Sustainable Transport Modes Research Articles

An assessment of Qatari school students’ perspective towards using sustainable modes of transport to school

Being a car dependent country traditionally, private cars are widely used for various trip purposes in State of Qatar including school trips. This paper assesses the factors affecting sustainable mode choices, walking or using school bus to/from school from school students’ perspective. A data of 2639 students from public schools was used to conduct the quantitative analysis. The results suggested that gender, age, and nationality were important demographic factors in determining mode choice to/from school. Further, the selection of school bus was dependent on general safety factors, policy of loading or unloading, service cost, dropoff/pickup points, and availability of driver assistant. Additionally, three obstacles limited school bus usage by students, which were long trip timing, unavailability of service, unsafe service, inconvenient time, and private or personal reasons. These results can be used to address students’ concerns/issues while planning and policy making which can help to increase school bus usage among school students and reduce reliance on private cars.

Development of Transport Management Software

The growth of technology and the increasing competitiveness and opportunity of digital services have meant that the traditional logistic sector has changed dramatically. New technology and globalization affect the need to use solutions known to the IT industry. In recent years, we have seen significant technological growth, from smart devices communicating with each other via the IoT (Internet of Things) to artificial intelligence, and the changes it brings in the future. However, the role of user interface design in enhancing the user experience of mobile applications, such as journey planners, has been overlooked. Given the knowledge of the environmental impact of transport systems, it is important to convince users to use more sustainable modes of transport. Transport systems account for 20% to 25% of global energy consumption and carbon emissions. The aim of the article is to explain the process of creating a user interface for a public transport journey planner application that serves as a personal travel assistant. In addition, a competitive audit and research on a small group of users was also carried out. The conducted research and the audit of the competition were crucial in the ideation process, which became the basis for the first wireframes of the application. This was made possible by using the powerful Figma tool, which allowed for a more detailed design of the application’s appearance and functions. The resulting design of the application interface is a proposed solution to some of the user problems detected during the study. The final prototype needs to be turned into a real product to serve users. For this purpose, a developer must be provided with the prototype, who will bring the application to life using programming tools.

Carbon footprint effects of shifting from flights to night trains for Swedish tourism

Changes in travel behaviour are needed to tackle the climate impact associated with long-distance flights, including a switch to sustainable transport modes. In this paper, we analyse scenarios of carbon footprint reduction associated with a switch from flights to night trains for holidays in Europe for the case of Sweden, including outbound, inbound and domestic tourism. We use a prospective lifecycle assessment framework combined with results from a stated preference experiment to determine the impact of future mode shift behaviours. Our results indicate that a mode shift could be triggered by progressive night train policies resulting in (i) fewer transfers and (ii) price levels similar to those of flights. The shifts from flights to night trains could result in 9% lower cumulative carbon footprint in relation to a baseline travel demand scenario for the period 2025–2050. Decarbonization of long-distance travel in line with the Paris Agreement would likely require a combination of many different types of measures including a shift to low-carbon fuels.

Exploring Melbourne Metro Train Passengers’ Pre-Boarding Behaviors and Perceptions

The focus on sustainable transportation has increased interest in promoting sustainable modes of transport, such as rail. Understanding train passengers’ behaviors and perceptions is essential to enhance their travel experience and increase train ridership. Pre-boarding behaviors and perceptions are crucial in shaping the overall train travel experience. However, there are limited studies that have developed a systematic framework for investigating train passengers’ pre-boarding behaviors and perceptions. This paper examines the train passenger’s pre-boarding behaviors and perceptions about the station and platform. The study adopts a mixed-methods approach by developing a pre-boarding decision framework and combining it with questionnaire surveys to explore passengers’ behaviors and perceptions on the platform before boarding. A total of 429 valid responses from Melbourne metro train users were used for analysis. Descriptive statistics and correlation techniques were applied to identify patterns and relationships. The findings reveal common pre-boarding behaviors and perceptions. Furthermore, the study uncovers factors influencing these behaviors and perceptions, such as passenger demographics, travel patterns, and specific trip characteristics. For example, carrying large items and travel frequency significantly impact passengers’ travel experience in the pre-boarding phase. Waiting time, group travel, carrying small items, gender, and age group also significantly impact some pre-boarding behavior variables. Travel time, on the other hand, makes no significant impact on any of the pre-boarding variables that we examined. This research provides valuable insights for rail service operators and policymakers to enhance the pre-boarding experience, optimize station design, and improve passenger satisfaction.

ANALISIS PEMILIHAN MODA TRANSPORTASI UMUM MENUJU STASIUN GAMBIR MENGGUNAKAN TRANSPORTASI ONLINE DAN BUS TRANSJAKARTA

Public transportation acts as a sustainable mode of transportation to reduce the increasing level of pollution and traffic congestion. Public transportation has many types, in Jakarta, for example, online transportation and Transjakarta buses. This study aims to analyze the characteristics of online transportation travellers and Transjakarta buses, analyze the chances of choosing between the two modes of public transportation, and determine the level of service according to the community. Data collection using questionnaires distributed at Gambir station. Characteristics of travelers were analyzed using descriptive statistics. The probability of choosing a mode is analyzed using the binary logit difference model with the help of multiple linear regression equations. The level of service of the two modes of public transportation was analyzed using a Likert scale. The results showed that the characteristics of online transportation travellers are dominated by female, of productive age, highly educated, worked as an employee, earned quite a lot of money, traveled for vacation or recreational purposes, and the reason for choosing the mode was getting to the destination quickly. The characteristics of Transjakarta bus travellers are dominated by male, productive age, highly educated, work as an employee, earn quite high, travel for vacation or recreational purposes, and the reason for choosing the mode is the low price. The dominant choice of public transportation mode is online transportation. The level of online transportation services for the security and convenience aspects is in the good category, and the ease and reliability aspects are in the very good category.Keywords: characteristics of travellers, transportation mode, likert scale

Deep trip generation with graph neural networks for bike sharing system expansion

Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-scale pilot and gradually expand the system to cover more areas. For station-based BSSs, this means planning new stations based on existing ones over time, which requires prediction of the number of trips generated by these new stations across the whole system. Previous studies typically rely on relatively simple regression or machine learning models, which are limited in capturing complex spatial relationships. Despite the growing literature in deep learning methods for travel demand prediction, they are mostly developed for short-term prediction based on time series data, assuming no structural changes to the system. In this study, we focus on the trip generation problem for BSS expansion, and propose a graph neural network (GNN) approach to predicting the station-level demand based on multi-source urban built environment data. Specifically, it constructs multiple localized graphs centered on each target station and uses attention mechanisms to learn the correlation weights between stations. We further illustrate that the proposed approach can be regarded as a generalized spatial regression model, indicating the commonalities between spatial regression and GNNs. The model is evaluated based on realistic experiments using multi-year BSS data from New York City, and the results validate the superior performance of our approach compared to existing methods. We also demonstrate the interpretability of the model for uncovering the effects of built environment features and spatial interactions between stations, which can provide strategic guidance for BSS station location selection and capacity planning.

An exploration of the preferences and mode choice behavior between autonomous demand-responsive transit and traditional buses

With the advancement in autonomous driving techniques, autonomous demand-responsive transit (ADRT) is a newly emerging sustainable transport mode for the future, which will provide more flexible services to public users. ADRT offers benefits such as flexible stops and routes and comfortable seats, but it also involves risks due to the vehicles being driverless. This paper particularly investigates users’ preferences and attitudes towards ADRT, and mode choice behavior between ADRT buses and traditional buses. A survey with Likert scale statements and stated preference (SP) choice scenarios is designed and conducted to explore users’ attitudes towards the safety risks of autonomous vehicles (AVs), social concerns, service flexibility concerns when using AVs, interest in new things, and shuttle mode choices. An integrated choice and latent variable (ICLV) model is adopted to explore users’ psychological factors through latent variables and to integrate them into mode choice behavioral modeling. Estimated results indicate that users’ attitudes towards AV safety risks, their social concerns, and their flexibility concerns with ADRT strongly influence their mode choices and are strongly related to sociodemographic and travel-related factors such as age, gender, income, education, number of family members. In general, a young age, a high education level, a higher income, private car ownership, and better knowledge of AVs are positively related to attitudes towards ADRT. Females, users from large families, and users with driving licenses or long commuting times are less willing to adopt ADRT. The study's outcomes highlight significant heterogeneities among users and can be highly valuable for policymakers, such as government authorities, in providing social support and designing policies targeting specific population groups. This will be beneficial in attracting more users to this emerging mobility service and contributing to sustainable urban development.

Assessing the Impact of Public Behavior and Industrial Emissions on Ambient Air Quality in Pakistan

Air pollution is a growing environmental concern worldwide, and Pakistan is no exception. The study investigates the impact of industrial emissions, as well as public behavior, on air pollution in Pakistan. For this purpose, a questionnaire-based survey was conducted among a sample size of 100 participants, including students, professors, and government employees. The goal was to assess their attitudes, practices, and understanding regarding air pollution, as well as their perception and contribution to the relevant issues and their role concerning industries and vehicles. The findings revealed significant insights into the prevailing situation. The respondents recognized the contributions of industrial emissions to pollution levels. However, a substantial portion of the participants exhibited limited knowledge regarding specific pollutants and their sources. These responses clarified certain facets regarding public behavior, including transportation choices, waste management practices, and energy-efficient technologies. It was evident that sustainable modes of transportation, such as cycling or public transportation, were underutilized, with personal vehicles being the primary choice. Furthermore, significant challenges were identified as a lack of proper waste management practices and the low adoption of energy-efficient technologies. Based on these findings, it is crucial to implement targeted interventions and raise awareness to address air pollution in Pakistan effectively. The study highlighted that there is adire need for increased education and public outreach initiatives to improve knowledge regarding air pollutants and their sources. Policies that support environmentally friendly transportation choices and encourage industry adoption of cleaner technologies should also be developed.

Using Deep Learning and Google Street View Imagery to Assess and Improve Cyclist Safety in London

Cycling is a sustainable mode of transportation with significant benefits for society. The number of cyclists on the streets depends heavily on their perception of safety, which makes it essential to establish a common metric for determining and comparing risk factors related to road safety. This research addresses the identification of cyclists’ risk factors using deep learning techniques applied to a Google Street View (GSV) imagery dataset. The research utilizes a case study approach, focusing on London, and applies object detection and image segmentation models to extract cyclists’ risk factors from GSV images. Two state-of-the-art tools, You Only Look Once version 5 (YOLOv5) and the pyramid scene parsing network (PSPNet101), were used for object detection and image segmentation. This study analyzes the results and discusses the technology’s limitations and potential for improvements in assessing cyclist safety. Approximately 2 million objects were identified, and 250 billion pixels were labeled in the 500,000 images available in the dataset. On average, 108 images were analyzed per Lower Layer Super Output Area (LSOA) in London. The distribution of risk factors, including high vehicle speed, tram/train rails, truck circulation, parked cars and the presence of pedestrians, was identified at the LSOA level using YOLOv5. Statistically significant negative correlations were found between cars and buses, cars and cyclists, and cars and people. In contrast, positive correlations were observed between people and buses and between people and bicycles. Using PSPNet101, building (19%), sky (15%) and road (15%) pixels were the most common. The findings of this research have the potential to contribute to a better understanding of risk factors for cyclists in urban environments and provide insights for creating safer cities for cyclists by applying deep learning techniques.

Effective Speed: Can Cost Effective Transportation Be Sustainable (Reducing Emissions and External Costs)?

Given the need to reduce fuel emissions from transport many research studies have been devoted to the development of technologies and identification of new policies to tackle this problem. The majority of these strategies either increase the costs (e.g., new technology), are more time-consuming (i.e., choosing a more sustainable mode of transport), or encourage consumers to forgo travel plans (i.e., flights to holiday destinations). Implementing any of these changes is challenging for a society where cost, quality and time are the key motivators. The paper differs from previous research, given that the focus is not to slow down global warming, through the development of new technologies, or through inconveniences to lifestyle. Instead, the focus is to improve the sustainability of transport using current technology without increasing the cost or time factor. By using the concept of effective speed, this paper estimates the possible reduction in emissions, external costs and land use if people can be persuaded to choose the most cost-effective mode of transport. The effective speed is calculated by dividing the distance travelled by the time spent (i.e., travelling to work and earning the money to pay for the commute). This case study uses data from a survey of residents in New York City (NYC) and incorporates supporting data about commuting patterns in Germany. If people use their most cost-effective mode of transport in NYC, it is possible to have emission reductions of up to 14.7%, external cost reduction of 11.6% and a reduction in the time–area requirements of 16.5%. The results of this paper highlight that people do not always need to spend more time or money on their transport activity to travel in a more sustainable way. Indeed, encouraging people to use a mode of transport with a faster effective speed may even reduce the external effects for some.

A Bicycle-Embedded Electromagnetic Harvester for Providing Energy to Low-Power Electronic Devices

Bicycles are rapidly gaining popularity as a sustainable mode of transportation around the world. Furthermore, the smart bicycle paradigm enables increased use through the Internet of Things applications (e.g., GPS tracking systems). This new paradigm introduces energy autonomy as a new challenge. The energy harvesting technology can capture the energy present in the cycling environment (e.g., kinetic or solar) to give this autonomy. The kinetic energy source is more stable and dense in this environment. There are several wheel kinetic harvesters on the market, ranging from low-complexity dynamos used to power bicycle lights to smart harvester systems that harvest kinetic energy while braking and cycling and store it for when it is needed to power sensors and other electronics loads. Perhaps the hub and the “bottle” dynamos are the most commercially successful systems because of their cost-effective design. Furthermore, the bottle generator is very inexpensive, yet it suffers from significant energy losses and is unreliable in wet weather due to mechanical friction and wheel slippage in the wheel/generator contact. This paper proposes a cost-effective bicycle harvester based on a novel kinetic-electromagnetic transducer. The proposed harvester allows for the generation and storage of harnessed kinetic energy to power low-power electronics loads when the user requires it (e.g., cell phone charging, lighting). The proposed harvester is made up of a power processing unit, a battery, and an optimized transducer based on a Halbach magnet array. An extensive full-wave electromagnetic simulation was used to evaluate the proposed transducer. Circuit simulation was also used to validate the proposed power unit. The proposed harvester generates a simulated output power of 1.17 W with a power processing unit efficiency of 45.6% under a constant bicycle velocity of 30 km/h.

The Impacts of Popular Participation in the Urban Landscape: A Case Study in Lorena, SP.

Despite being an intrinsic factor in people's daily lives, active mobility still presents itself as a challenge in the model of Brazilian cities due to the lack of adequate and inviting structure for walking. This case study was carried out in a city located in the countryside of the State of São Paulo, with the purpose of enabling a higher quality in walking, reinforcing walkability and the importance of more sustainable modes of transportation, fostering urban vitality and reactivating neglected areas by promoting a network articulation to develop sustainable initiatives. In this way, it seeks to highlight the importance of popular participation in the construction and requalification of urban public spaces, not only in terms of displacement, but also in the occupation of idle spaces through the use of methods and tools, such as tactical urbanism, which proposes interventions in the city in the context of the micro scale, from short-term solutions, low cost and multipliable. The study presents the challenges and the results of the project, arising from the collection and systematization of data, and from the collective construction and continuous appropriation of space by the population.

Sustainable transport modes, travel satisfaction, and emotions: Evidence from car-dependent compact cities

This study investigates how the use of sustainable transport modes relates to travel satisfaction (general evaluation of travel) and travel affect (emotions during travel) in car-dependent compact cities. Thereby, the study provides evidence on sustainable mobility and travel-related well-being in a context of compact urban form but inadequate provisions for public transport, walking, and cycling. A mixed-methods approach was applied comprising quantitative and qualitative analyses of data from the two major cities of Greece, i.e., Athens and Thessaloniki. Travel satisfaction and travel affect are found to be highest for those who walk for commuting, independently of travel time and other factors. Conversely, travel satisfaction and travel affect are lowest for public transport users, largely due to very long travel times but also poor public transport services in one of the two cities. Results indicate that the experience of traveling by public transport, car, and motorcycle within urban areas greatly depends on transport provision and policies. Overall, findings support the idea that to shift to pleasant, satisfying, and sustainable mobility in car-dependent compact cities, car restrictions should be accompanied by massive improvements in public transport, high-quality walking and cycling infrastructure, and an integrated coordination of different modes.

Understanding Ride-Hailing Sharing and Matching in Chicago Using Travel Time, Cost, and Choice Models

Ride-hailing data is sparingly available throughout the U.S., which limits researchers’ understanding of the mode. Chicago is one of a few cities that have mandated ride-hailing companies to submit detailed trip data to their local transportation agency. The dataset is one of the few to contain trip-level attributes such as fare, travel time, and trip length. Most research using the Chicago dataset has focused on understanding why people use ride-hailing. This study focuses on why ride-hailing passengers choose shared over private trips and what influences the shared trips to be matched. Trips to/from airports are less likely to be shared. Trips to/from low-income areas are more likely to be shared. Longer shared trips are more likely to be matched, shared trips to/from dense areas are more likely to be matched, and shared trips between areas with a high number of shared trips are more likely to be matched. Matching an additional shared trip with another adds approximately 4 min to a trip. Ride-hailing users’ value of time is found to be $48.23 per hour. Understanding travel behavior is important for all modes of transportation including ride-hailing. The results of this paper can be applied to guide polices aiming to promote more sustainable transportation modes.

Understanding children's cycling route selection through spatial trajectory data mining

A sustainable alternative transportation mode to address growing transportation and environmental stress is cycling, which is eco-friendly and healthy for humans. Improving the quality of the bicycling experience is crucial for increasing bicycle use. Good bicycling experience is more critical for child bicyclists because they are less experienced and need more space for error. Therefore, a scientific assessment of child bicyclist perception of selected route safety, comfort, and environment is of great interest. Finding an effective way to learn child bicyclist behavior and help them reduce cycling risk is necessary. In response to this need, we utilize a data mining model to develop a methodology for measuring children's bicycling route safety conditions by evaluating multiple road safety-related features. The proposed method uses a set of route features representing the situation of street environments extracted from state data and first-hand children’s bicycle trajectory data collected using Global Positioning Systems (GPS) from volunteer children bicyclists. A random forest (RF), a well-known classifier, is adopted to predict child bicyclists' behavior. We extract the different route segments between children’s selected routes and the shortest path to learn the child bicyclists' behavior and use the selected best features to interpret their changing cycling behavior. The result shows that children bicyclists' behavior could be analyzed by giving trajectory and nearby road safety situation data. Our model achieves a promising accuracy with an average rate of 92% over multiple scenarios, demonstrating the proposed method's feasibility and the effectiveness of selected features. In addition, we compare our feature effectiveness with the state's generated road safety score data to evaluate the feature robustness. Our features outperform the safety score feature with an average of 10% improvement in prediction accuracy. Furthermore, our method proposes a model framework that can be applied to different study regions and adult bicycling behavior learning.

Developing approaches and strategies to promote increased active mobility in urban city neighbourhood

With the world’s population expanding quickly, cities must deal with problems such as traffic jams, air pollution, car accidents, and urban sprawl. Walking and cycling are non-motorized modes of transport that require little infrastructure and don’t utilize any fossil fuel energy. In comparison to motorized modes, they are easily adaptable and inexpensive to maintain for both users and governments. Therefore, the goal of this paper is to identify strategies for greater active mobility through non-motorized modes of transport. Through a document study, methods that can be utilized as incentives to improve walkability and bikeability in the Elgeseter neighbourhood of the City of Trondheim, Norway have been identified. The analysis addresses following research question: “What may persuade Elgeseter district commuters or travellers to modify their behaviour toward more walking and biking?” This study reveals five topics of interest: Identifying existing difficulties in the district, Evaluation of current pedestrian, bicycle, and vehicle links, as well as meeting places and open spaces, and identification of opportunities to improve them, How to outline planning concepts, Methods and design recommendations for enhancing walking and micro transportation, as well as The importance of accessibility, safety, and comfort in attracting people to active mobility. The main argument is that a multi-dimensional approach is required to persuade more people to choose environmentally friendly, sustainable modes of transportation as a lifestyle choice, and function better than compulsion. The research presented in this paper contributes to our understanding of the factors that influence commuters’ decision to engage in active mobility in their urban city neighbourhood.

THE IMPACT OF THE TRAMWAY ON PEDESTRIAN ACCESSIBILITY IN THE NEW CITY OF ALI MENDJELI IN CONSTANTINE

The new city of Ali Mendjeli in Constantine has capitalized on the extension of the tramway line by adopting a sustainable mode of transportation that has significantly reduced car usage and alleviated congestion in the city. The introduction of the tramway has also led to the creation of outdoor amenities such as widened sidewalks, green spaces, and resting areas, which have promoted pedestrian activity. The objective of our research was to examine the impact of the tramway's arrival on pedestrian accessibility in this city. To achieve this, we employed a mixed methodology that combined quantitative and qualitative tools. We distributed a questionnaire to a sample of residents in the new city of Ali Mendjeli and conducted on-site observations as well. The findings of our study clearly demonstrated that the arrival of the tramway in the new city of Ali Mendjeli and the accompanying outdoor enhancements along its route have had a positive impact on pedestrian accessibility in the city.

Effective Speed: Factors That Influence the Attractiveness of Cost Effective and Sustainable Modes of Transport in Cities

Background: People invest hours of their working day to pay for their vehicle. Is this a worthwhile use of their time? Wouldn’t public transport be a more economical solution for those on low income? Which demographic group typically chooses the most effective mode of transport? Aim: The two studies presented in this paper answer those fundamental questions based on the effective speed concept. This is calculated by dividing the distance travelled by time (i.e., minutes spent travelling and earning the money to afford it). Methodology: A digital model of the residents in Berlin is created to calculate the ‘effective speed’ for everyone. In the second study, based on a survey of residents in New York City, the best mode of transport for each respondent based on their effective speed has been identified and compared to the mode chosen. Results: The results of the Berlin case study show that the mode share for cars would be less than 6% if people choose the mode of transport with the highest effective speed. A similar picture is seen in NYC. Factors that significantly affect whether people who should use public transport, do so, include: age, gender, education, place of work or home.

Improving the efficiency of patient diagnostic specimen collection with the aid of a multi-modal routing algorithm

The Sustainable Specimen Collection Problem (SSCP), in which diagnostic specimens are collected from GP surgeries (doctor’s office/clinics) and subsequently transported to a hospital laboratory for analysis using more sustainable transport modes, is introduced in this paper. Using a weighted objective function, we solve a new multi-objective problem using cycle consolidation to limit driving time and the numbers of vans used whilst improving overall service quality, reducing costs and emissions. This particular heterogeneous vehicle routing problem is explored and applied to two real-world case studies in the UK, where 97 and 22 sites (respectively) are currently served, using a column generation based heuristic algorithm with some additional improvement heuristics. The results demonstrated a potential improvement in the system’s maximum delivery time between 41% and 74% compared to business-as-usual activity using solely road vehicles. Road vehicle (van) fleets could be reduced by up to 40%, and the total driving time across the fleet by between 41% and 65%. Operational costs were estimated to increase by up to 38%, though additional workloads for gig-economy cycle couriers and improvement in specimen quality and service reliability may make this trade-off worthwhile. Tailpipe CO2 emissions were also reduced by up to 43%. The proposed algorithm was effective, reducing computational time by up to 99% whilst achieving an average of 5% deviation from optimality.

Designing and planning sustainable customized bus service for departing attendees of planned special events: A two-phase methodology integrating data-driven and demand-responsive

Planned special events (PSE), such as concerts and sporting events, will seriously affect the normal operations of the transportation system, especially at the egress stage. The huge taxi and ride-hailing trips of departing attendees is one of the prime reasons for traffic congestion, meanwhile, insufficient vehicles and traffic congestion around the venue also lead to excessive passenger waiting time. Customized bus (CB) service is an emerging green sustainable transportation mode, and its characteristics are suitable for solving corresponding problems. This paper designs a novel CB service mode and constructs a corresponding two-phase planning methodology integrating data-driven and demand-responsive. The first phase includes a potential demand identification model based on graph semi-supervised learning and an integrated algorithm to deploy CB stops. The second phase is CB route planning with heterogeneous vehicles based on attendee choices. A concert held in Tianjin, China was selected as case study to evaluate the designed CB service. The results show that compared with the corresponding taxis and ride-hailing trips, the generated CB services can save 37.71% of the average travel time, 36.56% of the expenses per passenger and 89.58% of the spatial road occupancy, which can greatly alleviate traffic congestion, reduce carbon emissions and improve attendee travel.