Stated choice: a study in predictive validity using an aggregate truth set

Stated Choice models expand the ability of transportation planners to forecast future trends. The Stated Choice approach can forecast demand for new services or policies. However, Stated Choice models are subject to a range of experimental error not found within Revealed Preference (RP) designs. Primary among the concerns facing researchers is the ability of respondents to understand and operate upon hypothetical choice scenarios in a manner that will reproduce choices made under actual situations. These concerns are specified in the magnitude of a scaling factor. Efforts to estimate the scaling factor has proceeded by linking real decisions taken from a revealed preference survey with comparable decisions made under hypothetical conditions. However, where the alternative is new, actual decision data is not available. This study examines the level of error incorporated in a study where no RP data is available. The test of predictive validity focuses on the switching behavior of commuters at a single employment site. The actual data used to test the forecast is limited to company wide or aggregate ridership levels on the public transit mode taken two years after estimation of the SC model. The Fowkes and Preston hypothesis is examined and shown to bound the future actual value between forecasts derived from probabilistic and deterministic methods. The results show that with the passage of time, the probabilistic method approaches the reported ridership levels within 15 percent error.     

Development and validation of a direct mode choice model

A direct discrete mode choice model is introduced using relative attributes of competing modes as well as socioeconomic characteristics of travelers. The model is calibrated and validated for two available historic databases in the Dallas–Fort Worth region. The validation is conducted against the outputs of a current nested logit model used by the regional planning organization as well as the observed values based on transit ridership surveys for a newly inaugurated commuter rail service. The calibrated model is applied after the introduction of this new transit mode. The results show that the estimated mode shares by the proposed model have a statistically better consistency with the observed values than the estimates of the conventional nested logit model. Unlike the logit model, the structure of the direct model based on relative attributes also has the advantage of not needing recalibration each time a new travel mode is introduced. The model is found to be easier to calibrate and produces more accurate results than the nested logit model, commonly used by many metropolitan planning organizations.     

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers’ observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior.     

Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach

This paper assesses the demand for a flexible, demand-adaptive transit service, using the Chicago region as an example. We designed and implemented a stated-preference survey in order to (1) identify potential users of flexible transit, and (2) inform the service design of the flexible transit mode. Multinomial logit, mixed-logit, and panel mixed-logit choice models were estimated using the data obtained from the survey. The survey instrument employed a d_p-efficient design and the Google Maps API to capture precise origins and destinations in order to create realistic choice scenarios. The stated-preference experiments offered respondents a choice between traditional transit, car, and a hypothetical flexible transit mode. Wait time, access time, travel time, service frequency, cost, and number of transfers varied across the choice scenarios. The choice model results indicate mode-specific values of in-vehicle travel time ranging between $16.3 per hour (car) and $21.1 per hour (flexible transit). The estimated value of walking time to transit is $25.9 per hour. The estimated value of waiting time at one’s point of origin for a flexible transit vehicle is $11.3 per hour; this value is significantly lower than the disutility typically associated with waiting at a transit stop/station indicating that the ‘at-home’ pick-up option of flexible transit is a highly desirable feature. The choice model results also indicate that respondents who use active-transport modes or public transit for their current commute trip, or are bikeshare members, were significantly more likely to choose flexible and traditional transit than car commuters in the choice experiments. The implications of these and other relevant model results for the design and delivery of flexible, technology-enabled services are discussed.     

Forecasting ridership for a metropolitan transit authority

The recent volatility in gasoline prices and the economic downturn have made the management of public transportation systems particularly challenging. Accurate forecasts of ridership are necessary for the planning and operation of transit services. In this paper, monthly ridership of the Metropolitan Tulsa Transit Authority is analyzed to identify the relevant factors that influence transit use. Alternative forecasting models are also developed and evaluated based on these factors—using regression analysis (with autoregressive error correction), neural networks, and ARIMA models—to predict transit ridership. It is found that a simple combination of these forecasting methodologies yields greater forecast accuracy than the individual models separately. Finally, a scenario analysis is conducted to assess the impact of transit policies on long-term ridership.     

A simultaneous route-level transit patronage model: demand,supply, and inter-route relationship

In this paper we present a route-level patronage model that incorporates transit demand, supply and inter-route effects in a simultaneous system. The model is estimated at the route-segment level by time of day and direction. The results show strong simultaneity among transit demand, supply and competing routes. Transit ridership is affected by the level of service, which in turn is determined by current demand and ridership in the previous year. The model demonstrates that a service improvement has a twofold impact on ridership; it increases ridership on the route with service changes, but it also reduces the ridership on competing routes so that the net ridership change is small. The model is thus useful for both system-level analysis and route-level service planning.     

Policy Simulation for New BRT and Area Pricing Alternatives Using an Opinion Survey in Jakarta

Abstract

An area pricing scheme for Jakarta, Indonesia, is currently under review as a transportation control measure along with the operation of new bus rapid transit (BRT) system. While this scheme may be effective for congestion reduction in the central business district (CBD), provision of alternative means of transportation for auto users that are ‘pushed-out’ is of great importance to obtain public acceptance. Hence, it is necessary to simulate simultaneously the area pricing scheme and the BRT development which may serve as an alternative for assumed ‘pushed-out’ auto users. Utilizing data from an opinion survey, this paper studies how BRT and auto ridership are likely to vary as a function of traveler and system attributes. Additionally, the study attempts to evaluate the way this new travel mode is distinguished from other existing conventional transportation alternatives in Jakarta. The survey data contains socioeconomic information of over 1000 respondents as well as details of to-work/school trips to the CBD including mode, travel cost, time, etc. Respondents were asked about their willingness to shift from their current mode to BRT to make the same travel for different BRT fare levels. Modeling efforts suggest that a mixed logit model performs better in explaining choice behavior. Therefore, this model was used for policy simulation. The simulation results brought about many implications as to the tested policies. While the developed models may be applied only to future BRT corridors in which the survey was conducted, they capture the key variables that are significant in explaining mode choice behavior and present great potential for practical use in policy simulation and analysis in a large metropolitan area of the developing world.     

Values,attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice

Values lie at the heart of an individual’s belief system, serving as prototypes from which attitudes and behaviors are subsequently manufactured. Attitudes and behaviors may evolve over time, but values represent a set of more enduring beliefs. This study examines the influence of values on travel mode choice behavior. It is argued that personal values influence individual attitudes towards different alternative attributes, which in turn impact modal choices. Using data from a sample of 519 German commuters drawn from a consumer panel, the study estimates an integrated choice and latent variable model of travel mode choice that allows for hierarchical relationships between the latent variables and flexible substitution patterns across the modal alternatives. Results from the empirical application support the value-attitude-behavior hierarchical model of cognition, and provide insights to planners and policy-makers on how better to sell public transit as a means of travel.     

Mixed RP/SP models incorporating interaction effects

In order to analyse the impact of a new train service in Cagliari (Italy) a databank including information from a revealed preference (RP) and a stated preference (SP) survey was set up. The RP data concern choice between car, bus and train; the SP data consider the binary choice between a new train service (quicker, more frequent, with a lower fare and more stations than the current one) and the alternative currently chosen by car and bus users. Logit models allowing for correlation among RP alternatives were estimated for this mixed RP/SP data set using the artificial tree structure method. The analysis included level-of-service variables measured with an unusually high level of precision, latent or second order variables (such as comfort), inertia and interaction variables. Different specifications of the utility function were tested, including the expenditure rate model, and the effects of these specifications on modelling results are highlighted. Our results show that for a population mainly composed of fixed income workers, the expenditure rate model is superior to the traditional wage rate model, yielding lower and more significant subjective values of time. Moreover, we found that the non-linear specifications appear to be more suitable as not only better model results were obtained, but also the real distribution of the error terms was revealed (i.e. highlighting correlation among public transport options).     

Factors influencing the propensity to cycle to work

This paper describes the development of a mode choice model for the journey to work with special emphasis on the propensity to cycle. The model combines Revealed Preference (RP) and Stated Preference (SP) data to form a very large and comprehensive model. RP data from the National Travel Survey was combined with a specially commissioned RP survey. A number of SP surveys were also undertaken to examine the effects of different types of en-route and trip end cycle facilities and financial measures to encourage cycling.The development of the model is described in detail. The model was used to forecast trends in urban commuting shares over time and to predict the impacts of different measures to encourage cycling. Of the en-route cycle facilities, a completely segregated cycleway was forecast to have the greatest impact, but even the unfeasible scenario of universal provision of such facilities would only result in a 55% increase in cycling and a slight reduction in car commuting. Payments for cycling to work were found to be highly effective with a £2 daily payment almost doubling the level of cycling. The most effective policy would combine improvements in en-route facilities, a daily payment to cycle to work and comprehensive trip end facilities and this would also have a significant impact on car commuting.     

Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models

A growing base of research adopts direct demand models to reveal associations between transit ridership and influence factors in recent years. This study is designed to investigate the factors affecting rail transit ridership at both station level and station-to-station level by adopting multiple regression model and multiplicative model respectively, specifically using an implemented Metro system in Nanjing, China, where Metro implementation is on the rise. Independent variables include factors measuring land-use mix, intermodal connection, station context, and travel impedance. Multiple regression model proves 11 variables are significantly associated with Metro ridership at station level: population, employment, business/office floor area, CBD dummy variable, number of major educational sites, entertainment venues and shopping centers, road length, feeder bus lines, bicycle park-and-ride (P&R) spaces, and transfer dummy variable. Results from multiplicative model indicate that factors influencing Metro station ridership may also influence Metro station-to-station ridership, varied by both trip ends (origin/destination) and time of day. In comparison with previous case studies, CBD dummy variable and bicycle P&R are statistically significant to explain Metro ridership in Nanjing. In addition, Metro travel impedance variables have significant influence on station-to-station ridership, representing the basic time-decay relationship in travel distribution. Potential implications of the model results include estimating Metro ridership at station level and station-to-station level by considering the significant variables, recognizing the necessity to establish a cooperative multi-modal transit system, and identifying opportunities for transit-oriented development.     

Analysing Demand for Suburban Trips: A Mixed RP/SP Model with Latent Variables and Interaction Effects

We analyse mode choice behaviour for suburban trips in the Grand Canary island using mixed revealed preference (RP)/stated preference (SP) information. The SP choice experiment allowed for interactions among the main policy variables: travel cost, travel time and frequency, and also to test the influence of latent variables such as comfort. It also led to discuss additional requirements on the size and sign of the estimated model parameters, to assess model quality when interactions are present. The RP survey produced data on actual trip behaviour and was used to adapt the SP choice experiment. During the specification searches we detected the presence of income effect and were able to derive willingness-to-pay measures, such as the subjective value of time, which varied among individuals. We also studied the systematic heterogeneity in individual tastes through the specification of models allowing for interactions between level-of-service and socio-economic variables. We concluded examining the sensitivity of travellers’ behaviour to various policy scenarios. In particular, it seems that contrary to political opinion, in a crowded island policies penalising the use of the private car seem to have a far greater impact in terms of bus patronage than policies implying direct improvements to the public transport service.     

The impact of real-time information on bus ridership in New York City

In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and/or learn about the expected arrival time of their transit vehicles. Though these services are widely used, their impact on overall transit ridership remains unclear. The objective of this research is to assess the effect of real-time information provided via web-enabled and mobile devices on public transit ridership. An empirical evaluation is conducted for New York City, which is the setting of a natural experiment in which a real-time bus tracking system was gradually launched on a borough-by-borough basis beginning in 2011. Panel regression techniques are used to evaluate bus ridership over a three year period, while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. A fixed effects model of average weekday unlinked bus trips per month reveals an increase of approximately 118 trips per route per weekday (median increase of 1.7% of weekday route-level ridership) attributable to providing real-time information. Further refinement of the fixed effects model suggests that this ridership increase may only be occurring on larger routes; specifically, the largest quartile of routes defined by revenue miles of service realized approximately 340 additional trips per route per weekday (median increase of 2.3% per route). Although the increase in weekday route-level ridership may appear modest, on aggregate these increases exert a substantial positive effect on farebox revenue. The implications of this research are critical to decision-makers at the country’s transit operators who face pressure to increase ridership under limited budgets, particularly as they seek to prioritize investments in infrastructure, service offerings, and new technologies.     

Ridership estimation procedure for a transit corridor with new bus rapid transit service

Ridership estimation is a critical step in the planning of a new transit route or change in service. Very often, when a new transit route is introduced, the existing routes will be modified, vehicle capacities changed, or service headways adjusted. This has made ridership forecasts for the new, existing, and modified routes challenging. This paper proposes and demonstrates a procedure that forecasts the ridership of all transit routes along a corridor when a new bus rapid transit (BRT) service is introduced and existing regular bus services are adjusted. The procedure uses demographic data along the corridor, a recent origin–destination survey data, and new and existing transit service features as inputs. It consists of two stages of transit assignment. In the first stage, a transit assignment is performed with the existing transit demand on the proposed BRT and existing bus routes, so that adjustments to the existing bus services can be identified. This transit assignment is performed iteratively until there is no adjustment in transit services. In the second stage, the transit assignment is carried out with the new BRT and adjusted regular bus services, but incorporates a potential growth in ridership because of the new BRT service. The final outputs of the procedure are ridership for all routes and route segments, boarding and alighting volumes at all stops, and a stop‐by‐stop trip matrix. The proposed ridership estimation procedure is applicable to a new BRT route with and without competing regular bus routes and with BRT vehicles traveling in dedicated lanes or in mixed traffic. The application of the proposed procedure is demonstrated via a case study along the Alameda Corridor in El Paso, Texas. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of switching models from revealed preferences and stated intentions

This paper proposes a methodology for modeling switching behavior using simultaneously cross-sectional revealed preference (RP) data and stated intentions, a type of stated preference (SP) data. With explicit consideration of biases and random errors potentially contained in SP data, combined estimation with RP and SP data can exploit the advantages of both data sources. An empirical analysis of commuters' mode choice shows that the stated intention data have predictive validity if their biases and errors are properly corrected.     

The impact of fare and gasoline price changes on monthly transit ridership: Empirical evidence from seven U.S. transit authorities

This paper presents eight empirical models of monthly ridership for seven U.S. Transit Authorities. Within the framework of these models, the impacts upon monthly ridership from changes in the real fare and gasoline prices are examined. Important findings are: (1) the elasticities of monthly transit ridership with respect to the real fare are negative and inelastic, ranging from 0.042 to 0.62; and (2) the elasticities of monthly transit ridership with respect to the real gasoline price are positive and inelastic, ranging from 0.08 to 0.80. Such results have important policy implications for decisions based on the relationships of price, revenue, and ridership; and for assessing the impacts of changing gasoline prices upon urban modal choice.     

Structural equation modeling of user satisfaction of bus transit service quality based on stated preferences and latent variables

Few studies have been conducted on the service quality (SQ) of bus transit in developing countries. This paper presents a structural equation modeling (SEM) approach to identifying the relationships among major attributes that affect the SQ of bus transit in the city of Dhaka in Bangladesh. Specifically, 22 bus transit SQ attributes, drawn from 655 questionnaires, are used to develop different SEM models for the city. Along with stated preferences, the effect of three latent variables on SQ is analyzed. Among the developed models, the best model is selected by using different statistical approaches. With the best model, selected attributes are rated according to their relative importance on SQ. Acknowledging limited resources of a developing nation, this study gives a clear way ahead to planners, operating companies and transport managers to design appropriate transport policies which will ensure more effective services to current bus users as well as attracting new passengers.     

Examining the influence of multidestination service orientation on transit service productivity: a multivariate analysis

Between 1990 and 2000, U.S. transit agencies added service and increased ridership, but the ridership increase failed to keep pace with the service increase. The result was a decline in service effectiveness (or productivity). This marks the continuation of a long-running and often-studied trend. The scholarly literature attributes this phenomenon, at least in part, to transit agency decisions to decentralize their service rather than focus on serving the traditional CBD market. Many scholars argue that a decentralized service orientation is both ineffective and inefficient because it attracts few riders and requires large per-rider subsidies. This research tests whether a non-traditional, decentralized service orientation, called multidestination service, results in reduced service productivity. Contrary to what the literature suggests, we find that MSAs whose transit agencies pursued a multidestination service orientation did not experience lower productivity. These results indicate that policies that have encouraged the growth of decentralized transit services have not necessarily been detrimental to the industry.

Investigating preference heterogeneity in Value of Time (VOT) and Value of Reliability (VOR) estimation for managed lanes

This paper presents an empirical study in investigating user heterogeneity of Value of Time (VOT) and Value of Reliability (VOR). Combined Revealed Preference (RP) and Stated Preference (SP) data were used to understand traveler choice behavior regarding the usage of managed lanes (MLs). The data were obtained from the South Florida Expressway Stated Preference Survey, which focused on automobile drivers who had traveled on the I-75, I-95, or SR 826 corridors in South Florida. Mixed logit modeling was applied and indicated an average value of $13.55 per hour for VOT and $16.13 per hour for VOR. Potential sources of heterogeneity in user sensitivities to time, reliability, and cost were identified and quantified by adding interaction effects of the variables in the mixed logit model. The findings indicated that various socioeconomic demographic characteristics and trip attributes contributed to the variations in VOT and VOR at different magnitudes. The results of this study contribute to a better understanding on what attributes lead to higher or lower VOT and VOR and to what extent. These findings can be incorporated into the demand forecasting process and lead to better estimates and enhanced analytical capabilities in various applications, such as toll feasibility studies, pricing strategy and policy evaluations, and impact analysis.