Estimating heterogeneity of car travelers on mode shifting behavior based on discrete choice models

In order to understand the mode shift behavior of car travelers and relieve traffic congestion, a Stated Preference survey has been conducted in the city of Ji'nan in China to analyze bus choice behavior and the heterogeneity of car travelers. Several discrete choice models, including multinomial logit, mixed logit and latent class model (LCM) are developed based on these survey data. A comparative analysis indicates that the LCM has the highest precision and is more suitable to analyze the heterogeneity of car travelers. The LCM divides car travelers into three classes. Different classes have different sets of influencing factors in the model. Policy recommendations are also proposed for those classes to promote bus shift from car travelers based on the model results. Finally, sensitivity analysis on parking fees and fuel cost is carried out on the LCMs under different bus service levels. Car travelers have different sensitivities to the influencing factors. The conclusions indicate that the LCM can reflect the heterogeneity and preferences of car travelers and can be used to understand how to shift the behavior of car travelers and make more effective traffic policy.     

Transport reviews prize

This paper reviews the provision of public transport in the Cape Town Metropolitan Area and the proposals for its future development. A vitally distinguishing characteristic of public transport in Cape Town and other South African cities is the fractured market with great discrepancies between different segments. These range from those with high incomes and preferences similar to those found in typically First World countries to a great majority living at or below minimum poverty levels with virtually no choices and a very different set of needs and preferences. Although scope for improving service levels and satisfying user needs is unlimited, in South Africa resources for transport are facing severe competition from other macrolevel social and economic imperatives, such as combating crime and addressing discrepancies in health and education needs. This situation represents a huge challenge to those responsible for planning and developing public transport strategies. Evidence is presented that current public transport services are unsustainable in terms of increasing subsidy requirements while also not effectively meeting user needs. Proposals to restructure the public transport system and to use Stated Preference techniques to identify user needs are put forward. Given the fractured market for public transport in metropolitan Cape Town, Stated Preference techniques will require innovative adaptations and different approaches to those most commonly applied elsewhere in the world and this paper provides some broad guidelines.     

Valuation of improved railway rolling stock: A review of the literature and new evidence

The paper provides a comprehensive review of a large amount of previously unpublished British evidence about the valuation of new railway rolling stock and its effects on demand. An important conclusion is that incentives to bias Stated Preference responses and unfamiliarity with the rolling stock being considered have led to inflated values. This has broader implications for the use of Stated Preference techniques. Also provided is fresh empirical evidence for a range of different types of rolling stock and specific rolling stock attributes. A novel aspect of the research was the use of disaggregate Revealed Preference choice data. The estimated rolling stock values are much lower than those obtained from previous studies.     

Development and comparison of choice models and tolling schemes for high-occupancy/toll (HOT) facilities

A high-occupancy/toll (HOT) lane is an increasingly popular form of traffic management strategy which reserves a set of freeway lanes for HOVs and transit users, while allowing low-occupancy vehicles (LOVs) to enter for a fee. In turn, HOT lanes maintain a minimal level of service by regulating the volume of entering LOVs. The focus of this paper is how to model the choice process of individual drivers, which dictates the volume of LOVs that choose to pay and take the HOT lane. Such models and the insights they provide can be very helpful for the toll setting process. Two simple formulations (an all-or-nothing assignment and an additive logit model) are compared with a proposed formulation based on the population value of time (VOT) distribution. Both static and dynamic toll setting algorithms are studied based on the proposed lane choice model, and their performance is compared under deterministic traffic behavior.     

Stated preference methods and travel demand forecasting: An examination of the scale factor problem

Stated Preference choice models may have incorrect scale properties, because their coefficients incorporate the influence of factors which do not affect actual behaviour, and this paper examines the consequences of this scale factor problem for demand forecasting. Empirical evidence is presented which suggests that the scale factor problem is not serious and the results are generalised to consider the consequences of the scale factor problem in situations other than those prevailing in the empirical example. The paper also examines the predictive performance of three procedures for forecasting changes to an existing demand in the light of the scale factor problem. This indicates that reliance should not be placed on the incremental logit model and that a simple procedure which amends a standard logit model forecast in the light of its performance in explaining the observed initial demand is generally preferred to unadjusted logit model forecasts.     

Establishing value of time for the inter-island passenger transport of the Western Visayas region,Philippines

Transportation infrastructure planning process requires cost–benefit analysis in the evaluation of project proposals. Value of time (VOT) facilitates the conversion of travel time savings, which is a significant proportion of benefits in monetary terms. In cases where VOT has not been established, planners resort to crude estimates that often results in erroneous or biased measurements of benefits. This is the case of the Western Visayas region in the Philippines where transportation studies are rare. Secondary cities and its peripheral regions have often been overlooked subjects of transportation studies. In this study, multinomial logit models using revealed preference data were estimated to facilitate the calculation of the VOT. The total cost, square of the total cost, and total time were identified as significant explanatory variables affecting mode choice. The square of the total cost term was introduced in the models in order to account for income effect. Results indicate that VOT estimates for the inter-island passenger transportation between Iloilo and Negros Occidental generally range from 78.15PHP to 179.15PHP (1.91USD to 4.37USD) depending on trip and traveller characteristics.     

Stated choice valuation of urban traffic air pollution and noise

In this study environmental problems related to urban traffic are valued by Stated Choice. Stated Choice is a Stated Preference method in which non-market goods are assessed relatively to each other and not in absolute amounts as with the more common Contingent Valuation Method. The use of Stated Choice to estimate people’s willingness to pay to reduce environmental problems caused by road traffic can be seen as an extension of the established use of Stated Choice in transport research. Despite the fact that the results from this study have been used by the Norwegian Public Roads Administration in their cost benefit analyses since 1995, this paper points to the fact that considerable uncertainty remains with regard to (1) the area of validity of the results, (2) the impact on the valuations of interaction effects and other general methodological problems with Stated Preference methods, and (3) problems related to the complex choice situation of Stated Choice.     

Inconsistent choices in Stated Choice data;Use of the logit scaling approach to handle resulting variance increases

The scaling approach is a statistical estimation method that allows for differences in the amount of unexplained variation in different types of data, which can then be used together in the analysis. This approach has been mostly used in context of combining Stated Preference and Revealed Preference data, but has also been used as a method of identifying systematic differences in the variance of choices within a single Stated Preference data set, e.g. for investigation of learning and fatigue effects. This paper investigates whether a scaling approach is suitable for handling inconsistencies in Stated Choice data. Both the number of inconsistent choices, based on a test of violations of the transitivity axiom, and education are used as scaling variables. Scaling effects appear to exist due to inconsistent choices, and the amount of unexplained variance is shown to increase as the number of inconsistent choice increase. Scaling due to inconsistencies significantly improves the models and reduces the valuations of travel time. In addition, the scaling approach makes the valuations of travel time from the Stated Choice data more consistent with the valuations from Contingent Valuation data included in the same study. In spite of the fact that education is the only significant explanatory variable for the number of inconsistent choices, scaling due to education gives no significant improvement in the model.     

Pareto-improving congestion pricing and revenue refunding with multiple user classes

This study investigates Pareto-improving congestion pricing and revenue refunding schemes in general transportation networks, which make every road user better off as compared with the situation without congestion pricing. We consider user heterogeneity in value of time (VOT) by adopting a multiclass user model with fixed origin–destination (OD) demands. We first prove that an OD and class-based Pareto-improving refunding scheme exists if and only if the total system monetary travel disutility is reduced. In view of the practical difficulty in identifying individual user’s VOT, we further investigate class-anonymous refunding schemes that give the same amount of refund to all user classes traveling between the same OD pair regardless of their VOTs. We establish a sufficient condition for the existence of such OD-specific but class-anonymous Pareto-improving refunding schemes, which needs information only on the average toll paid and average travel time for trips between each OD pair.     

Passengers’ valuations of train seating layout,position and occupancy

The layout of seating within train carriages, of which there are numerous possibilities, and also the occupancy of that seating can be expected to impact on passengers’ experiences of a train journey. However, there is very little evidence on how rail passengers value different seating experiences. On the back of exploratory research, and including attitudinal evidence, this paper provides significant and original insights into rail passengers’ preferences in this area. The primary evidence base is a Stated Preference experiment, complemented by a novel Revealed Preference exercise that uses CCTV footage to observe where rail passengers prefer to sit.The valuations, expressed as travel time multipliers, obtained from the Stated Preference exercise are generally plausible and exhibit a wide range according to the precise seating configuration, the occupancy level and the seating position within a layout. The innovative Revealed Preference analysis provides an encouraging degree of support to the Stated Preference results which, reassuringly, can themselves be reconciled with related valuations widely used in the railway industry in Britain and also the findings of the exploratory and attitudinal research. Whilst we are interested here solely in obtaining valuations, we demonstrate how they can be used in demand forecasting.     

An investigation on the performances of mode shift models in transit ridership forecasting

This paper aims at investigating the over-prediction of public transit ridership by traditional mode choice models estimated using revealed preference data. Five different types of models are estimated and analysed, namely a traditional Revealed Preference (RP) data-based mode choice model, a hybrid mode choice model with a latent variable, a Stated Preference (SP) data-based mode switching model, a joint RP/SP mode switching model, and a hybrid mode switching model with a latent variable. A comparison of the RP data-based mode choice model with the mode choice models including a latent variable showed that the inclusion of behavioural factors (especially habit formation) significantly improved the models. The SP data-based mode switching models elucidated the reasons why traditional models tend to over-predict transit ridership by revealing the role played by different transit level-of-service attributes and their relative importance to mode switching decisions. The results showed that traditional attributes (e.g. travel cost and time) are of lower importance to mode switching behaviour than behavioural factors (e.g. habit formation towards car driving) and other transit service design attributes (e.g. crowding level, number of transfers, and schedule delays). The findings of this study provide general guidelines for developing a variety of transit ridership forecasting models depending on the availability of data and the experience of the planner.     

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.     

Carpooling and carpool clubs: Clarifying concepts and assessing value enhancement possibilities through a Stated Preference web survey in Lisbon, Portugal

The increase of urban traffic congestion calls for studying alternative measures for mobility management, and one of these measures is carpooling. In theory, these systems could lead to great reductions in the use of private vehicles; however, in practice they have obtained limited success for two main reasons: the psychological barriers associated with riding with strangers and poor schedule flexibility. To overcome some of the limitations of the traditional schemes, we proposed studying a carpooling club model with two main new features: establishing a base trust level for carpoolers to find compatible matches for traditional groups and at the same time allowing to search for a ride in an alternative group when the pool member has a trip schedule different from the usual one. A web-based survey was developed for the Lisbon Metropolitan Region (Portugal), including a Stated Preference experiment, to test the concept and confirm previous knowledge on these systems’ determinants. It was found through a binary logit Discrete Choice Model calibration that carpooling is still attached with lower income strata and that saving money is still an important reason for participating in it. The club itself does not show promise introducing more flexibility in these systems; however, it should provide a way for persons to interact and trust each other at least to the level of working colleagues.     

Capturing value of reliability through road pricing in congested traffic under uncertainty

Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.     

A Stated Preference Experiment for Measuring Service Quality in Public Transport

Abstract

This paper develops a Stated Preference (SP) experiment that provides a way to measure service quality in public transport. The paper introduces an empirical procedure for optimising the SP experiment. This procedure permits the identification of the choice alternatives defining the experiment by simulating the choices of a user sample. By using the data collected from an experimental survey, a Multinomial Logit model was calibrated. This model is a way of identifying the importance of service quality attributes on global customer satisfaction and calculating a Service Quality Index, which provides an operationally appealing measure of current or potential service effectiveness.     

Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles

Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nationwide online panel of 1260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3500 for partial automation and $4900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.     

Novel approaches to forecasting the demand for new local rail services

Revealed Preference(RP) studies based on actual behaviour suffer from a number of statistical problems. Furthermore, RP methods are of little use when the effects of a new or radically altered service need to be considered. As a result, for a case study of demand forecasting for new passenger rail services, a new approach has been developed. Our starting point is to seek what we call Stated Intentions (SI) responses as to the likely usage of a new rail service. However, due to a combination of systematic biases, these responses may be taken to be gross overestimates. A check on the biases of this SI data may be supplied by a Stated Preference (SP) survey. Respondents are asked to make hypothetical choices which are sufficiently complex for there to be little chance of policy bias. It is ensured that choices presented contain useful ‘boundary values,’ being the relative valuation for which respondents would be indifferent between two offered alternatives. It is, however, crucial to ensure that the SP survey is simple enough for respondents to manage, since excessive error variability in the responses will cause the calibrated coefficients to be rescaled, presenting problems for forecasting. From the SP surveys, it is estimated that SI data overstates usage of new rail services by around 50%, even if it is assumed that nonrespondents to the SI survey are nonusers. It is concluded that an SI/SP approach can potentially provide accurate forecasts, but there are a number of practical constraints that may prevent this.     

Economical welfare maximisation analysis: assessing the use of existing Park-and-Ride services

This paper assesses travellers’ responses to the use of existing Park-and-Ride (P&R) services based on an economical welfare maximisation approach. Specifically, the paper presents a modelling framework to estimate consumer surplus and producer surplus (business profits) on the basis of modal choice probabilities. The paper draws on evidence from Stated Preference surveys conducted around two P&R sites in Sapporo, Japan, where P&R services occupy a modest market space. Overall, the results suggest that business profit increases when economical welfare is maximised, as a consequence of increased demand. It is also shown that P&R choice is not only influenced by parking fees, but also by the fares and other attributes of alternative transportation modes. Accordingly, the interactions of P&R with alternative transportation modes should be taken into consideration in any strategic transportation policies oriented towards motivating sustainable transport mode choices.