Towards a more equitable distribution of resources: Using activity-based models and subjective well-being measures in transport project evaluation

In this paper, we develop an innovative and comprehensive transport evaluation criterion to better account for equity considerations in transport project evaluation. This work explores transportation benefits from the consumer’s perspective to accessibility as a key benefit generated by any transportation project. To assess the full benefits of transportation project implementation for various consumers and calculate the improvement in accessibility, it is best to use Activity-Based Models (ABM). ABMs have two important advantages for equity analysis, which have not been utilized in the literature so far: first, ability to analyze results by various groups of the population; second, these models can utilize the Activity Based Accessibility (ABA) measure to estimate the overall benefits from transport investments and policies. The ABA measure allows one person to have different accessibilities for different choice situations, depending on his/her characteristics. We suggest including social and spatial factors in social welfare assessment by introducing the concept of accessibility gains to key social activities. Specifically, it is suggested to incorporate subjective well-being consideration into a new evaluation framework “Equity Benefit Analysis” (EBA). we use an alternative measure, “Subjective Value of Accessibility gains” (SVOA), which is based on the ABM accessibility measure as well as on Subjective Well-Being (SWB) measure, as the key benefit taken into account in the evaluation process. The SVOA is not intended to replace the current practice of analyzing equity by comparing various impacts on different groups of the population, but can aid by providing policymakers with a single measure advancing both equity and efficiency considerations and facilitating comparison among alternatives. Initial case study results indicate the SVOA can show higher benefits to policies focusing on the needs of vulnerable social groups that compared traditional measures.     

Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

The first analytical stochastic and dynamic model for optimizing transit service switching is proposed for “smart transit” applications and for operating shared autonomous transit fleets. The model assumes a region that requires many-to-one last mile transit service either with fixed-route buses or flexible-route, on-demand buses. The demand density evolves continuously over time as an Ornstein-Uhlenbeck process. The optimal policy is determined by solving the switching problem as a market entry and exit real options model. Analysis using the model on a benchmark computational example illustrates the presence of a hysteresis effect, an indifference band that is sensitive to transportation system state and demand parameters, as well as the presence of switching thresholds that exhibit asymmetric sensitivities to transportation system conditions. The proposed policy is computationally compared in a 24-hour simulation to a “perfect information” set of decisions and a myopic policy that has been dominant in the flexible transit literature, with results that suggest the proposed policy can reduce by up to 72% of the excess cost in the myopic policy. Computational experiments of the “modular vehicle” policy demonstrate the existence of an option premium for having flexibility to switch between two vehicle sizes.     

A mathematical programming technique for matching time-stamped records in logistics and transportation systems

Time-stamped data for transportation and logistics are essential for estimating times on transportation legs and times between successive stages in logistic processes. Often these data are subject to recording errors and omissions. Matches must then be inferred from the time stamps alone because identifying keys are unavailable, suppressed to preserve confidentiality, or ambiguous because of missing observations. We present an integer programming (IP) model developed for matching successive events in such situations and illustrate its application in three problem settings involving (a) airline operations at an airport, (b) taxi service between an airport and a train station, and (c) taxi services from an airport. With data from the third setting (where a matching key was available), we illustrate the robustness of estimates for median and mean times between events under different random rates for “failure to record”, different screening criteria for outliers, and different target times used in the IP objective. The IP model proves to be a tractable and informative tool for data matching and data cleaning, with a wide range of potential applications.     

The acceptability of road pricing in Vienna: the preference patterns of car drivers

The acceptability of road pricing has attracted considerable attention among researchers over the last decade, as is evident in the amount of literature about transport and environmental economics. The general conclusion from these studies has been that there is low acceptability for road pricing among car users. In this paper, we add more knowledge to the existing literature by conducting an acceptability study of road pricing in Vienna, where such a study has never been conducted before. We used a replication study approach where a previous approach used in the EU research project AFFORD (acceptability of fiscal and financial measures and organisational requirements for demand management) was replicated for Vienna and further supplemented with a conjoint analysis. In order to examining whether the Vienna study confirms previous findings. We investigated the acceptability of two concrete policy packages factors influencing this acceptability, and preference patterns that can be used in designing a road pricing policy for Vienna. The survey reveals a higher acceptability if road pricing schemes lead to perceived personal benefits. According to the multivariate analyses, the “personal outcome expectations”, “social norm” and “perceived effectiveness” variables account for more than 50 % of the criterion variance and therefore these are the most influential factors. Road pricing schemes can be an effective transport management instrument for a city particularly if they are associated with direct investment in public transport and public infrastructure. Thus, personal benefits can be perceived more easily and direct effects can be expected.     

The characteristics,uses and potentials of taxicab transportation

Taxicab transportation is a significant segment of urban transportation. Taxicabs, along with other “paratransit” type systems, provide service with characteristics between the automobile and mass transportation. Consequently, they are well suited to a number of special purposes. Taxicabs currently serve a wide range of trip purposes by travellers with varied socio-economic characteristics. Taxicab transportation is most attractive for serving lower density area and off-peak travel particularly where there is only minimal mass transit service. In this regard, taxicabs are a supplement to conventional mass transit. The use of taxicabs for collection and distribution functions for both passengers and freight is gradually being realized. The multiple use of taxicabs offers advantages of increasing taxicab productivity and reducing individual trip costs. Many of the problems related to taxicabs are regulatory and institutional in nature. Unless these constraints are eased or removed, wider application of taxicab transportation, including productivity gains, will be limited.     

Profit-based maritime container assignment models for liner shipping networks

We propose the problem of profit-based container assignment (P-CA), in which the container shipment demand is dependent on the freight rate, similar to the “elastic demand” in the literature on urban transportation networks. The problem involves determining the optimal freight rates, the number of containers to transport and how to transport the containers in a liner shipping network to maximize the total profit. We first consider a tactical-level P-CA with known demand functions that are estimated based on historical data and formulate it as a nonlinear optimization model. The tactical-level P-CA can be used for evaluating and improving the container liner shipping network. We then address the operational-level P-CA with unknown demand functions, which aims to design a mechanism that adjusts the freight rates to maximize the profit. A theoretically convergent trial-and-error approach, and a practical trial-and-error approach, are developed. A numerical example is reported to illustrate the application of the models and approaches.     

Factors influencing the choice of shared bicycles and shared electric bikes in Beijing

China leads the world in both public bikeshare and private electric bike (e-bike) growth. Current trajectories indicate the viability of deploying large-scale shared e-bike (e-bikeshare) systems in China. We employ a stated preference survey and multinomial logit to model the factors influencing the choice to switch from an existing transportation mode to bikeshare or e-bikeshare in Beijing. Demand is influenced by distinct sets of factors: the bikeshare choice is most sensitive to measures of effort and comfort while the e-bikeshare choice is more sensitive to user heterogeneities. Bikeshare demand is strongly negatively impacted by trip distance, temperature, precipitation, and poor air quality. User demographics however do not factor strongly on the bikeshare choice, indicating the mode will draw users from across the social spectrum. The e-bikeshare choice is much more tolerant of trip distance, high temperatures and poor air quality, though precipitation is also a highly negative factor. User demographics do play a significant role in e-bikeshare demand. Analysis of impact to the existing transportation system finds that both bikeshare and e-bikeshare will tend to draw users away from the “unsheltered modes”, walk, bike, and e-bike. Although it is unclear if shared bikes are an attractive “first-and-last-mile solution”, it is clear that e-bikeshare is attractive as a bus replacement.     

Ridesharing in North America: Past,Present, and Future

Since the late 1990s, numerous ridematching programmes have integrated the Internet, mobile phones, and social networking into their services. Online ridematching systems are employing a range of new strategies to create “critical mass”: (1) regional and large employer partnerships, (2) financial incentives, (3) social networking to younger populations, and (4) real-time ridematching services that employ “smartphones” and automated ridematching software. Enhanced casual carpooling approaches, which focus on “meeting places”, are also being explored. Today, ridesharing represents approximately 8–11% of the transportation modal share in Canada and the USA, respectively. There are approximately 638 ridematching programmes in North America. Ridesharing's evolution can be categorized into five phases: (1) World War II car-sharing (or carpooling) clubs; (2) major responses to the 1970s energy crises; (3) early organized ridesharing schemes; (4) reliable ridesharing systems; and (5) technology-enabled ridematching. While ridesharing's future growth and direction are uncertain, the next decade is likely to include greater interoperability among services, technology integration, and stronger policy support. In light of growing concerns about climate change, congestion, and oil dependency, more research is needed to better understand ridesharing's impacts on infrastructure, congestion, and energy/emissions.     

Car today,gone tomorrow: The ephemeral car in low-income,immigrant and minority families

Most transportation research in the United States uses cross-sectional, “snapshot” data to understand levels of car access. Might this cross-sectional approach mask considerable variation over time and within households? We use a panel dataset, the Panel Study of Income Dynamics (PSID), for the years 1999–2011 to test this question. We find that for most families, being “carless” is a temporary condition. While 13 % of families in the US are carless in any given year, only 5 % of families are carless for all seven waves of data we examine in the PSID. We also find that poor families, immigrants, and people of color (particularly, blacks) are considerably more likely to transition into and out car ownership frequently and are less likely to have a car in any survey year than are non-poor families, the US-born, and whites.     

Transition to a cyclable city: Latent variables affecting bicycle commuting

An understanding of the key factors influencing bicycle commuting is essential for developing effective policies towards a cyclable city. This paper contributes to this line of research by proposing a methodology for including cycling-related indicators in mobility surveys based on the Theory of Planned Behaviour (TPB), and applying an exploratory factor analysis (EFA) to evaluate the structure of latent variables associated with bicycle commuting. The EFA identified six cycling latent variables: Lifestyle, Safety and comfort, Awareness, Direct disadvantages, Subjective norm, and Individual capabilities. These were complemented with a latent variable related to habit: Non-commuting cycling habit. Statistical differences and regression analysis were applied with the cycling latent variables. The study also includes the relationship between objective factors and bicycle commuting, which reveals minor associations. This methodology was applied to the “starter cycling city” of Vitoria-Gasteiz (Spain). The results confirm that in this context – in transition to a cyclable city – safety and comfort issues are not the main barriers for all commuters, although more progress needs to be made to normalise cycling. A set of customised policy initiatives is recommended in the light of the research findings, including marketing campaigns to encourage non-commuting cycling trips, bicycle measures to target social groups as opposed to individuals, bicycle-specific programs such as “Bike-to-work Days”, and cycling courses.     

Efficiency and equity issues in special fees for financing transportation infrastructure

Theoretically, charges for building and operating facilities and services should be efficient (proportional to one's use of or benefit derived from the service) and equitable (proportional to one's ability to pay). A number of “innovative” financing techniques have been used recently, whereby the private sector (developers, property owners, businesses, and users) pays entirely or partially for the cost of building and operating transportation facilities or services. These techniques are particularly popular when the impact from the land use developments on the transportation system is such that substantial improvements are warranted in order to avoid serious congestion problems. The purpose of this paper is to investigate special fee assessments (a popular sub-set of “innovative” financing techniques) used for financing highway and mass transportation in order to determine their efficiency and equity implications. The need for such an investigation arises from the fact that everyone residing or doing business in a particular area does not benefit equally from a given transportation system, and neither is everyone able to contribute equally toward that system's building and operation. This paper presents some of the most commonly used special fee assessment techniques and suggests fee structures that are “optimal” on the basis of the benefits that contributors derive from the given transportation system, as well as their ability to pay for it.     

Travel mode choice among same-sex couples

Same-sex partnered individuals are far more likely to use transit, walk, and cycle, and to a lesser extent, use carpools than are people in straight couples. As society becomes more tolerant, gay and lesbian populations are an increasingly visible social group, yet they have received scant attention by transportation scholars. This paper builds on this nascent literature by documenting and attempting to explain these dramatic differences by controlling for factors known to influence mode choice.We perform two separate analyses employing two distinct datasets. The first analysis examines journey-to-work data from the American Community Survey. The second analysis focuses in specifically on non-motorized (walking, biking) travel using use self-reported walk and bike frequency from the 2009 National Household Travel Survey. In both, we find that characteristics of the neighborhoods in which gays and lesbians live, as well characteristics of the individuals themselves, only explain part of the increased propensity to use “alternative” modes of transportation; a strong residual effect remains.     

Transport effects of e-commerce: what can be learned after years of research?

This paper offers an extensive review of conceptual and quantitative studies on the implications of business-to-consumer (b2c) e-commerce on mobility. To create a more comprehensive understanding of the mobility implications we also discuss the complementary side: freight transport. Most studies conducted thus far have looked at the consequences of b2c e-commerce for either personal travel or goods movement, but not for both. The added value of this review article is that it not only explores the conclusions drawn in the wide-ranging published research, but also attempts to review the sampling strategies, definitions, assumptions and methodologies that lead to the diverse conclusions. For example, the paper discusses the differences in how “e-shopping” is defined (whether it includes browsing or only purchasing) and with what frequency a respondent e-shops (however it is defined) in order to be considered an “e-shopper”. The review describes how product differentiation is necessary to scrutinize the mobility effects of e-commerce. It points to studies which tend to have a dual conclusion. We try to observe whether complementary effects are given the same level of attention as substitution effects. Each of these factors can have sizable impacts on the quantitative conclusions reached. Our aim is that, by calling attention to these issues, the conclusions of studies will be discussed in a rigorous way to improve our knowledge of the transportation impacts of online shopping.     

Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing

In the process of rapid development and urbanization in Beijing, identifying the potential factors of carbon emissions in the transportation sector is an important prerequisite to controlling carbon emissions. Based on the expanded Kaya identity, we built a multivariate generalized Fisher index (GFI) decomposition model to measure the influence of the energy structure, energy intensity, output value of per unit traffic turnover, transportation intensity, economic growth and population size on carbon emissions from 1995 to 2012 in the transportation sector of Beijing. Compared to most methods used in previous studies, the GFI model possesses the advantage of eliminating decomposition residuals, which enables it to display better decomposition characteristics (Ang et al., 2004). The results show: (i) The primary positive drivers of carbon emissions in the transportation sector include the economic growth, energy intensity and population size. The cumulative contribution of economic growth to transportation carbon emissions reaches 334.5%. (ii) The negative drivers are the transportation intensity and energy structure, while the transportation intensity is the main factor that restrains transportation carbon emissions. The energy structure displays a certain inhibition effect, but its inhibition is not obvious. (iii) The contribution rate of the output value of per unit traffic turnover on transportation carbon emissions appears as a flat “M”. To suppress the growth of carbon emissions in transportation further, the government of Beijing should take the measures of promoting the development of new energy vehicles, limiting private vehicles’ increase and promoting public transportation, evacuating non-core functions of Beijing and continuingly controlling population size.     

Dynamic diurnal social taxonomy of urban environments using data from a geocoded time use activity-travel diary and point-based business establishment inventory

In this paper, we explore the diurnal dynamics of joint activity participation in a small city in Pennsylvania, USA, using behavioral data and an inventory of business establishments. We account for the variation caused by the collective impact of social, temporal and spatial choices of individuals to produce predicted space–time visualizations of activity participation. The focus is on how social contexts of an activity impact the temporal and spatial decisions regarding the activity locations and how this impact varies depending on activity types. A comparison across activity types and social interaction types is made among spatial patterns during a day. The CentreSIM dataset, which is a household-based activity diary survey collected in Centre County (Pennsylvania, USA) in 2003, provides very detailed social interaction information enabling the analysis of social, spatial and temporal aspects of activity participation. In this paper we use this information to develop a spatio-temporal interpolation method and demonstration based on kriging. In this way, we extract the dynamic social taxonomy of places from the behavioral information in the dataset and suggest how urban and transportation models can be informed from the dynamics of places by observing “what is taking place” (activities being pursued in the context of this paper) combined with “what exists” (business establishments) or “what is available” (businesses that are open). The method here can also be used to improve the design of urban environments (e.g., filling gaps in desired activity locations), manage specific places (e.g., extending the opening and closing times of businesses), study transportation policies that are sensitive to time of day (e.g., pricing of parking to discourage crowding and traffic congestion), and modeling of spatio-temporal decisions of social activities in travel demand models (e.g., to guide the development of model specification and representation of the space in which behavioral models are applied).     

Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data

Arrival processes are important inputs to many transportation system functions, such as vehicle prepositioning, taxi dispatch, bus holding strategies, and dynamic pricing. We conduct a comprehensive survey of the literature which shows that many transport systems employ basic homogeneous arrival process models or static nonhomogeneous processes. We conduct an empirical experiment to compare five state of the art arrival process short term prediction models using a common transportation system data set: New York taxi passenger pickups in 2013. Pickup data is split between 672 observations for model estimation and 96 observations for validation. From our experiment, we obtain evidence to support a recent model called FM‐IntGARCH, which is able to combine the benefits of both time series models and discrete count processes. Using a set of seven performance metrics from the literature, FM‐IntGARCH is shown to outperform the offline models—seasonal factor method, piecewise linear model—as well as the online models—ARIMA, Gaussian Cox process. Implications for operating data‐driven “smart” transit systems and urban informatics are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

The social context of informal commuting: Slugs, strangers and structuration

Despite considerable interest in the role of social interactions and social context on transportation, there have been very few attempts to explore specific cases of social interaction influencing transportation systems. This paper explores the social practice of slugging, an informal system of carpooling in the Washington, DC area. Slugging emerged in response to the establishment of Virginia’s High Occupancy Vehicle (HOV) lanes in the early 1970s, as single drivers picked up riders alongside the road (slugs) in order to meet the requirements for driving in the less congested HOV lanes. Drawing on the work of sociologist Anthony Giddens, as well as the sociological insights of Georg Simmel and Stanley Milgram, we suggest that the practice of slugging highlights the processes of institutionalization and structuration. This paper details how the region’s mass transportation policies and urban culture have combined to result in an institutionalized practice with particular norms and logics of behavior. We conclude that looking at specific cases where social context has affected transportation, like slugging, could provide useful insights on the impact of social context on transportation policies and systems.     

Uncertain socioeconomic projections used in travel demand and emissions models: could plausible errors result in air quality nonconformity?

A sensitivity analysis of plausible errors in population, employment, fuel price, and income projections is conducted using the travel demand and emissions models of the Sacramento, CA, USA, region for their transportation plan. The results of the analyses indicate that plausible error ranges for household income and fuel prices are not a significant source of uncertainty with respect to the region's travel demand and emissions projections. However, plausible errors in population and employment projections (within approximately one standard deviation) may result in the region's transportation plan not meeting the conformity test for nitrogens of oxides (NO_x) in the year 2005 (i.e., an approximately 16% probability). This outcome is also possible in the year 2015 but less likely (within approximately two standard deviations or a 2.5% probability). Errors in socioeconomic projections are only one of many sources of error in travel demand and emissions models. These results have several policy implications. First, regions like Sacramento that meet their conformity tests by a very small margin should rethink new highway investment and consider contingency transportation plans that incorporate more aggressive emissions reduction policies. Second, regional transportation planning agencies should conduct sensitivity analyses as part of their conformity analysis to make explicit significant uncertainties in the methods and to identify the probability of their transportation plan not conforming. Third, the US Environmental Protection Agency (EPA) should clarify the interpretation of “demonstrate” conformity of transportation plans; that is, specify the level of certainty that it considers a sufficient demonstration of conformity.     

Transportation modeling: Lessons from the past and tasks for the future

In spite of the recent progress made in household activity analysis and travel budget studies, urban transportation modeling still remains a “not-too-well developed” research field. There are conflicting theories, analysis units are not uniform, terms are not precisely defined, basic studies of sub-systems involved are not yet completed, and many models lack behavioral background as well as basic attributes such as simplicity, sensitivity, compatibility, transferability and forecasting ability. Gaps in methodology may be partially responsible for this situation. There is an urgent need for simple, yet not primitive, easily applicable urban transportation models which can respond to the technical needs of planners and engineers. Lessons from the past, as well as experiences from other disciplines, suggest that future research should concentrate on: (1) new, “unconventional” approaches based on systematic, basic studies of all sub-systems involved; (2) proper definition and stratification of an analysis unit; (3) revision and unification of definitions, classifications, etc., in order to improve the behavioral background of the models; (4) dynamic rather than static approaches, able to describe feedbacks between transportation and land-use as well as between transportation demand and supply; (5) interrelations between subsequent sub-models, particularly between car availability, trip generation and modal split; (6) developing models which are not only sensitive to transportation policies but also to other local policies (e.g. land use, city development, social, etc.).