Analysis of Inland Mode Choice Decisions and Travel Characteristics of Air Passengers: A Case Study in Bandaranaike International Airport (BIA)

Mode choice behavior directly affects the layout of the urban transportation system and serves as the foundation for the development of policies about the planning and administration of urban transportation. This study focused on the city’s various transportation options, aiming to pinpoint and investigate several factors influencing transportation mode selection. The data included the traffic survey; traveler interviews were gathered using a questionnaire survey, structured interviews, and secondary documents. The collected data from the questionnaire survey were then analyzed using a multinomial logit (MNL) model to assess the relationships between different parameters and mode choice. The investigation considered several concerns, including travel distance, travel time, travel cost, safety, environmental impact, health benefits, and comfort. Both qualitative and quantitative methods of sustainability have been integrated into this study. The MNL model’s pseudo‐R‐squared value illustrates the apparent correlation between the independent and dependent variables. The multilayer perceptron (MLP) model was used as a comparison model. The results show that MLP has higher predictive performance than the MNL model in assessing transport mode choice in the city. The study reveals that travel distance, time, availability, health benefits, comfort, safety, cost, and environmental impact significantly influence mode choice for work trips. Public services are safer, less environmentally impactful, and more accessible, while walking is safest, offers health benefits, and is more environmentally friendly but is preferred by the youngest. Private vehicle users offer more safety but are less cost‐effective. Minibus users provide better cost‐benefit and safety but take longer travel times. Overall, the study was used to understand passenger preferences and critical factors in transport options, thereby aiding policymakers in making informed decisions and suggestions for improving the transport system in similar cities.

Hyperloop, projected as fast and efficient, and envisaged as the future of high-speed transportation, does not have much published information about its demand estimation. This paper aims to estimate the willingness of air and car passengers to shift to hyperloop. A nested logit model was used to analyze stated preference data gathered from the air and car travelers along the Bangkok–Chiang Mai sector in Thailand. The variables contributing the most to the modal shift towards hyperloop are total travel cost, total travel time, monthly income, gender, education level, bearer of trip expenses, and number of trips in the last 6 months and duration of stay at the destination. The highest value of elasticity for hyperloop is obtained for the total travel cost followed by total travel time and monthly income. It is concluded that hyperloop will be the predominant mode of transportation between the Bangkok–Chiang Mai sectors with a modal share of almost 50% by the year 2025. Survey results also revealed that the preferences of the passengers in order of priorities for long distance travel are comfort, low travel cost, less travel time, safety, high frequency of travel mode and low CO2 emission. The main contribution of this paper is to provide an insight on factors that may contribute towards a possible shift in mode from car and air to hyperloop. The study will be beneficial to policy makers in developing a strategy for a more efficient mass transportation system using new and emerging technologies.

Mode choice in transportation is influenced by factors such as comfort, safety, travel time, cost, and reliability, with most commuters in Banda Aceh preferring private vehicles due to flexibility and efficiency. Travel time and travel cost are identified as major barriers preventing people from switching to public transport, which is often considered less attractive. While studies abroad examine elasticity from multiple perspectives, research in Indonesia has mainly focused on vehicle attributes. The objective of this paper is to analyze the elasticity of travel time and travel cost for users of private vehicles and public transportation. The study employs a binary probit model to estimate elasticity values, utilizing data from a Stated Preference survey of transportation users. The results show that both trip attributes and individual characteristics significantly influence transport mode choice. Longer travel distances, higher travel time, and increased operating costs raise the likelihood of choosing public transport, while being male and having higher education also increase the tendency to select it. Conversely, motorcycle ownership strongly favors private mode choice, and holding a valid driver’s license is an important determinant of travel behavior. The elasticity analysis further reveals that reducing travel and access times for the TransK bus could significantly increase its usage, while policies restricting private vehicle use, such as the odd-even system, may also encourage shifts toward public transport. The study concludes that mode choice in Banda Aceh is mainly driven by travel time, with TransK highly responsive to time improvements, while private vehicles dominate despite cost changes. Encouraging public transport use requires faster, more reliable bus services and restrictions on private vehicle advantages.

Objective: Several studies before the COVID-19 pandemic documented the positive impact of telehealth on patients' travel distance, time, out-of-pocket costs, and greenhouse gas emissions. The objective of this study was to calculate these outcomes following the increased use of ambulatory telehealth services within five large University of California (UC) health care systems during the COVID-19 pandemic. Methods: We analyzed retrospective ambulatory telehealth data from the five UC health care systems between March 1, 2020, and February 28, 2022. Travel distances and time saved were calculated using the round-trip distance a patient would have traveled for an in-person visit, while cost savings were calculated using Internal Revenue Services' (IRS) 2022 standard mileage reimbursement rates. In addition, we estimated the injuries and fatalities avoided using the national motor vehicle crash data. Greenhouse gas emissions were estimated using the 2021 national average vehicle emission rates. Results: More than 3 million (n = 3,043,369) ambulatory telehealth encounters were included in the study. The total round-trip distance, travel time, and travel cost saved from these encounters were 53,664,391 miles, 1,788,813 h, and $33,540,244, respectively. These translated to 17.6 miles, 35.3 min, and $11.02 per encounter. By using telehealth, 42.4 crash-related injuries and 0.7 fatalities were avoided. The use of telehealth for ambulatory services during this time eliminated 21465.8 metric tons of carbon dioxide, 14.1 metric tons of total hydrocarbons, 212.3 metric tons of exhaust carbon monoxide, and 9.3 metric tons of exhaust nitrogen oxide emissions. Conclusions: Telehealth use for ambulatory services in a statewide academic Health System during COVID-19 had a positive impact on patient travel distance, time and costs, injuries and fatalities in motor vehicle accidents, and greenhouse gas emissions. These significant advantages of telehealth should be considered when planning future health services.

An overwhelming majority of inhabitants in Ho Chi Minh City use motorbike as their primary means of transport, causing severe urban traffic congestion. As an effort to combat congestion, the local and central government started constructing the first urban railway line (metro) in 2009. In this study, travel demand for different modes of transport was investigated using discrete choice experiment. In addition to conventional choices such as motorbike and bus, we included new technology-assisted taxi service and the first urban railway (Ben Thanh–Suoi Tien) as a hypothetical choice. The data set contains 267 respondents spanning 19 communes across the railway. The results pointed out that mode choice is influenced by both total travel time and total travel cost. The findings also highlighted the importance of transiting time and transiting cost in public transports. In terms of welfare, metro users are willing to pay 976 and 667 VND for a minute reduction of transiting time and transporting time, respectively. When a motorbike user switches to metro mode, monetary welfare of that individual rises by approximately 56,000 VND.

This paper explores air passengers' behavior of airport access mode choice and develops airport access mode choice models for two major domestic airports in Korea, Gimpo Airport and Daegu Airport. First, descriptive analyses are conducted to identify whether the key explanatory variables such as trip purpose, travel time, and travel cost are statistically different among airport access modes. Second, logistic regression models are proposed for three types of airport access mode choices: auto and public transit, auto and bus, and auto and subway. The model results indicate that the choice of airport access mode is significantly affected by travel characteristics and sociodemographic variables. This study also estimates access mode choice models for business and nonbusiness travel, indicating that access mode choice is significantly more affected by demographic characteristics of nonbusiness travel air passengers than those for business travel passengers.

Commuters tend to shift from public to private transport modes due to various reasons. This results in an increased traffic volume in the urban road network. The ultimate consequence is traffic congestion which creates massive economic losses and adverse environmental pollution. To provide a feasible solution for the above problems, this study is set to examine the factors affecting transport mode choice. Factor analysis was used to identify the factors influencing the mode choice variability of the commuters. Parameter estimation is done using the Multinomial Logit (MNL) model based on the utility maximization theory. The adjusted likelihood ratio index is used to show the model fits. Also, t-statistics and the respective sign of parameters are used to show the validity of estimated parameters. The outcome of the factor analysis shows that age and occupation significantly affect the public transport mode choice under personal characteristics. For private mode choice, the contributing factors are gender, age, occupation and monthly income. Trip distance, invehicle travel time, and travel cost are significantly influencing both public and private transport mode choices. Results of the estimated parameters and elasticity analysis suggest that more commuters can be attracted to public transport by reducing the total travel time of buses.

Do air passengers behave differently to other regional travellers?: A travel mode choice model investigation

In a global market the manufacturing industries have to minimized the production cost and improve the efficiency. The effective design of plant layout can significantly minimized the operational costs of manufacturing companies. An adequate plant layout design can improve the performance of the production line, their flexibility, efficiency. Proper plant layout design is one of the keys of success in factory management. In this study ongoing production process layout are studied and a new layout will be developed to improve the efficiency and reduce the production cost. The major problem faced by the workshop is high flow intensity between machines which have high interrelationship. This leads to high travelling time and high travelling cost. Two alternative layouts are developed by using Systematic Layout Planning, which is a systematic way of generating layout alternatives. The alternative layout involves transferring the machines which have high interrelationship close to each other. The alternative layouts are evaluated using weighting placement value (WPV) and placement rating (PR) in Computerized Relationship Layout Planning (CORLAP) algorithm. The best alternative is chosen based on the performance measures which have the most significant improvement, total travel distance, total travel time, total travel cost, number of cross-over, output, average resource utilization, total average WIP level, total average waiting time and total time spent in the system. The constructed layout of the workshop efficiency is 30.16% while the alternative 1 and alternative 2 layouts is 71.4% and 73% respectively. The annual production saving cost of the alternative layout is 34,982 birr.

The systematic layout planning (SLP) procedure of designing facilities layout which is widely used by enterprises provides the framework to this study's objective of identifying weaknesses in the production process of XYZ Company, a snack food manufacturing company. The observation and evaluation of the current production process indicate the incidence of bottlenecks, especially at the cooling station which interferes with the production flow. However, the fundamental problem that could inhibit the long-run production flexibility is its suboptimum facilities layout. This leads to high travelling time and high travelling cost. The study found weaknesses from the point of facilities layout flexibility (FLF), productive area utilisation (PAU) and closeness gap (CG). With these weaknesses, it is unlikely that the company would be able to respond both in quantity and quality, as well as to provide working conditions that are conducive to the workers. Two alternative layouts are proposed using the Systematic Layout Planning, which is a systematic way of generating layout alternatives. The proposed layouts are evaluated using the current layout and ARENA simulation software. The best alternative chosen based on the performance measures which have the most significant improvement which are total travel distance, total travel time, total travel cost and output. In line with the factory's future expansion and the company' overall diversification programme, the study also recommends that it embarks on a vertical integration by going into tapioca planting. This would ensure constant supply of raw materials, both in quantity and quality.

Stochastic supply and fluctuating travel demand lead to stochastic travel times and travel costs for travelers. This paper will firstly focus on modeling of travelers’ departure time/route choice behavior under stochastic capacities. By analytically proving the equivalency of the scheduling approach and the mean variance approach, a generalized travel cost function is derived to model travelers’ departure time/route choice behavior under uncertainty. The proposed generalized travel cost function, which is more behaviorally sound and flexible, will be adopted to model a reliability-based long term user equilibrium with departure time choices. A reliability-based dynamic network design approach is proposed and formulated in which the numbers of lanes on all potential links are the design variables. A combined road network-oriented genetic algorithm and set evaluation algorithm is proposed to solve the dynamic network design problem. A new systematic approach is proposed to eliminate the infeasible, unrealistic and illogical lane designs in order to reduce the solution space and to save computation time. The proposed reliability-based dynamic network design approach is applied to a hypothetical network, and its solutions are compared to a corresponding static network design approach. It is concluded that the static network design approach may lead to poor designs. In general static traffic assignment underestimates the overall total network travel time and total network travel costs. The dynamic network design approach appears to result in a fairly good allocation of road capacity over space and makes the best utilization of the network capacity over time. A version of the Braess paradox appears in case of reliability-based cost functions in both static and dynamic networks.

Objectives: A well-known NP-complete problem is the travelling salesman problem (TSP). It has numerous engineering and scientific applications. In this article, we have proposed a multi-conveyance TSP where different conveyances are present to travel from one city to another city. This is an extension to classical TSP. In this TSP, the salesman visits all the cities only once during his/her tour, using different conveyances to travel from one city to another. The cost of travelling between cities using various modes of conveyance varies. The objective of this research is to find the minimum cost tour using an ant colony optimization (ACO) based approach by satisfying the constraints of the proposed multi-conveyance TSP. Method: The considered TSP has been solved using a novel ACO technique. The proposed ACO is adopted with the Roulette-wheel selection and “tuning solution” techniques. We have used a few benchmark datasets from TSPLIB to check the effectiveness of the proposed algorithm. The experimental findings for a few benchmarks TSP instances show that almost always, the proposed ACO is able to find a better result. Then, we used some redefined and randomly generated datasets for experiments. The experimental outcomes for various input datasets are also very encouraging. Findings: The goal of the proposed TSP is to find a complete tour with a minimum cost without exceeding the total travel cost and total travel time. Thus, there are two novel constraints in the classical TSP. Novelty: A unique aspect of the proposed research is the use of several conveyance facilities and a fixed total travel time and cost. This is new, as these three factors are integrated into a single TSP model. Keywords: Travelling Salesman Problem; Travel Cost; Travel Time; MultiConveyance TSP; Ant Colony Optimization

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

Travel Burden and Travel Costs of Bispecific Antibodies in Patients with Relapsed/Refractory Diffuse Large B-Cell Lymphoma and Relapsed/Refractory Follicular Lymphoma

**Read paper on the following link:** https://ifaamas.org/Proceedings/aamas2022/pdfs/p678.pdf **Abstract:** System optimum (SO) routing, wherein the total travel time of all users is minimized, is a holy grail for transportation authorities. However, SO routing may discriminate against users who incur much larger travel times than others to achieve high system efficiency, i.e., low total travel times. To address the inherent unfairness of SO routing, we study the ${\beta}$-fair SO problem whose goal is to minimize the total travel time while guaranteeing a ${\beta\geq 1}$ level of unfairness, which specifies the maximum possible ratio between the travel times of different users with shared origins and destinations. To obtain feasible solutions to the ${\beta}$-fair SO problem while achieving high system efficiency, we develop a new convex program, the Interpolated Traffic Assignment Problem (I-TAP), which interpolates between a fair and an efficient traffic-assignment objective. We evaluate the efficacy of I-TAP through theoretical bounds on the total system travel time and level of unfairness in terms of its interpolation parameter, as well as present a numerical comparison between I-TAP and a state-of-the-art algorithm on a range of transportation networks. The numerical results indicate that our approach is faster by several orders of magnitude as compared to the benchmark algorithm, while achieving higher system efficiency for most levels of unfairness. We further leverage the structure of I-TAP to develop two pricing mechanisms to collectively enforce the I-TAP solution in the presence of selfish homogeneous and heterogeneous users, respectively, that independently choose routes to minimize their own travel costs. We mention that this is the first study of pricing in the context of fair routing for general road networks (as opposed to, e.g., parallel road networks).