Measuring the structural determinants of urban travel demand

To be best prepared for tomorrow's cities we need to forecast urban travel demand. To this end, this study calibrates an urban travel demand model, which uses the principal structural variables that have been identified in the literature. It uses a robust econometric method, which has been little applied in the sphere of transportation. The results show that two variables stand out from the others: the user cost of transport – by private car and public transport – and urban density. It is surprising, but explicable with the available data, that the demand functions estimated for a given country are independent from the group of countries to which it belongs.     

Estimating social travel demand of senior citizens in the Netherlands

Like many other countries, the Netherlands is experiencing a sharp rise in the ageing population. As age increases, people’s mobility may decrease. However, older people have more leisure time compared to their younger (working) counterparts, and potentially spend more time on social activities. Therefore, this group can possibly increase social travel demand. However, to date, the travel demand for social activities of senior citizens has received only little attention. This paper studies trip-making for social purposes, with a special focus on the demographic ageing factors. Using social activity diary data, models are estimated to predict the number of social trips, the travel distance and mode of transport for social trips. The results indicate that the elderly of today seem to be as mobile as their younger counterparts with respect to the number of social trips. High education and involvement in clubs on average result in more social trips and full time work is found to result in fewer social trips. With regard to trip distance the results show that the average travel distance does not decrease as people get older. Full time work is found to result in longer social trips. Shorter trips were found for people in urban as well as rural areas. Trips for the purpose of visiting or joint activities tend to be longer than average. With regard to transport mode choice the results indicate that older seniors (75+) are less likely to choose the bicycle, relative to driving. No other significant age effects were found. Significant effects were found for gender, household structure, education level, car ownership, having a disability, urban density, distance and the purpose of the social activity.     

Determinants of air travel demand in Middle Income Countries

The aim of this paper is to calculate the effects of air travel demand determinants in Middle Income Countries (MICs). Through static and dynamic panel data models from 32 countries during the period from 2002 to 2008, we found that the income elasticity is the most important determinant and that it is slightly higher than one. Income growth multiplied by income elasticity accounts for 75 percent of total passenger growth. Public policies such as an open skies agreements with the European Union have a positive effect on passenger growth, whereas structural changes, such as Low Cost Carrier (LCC) growth, have a marginal effect.     

Spatial and temporal transferability of relationships between travel demand,trip cost and travel time

The paper draws together results from several major revealed preference (RP) and stated preference (SP) data sets, collected for large-scale disaggregate models and VTTS research, respectively, to comment on trends which appear in time and cost trading as between different time periods and different countries.     

Impact analysis of September 11 on air travel demand in the USA

This study uses an intervention model to look at the impact of the September 11, 2001 terrorist attack on air transport passenger demand in the US. The result showed that both domestic and international air traffic was significantly impacted for 1 and 2 months, respectively. The impact pattern was thus abrupt and temporary, instead of gradual and permanent. The approach also provides better forecasts than the seasonal ARIMA benchmark.     

Analysing Chinese citizens' intentions of outbound travel: a machine learning approach

Due to the tremendous expansion of Chinese outbound travel market, it is of great significance to identify the characteristics of potential Chinese outbound tourists so as to develop targeted marketing strategies. As both extrinsic and intrinsic characteristics of individuals are important in influencing their decision-making process, this study explores potential Chinese outbound tourists from these two aspects. Demographic and psychographic factors, as the proxy of extrinsic and intrinsic characteristics, respectively, are incorporated into this study to construct the relationship between tourists' characteristics and their intentions of outbound travel. An advanced machine learning approach, called twice-learning, is employed for modelling in this study. As an intelligent data analysis tool, this method is able to construct models that can provide insight into the ground-truth relationships hidden beneath the data in an essentially comprehensible way, without being limited by the typical assumptions held by the traditional data analysis methods. By applying this method, the important personal factors that influence Chinese citizens' intentions of outbound travel are detected, and the typical groups of potential Chinese outbound tourists are characterised. The findings would be beneficial for destination marketers to develop marketing strategies on positioning and advertising which are tailored to potential Chinese outbound travel market.     

Dynamic analysis of air travel demand in competitive island markets

This paper describes a dynamic demand model, referred to as dynamic abstract mode model, for estimating both the short- and long-term responses of air passengers to changes in relative air-sea travel cost components in competitive markets. The implementation of the model in the competitive market of Aegean islands in Greece demonstrates the importance of considering the past volumes of air passengers and relative travel cost components to explain current air travel demand.     

Design of limited-stop bus service with capacity constraint and stochastic travel time

This paper develops a mathematical model for the optimal stopping design of limited-stop bus service, which allows each bus vehicle to skip some stops. To better reflect the reality, this paper considers the vehicle capacity and stochastic travel time. Also, vehicles are all allowed to skip stops whereas any stop is not allowed to be skipped by two consecutive vehicles. A hybrid artificial bee colony (ABC) and Monte Carlo method is developed to solve the optimal stopping strategy. Finally, the model and solution method are validated by a numerical example, and a sensitivity analysis is performed on the passenger demand.     

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.     

Price elasticities of demand for passenger air travel: a meta-analysis

The demand for air transport is largely determined by the spending capacity of customers. This paper aims to offer more insight into the determinants of price elasticities in the aviation sector. It seeks to identify both common and contrasting factors that influence the price elasticities, on the basis of a comparative analysis among a large number of empirical studies in this field. By means of meta-analytical methods, the relative importance of several driving forces such as distance, type of ticket and the nature of study is investigated.     

Wealth effect from real estate and outbound travel demand: the Malaysian case

This study uses quarterly data from Malaysia (2000–2011) to examine the relationship between the wealth effect from real estate (WERE) and outbound tourism while controlling for other relevant outbound tourism determinants. By applying time-series cointegration regressions, the results show that WERE has a positive and significant impact on Malaysian outbound travel demand. Then, we exclude the departures for business purposes from the total departures in order to have a better understanding of the impact of WERE on the consumption of a luxury good like international travel for leisure purposes. Similarly, we find that WERE increases Malaysian international travels for leisure purposes. The findings provide some implications for Malaysian policy-makers as well as tourism and travel agents.     

The demand for reliable transit service: New evidence using stop level data from the Los Angeles Metro bus system

This study explores the role of service reliability in determining bus transit ridership. Using stop level service supply, demand, and performance data from the Los Angeles Metro bus system, I investigate whether reliability of a directional line serving a stop influences the number of passengers boarding the line at that stop, controlling for various other established factors affecting demand. This cross-sectional analysis of the variation in line boardings across about 1300 sample schedule time point bus stops served by about 300 directional bus lines over a six-month period uses a historical archive of real-time geo-referenced vehicle location data, and focuses on five different time periods, peaks and off-peaks, of a typical weekday. By evaluating two measures that capture different dimensions of bus service reliability, and by estimating a series of regression models, I find systematic evidence that higher average service punctuality (or schedule adherence) and lower variation in schedule deviation over time are associated with greater ridership, all else equal, particularly during the peak periods. This study also provides first empirical evidence that the effect of reliability on peak-period ridership is moderated by headway. The demand for reliability seems to be higher for lines with relatively longer headways. The findings indicate that service reliability influences transit mode choice and/or line/route selection, and suggest that system-wide ridership gains can be expected from reliability improvements. From an urban planning perspective, this study provides more evidence that good service quality can effectively compliment transformations in the urban fabric brought about by coordinated land use — transit plans to promote transit use.     

Modeling job accessibility using online map data: An extended two-step floating catchment area method with multiple travel modes

Current quantitative measures of job accessibility rarely consider the interaction between job opportunities and labor force, and the effects of dynamic travel mode choice. Drawing upon multiple open-source datasets, we develop a job accessibility index by extending the two-step floating catchment area method (2SFCA). The job accessibility indices are calculated for different commuting scenarios concerning distance, time, and travel modes. The results suggest that job accessibility is very sensitive to travel modes, and using a single travel mode would contribute to a biased job accessibility index. The job accessibility indices with combined travel modes are more geographically balanced than using a single travel mode. Furthermore, the new index is employed to examine the spatial pattern of job accessibility and explore the relationship between job accessibility, housing, and population in the Pudong district, Shanghai. The new job accessibility indices manifest the impacts of ring roads on the spatial distribution of job accessibility. A comparative analysis shows that the floating population has poor driving-based job accessibility but can access job opportunities using public transit. Also, poor job accessibility leads to low rent prices but has little impact on medium-high rent. Both transit-based and drive-based job accessibility indices are positively related to housing prices. Our study highlights the importance of considering dynamic travel mode choice in job accessibility research. The research outcomes also contribute to the literature on spatial mismatch by revealing the unique relationship between job accessibility, housing, and population in urban China.     

Elasticities of demand for leisure air travel: A system modelling approach

In this study, the UK outbound demand for leisure air travel has been examined using a demand system which takes into account the ways in which the expenditure on air fares interacts with both the expenditure on non-fare components of travel abroad and with expenditure on domestic leisure. The findings suggest that there is a strong link between total consumer expenditure and expenditure on air travel with the expenditure elasticity greater than one but that, considered in aggregate, the demand for air travel is moderately inelastic with respect to air fares alone. However, the findings also show that interactions between air travel expenditure, other costs of travel abroad, as well as expenditure on leisure activities in the UK, involve sizeable cross-price elasticities.     

Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion

The economic downturn and the air travel crisis triggered by the recent coronavirus pandemic pose a substantial threat to the new consumer class of many emerging economies. In Brazil, considerable improvements in social inclusion have fostered the emergence of hundreds of thousands of first-time fliers over the past decades. We apply a two-step regression methodology in which the first step consists of identifying air transport markets characterized by greater social inclusion, using indicators of the local economies’ income distribution, credit availability, and access to the Internet. In the second step, we inspect the drivers of the plunge in air travel demand since the pandemic began, differentiating markets by their predicted social inclusion intensity. After controlling for potential endogeneity stemming from the spread of COVID-19 through air travel, our results suggest that short and low-density routes are among the most impacted airline markets and that business-oriented routes are more impacted than leisure ones. Finally, we estimate that a market with 1% higher social inclusion is associated with a 0.153%–0.166% more pronounced decline in demand during the pandemic. Therefore, markets that have benefited from greater social inclusion in the country may be the most vulnerable to the current crisis.     

Analysing and modelling some effects of solutions for matching the airport runway system capacity to demand

The paper deals with analysing and modelling some effects of three solutions for matching the airport runway system (landing) capacity to corresponding demand. These are: i) charging congestion applied to the NY LaGuardia airport (New York, USA); ii) deployment of the innovative operational procedures supported by the new technologies developing in the scope of European SESAR (Single European Sky ATM Research) and U.S. NextGen (Next Generation) program applied to the system of two closely-spaced parallel runways at Dubai International airport (Dubai, UAE (United Arab Emirates)); and iii) building the new additional runway(s) applied to the runway system of three London airports - Heathrow, Gatwick, and Stansted (London, UK).The results have shown that each of the considered solutions can contribute to more efficient and effective matching of the airport runway (landing) capacity to the current and expected (prospectively growing) airside demand under given (specified) conditions.     

Profiling the segments of senior tourists throughout motivation and travel characteristics

Interest in tourism market segmentation of seniors has been increasing in recent years as the tourism industry has begun to be aware of their importance for the sector. This is a very heterogeneous group, with greater purchasing power and lifestyle-oriented entertainment and enjoyment of leisure time by performing travel than previous generations. This study strives to contribute towards the hitherto scarce research on segmentation in senior tourism. Specifically, this work aims to determine the existence of various profiles of senior tourism by means of using socio-demographic variables, motivation and characteristics of travel of seniors. For this, two complementary analysis techniques, the nonlinear canonical correlation (OVERALS) and a two-stage cluster analysis, were used. Five market segments were identified according to the behavioural variables analysed that allow marketers to target this group in the most convenient manner and to exploit new market opportunities.     

Predicting the impact of Trans Java Toll Roads on demand for intercity air travel in Indonesia

This paper aims to explore the factors influencing a potential air passenger shift to autos due to the completion of the Trans Java Toll Road and an increase in airfares. The study focuses on intercity trips for nonbusiness purposes. A face-to-face interview survey of 751 air passengers was conducted in three main airports on Java Island, Indonesia. Both the theory of planned behavior and the discrete choice model were applied to understand the factors for influencing toll road use among air passengers. The empirical results reveal that psychological factors, consisting of attitudes, subjective norms, and perceived moral obligations significantly influenced intentions to use the toll road. Meanwhile, perceived control of external resources was essential for airline passengers in their intention to use toll roads. Sociodemographic factors such as age, gender, income, frequency of airplane use, travel time, and cost also significantly influenced the decision to drive. Female, older passengers, high-income passengers, and frequent flyers were more likely to continue flying. The finding confirms that the Trans Java Toll Road affected a decline in air demand on Java Island. However, traveling with family members, rather than airfare hikes, became a more substantial reason for air passengers to switch to driving the toll roads. This research found that air passengers were inelastic related to changes in travel time, while 6% were willing to switch due to airfare increases of 10%.     

What is driving the continued growth in demand for air travel? Customer value of air transport

This article introduces the concept of customer value as a driving factor for continued growth in demand and eliminates a subversion of the customer-value theory for the air-transport industry. This theoretical framework includes risk as subcomponents of perceived relative benefits as well as perceived relative costs. Based on data of a survey the weight of the different influential factors is evaluated. Management implications as well as methodological learning for the measurement of customer value are derived. The key conclusion thereby is that a stronger focus on customer value will be a key challenge of airline management. Important developments take place at the lower and upper end of the price and quality ranges. Thereby the orientation should be directed by the real, often hidden and not only to the stated preferences of customers.     

Role of user's socio-economic and travel characteristics in mode choice between city bus and informal transit services: Lessons from household surveys in Visakhapatnam,India

Informally operated paratransit or Intermediate Public Transport (IPT) systems provide demand responsive transit in many developing countries, often competing with formal public transport systems. Literature on the relative user characteristics of the two modes and their choice behaviour between the systems is limited. This article addresses the gap by presenting a methodology to derive a comprehensive understanding of socio-economic and travel demand characteristics of all transit users in a city. The household survey based data collection and analysis framework is demonstrated for the case of Visakhapatnam, a medium sized Indian city. The variables impacting users' choice between the formal and informal modes were derived through binary logistic regression. It was observed that gender, income and travel time have a significant influence on users' choice between the modes, with waiting time having the maximum impact on mode choice. Therefore, the high frequency services offered by paratransit attract users making shorter trips.