The role of airport proximity on warehouse location and associated truck trips: Evidence from Toronto,Ontario

This paper examines the two-way relationship between land use and transportation from the perspective of warehouses in the Toronto Census Metropolitan Area. A location choice model is first developed to reproduce the decision process of firms selecting suitable locations to operate warehousing facilities. The results highlight that transportation plays a prominent role in the location of warehouses with proximity to airport, highway infrastructure, and rail to a lesser extent. However, the proportion of industrial land use provides the largest role in the model suggesting that available land and zoning is the most important factor. This paper also studies the impact of warehouse location on freight transportation trips. The resulting analysis highlights that GPS derived trips arising from warehouses near a major airport in Toronto tend to travel 1.8 times further than trips pertaining to other warehouses in the region. This suggests that trips related to the airport are more likely to be connected to a larger supply chain process.     

A literature review and citation analyses of air travel demand studies published between 2010 and 2020

Accurate forecasting of air travel demand is vital for the resource planning of the air transportation industry. Therefore, identifying contributing factors and understanding the effect of these factors in causing the variation of air travel demand have been one of the key focus areas in air transportation research. This article reviews 87 air travel demand studies published from 2010 to 2020 and summarizes these studies using their input data and primary analytical methods. We also devise and conduct three citation analyses to further explore the relationships among the reviewed studies. Our review finds that a typical empirical study of air travel demand analysis would focus on the demand at the national level, employ time-series data concerning socio-economic and airline operational factors and use time-series based methods to estimate the relationship among the selected time-series. These studies are mostly applying existing analytical frameworks to specific problems rather than developing original methods, therefore their relationship to each other is parallel rather than sequential. A small number of references are frequently cited by the reviewed studies primarily because of their methodological contribution to time-series analysis. A common limitation of existing literature is that very few reviewed studies provide validation of their analyses. In addition, methods that are not regression or time-series based have very limited application in this area so far, so are the non-convention data such as mobility data or social media data. Besides providing a systematic summary of recent publications in a specific field, this review uses a relatively objective and replicable framework to compare and link studies by their references, which can be visualized by the figures included in this review. This review is expected to benefit future researchers that are interested in either air transportation or the application of time-series forecasting in an applied domain.     

The multiplicity of self-selection: What do travel attitudes influence first,residential location or work place?

Residential self-selection has been widely considered as an important issue in quantifying the impacts of the residential built environment on travel behavior and much empirical evidence regarding the nature and magnitude of the self-selection effect has been reported. Nevertheless, people may be based on travel attitudes/needs to self-select not only residential location, but also work place, car ownership, etc. In other words, the impacts of long-term decisions other than residential location choices (e.g., decisions on work place, car ownership, etc.) on travel behavior may also be biased by the self-selection effect. However, self-selection concerning these long-term decisions has not been explored much in the travel behavior literature. The role of residential self-selection would not be properly evaluated if self-selections concerning other long-term decisions were not considered because they are often related. This paper addresses this research gap in the travel behavior literature by exploring the multiplicity of travel-based self-selection. We jointly examine the possible self-selections concerning residential location, workplace, commuting distance and car ownership in an integrated framework, taking into consideration the interrelationships among these decisions. Data are derived from an activity-travel diary survey conducted in 2016 in Beijing, China. We classify the respondents into two groups based on the choice order of their current residential and work locations and conduct a comparative analysis using structural equation models. It is found that self-selection exists in all long-term choices examined in the study. The choices of residential location and work place are found to be mutually dependent. Consequently, both choices have indirect impacts on travel behavior through the other choice.     

Transport infrastructure investment,accessibility change and firm productivity: Evidence from the Seoul region

This study explores how and to what extent transport infrastructure investment affects the level of agglomeration and consequently impacts on firm productivity, using transport network data and micro-data for manufacturing firms in the Seoul region. A first-difference model with novel instrument variables is developed to obtain more precise results than those in previous studies, and the partial arbitrariness that exists in previous studies is reduced by using the estimated spatial-decay parameter. An accessibility indicator derived from a multi-modal travel model is also applied to increase the accuracy in the measurement of agglomeration. It is found that an increase in the degree to which transport infrastructure investment improves the level of agglomeration raises firm productivity, with a best estimated elasticity of 0.0452. The finding highlights the key role of transport infrastructure investment in strengthening the level of agglomeration and the resulting economic advantages for production and economic actors. It is suggested that a policy for transport projects be implemented to maximise their cost effectiveness and ensure that agglomeration is promoted in the area where they are developed.     

A dynamic hazard-based system of equations of vehicle ownership with endogenous long-term decision factors incorporating group decision making

The transportation system affects all aspects of our daily lives including relatively long-term decisions on work and home location choice and automobile ownership decisions. The interdependency existing among these three decisions jointly influences household mobility and overall travel patterns. Therefore, a dynamic modeling framework that can account for the effects of interdependencies between vehicle transaction behavior and residential and job location choices is highly desirable. These decisions are made in the household level while individuals’ decisions influence the overall outcome; therefore, it is also important to incorporate a group decision making process within such modeling frameworks.This study introduces a dynamic model for vehicle ownership, residential mobility, and employment relocation timing decisions. These decisions are modeled at the individual level and then sequentially aggregated to the household level if it is required. A hazard-based system of equations is formulated and applied in which work location and residential location changes are included as endogenous variables in the vehicle transaction model while other important factors such as land-use and built environment variables, household dynamics, and individuals’ socio-demographics are also considered.     

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.     

Airline services marketing by domestic and foreign firms: differences from the customers’ viewpoint

Based on a survey of 1014 passengers of five European airlines, this paper reveals differences between passengers on the Turkish domestic airline and those on four foreign airlines on the same flight destinations with respect to demographic profiles, behavioral characteristics, understanding of airline service dimensions, and satisfaction levels. Differences between the two passenger groups are highlighted in terms of age, sex, education, occupation, sector affiliation, location of domicile, travel purpose, travel frequency, service expectations, and satisfaction levels. It is concluded that the differences in consumer profiles and expectations are valuable clues for domestic and foreign airline firms in understanding their consumers and in designing their marketing strategies.     

How could the station-based bike sharing system and the free-floating bike sharing system be coordinated?

The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes.     

Carer-employees’ travel behaviour: Assisted-transport in time and space

Assisted-transport demand is a daily caregiving task that affects carer-employee’s activity-travel behaviour; however, little is known about such behaviour and the types of constraints that impact carer-employee health. Combining the principles of Hägerstrand’s time geography and Mckie et al.’s caringscape terrain, this research develops a mixed-methods framework to classify the travel behaviour of carer-employees based on their travel experience and the space-time fixity of their weekly schedules. The mixed-methods framework consists of sentiment analysis and k-means clustering, both which are used to analyze 25 randomly selected participants within the Greater Toronto-Hamilton Area (GTAH). Participants were asked to reflect on their recorded one-week trips in a trip summary questionnaire. Sentiment analysis was used to thematically describe carer-employees’ travel behavior, whereas, k-means clustering generated travel behaviour profiles. “Time”, “pressure”, “parents”, “run”, and “long” were several thematic keywords describing the carer-employees’ travel behaviour. K-means clustering identified three relative types of carer-employees’ travel behaviours: 1) flexible, 2) between flexible and fixed, and; 3) fixed. These results provide critical information for the establishment of custom transport programs, such as maximum monthly telecommuting allotment; such programs are useful for employers to use in order to alleviate assisted-transport demand on their employees.     

Do firms flee traffic congestion?

Much transport policy aims to use congestion relief measures to support economic activity, but planners know relatively little about how individual firms respond to traffic congestion. This study helps fill this gap by exploring individual firm location responses to traffic congestion within the Philadelphia metropolitan area between 2003 and 2007. This study tests whether existing, basic-industry firms flee congested areas to minimize exposure to the congestion externality. Relocation responses are estimated and compared for five separate industries (finance and insurance, health care, manufacturing, real estate and leasing, and wholesale trade) using firm-level data collected by InfoUSA and obtained from ESRI. Results suggest that congestion influences firm location decisions, but that the scale of congestion is important. While firms appear to relocate out of areas with high regionally-scaled congestion, areas with high local congestion are associated with a lower likelihood of relocating. In sum, while regional congestion appears to be a drag, local congestion appears to function as an amenity – implying that there is truth in the competing notions among engineers and economists of congestion as a diseconomy and among urban designers of congestion as an amenity.     

Transportation network and venture capital mobility: An analysis of air travel and high-speed rail in China

Transportation network plays a critical role in reshaping the spatial geographical economy in terms of population, urban form, output and so on. But the impact of transportation on capital mobility is seldom revealed. Using venture capital investment events and airline network as well as high-speed rail network in China, this paper examines the effects of transportation network on capital mobility. We find that 1% decline in travel time will lead to increase in VC investment deals by 0.02. Heterogeneous results indicate that small firms, young firms, and emerging industries benefit more from the transportation network, which demonstrates the role of transportation network in alleviating information asymmetries. In addition, heterogeneity on VC flow direction offers suggestive evidence that transportation network is likely to alleviate development imbalance within wealthier cities while widening the gap between wealthier cities and poorer cities.     

What drives sustainable student travel? Mode choice determinants in the Greater Toronto Area

Examination of mode choice behavior is an important step in accurately predicting future travel demand. Despite having somewhat unique travel needs and challenges, there is a lack of knowledge in understanding the mode use behavior of university student population. The existing studies on university populations relied on a relatively smaller sample in investigating the behavior. Therefore, using world's largest university student's travel database, this study examines the factors affecting the mode choice behavior of a diverse university student population with student samples from four universities and their seven campuses located across the Greater Toronto Area (GTA) in Canada. Additionally, stratifying this diverse population using their attitudinal responses towards numerous travel modes, this study also estimates three additional mode choice models to obtain a more comprehensive understanding of how students in different markets, with different latent attitudes towards transportation, vary in terms of sustainable mode choice. A cluster analysis based on fourteen attitudinal responses, was conducted to stratify the sample whereas the popular multinomial logit approach was used to estimate the mode choice models. This study finds transit pass and bike ownership as important determinants that govern sustainable mode choice among the students in the region. The findings of this study could facilitate the sustainability offices at the four universities in making an informed policy decision in shifting the mode use behavior of students towards sustainable modes.     

Time-use data, analysis and modeling: toward the next generation of transportation planning methodologies

This paper argues that transportation planning methodologies must be built on the central thesis of the activity-based approach to travel demand modeling, namely, that travel is a derived demand that reflects people's desire and need to participate in activities. The paper discusses why this foundation for transportation planning methodologies is necessary to address contemporary planning and policy analysis issues. The paper also argues that the introduction of time-use data, analysis and modeling is a key element in the development of the next generation of transportation planning methodologies. Following a brief review of time-use studies, the paper discusses a number of planning and policy analysis areas in which time-use data will be of particular value, including the evaluation of induced or suppressed travel demand. The concepts advanced in the paper are illustrated with two brief numerical examples. These examples show how model systems based on time-use data can be used to (i) estimate the number of induced trips that would result from a reduction in commute travel time, and (ii) evaluate the impacts of alternative transportation improvement projects.     

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.     

A weighted logit freight mode-choice model

The paper describes the estimation of a weighted discrete choice model applicable for analysing choice of mode and crossing for freight in the Oresund region. The study finds that, by applying a weighted logit approach, it is possible to establish a suitable decoupling of agents and shipments. Moreover, by assessing preferences on the basis of a representative baseline OD matrix it is possible to better reflect the dependence between mode substitution effects and geography/infrastructure. The paper presents demand elasticities with respect to monetary cost and travel time as well as value-of-time estimates for five modes and thirteen commodity groups.     

TRIBUTE: Trip-based urban transportation emissions model for municipalities

This research aims at developing modeling and scenario-comparison tools to explore the impacts of various transportation and land use planning policies on changing travel behavior and eventually greenhouse gas (GHG) emissions. A Trip-Based Urban Transportation Emissions (TRIBUTE) model is developed. Data required for TRIBUTE comes from household travel surveys and emissions inventories, which is a major advantage in cases where a detailed transportation network model is unavailable. TRIBUTE is composed of two main parts: a mode choice model and an emissions forecasting model. The mode choice model is responsible for estimating modal shares of alternative modes of travel in response to changes in personal, modal, and land use attributes. The emissions forecasting model translates the modal shares into vehicle kilometers traveled, and subsequently GHG emissions. TRIBUTE is a macroscopic model intended to assist municipalities evaluate alternative transportation and land use policy scenarios and eventually select the one(s) that help them meet their future GHG emission targets. This paper reports on the conceptual framework of the developed model and presents a case study.     

Space-time dynamics: A modeling approach for commuting departure time on linked datasets

Commuters' departure time related decisions are important in time geography. Analytic tools have been proposed to capture the inherent choice determinants both in time and space. Although the dynamic aspects of the problem have been identified, most of the existing studies are based on static models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters' departure time choices and the evolution of en route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters' reaction to changes in travel time and traffic conditions over time. A regional household travel survey is linked to travel information obtained from the Google Maps application program interface (API), creating a synthetic longitudinal dataset. Two decision rules are applied to model commuters' response to the evolution of traffic. The results indicate that travel time, distance to work location, flexibility in working schedule, expected arrival time, and commuters' sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. Both the dynamic model and the proposed data linkage method contribute to the understanding of human activities in space and time and can be used to enhance transportation demand analysis and urban policy studies.     

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.     

Drone service response: Spatiotemporal heterogeneity implications

Unmanned Aerial Vehicles, often called drones, have rapidly emerged for commercial and personal use in recent years. Drones are a promising and effective transportation mode because they can travel faster than traditional ground-based vehicles, particularly when obstacles limit quick response or in cases of congestion. An important consideration for drones is that travel times are impacted in various ways by real-time local conditions, including weather and terrain. While goods and supplies can be acquired at more traditional outlets (e.g., stores, warehouses, restaurants, hospitals, fire stations, etc.), drones are being increasingly relied upon to extend access, particularly for special services associated with food, drug, and equipment delivery. The reason is that they can reliably access almost anywhere, providing quick response without the need for more expensive (and larger) vehicles that are restricted to congested roadways. How to locate drone base stations and allocate service in order to optimize overall response is a challenging task, especially given spatiotemporal heterogeneity in distributed demand and service response times/costs that can vary over a day. This paper introduces an extension of p-median problem to aid in the deployment of a drone system that accounts for continuous planar travel costs. Results show that drone travel times can be significantly reduced across a region. A key feature in this work is the representation of both demand and flight trajectories across a continuous terrain.     

Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system

In recent years, there has been increasing attention on bicycle-sharing systems (BSS) as a viable and sustainable mode of transportation for short trips. However, due to the relatively recent adoption of BSS, there is very little research exploring how people consider these systems within existing transportation options. Given recent BSS growth around the world, there is substantial interest in identifying contributing factors that encourage individuals to use these systems. The current study contributes to this growing literature by examining BSS behavior at the trip level to analyze bicyclists’ destination preferences. Specifically, we study the decision process involved in identifying destination locations after picking up a bicycle at a BSS station, using a random utility maximization approach in the form of a multinomial logit model (MNL). The quantitative frameworks developed have been estimated using 2013 data from the Chicago’s Divvy system. In our modeling effort, we distinguish between BSS users with annual membership and short-term customers with daily passes. The developed model should allow bicycle-sharing system operators to plan services more effectively by examining the impact of travel distance, land use, built environment, and access to public transportation infrastructure on users’ destination preferences. Using the estimated model, we generated utility profiles as a function of distance and various other attributes, allowing us to represent visually the trade-offs that individuals make in the decision process. To illustrate further the applicability of the proposed framework for planning purposes, destination station-choice probability prediction is undertaken.