Trip Length Research Articles

Commuting performance in Shanghai: Efficiency and orderliness

The literature on excess commuting has grown significantly over the past three decades. However, there are still some important open questions about the resulting statistics. Despite researchers proposing several commuting benchmarks and excess metrics, there is poor comparability between empirical studies. Many studies use average trip length to evaluate commuting efficiency of the whole region, while very few studies visualize the spatial organization of (excess) commuting patterns and evaluate its commuting orderliness. Based on a finely disaggregated data base --- the National 1% Population Sampling Survey in Shanghai, this study investigates commuting patterns and their relationship to urban land use arrangement by detailed industrial sectors from the perspectives of both average trip length and spatial organization measured by entropy value. It offers insights into social inequalities, spatial mismatch and commuting performance by identifying which groups of workers make more contribution to urban traffic congestion and have more inefficient and disorderly commutes. The results suggest that there are significant commuting differences between the groups, both socially and spatially. Overlooking the heterogeneity of commuter behaviors and socioeconomic characteristics may overestimate or underestimate excess commuting. Skilled workers commute further and experience more excess commuting than less-skilled workers, which is very different from American cities and brings forth new insights into traditional spatial mismatch. This paper also finds that workers in the manufacturing sector commute longer than those in the wholesale and retail sector but the commuting pattern of the former is more organized and orderly than the latter, indicating that long-distance commuting patterns might not necessarily be inefficient and disorderly and vice versa.

Environmental perceptions of global business travel by Swiss companies in the Zurich airport region

Background: This article presents findings from research conducted before the coronavirus disease 2019 (COVID-19) pandemic on companies located in the Zurich airport region of Switzerland, regarding the needs for global business travel and its impacts. Methods: The study involved a mixed methods approach. Five hypotheses were tested using inferential statistics on data obtained from pre-tested closed questions in a web-based survey. Deeper context was explored through an interview-based case-study conducted at a Swiss pharma company. Results: Supporting alternative hypothesis 3 (Ha(3)), a significant positive relationship was found between travel frequency and business growth, F(1, 100) = 11.31, p = 0.0011. Supporting Ha(4), corporate culture had a significant positive relationship with business travel frequency (F(1, 100) = 15.50, p = 0.0002) and average trip length (F(1, 100) = 6.39, p = 0.01). And thirdly supporting Ha(5), corporate social responsibility had a significant relationship with global business travel (91%). Ho(2) and Ho(3) were accepted. The case study found that smart corporate travel policies and regulations should be instantiated to enhance our environment, which would also benefit employee wellbeing. Travel can be reduced significantly despite being demonstrated that physical co-presence is important for building trust. The case study suggests tools to support the monitoring and management of global business travel by organizations. Conclusions: COVID-19 has impacted travel for business significantly, and future research will be necessary to assess its impact. The article explores the ongoing research in this area, and several relevant implications are proposed for future leaders. The case study found willingness to pay both corporate and individual green taxes, and a deficiency in corporate communication around the environment. Business travel is needed to build trust; however, it can be reduced.

Infrastructure planning for ride-hailing services using shared autonomous electric vehicles

Ride-hailing services using shared autonomous electric vehicles (SAEVs) are soon expected to serve a significant portion of the public transportation demands, especially in metropolitan cities. The transportation demands will arise randomly across all transportation nodes and during all hours of operation. This spatio-temporal nature of the transportation demand together with the constraints of trip length, battery size and consumption rate, and power network will guide the actions of the SAEVs. These actions are pair-with-demand, park, charge, and relocate, which in turn will determine the necessary infrastructure plan for ride-hailing services. The plan will comprise SAEV fleet size, charging hub locations across the nodes of the transportation network, and the number of charging stations in each hub. We formulate a profit-maximizing mixed integer linear model comprising a space-time-battery representation of the transportation network and a DC power flow representation of the power network. The model, intended for developing infrastructure plans for ride-hailing services, is robustified to accommodate randomness in travel and electric power demand. The model solution is demonstrated via an illustrative numerical case study representing the Tampa Bay region of Florida, USA. The results show how the infrastructure plans and their profitability depend on the demand fulfillment goal, battery size, battery consumption rate, and power network configurations. A linear relaxation of some of the integer variables of the model to reduce computational time is discussed.

Gender Differences in Travel Behavior, for Daily Trips in Kathmandu Valley

Kathmandu valley is the main center for economic activities and with it travel demand has increased drastically in the Valley in the recent years, along with rapid growth in population. This paper brings into highlight, the role of gender on urban mobility, for daily trips, by assessing the gender differences in travel behavior of workers and students, in commuting their work and educational trips respectively. Household survey was carried out in different parts of the study area, using random sampling. Data was analyzed to associate gender with travel mode choice and trip length. The statistical analysis of the travel data shows that there is a significant level of association between gender and the way, they travel. The modal share and trip length varies significantly between men and women, more prominently for work trips. For educational trips, the variation is observed comparatively, to a lesser extent. For work trips, men are using more of the private vehicles and relying less on public transport and non-motorized means of travel, as compared to women. It is also revealed that that males are commuting longer trip distances as compared to females. From the findings, recommendations for gender equality in urban transport and promotion of sustainable urban mobility, emphasizing the use of public transport and non-motorized modes of transport.

Effect of cold start and vehicle trip characteristics on spatio-temporal distribution of vehicle emissions

Quantifying the effect of light-duty vehicle trip characteristics and cold-start extra emissions (CSEEs) on the spatio-temporal distribution of vehicle emissions at the urban scale facilitates dynamic modeling of emissions and assessment of human exposure in denser settings. The results of Beijing demonstrate that cold starts and CSEEs are predominantly distributed in peak hours, especially morning peaks. A total of 80.8% of the morning peak trips are cold-start trips, and CSEEs account for 64.2%, 50.1%, and 8.3% of carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NO X ) in the winter morning peaks, respectively. The fraction of CSEEs for CO, HC, and NO X increases by 49.1%, 54.6%, and 36.7%, respectively, when trip length decreases from more than 35 km to less than 5 km. By converting 50% of the short automobile trips to non-motorized modes, CSEEs decrease by 12.9%, 13.4%, and 12.9% of CO, HC, and NO X of the entire road network. CSEEs are mainly distributed on local roads and minor arterials with high air pollution exposure for pedestrians and cyclists, accounting for 68.1% and 21.4%.

Breeding Thin-Billed Prions Use Marine Habitats Ranging from Inshore to Distant Antarctic Waters.

Pelagic seabirds cover large distances efficiently and thus may reach a variety of marine habitats during breeding. Previous studies using stable isotope data and geolocators suggested that Thin-billed Prions breeding in the Falkland Islands in the Southwest Atlantic may forage in temperate waters over the Patagonian Shelf or cross the Drake Passage to forage in Antarctic waters south of the Polar Front. We deployed miniature GPS dataloggers to track Thin-billed prions in the Falkland Islands during incubation (3 seasons) and chick-rearing (2 seasons). Thin-billed Prions had a wide distribution during incubation, covering latitudes between 43 and 60° S, with trip lengths of ca. 2000 km over seven days, on average. Thin-billed Prions from two nearby sites (60 km apart) were spatially segregated in their incubation trips, with New Island Thin-billed Prions foraging over the Patagonian Shelf, compared to Thin-billed Prions from Bird Island, that foraged in the region of the Polar Front. During chick-rearing, Thin-billed Prions from New Island undertook both long trips to the Patagonian Shelf and south of the Polar Front (30% of trips were 5-11 days), and short trips (70% of trips were 1-4 days) when they foraged more locally, including in inshore waters around the Falkland Islands. Females carried out more trips to distant sites. Thus, Thin-billed showed a high flexibility in foraging areas, habitats and foraging trip durations, which enable them to benefit from both, temperate and Antarctic environments.

Exploring the Spatial-temporal dynamics of travel patterns and air pollution exposure of E-scooters

Dockless mobility has been the biggest disruptive force in the shared mobility industry solving the “first-last” mile issue. With their high adoption levels combined with little to no regulation regarding their usage, these users have been driving along with motorized vehicles exposing them to major concerns. The users are exposed to high levels of traffic-related air pollution (TRAP) due to their direct exposure to vehicle exhaust. This study aims to understand the spatial and temporal dimensions of this emerging transportation mode in terms of travel behavior patterns, geographical aspects of travel, interactions between the travel route taken with the existing vehicle traffic, and resulting air pollution and exposure. The travel behavior patterns are evaluated through spatial-temporal analysis of a sample of e-scooter trip data collected in 2018 in the City of Austin and an online travel behavior survey. The analysis identified areas with peak usage, and peak ridership time. The survey results found the e-scooter user demographics to be mostly white males, in the 26–45 age range, with an undergraduate degree and working full-time. Secondly, key responded in influencing the use of an e-scooter are trip length, connectivity to transit, congestion and parking issues, and pollution reduction. Thirdly, e-scooter predominantly replaced personal vehicles and shared ridership in case of home-to-work trips and replaced walking for connecting to transit stops. The exposure to TRAP was obtained by integrating the spatial-temporal dynamics of e-scooter trips with spatial-temporal dynamics of pollutant concentrations modeled from traffic. Exposure analysis found peak exposure levels during midday and evening periods focused in the Central Austin area. This area houses the University of Texas campus and several neighborhoods with lots of shopping, restaurants, bars, and live music avenues. The findings are useful for policymakers and planners when planning for infrastructure changes air pollution control measures, incentive programs, and policies to motivate shared mobility.

The shareability potential of ride-pooling under alternative spatial demand patterns

ABSTRACT In In this study, we set out to explore how various spatial patterns of travel demand drive the effectiveness of ride-pooling services. To do so, we generate a broad range of synthetic, yet plausible demand patterns. We experiment with the number of attraction centres, the dispersion of destinations around these centres, and the trip length distribution. We apply a strategic ride-pooling algorithm across the generated demand patterns to identify shareability potential using a series of metrics related to ridepooling. Our findings indicate that, under a fixed demand level, vehicle-hour reduction due to ride-pooling can range between 18 and 59%. These results depend on the concentration of travel destinations around the centre and the trip length distribution. Ride-pooling becomes more efficient when trips are longer and destinations are more concentrated. A shift from a monocentric to a polycentric demand pattern is found to have a limited impact on the prospects of ride-pooling.

Rechargeable Battery Cabinet Deployment for Public Bike System

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${e}$ </tex-math></inline-formula> lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings.

Transfer station choice in a multimodal transit system: An empirical study

Connectivity between modes and lines constitutes a core aspect for multimodal transport system user experiences. Using a large-scale transit smart card dataset, we reveal transfer station choices in bus–rail intermodal trips frequently deviate from the “nearest-station” heuristics, motivating research to better explain real transfer behaviors and define more realistic station catchment areas. The investigation is centered on modeling transfers from bus to rail using multinomial logistic regression discrete choice models applied to 200k observations from four weeks of transit smart card observations. The results show that: (1) for trips requiring an intermodal transfer, riders select the nearest rail station in only 40% of cases; (2) commuters’ transfer station selections are jointly influenced by the trip attributes of all involved modes, with the bus ride distance contributing the most, followed by the walking distance; and (3) commuters’ sensitivity to travel distances exhibits nonlinear trends: sensitivity to bus ride distance dampens gradually as a function of trip length, while the sensitivity to walking distance exhibits a concave shape peaking at around 0.6 km. Catchment area modeling reveals that (4) location attributes (e.g., distance from the city center) play a greater role in defining the station catchment area than other attributes (e.g., land use and built environment). Specifically, the catchment area radius is larger for stations located further away from the city center or from the nearest interchange station. Moreover, (5) stations with more amenities (e.g., higher density of shops and greater accessibility) are likely to attract commuters from a larger area. This study strengthens the connections between station selection patterns and station catchment area analysis, to better guide intermodal transit design and operations, such as improving predictions of passenger flows at transfer stations.

A Study of the Impact of APTS on Service Quality Perceptions of Elderly and Disabled Riders

New transportation technology that directly impacts consumers should be evaluated by the people who are affected. Automated dispatching has become standard practice for paratransit services. This article summarizes a study analyzing consumer response to the Mobility Manager at a demonstration site in Winston-Salem, North Carolina.The Mobility Manager was applied to the TransAID demand-responsive mini-bus service for people who are elderly or who have disabilities. Survey data from two questionnaires, before and after the implementation of the Mobility Manager for the same subjects, were used to examine travel behavior and perceived service quality. These travelers reported service improvements such as easier telephone access and shorter travel times. The respondents’ travel patterns after implementation of the Mobility Manager remained stable. This article also provides econometric estimates of the change in the number of trips as a function of the change in travel attributes affected by implementation of the Mobility Manager. Changes in the number of trips by survey respondents were treated as a Poisson random variable. Results from a Poisson regression show that the primary beneficiaries of the Mobility Manager were riders with disabilities. Perceived service attributes that significantly affected changes in trips were length of trip, number of stops picking up additional passengers, and physical comfort.

A Study of the Impact of APTS on Service Quality Perceptions of Elderly and Disabled Riders

New transportation technology that directly impacts consumers should be evaluated by the people who are affected. Automated dispatching has become standard practice for paratransit services. This article summarizes a study analyzing consumer response to the Mobility Manager at a demonstration site in Winston-Salem, North Carolina.The Mobility Manager was applied to the TransAID demand-responsive mini-bus service for people who are elderly or who have disabilities. Survey data from two questionnaires, before and after the implementation of the Mobility Manager for the same subjects, were used to examine travel behavior and perceived service quality. These travelers reported service improvements such as easier telephone access and shorter travel times. The respondents’ travel patterns after implementation of the Mobility Manager remained stable. This article also provides econometric estimates of the change in the number of trips as a function of the change in travel attributes affected by implementation of the Mobility Manager. Changes in the number of trips by survey respondents were treated as a Poisson random variable. Results from a Poisson regression show that the primary beneficiaries of the Mobility Manager were riders with disabilities. Perceived service attributes that significantly affected changes in trips were length of trip, number of stops picking up additional passengers, and physical comfort.

A Pilot Study of Electrical Vehicle Performance, Efficiency, and Limitation in Kuwait’s Harsh Weather and Environment

Due to industrialization and an exponential increase in population, the demand for vehicles has increased in Kuwait. Utilizing fossil fuels to power vehicles that are in high demand has posed various environmental and medical implications. Considering the emission-free feature of electric vehicles (EVs), moving towards “EV readiness” is the need of the hour. This can be further enhanced by adopting alternative sustainable technologies such as photo voltaic (PV) charging of EVs, to potentially eliminate or significantly reduce the reliance on fossil fuel while simultaneously decreasing harmful emissions. As Kuwait does not manufacture cars, it imports all vehicles and their parts, including internal combustion engines (ICEs) and EVs. To find out the challenges to uplifting Kuwait’s “EV readiness” this study delved into the performance of a typical EV car in Kuwait’s weather conditions. This includes the investigation of parameters that influence the energy requirements in an electric vehicle, the change in the energy requirement in relation to several driving scenarios, and the efficiency of the EV battery. The results indicate that a significant amount of energy is being wasted for battery conditioning, which drastically reduces the distance covered during summer. The energy required for air-conditioning and battery conditioning are both positively correlated to the ambient temperature, while the time required to charge the battery has no relationship with the time of the day, traffic, or trip length. Additionally, the paper discusses some major challenges, such as lack of awareness, inadequate charging stations, and absence of policy. The paper recommends the most vital areas to be focused on for meeting the above challenges to make the transition to an “EV ready” state.

Calculation of Transit Performance Measures Using Smartcard Data

Smartcard automated fare collection systems (SCAFC) for transit have been considered primarily for their administrative function of controlling access to the service and for revenue management. However, it is likely that data from these systems also can be used to describe both transport supply and demand. This article illustrates the use of smartcard data to estimate various transit performance measures. Combined with well-established evaluation processes, such measures can help operators monitor their networks in greater detail. The performance of the network supply (vehicle-kilometers, vehicle-hours, commercial speed, etc.) and the statistics on passenger service (passenger-kilometers, passenger-hours, average trip length, etc.) can be calculated from these datasets for any spatial or temporal level of resolution, including route and bus stop levels.

Evaluation of Horizontal Equity under a Distance-Based Transit Fare Structure

Horizontal equity requires equal distribution of investment/benefit/costs among equal members of society. In other words, a transit passenger should pay as much as he/she uses. This study evaluated the measure of fulfillment of this rule in a case study, developed a distance-based fare structure, and shows that justice is better served by switching to the proposed structure. Fare elasticity of demand and probability distribution of transit passenger trip lengths were investigated through a field survey. Although mainly used in the measurement of inequality in income or wealth, the Gini index and the recovery ratio (revenue to cost for each transit passenger) in evaluating equity were used in this study. Results show that the Gini index would decrease from 0.38 to 0.17 after switching from a flat to a distance-based structure. Assessment of the ratio of revenue per mile over cost per mile (RPM/CPM) shows that switching to a distance-based fare structure makes the RPM/CPM curve significantly flatter, which indicates more similarity among passengers. As a byproduct, the amount of change in demand and revenue of the transit system also were formulated.

Examining user attitudes towards ride-hailing services – A SEM-MIMIC Ordered Probit approach

Ride-hailing services (RHS) are rapidly transforming the urban transportation landscape, and subsequently, users’ perception of mobility services. Hence, it is of utmost importance to understand the perceptual and latent attitudinal factors that drive such service usage. This gains more relevance in the context of a developing nation both because of its characteristically different transport environment- lifestyle interaction, and relatively fewer studies investigating RHS utilization. The present research uses revealed preference data from household surveys (N = 418) to estimate the usage propensity of RHS services in Kolkata, as it has the highest share of commuters among the four large metropolitan areas in India. A SEM-MIMIC Ordered Probit modelling framework has been developed, as it extracts the advantages of exploring latent constructs through a structural equation model (SEM) and examines their interaction with demographics and trip-specific factors with the Multiple Indicator Multiple Cause Model (MIMIC). This study relies on the confirmatory approach to establish the latent attitudinal factors which stem from accepted theories in travel behaviour, i.e., Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM). Subsequently, the Ordered Probit model estimates RHS use-frequency. The results highlight that the latent variables (LVs), viz., ride-hailing attitude and perceived usefulness, are the most significant while estimating RHS utilization. These findings encourage RHS providers to focus on service aspects (for example, security during rides, clean vehicles, reliability of rides, less wait time between booking and ride arrival), instead of only emphasizing on (cosmetic) changes to the booking interface. Besides, in contrast to the developed countries, subjective norms were found to have an inverse relationship with RHS usage, suggesting inhibition among the public, which is probably arising from the dearth of customer-friendly service, especially after being comparatively expensive. The model also suggests the supplementary role of RHS to public transit, which could be pivotal in its integration into mobility-as-a-service (MaaS) and also calls for regulatory actions. The demographics (e.g., age, gender, household income) and trip-specific (e.g., trip purpose, trip length, time-of-day) covariates add further meaning to the relationships among latent constructs. The results suggest a higher preference for RHS among non-car-owners, whereas frequent use of ride-hailing is observed to have a likely positive association with longer trip lengths. Overall, this research brings valuable and first-of-its-kind insights into attitudinal factors and their interaction with demographics and trip-specific covariates facilitating RHS utilization in the context of a developing nation.

Modeling trucking industry perspective on the adoption of connected and autonomous trucks

This paper predicts the future adoption of Connected Autonomous Truck (CAT) among freight transportation organizations by incorporating the distance decay function-based phenomenon of peer effects into an integrated choice and latent variable model (ICLV). We surveyed 400 freight transportation organizations operating within the United States to capture their preferences and perceptions towards adopting level-3 CATs. By analyzing the responses, we are able to generate a prediction for how quickly freight transportation organizations will choose to test and adopt CATs. Through ICLV, the authors captured the impact of organizations' optimistic attitudes towards their inclination to adopt CATs. The results showed that organizations with an optimistic attitude towards CATs, having members advocating CATs and nationwide coverage, are more likely to adopt CATs. In contrast, organizations with an average trip length of regular trucks of >500 miles are more likely to reject CATs. The peer effect results imply that the future adoption rate of level-3 CATs will be doubled (29%) or quadrupled (57%) from 13% based on the different peer effect coefficient values varying from 0.70 to 0.90. This study will provide key takeaways and policy inputs to planners, policymakers, and researchers to frame and implement plans to bolster the organizational adoption of innovations like autonomous technology.

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

Value of optimal trip and charging scheduling of commercial electric vehicle fleets with Vehicle-to-Grid in future low inertia systems

The electrification of transport is seen as an important step in the global decarbonisation agenda. With such a large expected load on the power system from electric vehicles (EVs), it is important to coordinate charging in order to balance the supply and demand for electricity. Bidirectional charging, enabled through Vehicle-to-Grid (V2G) technology, will unlock significant storage capacity from stationary EVs that are plugged in. To take this concept a step further, this paper quantifies the potential revenues to be gained by a commercial EV fleet operator from simultaneously scheduling its trips on a day-ahead basis, as well as its charging. This allows the fleet to complete its trips (with user defined trip length and distance), while taking advantage of fluctuating energy and ancillary services prices. A mathematical framework for optimal trip scheduling is proposed, formulated as a mixed-integer linear program, and is applied to several relevant scenarios of the present and future British electricity system. It is demonstrated that an optimal journey start time can increase the revenue of commercial fleets by up to 38% in summer and 12% in winter. This means a single EV from the maintenance fleet can make additional annual revenue of up to {\pounds}729. Flexible trip schedules are more valuable in the summer because keeping EVs plugged in during peak solar output will benefit the grid and the fleet operators the most. It was also found that a fleet of 5,000 EVs would result in the equivalent $\textrm{CO}_2$ of removing one Combined Cycle Gas Turbine from the system. This significant increase in revenue and carbon savings show this approach is worth investigating for potential future application.

Air taxi skyport location problem with single-allocation choice-constrained elastic demand for airport access

Witnessing the accelerated commercialization efforts for air taxi services in across metropolitan cities, our research focuses on infrastructure planning of skyports. We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in a city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, and varying transfer times at skyports. A case study of New York City is conducted considering a network of 149 taxi zones and 3 airports with over 20 million for-hire-vehicles trip data to the airports to discuss insights around the choice of skyport locations in the city, and demand allocation to different skyports under various parameter settings. Results suggest that a minimum of 9 skyports located between Manhattan, Queens and Brooklyn can adequately accommodate the airport access travel needs and are sufficiently stable against transfer time increases. Findings from this study can help air taxi providers strategize infrastructure design options and investment decisions based on skyport location choices.