) is available as open-source software and can be utilized by researchers and planners to envision alternative urban mobility futures in any place in the world.

Mapping PM2.5 Sources and Emission Management Options for Bishkek, Kyrgyzstan

Harsh winters, aging infrastructure, and the demand for modern amenities are major factors contributing to the deteriorating air quality in Bishkek. The city meets its winter heating energy needs through coal combustion at the central heating plant, heat-only boilers, and in situ heating equipment, while diesel and petrol fuel its transportation. Additional pollution sources include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with an average age of more than 10 years, 7.5 km2 of quarries, and a landfill. The annual PM2.5 emission load for the airshed is approximately 5500 tons, resulting in an annual average concentration of 48 μg/m3. Wintertime daily averages range from 200 to 300 μg/m3. The meteorological and pollution modeling was conducted using a WRF–CAMx system to evaluate PM2.5 source contributions and to support scenario analysis. Proposed emissions management policies include shifting to clean fuels like gas and electricity for heating, restricting secondhand vehicle imports while promoting newer standard vehicles, enhancing public transport with newer buses, doubling waste collection efficiency, improving landfill management, encouraging greening, and maintaining road infrastructure to control dust emissions. Implementing these measures is expected to reduce PM2.5 levels by 50–70% in the mid- to long-term. A comprehensive plan for Bishkek should expand the ambient monitoring network with reference-grade and low-cost sensors to track air quality management progress and enhance public awareness.

Road traffic injury among elderly people and its determinant factors: a cross-sectional study.

Injuries from Road Traffic Accidents (RTAs), particularly among vulnerable groups such as the elderly, are considered a significant public health concern. The aim of this study was to show epidemiology of RTIs among the elderly people in the Eastern Azerbaijan province of Iran. This cross-sectional study included all patients over 60 years old who admitted to the Shohada Hospital from 2006 to 2016. Data were extracted from the Hospital Information System. Injuries types were classified based on the International Classification of Diseases (ICD-10). Descriptive statistics (Mean, median and frequency) and inferential statistics (The Pearson Chi-square test) were used for data analysis through SPSS-24 software. A total of 3509 RTAs involving patients over 60 years old from 2006 to 2016. These traffic-related injuries comprised 15% of all recorded injuries (23,321 cases). The mean age of the patients was 69.91 ± 7.61 years, with a predominant male representation (2324 patients, 64.74%). The median Length of Stay (LOS) in hospital was 3 days. Injuries most commonly affected the knee and lower leg regions (27.5%). The most frequent types of accidents were those involving car passengers (40.47%) and pedestrians (36.33%). The majority of RTAs occurred during the summer season. There was a significant difference in the rate of RTAs based on variables such as sex, age, season, and LOS (p < 0.001). The study highlights traffic-related injuries among the elderly, mostly affecting the knee and lower leg regions, especially during the summer season. The efforts should include enhancing safety measures for car passengers and pedestrians, particularly during the high-risk summer months. Also, it emphasizes the need for public transportation to improve safety for older people due to lower accident rates and senior-friendly features.

Relationship between the Transport Performance and Financial Coverage in Urban Public Transport in the Czech Republic

Relationship between the Transport Performance and Financial Coverage in Urban Public Transport in the Czech Republic

Multimodal Signal Retiming Projects: A Survey-Based Exploration of Traffic Signal Professionals’ Practices and Challenges

In the realm of traffic signal operations, the Signal Timing Manual second edition (STM2) serves as a foundational guide for professionals engaged in multimodal signal retiming projects. However, it is acknowledged that the STM2 has its limitations, and real-world conditions often necessitate adaptations in the established procedures. Considering this context, this research endeavors to bridge this gap by conducting a comprehensive survey aimed at traffic signal professionals. This study presents the findings of a comprehensive survey conducted among traffic signal professionals to explore the methodologies, challenges, and practices involved in multimodal signal retiming projects. The survey aimed to obtain detailed insights into the current state of signal retiming, the types of data and tools utilized, and the adaptations necessary to address the complexities of multimodal urban transportation networks. The survey highlights and summarizes responses from 36 professionals across North America, providing insight into both the common strategies and unique challenges faced by those responsible for optimizing signal timings in diverse and dynamic urban environments. The survey results reveal a reliance on diverse tools and data types for signal optimization, highlighting the complexities of accommodating different transportation needs. The findings underscore the importance of tailored approaches and advanced technologies in enhancing signal retiming processes. The insights gained from this study will inform future strategies and enhance the effectiveness of signal retiming procedures in urban areas, thereby contributing to improved traffic management and multimodal transportation efficiency.

Electrifying the bus network with trolleybus: Analyzing the in motion charging technology

Currently, electric buses are becoming more and more popular, and their number in operation is increasing. The range of electric buses is also increasing and solutions that seem to be working almost without fixed infrastructure are being promised. However, this requires the use of high-capacity batteries, which increases the weight and price of the vehicle and causes high costs of battery replacement during operation. Moreover, if we take into account the growing demand for batteries, limited raw material resources, and the environmental impact of the battery production process, the optimization of battery capacity in vehicles may turn out to be a key issue. In this light, trolleybus becomes a sustainable and economically efficient bus electrification technology, if considered in an international scope and a medium- to long-term approach. The article provides a comprehensive study of challenges and potential solutions related to electric buses, which covers the theoretical analysis, technical aspects and practical applications, thus making a valuable resource for readers interested in sustainable urban transport systems. It presents the trolleybus technology, especially with modern solutions, as a sustainable and economically efficient tool for bus electrification. The article shows that the In Motion Charging (IMC) system reduces the need for high-capacity batteries under 100 kWh, which allows to extend their service life up to 15 years and, consequently, to reduce the number of buses needed for operation. The research was based on real measurement data from the transport system in Gdynia (Poland).

Efficiency and Equity Impacts of Urban Transportation Policies with Equilibrium Sorting

Efficiency and Equity Impacts of Urban Transportation Policies with Equilibrium Sorting

Identifying the Key Factors of Intermodal Travel Using Interpretative Ensemble Learning

Intermodal travel is considered an effective method for achieving sustainable urban transportation. Understanding the factors influencing intermodal travel is crucial. Due to the relatively small proportion of intermodal trips within cities, datasets are significantly imbalanced, leading to poor performance of traditional logit models. In this paper, we develop a novel interpretable ensemble learning (IEL) model to identify key factors through voting by five types of machine learning models. We test our model on two datasets with different numbers of features. The results show that travel duration, travel distance, vehicle ownership, and distance to the nearest metro station are the key factors influencing intermodal travel, cumulatively contributing nearly 70% in the JDS2021 dataset with 14 features and nearly 80% in the SHS2019 dataset with 8 features. Furthermore, we analyze the interpretability of our model and compare it with the logit model. Our model enriches the methodology for modeling intermodal travel behavior.