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The assessment of transport systems has traditionally focused on congestion and ridership as its core performance measures. These perspectives fail to account for the actual service people seek from the transport system—the ability to reach destinations. Recent studies have shifted to focus on accessibility as a performance indicator, but do not address the question whether the observed accessibility is sufficient for meeting people's daily needs. This paper contributes to the accessibility literature by (1) applying a people-centered approach to the performance assessment of transit systems and (2) exploring the factors explaining the differences in performance between regions. The paper proposes the Accessibility Sufficiency Index (ASI) as a performance standard. The ASI is based on a sufficiency threshold representing an accessibility level that is assumed to enable adequate access to destinations. The paper uses neighborhood transit job accessibility data to calculate separate ASI scores for different sufficiency thresholds for 49 large US metropolitan areas. Regression analyses show that transit system performance is shaped most strongly by transit vehicle revenue miles, mixed land uses, and activity centering. Importantly, the size of these effects varies by the employed sufficiency threshold, suggesting that transportation and land use factors affect transit performance at different spatial scales. The results have implications for the ways we evaluate transport and transit systems and for our understanding of the factors that affect their performance. [ABSTRACT FROM AUTHOR]

We compare responses from an online survey among 700 customers of transportation network companies (TNC) in Boston and Philadelphia to investigate TNC's impact on vehicle ownership, trip making, and mode choice. We first use a qualitative comparative analysis to examine changes in respondents' travel behavior and vehicle ownership after adopting TNC. We then use a random parameter logit regression analysis to investigate customers' preferences between transit and TNC based on a choice experiment. We find that in both cities, TNC allows customers, including those who currently do not own a car, to either delay purchasing a car or forgo a car altogether. TNC enables customers across income levels to take trips that they otherwise would not have taken. Meanwhile, TNC substitutes for more than complementing transit. The random parameter logit analysis indicates that when choosing between TNC and transit, individuals in both cities consider waiting time and overall travel time for transit to be more burdensome than those for TNC. Bostonians perceive the time spent walking to and from transit to be less burdensome, and the time spent traveling in vehicle to be more burdensome than do Philadelphians. Differences in built environment, mode share within transit systems, and income likely contribute to respondents' different values of time between the two cities. Our paper is the first to compare individual trade-off between transit and TNC in two cities with different urban settings and transit services. The findings have implications on transit service planning, station area improvements, parking regulations, and traffic management. [ABSTRACT FROM AUTHOR]

The Netherlands seems to exhibit the unique conditions that allow cycling on the country level instead of only the city level. Moreover, the national transit system seemingly provides one crucial condition: citizens use the train and cycling systems in an integrated manner, with combined bicycle-train transport recently demonstrating strong growth. Relatively little is known about bicycle-train users, i.e. the people who combine the bicycle and the train in a single trip. In this paper, we investigate their profiles and travel choices, in terms of the modes they choose for access and egress travel, their choice of stations, and their choice of type of bicycles. Studying this specific group can add to our understanding of the role of the train system in the success of cycling in the Netherlands, in turn helping improve policy transfer to metropolitan areas in other countries. In 2017, in cooperation with the Dutch National Railways, researchers surveyed a sample of train travellers, ultimately resulting in more than 3000 completed questionnaires. Descriptive analyses revealed that, compared to train travellers who do not or rarely cycle to/from train stations, bicycle-train users are on average more likely to be young people who are engaged in full-time employment or entrepreneurs, commute to work and hold university degrees. As for their cycling behaviour, bicycle-train travellers use bicycles much more often on the home-end of train trips than on the activity-end. Furthermore, bicycle-train travellers infrequently use suburban stations on the home-end, preferring large stations in the centres of major cities instead. For those who use bicycles, shared bicycles claim a considerable share on the activity-end of a train trip. [ABSTRACT FROM AUTHOR]

We examine the relationship between transportation access on the one hand and individuals' employment and labor earnings on the other. We improve on existing studies by bringing a large national panel data set to bear on this question, attempting to disentangle the mechanisms by which individuals improve their economic standing and, finally, comparing the economic benefits to the direct costs of car ownership. To do this, we use nine waves from the Panel Study of Income Dynamics from 1999 to 2015. We find that access to a car is a strong predictor of future economic benefit for individuals, and that at very high levels of transit access, carless individuals can also fare equally well. Access to an automobile is strongly associated with employment, job retention, and earning more money over time. Though having a car is associated with economic benefits, owning and operating a car is expensive; yet, our findings suggest that the benefits may outweigh the costs for most people living outside neighborhoods with truly excellent transit service. [ABSTRACT FROM AUTHOR]

Understanding the link between mode choice and travel satisfaction is essential for promoting sustainable travel by expanding utility theory to include also the eudaimonic value of travel. The study focuses on the hypothesis that more then it's functional value of arriving from A to B, mode choice creates travel experiences that answer high-order needs such as relatedness, autonomy and competence. This study enhances the framework for representing travel mode choice by incorporating the model of human needs as the missing link between mode choice and travel satisfaction. By developing and analysing a large-scale survey from the Greater Copenhagen Area in Denmark, this study empirically proves that commuting mode choice relates to travel satisfaction by answering functional, relatedness and growth needs. The Greater Copenhagen area represents a region where transit, bicycle and car each have large modal shares, hence enabling to validate the approach in a multi-modal environment. Higher bicycle satisfaction relates positively to cycling self-concepts and self-efficacy and negatively to car self-concepts. Greater car use satisfaction increases with car self-concepts and transit use difficulties, and decreases with functional difficulties in car use and better cycling self-efficacy. Higher transit satisfaction mainly relates to experiencing difficulties with other modes. These insights can be used when formulating transport policies and prioritising resources aimed at achieving sustainable mobility patterns. [ABSTRACT FROM AUTHOR]

The optimization model proposed in this paper is aimed to assist city councils (or other transport authorities) in the planning of integrated transit-parking policies. The objective is to minimize the joint operating deficit of the transit and parking operators while ensuring given minimum levels of (motorized) mobility in a city. The key decision variables are transit fares and parking fees. The impact of price changes on transit and car modal shares are described by logit functions of the generalized travel costs. The practical utility of the model is illustrated with a case study involving a midsize city in Portugal. [ABSTRACT FROM AUTHOR]

Travel behavior change has become an area of interest in many cities around the world, particularly to encourage people to change from car use to transit use. Previous research indicates that habits can act as a barrier to travel behavior change and that new use of transit can be associated with some negative characteristics such as increased anxiety and difficulty with wayfinding. However, thus far little research has focused on gaining an in-depth understanding of the process of travel behavior change from the perspective of the new transit user. The present research seeks to fill this gap through a rich qualitative exploration of the process of undertaking 'new' (to the participants) journeys on transit, how this experience differs from familiar or habitual travel, and the process of learning and habituation that is undertaken as unfamiliar travel is repeated and evolves into familiar travel. This is achieved by using Grounded Theory to collect and interpret data from 30 semi-structured interviews where participants described both familiar and unfamiliar trip experiences in their own words. The process of undertaking unfamiliar transit travel is characterized by a number of barriers and obstacles and it is characterized by uncertainty and anxiety. If unfamiliar travel is repeated, a change process occurs which includes a number of cognitive processes and adaptations to streamline the process of travel. From the findings, a number of recommendations to support long term travel behavior change are identified. [ABSTRACT FROM AUTHOR]

Practitioners often use demand models to predict how neighborhood-level land use, infrastructure, demographic, and other changes may impact transportation systems. Few of the models available to predict automobile, transit, bicycle, and pedestrian travel are based on easily-accessible data, which creates barriers for transportation agencies with limited data or modeling resources. We help fill this gap by developing a fractional multinomial logit model that estimates United States neighborhood work commute mode shares using existing, nationally-available data from 5000 randomly-selected, non-adjacent census tracts. After controlling for socioeconomic characteristics, the model shows that public transit, walk, and bicycle commuting are associated with higher population and employment density, more housing constructed prior to 1940, and more rental housing. Public transit and walk commuting are associated with being in a Northeastern state, automobile commuting is associated with being in a Southern state, and bicycle commuting is associated with being in a West Coast state. Validation of the model using a separate set of 1000 census tracts and application of the model in the Milwaukee metropolitan region show promise but also highlight several limitations of this approach. This sketch-planning model is a building block for future practically-oriented neighborhood commute mode share models. [ABSTRACT FROM AUTHOR]

Given the wide application of automatic fare collection systems in transit systems across the globe, smartcard data with on- and/or off-boarding information has become a new source of data to understand passenger flow patterns. This paper uses Nanjing, China as a case study and examines the possibility of using the data cube technique in data mining to understand space-time travel patterns of Nanjing rail transit users. One month of smartcard data in October, 2013 was obtained from Nanjing rail transit system, with a total of over 22 million transaction records. We define the original data cube for the smartcard data based on four dimensions—Space, Date, Time, and User, design a hierarchy for each dimension, and use the total number of transactions as the quantitative measure. We develop modules using the programming language Python and share them as open-source on GitHub to enable peer production and advancement in the field. The visualizations of two-dimensional slices of the data cube show some interesting patterns such as different travel behaviors across user groups (e.g. students vs. elders), and irregular peak hours during National Holiday (October 1st-7th) compared to regular morning and afternoon peak hours during regular working weeks. Spatially, multidimensional visualizations show concentrations of various activity opportunities near metro rail stations and the changing popularities of rail stations through time accordingly. These findings support the feasibility and efficiency of the data cube technique as a mean of visual exploratory analysis for massive smart-card data, and can contribute to the evaluation and planning of public transit systems. [ABSTRACT FROM AUTHOR]

Transportation agencies’ increased reliance on voter approved financing requires planners to better understand and address voters’ motivations to support ballot initiatives. These initiatives, which can mimic or subvert traditional planning processes, are also opportunities for voters to register backlash against planning initiatives. We examine the role of voters’ self-interest, as measured by personal travel patterns and their attitudes towards the impacts of various transportation policies, in predicting residents’ votes on two concurrent transport ballot measures in San Francisco, CA. Voters were asked to evaluate each initiative in terms of their personal, direct interest versus the interest of the city of San Francisco generally. We find that that drivers will vote against policies marketed to the public as benefiting drivers or defending “drivers’ rights” if they believe strongly in the benefits of alternative policies for non-users. Those who identified their interests as divergent from the city’s were more likely to vote against their perceived interest and for the city’s than to cast a vote consistent with their own interest. Among mode use patterns, only cycling significantly predicted votes: Frequent cycles voted as a cohesive and potentially decisive block in favor of transportation initiatives promoting non-automobile modes. [ABSTRACT FROM AUTHOR]

In the current economic climate, the British Government is revising a whole range of policy sectors to highlight areas where savings and cuts can be made. The policy of subsidising home-to-school transport for pupils who live beyond a set distance from the school which they attend has been in place since 1944 and this policy costs local authorities in England over £1 billion a year. The aim of this paper is to examine the outcomes of policy choices facing Government relating to subsidising the transport of pupils travelling between home and school. Specifically, the paper employs a multilevel modelling technique to develop a series of relationships between bus usage by school and the level of spending by local education authorities on home-to-school bus travel provision while controlling for other factors such as school quality, land-use patterns and various proxies for household incomes. The results suggest that there is a differential effect of funding on the total school-level bus mileage for primary (aged <11), secondary (aged 11-16) and post 16 schools. It is found that if local authority school budgets for bus travel provision were removed, then school-level bus mileage in England would decrease by 16, 27 and 10 % for primary, secondary and post 16 schools respectively. It is hoped that the results of the study will help inform practitioners and policy makers to select the policy responses that are most appropriate. [ABSTRACT FROM AUTHOR]

Bicycle usage can be affected by colder weather, precipitation, and excessive heat. The research presented here analyzes the effect of weather on the use of the Washington, DC, bikeshare system, exploiting a dataset of all trips made on the system. Hourly weather data, including temperature, rainfall, snow, wind, fog, and humidity levels are linked to hourly usage data. Statistical models linking both number of users and duration of use are estimated. Further, we evaluate trips from bikeshare stations within one quarter mile of Metro (subway) stations at times when Metro is operating. This allows us to determine whether Metro serves as a back-up option when weather conditions are unfavorable for bicycling. Results show that cold temperatures, rain, and high humidity levels reduce both the likelihood of using bikeshare and the duration of trips. Trips taken from bikeshare stations proximate to Metro stations are affected more by rain than trips not proximate to Metro stations and less likely when it is dark. This information is useful for understanding bicycling behavior and also for those planning bikeshare systems in other cities. [ABSTRACT FROM AUTHOR]

In recent years, transit planners are increasingly turning to simpler, faster, and more spatially detailed 'sketch planning' or 'direct demand' models for forecasting rail transit boardings. Planners use these models for preliminary review of corridors and analysis of station-area effects, instead of or prior to four-step regional travel demand models. This paper uses a sketch-planning model based on a multiple regression originally fitted to light-rail ridership data for 268 stations in nine U.S. cities, and applies it predictively to the Phoenix, Arizona light-rail starter line that opened in December, 2008. The independent variables in the regression model include station-specific trip generation and intermodal-access variables as well as system-wide variables measuring network structure, climate, and metropolitan-area factors. Here we compare the predictions we made before and after construction began to pre-construction Valley Metro Rail predictions and to the actual boardings data for the system's first 6 months of operations. Depending on the assumed number of bus lines at each station, the predicted total weekday ridership ranged from 24,767 to 37,907 compared with the average of 33,698 for the first 6 months, while the correlation of predicted and observed station boardings ranged from r = 0.33 to 0.47. Sports venues, universities, end-of-line stations, and the number of bus lines serving each station appear to account for the major over- and under-predictions at the station level. [ABSTRACT FROM AUTHOR]

Although it is apparent that providing useful information has a positive effect on transit riders, no studies to date have investigated bus operators' reactions to real-time arrival information and other potential rider information tools. In this study, the project team surveyed 253 bus operators to determine their views and values concerning the existing use of real-time information and to ask about future transit rider information applications. Almost all operators (93 and 91 % on two separate questions) were positive or neutral to the provision of real-time information. In addition, operators were receptive to building other new information applications, with all applications in the survey being supported by at least 60 % of the bus operators. The two most widely supported potential applications in the survey were additional tools to help blind and deaf-blind riders (89 % of bus operators favored) and an application that would aid riders in identifying physical stop, shelter and bus issues such as graffiti, broken parts or a need for lights (88 % of bus operators). Applications displaying data about past performance or current bus capacity received the least support (66 and 61 % respectively). This research gives a better understanding of the impact of rider information tools on bus operators, including the views and values of the operators, and the harms and benefits of such tools. [ABSTRACT FROM AUTHOR]

This paper moves beyond traditional models of car ownership in that we propose a framework for modeling household-level decisions to acquire specific types and numbers of mobility tools to fulfill the mobility needs of household members. The framework is applied to a data set collected during the winter and spring of 2000/2001 in the German city Karlsruhe via an interactive web-based stated response survey in which respondents could optimize their household mobility tool sets through on-line feedback concerning the estimated costs of the sets. In our analysis, bivariate ordered probit models are estimated for three combinations of mobility tools: season tickets (i.e., transit passes) and cars, season tickets and small cars and season tickets and large cars. In all instances, strong substitution effects are found – that is, as the number of season tickets increases, the number of cars decreases. This finding underscores the need to move beyond simple models of car ownership to comprehensive models of mobility tool ownership. As demonstrated by our research, failure to do so is likely to lead to biased results. [ABSTRACT FROM AUTHOR]

In the United States, federal funding for public transit often accounts for a large proportion of a local agency's budget, especially for capital investments. For this reason, local governments can be expected to plan a portfolio of projects that maximize federal contributions. This study examines the financial effects of federal transit subsidy policy on local transit investment decisions. Data from a System Planning Study for the Geary Corridor in San Francisco are used as an illustration. It is found that federal transit subsidy policy provides financial incentives for local decision-makers to select capital-intensive investment options that may not be efficient or effective. While federal financial incentives are not the only factor influencing local investment decisions, some reform of the current subsidy policy may be necessary to reduce the incentive for ineffective use of public resources. [ABSTRACT FROM AUTHOR]

This article reports on a field investigation into the ways that transportation agencies use quantitative and qualitative information for making strategic decisions regarding airport ground access. The study analyzes the value of this information for planning airport ground access improvements at seven major international airport sites.The major finding of the research is that quantitative modeling for strategic decision support is very difficult, costly and time consuming. Modelers are confident that the models are accurate and reliable but executives generally lack confidence in the results. Transportation officials believe that the information supplied is flawed by a number of defects that minimize its value for strategic decision makers. The information defects described in this article provide an analysis of the structural difficulty of using quantitative modeling for transportation problems of strategic importance. To date, qualitative information is not frequently used, but some transportation agencies are considering its application to designing transportation services. Although this study is limited to airport ground access, the authors feel that this evidence, in conjunction with the evidence from other studies in the transportation area, dictates a need for wariness in the development of decision support systems for transportation planners. Developers of decision support systems for transportation planners must be aware of modeling costs and defects and consider how to improve the timeliness, relevance and credibility of information quantitative models provide transportation executives. Fundamentally it is important to recognize that decision makers tend, either singularly or in concert with other individuals or groups, to be the champions of a long-term vision for the community. When modeling produces inconsistent or wide ranging results that contradict their position, decision makers may not only discard modeling activities, but lose confidence in the models altogether. As a consequence, transportation planners are faced with the challenge of how to improve quantitative modeling. The most reliable and effective means for improvement is incorporation of qualitative techniques which provide greater understanding of customer perceptions and human behavior. [ABSTRACT FROM AUTHOR]

This study explores the worth consumers place on mode-destination accessibility for the AM journey to work trip. To accomplish this, a multinomial mode-destination choice model is estimated and the denominator of the specified logit model is used as an estimate of mode-destination accessibility. To improve the interpretability of this measure, compensating variation is then applied to convert the mode-destination accessibility to units of dollars per AM journey to work trip. The model is estimated using travel survey data from the Puget Sound Region in Washington state. It is reasonable to assume, for example, that the worth placed on mode-destination accessibility varies by mode, by destination, and by market segment (e.g., low income, high income). Less intuitive, however, are the magnitude and direction of these variations. This paper presents a methodological approach, followed by an empirical evaluation, for examining the worth of journey to work mode- destination accessibility. The results have important policy implications and also provide a mechanism for incorporating a monetary value for accessibility in future cost-benefit analyses. [ABSTRACT FROM AUTHOR]

Recent federal legislation sets tough air quality goals for the nation but offers scant guidance to urban areas responsible for meeting those goals. Traditionally, transportation-related air quality policy emphasized cleaner vehicle and fuels technologies and alternatives to single-occupant vehicle travel such as car-pooling and mass transit. Analysis suggests, however, that meeting air quality goals will require the addition of policies to manage the growing demand for transportation. The introduction of market forces into transportation supply and demand decisions would support traditional transportation and air quality strategies, and produce additional mobility, air quality, and economic benefits. Recent transportation legislation offers states and localities the flexibility to meet mobility and air quality goals in an innovative, nontraditional manner. Drawing on theoretical analysis and recent empirical evidence we offer a package of measures for the consideration of state, and federal policy-makers. [ABSTRACT FROM AUTHOR]

This paper studies whether the many public urban transit providers in the San Francisco Bay Area could realize cost economies by consolidating into larger systems. In this discussion, any benefits of rationalization are excluded, since these could be achieved without organizational changes. The results support the formation of larger multi-modal systems in the Bay Area. But care must be taken in selecting the candidates for reorganization, and a broad-brush approach could result in important lost opportunities for cost reduction. But if these economies are taken into account, and transit grows in the right directions, the benefits could be substantial. [ABSTRACT FROM AUTHOR]

At the beginning of the 1980s, it seemed that a consensus was emerging among researchers that public transport service levels, fares or quality had a small but possibly important effect on car ownership, and that this should be taken into account for those purposes where car ownership forecasts are used for developing policy on public transport or roads. Somehow, the findings faded away, and had little effect on policy thinking or on analytical techniques. The results of six surveys carried out in South Yorkshire over the period 1972 to 1991, during which there were extreme changes in public transport policy, tend to support a prima facie case for reopening the question. At the household level, car ownership rates are shown to be more volatile than is often assumed, especially in multiple car households. This volatility provides a context within which the quality of public transport provision appears to influence both the level of car ownership and the relationship between changes in the level of car ownership and changes in public transport use. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe the development of a model designed to predict changes to ridership resulting from small changes in the service provided by a given bus line. The paper describes the methodology used and rationale for the methodology; the manipulation of data required to produce the model and for use with the model; and the models themselves. A brief description is provided of some results from hypothetical service changes. Because actual changes and their consequent ridership changes were not available to the author, only the hypothetical results can be reported. The model was intended to be able to respond to the following types of service changes at a route level, by service period and route type; including each of changes in headways by service period, elimination of service in a time period, route extensions and new routes, route shortenings and short-lines, changes in the service period (i.e., addition or elimination of a trip within a service period to extend or shorten it), and the combined effect of any two or more such actions. The primary output from the model was designed to be the change in boardings on the route, and possibly on other routes that would be impacted through transfer patterns. [ABSTRACT FROM AUTHOR]