A closer look at urban development under the emergence of autonomous vehicles: Traffic,land use and air quality impacts

Autonomous vehicles (AVs) hold great promise for increasing the capacity of existing roadways and intersections, providing more mobility to a wider range of people, and are likely to reduce vehicle crashes. However, AVs are also likely to increase travel demand which could diminish the potential for AVs to reduce congestion and cause emissions of greenhouse gases (GHG) and other air pollutants to increase. Therefore, understanding how AVs will affect travel demand is critical to understanding their potential benefits and impacts. We evaluate how adoption of AVs affects travel demand, congestion and vehicle emissions over several decades using an integrated travel demand, land-use and air quality modeling framework for the Albuquerque, New Mexico metropolitan area. We find that AVs are likely to increase demand and GHG emissions as development patterns shift to the region's periphery and trips become longer. Congestion declines along most roadways as expanded capacity from more efficient AV operation outpaces increasing demand. Most of the population can also expect a reduction in exposure to toxic vehicle emissions. Some locations will experience an increase in air pollution exposure and traffic congestion from changes in land-use and traffic patterns caused by the adoption of AVs.     

Tourism destination preferences of low-cost airline users in the East Midlands

Increasing demand for air travel, associated with the boom in low-cost airlines, has assisted tourism growth. Expanding opportunities from regional airports underpin an increased propensity to fly. This paper examines European tourism destination preferences and price sensitivity to fly by population segment from an air travel household survey in the East Midlands region of the United Kingdom. Of the eight typical low-cost airline destinations presented to respondents cultural locations such as Rome are preferred. Weekend break leisure trips are particularly attractive to those in employment. Price sensitivity, suggests that increasing financial pressures will have an impact upon growth, however demand for an annual holiday is important, particularly for families.     

Valuation of travel time for international long-distance travel – results from the Fehmarn Belt stated choice experiment

The geographical scope of travel varies from short distances in urban areas to long distances across cities and countries. While urban travel has been widely analysed in the literature, travel over longer distances and particularly across countries, has received much less attention. While this may be justified due to the number of travellers it cannot be justified when looking at the mileage consumption and its resulting environmental impacts. In this paper, we investigate international long-distance travel preferences related to travel between Scandinavia and Central Europe with particular focus on the Fehmarn Belt fixed link between Germany and Denmark to be opened in 2021. To facilitate long-term demand forecasts for the future fixed link, stated preference data were collected in 2011. Based on these data a discrete choice model for long-distance travellers was developed in order to estimate the value of travel time savings (VTTS). The final model, which was formulated as a nested logit model and included Box–Cox transformed travel time and cost attributes, revealed several interesting findings. Firstly, we found damping effects in both cost and time – most strongly in cost. Secondly, we found significant interactions among travel cost and time, and journey characteristics, such as distance and duration. This had direct impact on the VTTS, which was shown to decrease with distance and duration. Thirdly, we found that air travel implies a higher average VTTS, which is to be expected but rarely supported by empirical evidence.     

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.     

Analysis of the potential demand for battery electric vehicle sharing: Mode share and spatiotemporal distribution

Carsharing is considered one of the solutions to urban transport problems. As a new mode in the urban transport system in China, there are still initial questions of how carsharing will perform and what the impacts will be. Accordingly, this study considers battery electric vehicle sharing and investigates its potential demand, with Beijing as the case study. A nested logit model is established and calibrated to analyze mode choice behavior. Further, real trip data is used to estimate the potential demand for battery electric vehicle sharing. In addition, the temporal and spatial distribution of potential demand, the impact of battery electric vehicle sharing on the mode split, and the impact of pricing strategies are analyzed. The results show that an optimistic mode split of battery electric vehicle sharing is 4.23% when the average distance between travelers and stations is 0.5 km. The main source of potential demand is public transport. However, the substitution effect of battery electric vehicle sharing for private vehicles is weak. The potential trips are concentrated in the morning peak period, mainly starting in residential or integrative areas, and ending in commercial areas or green spaces. Commuting and long-distance trips are more sensitive to decreases in price, such that they are more likely to be completed as battery electric vehicle sharing trips. This price decrease could also increase the potential trip ratio during the evening peak period. These findings are useful to governments and operators for implementing policies such as station planning, relocation, and pricing strategies.     

An examination of the role of domestic destinations in satisfying holiday demands

The role of domestic destinations in satisfying demand for holidays has received less attention than research which outlines the challenges associated with long haul travel, particularly within the United Kingdom context. This paper examines the role of domestic holidays in satisfying demand in relation to international holidays, in the context of providing a better understanding of the environmental impacts of tourism. This is achieved through analysis of a household survey and a series of interviews with family groups of holiday-makers. The data was collected in the East Midlands region of the United Kingdom. Most respondents prefer domestic holidays in attractive coastal and rural destinations; they often take place in the summer and for shorter periods than overseas trips. Yet once at the destination there is commonality between tourism practices in domestic and international destinations. Socio-cultural aspects, life stage and financial considerations prove much more influential than environmental concerns in influencing choice. In response, the main policy recommendation would be to raise the relative cost of travelling abroad. The primary research recommendation would be to deepen the understanding of how substitution between destinations can occur.     

Disentangling the behavioural side of the first and last mile problem: the role of modality style and the built environment

The first and last mile (FLM) problem, namely the poor connection between trip origins or destination and public transport stations, is a significant obstacle to sustainable transportation as it is likely to encourage the use of cars for FLM travel, if not for the entire trip. This study examines the role of modality style and built environment in FLM mode choice behaviour, in order to identify the key features that might invoke a travel mode shift from cars to more sustainable travel options for both mandatory and discretionary trips. More specifically, this study draws on disaggregate data from the South East Queensland household travel survey and presents a latent class choice model to unravel modality style groups. Results reveal two distinct individual-level modality style groups: (1) driving and walking oriented; (2) multimodal travellers. Individuals in the second modality style group were found to be relatively inelastic to FLM travel time for mandatory trips, while individuals in the first group were largely unaffected by built environment characteristics and highly habitual in their mode choice behaviour for both mandatory and discretionary trips. Home residence environments with high road intersection density and public transport accessibility, and home residence environments with diverse land use mix, respectively encourage individuals within the second modality style to walk for mandatory trips, and discretionary trips. To this end, when place-based policies seek to change certain built environment features, individuals in the second modality style are more likely to shift their preference from cars to more sustainable modes. Finally, our findings have practical planning implications in targeting mode shift through highlighting the importance of considering the intersection of individual modality style in a given locale and mode choice behaviour. More specifically, our findings advocate for place-based policies that seek to target particular locales with the certain modality style deemed to be more predisposed to adopting a mode shift.     

The indirect effect of the built environment on travel mode choice: A focus on recent movers

Many studies have demonstrated that the built environment has a strong impact on people's travel mode choice. However, the built environment also influences elements such as travel distance and car ownership, which might be the true predictors of which travel modes are chosen. In this study, we analyse the effects of changes in residential neighbourhood on changes in travel mode (for commute trips and leisure trips), both directly and indirectly through changes in car ownership, travel distances and travel attitudes. This study applies a structural equation modelling approach using quasi-longitudinal data from 1650 recently relocated residents in the city of Ghent, Belgium. Results indicate that the built environment has strong direct effects on active leisure trips and car use. However, distance (for car use) and attitudes (for active travel) were found to be important mediating variables. In sum, the effect of the built environment on travel mode choice might be more complex than commonly assumed as it partly seems mediated by travel distance and travel attitudes.     

Choice behavior of tourism destination and travel mode: A case study of local residents in Hangzhou,China

The social and economic growth as result of promoting the rapid development of tourism in China has brought tremendous pressure on the urban transportation systems. Research of travel behavior concerning the characteristics of tourists has provided effective information for transportation planning. Due to different city plans, public transportation system design, car parking design and management, etc., the local situation in developed countries differs from the counterpart in China. However, little research has studied the factors influencing the choice of travel destinations in tourism. The research aims to study the tourism destination and mode choice behavior of tourists and provides suggestions to improve tourism transportation service system. An online questionnaire survey is used to collect data including the travel characteristics and personal attributes of local tourists in different holidays in Hangzhou, China. A multinomial logit model is constructed with the trip destination set as the dependent variable. Results show that age, residential type, car ownership, companion type and holiday length have a significant impact on destination choice. To determine what influences modal choice for such trips, a second logit model is established with travel mode set as the dependent variable with the explanatory variables of age, gender, companion type, car ownership, holiday length and travel destination found to be significant. The results demonstrated that people aged 26 to 44 prefer suburban areas, and they are the main group driving to their travel destination. Public transport use frequency decreases when the destination is located outside of the main tourist area. Finally, suggestions have been proposed to mitigate the congestion and parking problem based on model analysis from the perspective of the bus line setting, transfer improvements, and the policy to limit cars, respectively.     

Logistics facilities for intra and inter-regional shipping: Spatial distributions,location choice factors,and externality

Existing studies of urban logistics facility locations and their impacts on negative externality tend to focus on the movements of freight that start and end within a metropolitan area and overlook the role of the shipments with origin or destination outside of the urban area – external shipments – in characterizing logistics facilities. This research aims to fill such gap. Focusing on the Tokyo Metropolitan Area, we analyze locations and goods vehicle trips associated with four logistics facility groups that have different splits between intra and inter-regional trips for inbound and outbound shipments. The spatial density analysis and the location choice modeling highlight the differences in the distribution and location choice factors among those groups. The importance of trip distance minimization is more evident for the facilities that serve intra-regional shipments. Furthermore, we show that the traffic impacts of goods vehicle traffic associated with logistics facilities also differ in many ways among the above-mentioned groups. Finally, we discuss the policy insights to address their heterogeneity in the contribution to externality.     

Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation

The COVID-19 pandemic has had a substantial impact on the airline industry. Air travel in the United States declined in 2020 with significantly lower domestic and international flights. The dynamic change and uncertainty in the trend of COVID-19 have made it difficult to predict future air travel. This paper aims at developing and testing neural network models that predict domestic and international air travel in the medium and long term based on residents' daily trips by distance, economic condition, COVID-19 severity, and travel restrictions. Data in the United States from various sources were used to train and validate the neural network models, and Monte Carlo simulations were constructed to predict air travel under uncertainty of the pandemic and economic growth. The results show that weekly economic index (WEI) is the most important predictor for air travel. Additionally, daily trips by distance play a more important role in the prediction of domestic air travel than the international one, while travel restrictions seem to have an impact on both. Sensitivity analysis results for four different scenarios indicate that air travel in the future is more sensitive to the change in WEI than the changes in COVID-19 variables. Additionally, even in the best-case scenario, when the pandemic is over and the economy is back to normal, it still takes several years for air travel to return to normal, as before the pandemic. The findings have significant contributions to the literature in COVID-19's impact on air transportation and air travel prediction.     

Representing pedestrian activity in travel demand models: Framework and application

There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviors. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modeling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modeling pedestrian trips. The MPO's existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO's four-step model. The walk trips are distributed to destinations using a choice modeling approach, thus identifying pedestrian trip origins and destinations. In this paper, a proof-of-concept application is included to demonstrate the framework in successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modeling systems.     

Supply-and-demand models for exploring relationships between smaller airports and neighboring hub airports in the U.S.

Airport passenger leakage is the phenomenon of air passengers choosing to travel longer distances to access more extensive air services offered by airlines at an out-of-region hub (or, substitute) airport, instead of using their local airports. Airport leakage can cause further reduction in services offered by airlines at a local airport, thereby causing even further leakage, and so on, which can significantly impact an airport's role in the growth of the local economy. This paper explores the geographic and operational attributes of local-and-substitute airport pairs in the United States, explicitly accounting for the interactive feedback relationship between passenger volumes and air service characteristics that contribute to the onset, persistence, and exacerbation of airport passenger leakage. A two-stage least squares regression model of air passenger demand at small- and medium-sized airports is first presented, where local passengers may travel by vehicle to larger, out-of-region hub airports. The results confirm that airfare and passenger volume relationships exist between the local and substitute airport pairs included in the dataset, and that lower airfares at the substitute airport have a greater impact on airport choices made by larger travel groups. They also suggest the existence of positive feedback in that if an airport attracts increasingly smaller passenger numbers with fewer air services and fewer air services with fewer passengers, without external intervention airport leakage impacts may be irreversible and exacerbate over time. A conceptual market share equilibrium analysis is used to illustrate the mechanisms of a direct two-way feedback relationship between passenger volumes at a local airport and air service characteristics at both the local and substitute airports. With data, this quantitative framework can help guide airport planners in further assessing and verifying suspected passenger leakage issues at their airport. The results suggest that without intervention, airport leakage impacts may be difficult to reverse; further exacerbating the trend are technological advancements that make driving cheaper and easier (connected and autonomous vehicles). However, the results can also guide planners in choosing the types and degrees of infrastructure investments and airline incentives that may be used to expand or retain air services to attract passengers.     

Teen travel in the Greater Toronto Area: A descriptive analysis of trends from 1986 to 2006 and the policy implications

This paper extends research on urban form and travel behavior beyond adult travel by examining teen travelers aged 13–19 in the Greater Toronto Area. Data from the Transportation Tomorrow Survey (TTS) survey are used to study four main research questions: (1) How has teen mode choice changed from 1986 to 2006? (2) How do these choices vary as teens transition from the 13–15 age group to being of driving age (16–19)? (3) How do these choices vary across the different urban and suburban regions of the GTA? (4) What are some of the differences between teen travel and adult travel? Results show that in general, active transportation has decreased, while auto-passenger mode shares have increased across the region. The younger group walks more and the older group takes transit more for both school and discretionary travel. Jurisdictions with better transit supply and orientation have higher transit mode shares for school trips, but discretionary trips have very low transit mode shares. Walk mode shares for both school and discretionary travel are similar across all jurisdictions, regardless of whether they are urban or suburban. In contrast to adult travel in the GTA, built form characteristics and transit supply do not appear to have a direct relationship with teen mode choice. Urban form appears to exert an indirect influence on teen travel.     

A border effect in airport choice: Evidence from Western Europe

Some commentators suggest that air travelers are reluctant to use a foreign airport as a point of departure for their journey. Such reluctance has implications for marketing and policy measures, especially in border regions and in smaller countries, such as the Netherlands and Belgium. In this study, we tested this supposed barrier effect of national borders with empirical data.Our research is based on a subsample (n = 4083) of a large-scale survey among German, Dutch and Belgian adults. Participants were asked about their most recent flight, preferences regarding long-distance travel, and general travel behavior. The survey information about the most recent trip was linked to the Official Airline Guide with data from 18 airports in the Western European region. On average, however, only 10 airports offered a direct connection to the destination of the respondents’ most recent flight. A conditional logit model with varying choice sets allowed us to model only the relevant airports per respondent, while accounting for other known determinants in airport choice, such as travel time, airline competition, and number of available flights.The model results confirmed our expectations: people strongly prefer a departure airport situated in their own country. The model predictions suggest that without the border effect the share of people in our sample departing abroad would rise from 7.7% to 18.7%. Experience (with multiple airports as a point of departure) mitigates the barrier effect of the border. The performance of our model is good: the median probabilitie for the chosen alternative is 68%. Our research stresses the importance of the barrier effect of borders in airport choice: even when traveling across borders in the European sky, national borders remain important.     

Have the major forces driving leisure airline traffic changed?

Here, we examine the major forces that are driving leisure airline traffic to assess whether these have changed in recent years. Initially this is undertaken by considering global patterns of airline and tourism demand and then by a more detailed investigation of the UK situation. The research indicates that airline demand is becoming less sensitive to income changes and also that the share of income spent on air travel is not showing much growth. Both of these suggest that airline demand may be becoming more mature, with growth being increasingly driven by price reductions rather than income changes. Moreover, evidence from the UK shows that changing customer preferences, and subsequent industry developments to accommodate these, appear to be having a significant impact on the demand for different types of leisure air trips.     

The path and time efficiency of residents' trips of different purposes with different travel modes: An empirical study in Guangzhou,China

Improving residents' travel efficiency and reducing carbon emissions from travel are the key issues for sustainable development of urban transportation. This study first employed a circuity index to measure the path efficiency of residents' trips based on 2015 survey data in Guangzhou and developed a generalized additive model (GAM) to investigate the relationship between the path efficiency and travel distance for different purposes of trip and different travel modes. On this basis, it further evaluated the time efficiency of different travel modes for each trip. The results showed that there is a complex and nonlinear relationship between the path efficiency and travel distance, which differs between different purposes of trips and different travel modes. In general, trips by non-motorized transport have a lower circuity index and higher path efficiency than those by cars or public transport. Moreover, non-motorized transport is the time-efficiency optimal mode for almost half of the trips, especially for daily shopping trips. However, people prefer to choose public transport on their trips even though public transport is not the time-efficiency optimal mode for these trips. Generally, only about half of the residents chose the time-efficiency optimal mode for their trips. Those who did not choose the time-efficiency optimal mode tended to choose the modes with higher carbon-intensity. The conclusions of this study indicate that for improving travel efficiency and reducing carbon emissions from transport, more efforts should be focused on the non-motorized travel environment and developing relevant policies to encourage more walking and cycling.     

Analysis of active school transportation in hilly urban environments: A case study of Dresden

This paper analyses the way students travel to school and examines the influence of environmental conditions on travel patterns. More specifically, it studies how topographic changes affect the likelihood of choosing cycling as a transport mode. We use mode choice data on students' home-to-school commuting trips from a previous study by Müller et al. (2008). The results show that models perform better when they account for the topographic conditions of the urban environment. We included this information in the model by introducing the “energy exerted” variable, which significantly improves the model and the results. The implications of this study are manifold; it guides the consolidation or expansion of school-based transportation network planning in Germany and prompts further analysis of active transportation systems, such as bike, pedelec and e-bike sharing systems. Overall, transportation policy should seek to foster active transportation, as it provides the greatest benefits for society and has a direct impact on people's well-being, while notably reducing the negative environmental and socioeconomic impacts of motorized transport.     

Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price

This study aimed to explore the impacts of COVID-19 on car and bus usage and their relationships with land use and land price. Large-scale trip data of car and bus usage in Daejeon, South Korea, were tested. We made a trip-chain-level data set to analyze travel behavior based on activity-based travel volumes. Hexagonal cells were used to capture geographical explanatory variables, and a mixed-effect regression model was adopted to determine the impacts of COVID-19. The modeling outcomes demonstrated behavioral differences associated with using cars and buses amid the pandemic. People responded to the pandemic by reducing their trips more intensively during the daytime and weekends. Moreover, they avoided crowded or shared spaces by reducing bus trips and trips toward commercial areas. In terms of social equity, trips of people living in wealthier areas decreased more than those of people living in lower-priced areas, especially trips by buses. The findings contribute to the previous literature by adding a fundamental reference for the different impacts of pandemics on two universal transportation modes.     

Residential location and daily travel distances: the influence of trip purpose

This paper investigates the extent to which residential location influences daily distance travelled if travel purposes are differentiated. Statistical multilevel models are applied to Swedish National Travel Survey data from 2005–2006. Travel purposes are categorized by considering time–spatial constraints and hypothesized factors of personal freedom of choice. Results indicate that the influence of residential location on daily distance travelled is highly conditional on trip purpose in a nationwide Swedish context. Although statistically significant proportions of the variation in daily distance travelled to work, on service errands, and on weekdays were dependent on residential location, daily travel distances for leisure activities and on weekends varied greatly among people living in the same neighbourhood. From a policy perspective, these results suggest that measures intended to alter the built environment to reduce the volume of travel will be most efficient as regards work trips, while trips taken during free time are unlikely to be much affected. In addition, the multilevel models applied reveal several important interactions between the variation in travel distances across residential locations and individual characteristics of which researchers should be aware, especially when examining service trips.