Nested Logit Model Research Articles

Enhancing Urban Resilience through Strategic Parking Pricing in Jakarta’s Bus Rapid Transit Corridors

Although Jakarta has invested in various mass transport systems, these efforts have not successfully reduced private vehicle use. Due to this, this study aims to analyze the impact of implementing TransJakarta bus rapid transit corridor-based high parking tariffs on travel mode choice, including road- and rail-based public transport, ride-hailing, taxi, car, and motorcycle. Involving 478 private vehicle users and implementing a nested logit model, some variables, including respondents’ income, travel time, egress time, parking costs, parking distance, travel cost, and parking surcharge, are considered to understand to what extent these variables influence the use of proposed travel mode in the future. The nested logit model shows that not all variables significantly influence travel mode use, specifically related to rail-based public transport choice among motorcyclists. Meanwhile,parking distance insignificantly influences the choice of all travel modes except cars among car users. The results also indicate that increasing parking tariffs insignificantly influences the likelihood of both motorcyclists and car users shifting to public transport. Motorcyclists and car users tend to continue using motorcycles but change parking locations with higher tariffs. Additionally, some shifts towards ride-hailing services and TransJakarta Bus Rapid Transit are found, meaning that there is potential for these alternatives to play a significant role in reducing private vehicle use. Based on the model results, additional push-based policies, such as the odd-even license plate rule, are necessary to effectively support the transition from private vehicle use to public transport. Implementing these policies is expected to significantly contribute to reducing traffic congestion and promoting a sustainable and resilient urban environment.

Investigation of modal integration for transit-access trips in motorcycle dependent cities—The case study of Hanoi, Vietnam

Micro-mobility has the potential to address first -mile challenges, improving transit accessibility and encouraging public transit usage. However, users’ acceptability of modal integration between various micro-mobility options and public transit remains largely unexplored in the literature. Our study investigates the user behavior for first-mile options, focusing on four alternatives: walking, bicycling, motorcycling, and bus, to access urban mass rapid transit (UMRT) in Hanoi, Vietnam. Based on data collected from 1380 individuals, a Nested Logit Model (NLM) was proposed to analyze the determinants of users’ acceptability under each access mode option as well as evaluate further impacts of shifts in access mode choice on vehicle-kilometer traveled and emissions. The analysis shows that the availability of access modes might increase UMRT use by 47.83%. While this increase further generates additional vehicle-kilometer traveled due to the increase in park-and-ride users, this is offset overall by the large number of motorcycle users shifting to UMRT. Under the most optimistic scenario, modal integration for transit-access trips leads to an average reduction of 17.7% in net vehicle-kilometer traveled or 14.5% in net CO2 emissions or 10.9% in NOx from private vehicles. Our findings also imply that the introduction of parking fees for bicycling- or motorcycling-access trips, while impactful, does not significantly change UMRT choice. Therefore, the pricing schemes should be a focus of parking planning surrounding stations. Finally, a number of policy suggestions for parking planning and first-mile vehicles are presented.

Who will board urban air taxis? An analysis of advanced air mobility demand and value of travel time for business, airport access, and regional tourism trips in Iran

Recent advancements in the development of a comprehensive urban air mobility ecosystem promise the opportunity for its implementation in the near future. However, the utility of this emerging mode of transportation in comparison to the existing modes varies for different trip purposes and geographical contexts. It is, therefore, imperative to assess its usage potential for various scenarios before moving forward with implementation efforts. This paper presents the findings of three stated preference surveys aimed at characterizing prospective urban air mobility travel demand for weekly business, airport access, and regional tourism trips in Iran. Hybrid multinomial logit and hybrid nested logit models are estimated for each trip purpose. The results indicate that urban air mobility for weekly business trips represents the most viable market segment. Value-of-time estimates for weekly business trips are more than two and a half times higher than those for airport access trips, and tourism trips are valued even less. There is a trend towards delaying the adoption of air taxis to gain trust, and reducing ticket prices is seen as the most effective incentive for increasing the adoption rate. This study pioneers research on urban air mobility in a developing country context, offering valuable insights into market viability for investors and policymakers.

Preferences and welfare impacts of new metro serving airport access

The construction of a new rail infrastructureto connectairportswith cities plays a vital role in enhancing the accessibility of airport ground access and mitigating negative externalities resulting fromautomobiles. To identify air travelers’ preferences for airport ground access by considering a new metro line in Taiwan, we develop nested logit models, which combine revealed and stated preference data. The results reveal significant effects of travel time, travel cost, and individual characteristics; the metro and taxis are highly substituted for airport access. Consumer surplus or accessibility is not equally distributed across air travelers. Travelers who are female, young adults, lower income, and without vehicles in the household obtain considerable consumer benefits from using the new metro. Policy incentives to time and cost have a small impact on the increase in metro share, but consumer surplus surges considerably.

Merger simulation in second-score auctions: A nested logit model

We consider merger simulation based on the second-score auction (SSA) model and the “flat logit” assumption, and extend it to a more general “nested logit” assumption. We apply our framework to the 2012 acquisition of PowerReviews by Bazaarvoice and show that using flat logit instead of nested logit can potentially bias the predicted price effect from the merger substantially.

Assessment of carbon emissions from TOD subway first/last mile trips based on level classification

Many cities around the world have developed Transit-oriented development (TOD) based on subway systems, expecting to alleviate problems such as carbon emissions and pollution in the transportation sector. Although previous studies have proved the contributions to mitigate congestion and carbon emission pressure, there is a lack of evaluation on whether TOD can promote green mode choice and effectively reduce carbon emissions of first/last-mile trips in existing studies. This study aims to verify that subway stations with high TOD levels have a positive effect on reducing the carbon emissions of first/last-mile trips. We evaluate subway stations in Hangzhou and classify them into two categories based on their TOD levels. Then the carbon emission of ten typical subway stations is calculated based on survey data of first/last-mile trips and empirical formula. The nested logit model (NL) is used to analyze the correlation between the choice of first/last-mile mode and personal and environmental factors. The case results show that subway stations with a higher TOD level usually exhibit higher daily passenger flows (increased by approximately two-fold) and lower per capita carbon emissions (reduced by approximately 70 %). These findings proved that the high TOD level has a positive impact on carbon emissions of subway first/last mile trips, which could provide data support and insights for TOD development, especially in developing countries.

Willingness-to-Comply Analysis of Incentive Mechanisms for Alleviating Local Congestion in Metro Waiting Areas

Effectively equilibrating passenger distribution on metro platforms and carriages is important for relieving local congestion. This paper explores the role of incentive mechanisms in encouraging passenger queuing behaviours. To quantitatively analyse passenger compliance with the policy, a questionnaire survey was conducted in Fuzhou, China. According to the preliminary analysis of the survey data, passengers have various moving distance preferences under the incentive scenarios, namely, no movement, smaller distance and greater distance. Additionally, this paper establishes a nested logit model that considers travel purposes and moving distances. The empirical results show that although monetary and point-system incentives can effectively enhance passenger compliance with transfer queue-positioning requirements, when the moving distance is very small, people pay less attention to rewards. Compared to those commuting on weekends, passengers commuting on weekdays comply with policies more strongly, and the effect of implementing incentive policies is better; however, the effect of those policies is reduced among those travelling for leisure. Meanwhile, when travelling for leisure, as the number of companions increases, people’s willingness to follow guidance on where to wait increases. According to the results, the implementation of incentive-based waiting encouragement policies during peak working days can result in good compliance.

Analyzing housing supply location choice: a comparative study of the modelling frameworks.

The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing supply. Both approaches are evaluated regarding estimation, prediction, and microsimulation system integration. The findings enhance housing modelling literature and inform urban microsimulation's housing supply model configuration.

Simultaneous consideration of consumer preferences and seller revenue as a smart retail sales and management strategy

PurposeThe authors propose a dairy bundle, integrating strategies to jointly maximise producer revenue and consumer utility according to the latter's preferences.Design/methodology/approachAn algorithm based on a nested logit model identifies the bundle maximising producer revenue based on factors affecting consumer purchase behaviour. The data are drawn from a mall-intercept survey administered in Iran, with consumers stating a hypothetical choice among a comprehensive set of dairy products.FindingsDemographic characteristics and marketing mix elements significantly affect consumers' preferences. An algorithm based on the estimated dissimilarity parameter determines the best bundle of dairy products, simultaneously obtaining the highest utility and the highest expected revenue.Originality/valueConsumer preference and maximum producer or retail seller income are considered simultaneously. The bundling promotion strategy is widely used for food offerings and fresh foods and can be extended to other products.

Modeling Consumer Choice between Public and Private Health Care in Russia

Demand for public and private health care services is formed through consumer decision-making in two situations: to seek or not to seek medical help and which medical institution to attend. Theory suggests that choices in these situations occur sequentially and are explained by Andersen's behavioral model. This study tests these theoretical assumptions using three econometric approaches on the Russian National Health Telephone Survey 2022 data: (1) two separate probit regressions, (2) sample selection probit, and (3) nested logit. Nested logit confirmed the relevance of all demand-side characteristics (Andersen's model factors). Being male, older, and having health problems were associated with decreased probability of seeking health care and choosing private clinics. Being married, having a high level of education, being employed, having a medium or high monthly income, and living in larger cities were associated with increased probability of seeking health care and attending private clinics. Robustness check of the nested logit model accounted for the varying levels of availability of health care facilities in different cities. Despite the differences in the availability of public and private medical organizations, trends in consumer behavior remained the same. The author proposes to expand the practice of using nested logit models to analyze the behavior of consumers of medical services using data from Russia and other countries. Recommendations for increasing the efficiency of the Russian health care system and reducing inequality in its use by different consumer groups include transforming attitudes of Russians to their own health and promoting the accessibility of health care.

Exploring the differences between express and ride-pooling: A dual perspective on user perception and functional positioning in urban traffic system

Ride-hailing services, alongside public transportation (subway and bus), active modes (bike-sharing and walking), and private cars, are collectively reshaping urban transportation systems worldwide. However, the differences in user risk perception and functional positioning among different ride-hailing services, especially express and ride-pooling, are not yet clear. The study, conducted in Nanjing, China, designed two stated-preference questionnaires to assess the substitutability and complementarity of express and ride-pooling services. Four latent variables were introduced to quantify user risk perception: time risk, economic risk, service risk, and safety risk. The Integrated Choice and Latent Variable model used in this study is based on the Mixed Nested Logit model, which focuses more on the interdependence among different travel modes, as well as individual heterogeneity, compared with traditional logit models. The research findings indicate that in terms of risk perception, ride-hailing users have stronger service risk perception, weaker time risk perception, and weaker safety risk perception compared with public transportation users. Furthermore, compared with ride-pooling users, express users have stronger time risk perception and weaker economic risk perception. From a functional positioning perspective, ride-pooling services complement public transportation systems, while express services are preferred for long-distance door-to-door travel. Based on the research findings, this study offers a set of recommendations to support the sustainable growth of Transportation Network Companies (TNCs), guide government regulatory efforts, and explore opportunities for collaboration between the TNCs and government agencies, aiming to foster the robust development of urban transportation systems.

A Different Nested Logit Model Structure Consistent with Random Utility Theory for Various Freight Types – A Case Study for Istanbul

Freight transport significantly contributes to urban traffic but is often overlooked by decision-makers compared to passenger transport. Conventional transportation modelling studies often use aggregate approaches for freight transport, undoubtedly due to the difficulty of data collection. However, the nature of freight transportation is much more complex. For this reason, examining the determinants of freight vehicle preferences with discrete approaches is crucial for the contributions that can be put forward, especially in local studies. To address this apparent gap in the study of local disaggregated approaches to freight transport, we present a discrete modelling-based methodology to investigate the factors that determine freight vehicle preferences for shippers and senders. The estimated nested logit model is constructed with the RU2 approach, the second part of random utility theory, thus avoiding the theoretical inconsistencies that arise when generic coefficients are used. As a result, the model structures provided satisfactory results compared to the literature. It was revealed that the factors affecting freight vehicle choice preferences were influenced by packaging preferences and differed according to freight groups. This local study is the first nested logit study for freight modelling in Istanbul and it is aimed to shed light on future national studies.

Refining hospitality in a post-pandemic landscape: Tourist food choices through a latent class nested logit lens

In the post-pandemic landscape, scant research has been conducted on the timely, in-depth examination of how food-related attributes affect tourist food choices. To tackle this issue, this study employed a discrete choice experiment and latent class nested logit models for a nuanced exploration of tourist food preferences. This approach captured both tourists’ preference heterogeneity and substitution effects among fast, local, and hometown-style food options. Findings reveal that price, food taste rating, waiting time, service, and environment are key determinants of tourists’ food choices. Our model identified two distinct tourist classes, Local-food-image class and Anti-price-quality class, each displaying unique food preferences and substitution patterns. This research contributes to tourism and hospitality literature by offering an updated and nuanced understanding of post-pandemic tourists’ food preferences and decision-making trade-offs. It offers practical recommendations for targeted marketing and effective resource allocation, ultimately strengthening resilience and fostering sustainable development within hospitality, catering, and tourism sectors.

The trend of the structural change of the city taxi market in China: a modal split analysis in the context of different market access rules

ABSTRACT To elucidate the impact of online car-hailing (OCH) on traditional taxi services and in producing structural changes to urban taxi markets in China, we propose a model based on the market access rules and business models of the two modes that analyzes their interactions in the following arenas: fleet size, passenger waiting time, service level, and driver income. Then, we build a nested logit model to predict the modal splits and market shares for four travel modes (online car-hailing, traditional taxi, metro, and bus). Taking Ningbo as an example, we estimate the changes in the number of OCH vehicles, the modal splits of the four travel modes, and the new equilibrium in urban taxi service markets. We show that OCH puts significant pressure on the traditional taxi market due to the positive feedback between OCH fleet size and its service quality.

Optimal instant discounts of multiple ride options at a ride-hailing aggregator

Recently, ride-hailing aggregators have emerged to help passengers find the best ride options. It aggregates the results from different service providers, allowing sorting and filtering all in a single app. It is not unusual for an aggregator to offer instant discounts on the prices returned from the service providers to increase its Gross Transaction Value (GTV) or Total Transaction Volume (TTV). In this research, we study this optimal instant discount problem, which can be viewed as a new variant of the classical multi-product price optimization problem. We first use the Nested Logit model to predict the probability a passenger would complete the trip with each ride option. We then formulate the instant discount problem as a nonlinear optimization model with a budget constraint and a group of discount bound constraints. To solve this model, we construct a surrogate relaxation formulation with strong duality. We develop a Lagrangian-dual-based approach to decompose this problem into a series of subproblems, and then design heuristic methods to give feasible solutions. For both cases when the GTV or the TTV is maximized, we quantify the optimality gap, give its asymptotic properties, and establish conditions under which it becomes zero and tight. Finally, we use real data from Meituan, a leading ride-hailing aggregator in China, to validate the proposed approach. Results show that compared to the baseline methods, we can improve the GTV by 1.293%, improve the TTV by 0.475%, and decrease the magnitude of the optimality gap.

Demand management using responsive pricing and product variety to counter supply chain disruptions

We consider a retailer that offers multiple variants of two brands, all of which are substitutable, within a single product category. Due to supply disruptions, the retailer may not be able to always offer either or both brands. To mitigate potential loss in demand due to unavailability of one brand, the retailer may choose the product variety strategically and/or adjust the price of the available brand. The goal of this paper is to compare the relative impact of these two levers of demand management in the face of supply disruptions. To that end, we develop and analyze a model of a retailer buying from two brands (suppliers) subject to random supply disruptions. The retailer’s customer demand depends on price and product variety, and their impact on customer choice is captured through the nested logit model. The model also takes into account fixed design and operational costs associated with product variety. Our analysis reveals that strategic choice of product variety yields most of the benefit; price is a largely ineffective lever. We also show that when the supply of one brand is disrupted, the optimal price for the other brand is lower than when both brands are available, provided the outside option is equally or less affected by the disruption. However, even if the outside option is affected more, this result may not necessarily reverse. Finally, even though responsive pricing does not improve the profit substantially, it may reduce safety stock requirements.

Modeling joint row- and column-wise correlation in air passenger seat selection: A cross-nested logit approach

Advance seat selection (ASS) fees have gradually become an important revenue generator for the carriers worldwide. In this study, we investigate air passenger seat selection behavior in the ASS service while quantifying spatial correlation structure among seats, which is novel to the literature. Specifically, we employ a Cross-Nested Logit (CNL) model that captures the joint row- and column-wise correlation among seats. The CNL model, together with the Multinomial Logit and Nested Logit models involved as benchmarks, are applied to a rich dataset of 321670 ASS records, extracted from the database of a Chinese regional carrier. The empirical results suggest that the correlation among seats arises from their similarities in both row and column dimensions, with a stronger correlation in the column dimension. And modeling such joint correlation can not only enhance the in-sample model fit, but also facilitate more accurate out-of-sample revenue prediction. Moreover, the utility parameter estimates reveal that passengers exhibit non-linear price sensitivity, representable by a power function with a rational exponent of 0.37. Additionally, passengers display heterogeneous willingness to pay influenced by their flight, booking, and personal characteristics. And seat preferences can be affected by participating in loyalty programs.

Container ship investment Decisions―Newbuilding vs second-hand vessels

Ship investment is a crucial strategic issue for shipping companies as the container shipping industry is highly capital-intensive. In practice, a company's ship investment decision mainly concerns new shipbuilding and second-hand vessels. The investment of new container ships with advanced technology is high in cost and the payback period is long. Differently, the second-hand vessels' investment cycle is shorter, but there are certain drawbacks in loading capacity and maintenance. Therefore, it is significant to investigate the investment selection of new-building and second-hand ships in the container shipping market. Previous studies have analyzed this topic at aggregate or industry level. Few studies have attempted to reveal ship type preferences from observed individual data. Therefore, using the data from the Clarkson's World Fleet Register between 2000 and 2021, this study employs the Nested Logit model to analyze the container ship investment preferences, and explore the impact of different variables on decision-making from the perspective of individual companies. The empirical results suggest the higher substitution effect among alternatives within the same nest than that of crossing nests. Along with discussion on the effects of various factors, it discloses the higher preferences of larger types of vessels in responding to competitors' capacity expansion, especially the larger companies. Therefore, it is important to distinguish the larger companies' capacity expansion between responding to demand and competing with competitors, especially for those companies adopting follower strategies.

Pregnant women's and policymakers' preferences forthe expansion of noninvasive prenatal screening: A discrete choice experiment approach study.

Quantitative approaches for eliciting preferences for new interventions are mostly conducted by patients and rarely by policymakers. This study aimed to quantify the preferences of pregnant women and policymakers regarding the addition of a new test to prenatal screening programs for detecting chromosomal abnormalities. A discrete choice experiment was conducted to measure the respondents' preferences for a new prenatal test. A seven-attribute instrument was built based on interviews with pregnant women and policymakers. The data were analyzed using robust conditional logistic regression and nested logit models. In total, 272 pregnant women and 24 policymakers completed the questionnaire (response rates of 48% and 55%, respectively). Overall, all attributes were statistically significant in the pregnant women group, whereas only three attributes (test performance, degree of test result certainty, and cost) were statistically significant in the policymakers group. Statistically significant differences in test performance and information were observed between the two groups. Policymakers differed from pregnant women in their appraisal of attributes related to their preference for a new prenatal screening intervention. The low response rates observed in both groups suggest that further investigation of the relevance of this approach must be conducted.

Integrated Planning of Feeder Route Selection, Schedule Design, and Fleet Allocation with Multimodal Transport Path Selection Considered

In the shipping network optimization, the feeder liner companies not only need to decrease the operation cost by comprehensively optimizing the route, schedule, and fleet but also try to increase the operation income by attracting more shippers, with multimodal transport-path selection considered. Therefore, this paper proposes the integrated planning of feeder route selection, schedule design, and fleet allocation with shippers’ transport-path selection considered. We utilize the nested Logit model to analyze the shipper selection behavior for the multimodal transport path and then formulate a mixed-integer nonlinear programming model of integrated optimization for route, schedule, and fleet. To solve our nonlinear model, we propose a particle swarm optimization (PSO) framework embedded with CPLEX solver by combining the constraint relaxations and the linearization techniques with the heuristic rules. Based on the multimodal transport system in Northern China, computational experiments were conducted to verify the effectiveness of our model and algorithm. The sensitivity analyses of model parameters show that with the increase in ship rent, feeder liner companies should reduce the ship capacity or try to increase the number of port call. With the increase in fuel price, feeder liner companies should reduce the ship speed under the constraints of the arrival-time window and schedule interval. If shippers’ time utility coefficient is high, feeder liner companies should shorten the sailing time of ships by decreasing the number of port calls or shorten the at-port time of ships by increasing the service frequency. If the multimodal transport paths are cost-advantaged or if the waterway transport time has a small impact on multimodal transport time, feeder liner companies should weigh the increase in income and cost when using the measures of time saving. These management insights provide decision support for the operation practice of liner shipping network optimization.