Investigation and analysis of evidence of asymmetric churn in travel demand models

There is a large amount of research work that has been devoted to the understanding of travel behaviour and for the prediction of travel demand and its management. Different types of data including stated preference and revealed preference, as well as different modelling approaches have been used to predict this. Essential to most travel demand forecasting models are the concepts of utility maximisation and equilibrium, although there have been alternative approaches for modelling travel behaviour. In this paper, the concept of asymmetric churn is discussed. That is travel behaviour should be considered as a two way process which changes over time. For example over time some travellers change their mode of travel from car to bus, but more travellers change their mode from bus to car. These changes are not equal and result in a net change in aggregate travel behaviour. Transport planners often aim at producing this effect in the opposite direction. It is important therefore to recognise the existence of churns in travel behaviour and to attempt to develop appropriate policies to target different groups of travellers with the relevant transport policies in order to improve the transport system. A data set collected from a recent large survey, which was carried out in Edinburgh is investigated to analyse the variations in departure time choice behaviour. The paper reports on the results of the investigation.     

Choice of parking: Stated preference approach

Over recent years, parking policy has become a key element of transport policy in many countries. Parking policy measures can affect many different dimensions of travel behaviour but are likely to be most significant in terms of travellers' choice of parking type and location. This dimension of travel choice has, to date, received comparatively little attention, yet is of vital importance if we are to properly understand and predict the effects of parking policy measures.This paper presents two studies addressing this issue carried out in the United Kingdom and Germany. Both studies used a stated preference approach in order to collect disaggregate data on travellers responses to changes in parking attributes and used these data to build simple logit models of parking type choice. The studies were designed in order to allow comparable choice models to be estimated from the two datasets. The results obtained strongly indicate the need to separately identify the costs associated with different components of the parking activity (e.g., general in-vehicle time, parking search time, egress time) and also point to the existence of significant differences in the relative valuation of these components across different journey purposes. Where possible, the results of the choice modelling exercises are also compared with existing revealed and stated preference results and are found to be generally in line with prior expectations.     

Bayesian network-based formulation and analysis for toll road utilization supported by traffic information provision

Congestion pricing has been proposed and investigated as an effective means of optimizing traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies. Meanwhile, advanced traffic information dissemination systems, such as Advanced Traveler Information System (ATIS), have been developed and deployed to provide real-time, accurate, and complete network-wide traffic information to facilitate travelers’ trip plans and routing selections. Recent advances in ATIS technologies, especially telecommunication technology, allow dynamic, personalized, and multimodal traffic information to be disseminated and impact travelers’ choices of departure times, alternative routes, and travel modes in the context of congestion pricing. However, few studies were conducted to determine the impact of traffic information dissemination on toll road utilizations. In this study, the effects of the provisions of traffic information on toll road usage are investigated and analyzed based on a stated preference survey conducted in Texas. A Bayesian Network (BN)-based approach is developed to discover travelers’ opinions and preferences for toll road utilization supported by network-wide traffic information provisions. The probabilistic interdependencies among various attributes, including routing choice, departure time, traffic information dissemination mode, content, coverage, commuter demographic information, and travel patterns, are identified and their impacts on toll road usage are quantified. The results indicate that the BN model performs reasonably well in travelers’ preference classifications for toll road utilization and knowledge extraction. The BN Most Probable Explanation (MPE) measurement, probability inference and variable influence analysis results illustrate travelers using highway advisory radio and internet as their primary mode of receiving traffic information are more likely to comply with routing recommendations and use toll roads. Traffic information regarding congested roads, road hazard warnings, and accident locations is of great interest to travelers, who tend to acquire such information and use toll roads more frequently. Travel time formation for home-based trips can considerably enhance travelers’ preferences for toll road usage. Female travelers tend to seek traffic information and utilize toll roads more frequently. As expected, the information provided at both pre-trip and en-route stages can positively influence travelers’ preferences for toll road usage. The proposed methodology and research findings advance our previous study and provide insight into travelers’ behavioral tendencies concerning toll road utilization in support of traffic information dissemination.     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.     

Door-to-door travel times in RP departure time choice models: An approximation method using GPS data

A common way to determine values of travel time and schedule delay is to estimate departure time choice models, using stated preference (SP) or revealed preference (RP) data. The latter are used less frequently, mainly because of the difficulties to collect the data required for the model estimation. One main requirement is knowledge of the (expected) travel times for both chosen and unchosen departure time alternatives. As the availability of such data is limited, most RP-based scheduling models only take into account travel times on trip segments rather than door-to-door travel times, or use very rough measures of door-to-door travel times. We show that ignoring the temporal and spatial variation of travel times, and, in particular, the correlation of travel times across links may lead to biased estimates of the value of time (VOT). To approximate door-to-door travel times for which no complete measurement is possible, we develop a method that relates travel times on links with continuous speed measurements to travel times on links where relatively infrequent GPS-based speed measurements are available. We use geographically weighted regression to estimate the location-specific relation between the speeds on these two types of links, which is then used for travel time prediction at different locations, days, and times of the day. This method is not only useful for the approximation of door-to-door travel times in departure time choice models, but is generally relevant for predicting travel times in situations where continuous speed measurements can be enriched with GPS data.     

Modeling the impact of pre-trip information on commuter departure time and route choice

The objective of this paper is to investigate the impact of pre-trip information on auto commuters’ choice behavior. The analysis is based on an extensive home-interview survey of commuters in the Taichung metropolitan area in Taiwan. A joint model for route and departure time decisions with and without pre-trip information is formulated. The model specifications are developed for both the systematic and random components. In particular, econometric issues associated with specifying the random error structure are addressed for parameter estimation purposes. Insights into the effects of attributes are obtained through the analysis of the model's performance and estimated parameter values. A probit model form is used for the joint model, allowing the introduction of state dependence and correlation in the model specification. The results underscore the important relationship between the different characteristics and the propensity of commuter choice behavior under two scenarios, with and without pre-trip information.     

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.     

Activity–travel behaviour of non-workers from Bangalore City in India

This paper presents exploratory and statistical analyses of the activity–travel behaviour of non-workers in Bangalore city in India. The study summarises the socio-demographic characteristics as well as the activity–travel behaviour of non-workers using a primary activity–travel survey data collected by the authors. Where possible, the research also compares the analysis findings with the case studies on activity–travel behaviour of non-workers, carried out in developed and developing countries. This gives an opportunity to understand the differences/similarities in the activity–travel behaviour of non-workers across diverse socio-cultural settings. The preliminary exploratory analysis shed light on the differences in activity participation, trip chaining, time-of-day preference for trip departure, and mode use behaviour of non-workers in Bangalore city. Statistical models were developed for investigating the effects of individual and household socio-demographics, land use parameters, and travel context attributes on activity participation, trip chaining, time-of-day choice, and mode choice decisions of non-workers. A few important results of the analysis are the influence of viewing television at home on out-of-home activity participation and trip-chaining behaviour, and the impact of in-home maintenance activity duration on time-of-day choice. Further, based on the findings of the initial analyses, an attempt has been made in this study to develop an integrated model that links time allocation, time-of-day choice, and trip chaining behaviour of non-workers. The study also discusses the implications of the research findings for transportation planning and policy for Bangalore city.     

Enhancing stated preference surveys in transportation research: the contribution of geographic information systems

In this paper we argue that visualization, data management and computational capabilities of geographic information systems (GIS) can assist transportation stated preference research in capturing the contextual complexity of many transportation decision environments by providing respondents with maps and other spatial and non-spatial information in graphical form that enhance respondents' understanding of decision scenarios. We explore the multiple inherent contributions of GIS to transportation stated preference data collection and propose a framework for a GIS-based stated preference survey instrument. We also present the design concepts of two survey prototypes and their GIS implementation for a sample travel mode choice problem.     

Modeling Travel Time Under ATIS Using Mixed Linear Models

The objective of this paper is to model travel time when drivers are equipped with pre-trip and/or en-route real-time traffic information/advice. A travel simulator with a realistic network and real historical congestion levels was used as a data collection tool. The network included 40 links and 25 nodes. This paper presents models of the origin-to-destination travel time and en-route short-term route (link) travel time under five different types and levels of advanced traveler information systems (ATIS). Mixed linear models with the repeated observation's technique were used in both models. Different covariance structures (including the independent case) were developed and compared. The effect of correlation was found significant in both models. The trip travel time analysis showed that as the level of information increases (adding en-route to the pre-trip and advice to the advice-free information), the average travel time decreases. The model estimates show that providing pre-trip and en-route traffic information with advice could result in significant savings in the overall travel time. The en-route short-term (link) travel time analysis showed that the en-route short-term (link) information has a good chance of being used and followed. The short-term qualitative information is more likely to be used than quantitative information. Learning and being familiar with the system that provides the information decreases en-route short-term delay.     

Over-reporting vs. overreacting: Commuters’ perceptions of travel times

In a large-scale, real-life peak avoidance experiment, we asked participants to provide estimates of their average in-vehicle travel time during their morning commute. After comparing the reported travel times with the actual corresponding travel times, we found that the average travel times were overstated by a factor of 1.5. We showed that driver- and link-specific characteristics partially explained these exaggerations. Using the stated and revealed preference data, we investigated whether the driver-specific reporting errors were consistent with the drivers’ scheduling behaviors in reality and in hypothetical choice experiments. In both cases, we found no robust evidence that drivers behave as if they misperceive travel times to a similar extent as those they misreported, thereby implying that the reported travel times did not represent the actual or perceived travel times in a truthful manner. The results of this study suggest that caution should be recommended when reported travel time data are used in an uncritical manner during transport research and when determining policy.     

The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis

The effect of travel time variability (TTV) on route choice behavior is explored in this study. A stated preference survey is conducted to collect behavioral data on Shanghai drivers’ choice between a slow but stable route and a fast but unreliable route. Travel time and TTV are respectively measured by mean and standard deviation of random travel time. The generalized linear mixed model (GLMM) is applied to quantify trade-offs between travel time and TTV. The GLMM based route choice model effectively accounts for correlations among repeated observations from the same respondent, and captures heterogeneity in drivers’ values of TTV. Model estimation results show that, female drivers and drivers with rich driving experience are less likely to choose a route with high TTV; smaller expected travel time of a route increase the probability of its being chosen; all drivers have intrinsic preference for a route with smaller expected travel time, but the degree of preference may vary within the population; TTV on average has negative effects on route choice decision, but a small portion of drivers are risk-prone to choose a fast but unreliable route despite high TTV.     

An investigation into passenger preference for express trains during peak hours

Seoul South Korea metro line 9 has a relatively short (27 km) length and dispatches express and local trains alternately with a 3-min headway. Despite a minimal time saving and more crowded conditions with regard to express trains, the passenger dependence on them is growing. The present study investigated the potential reasons for this seemingly irrational phenomenon during peak hours. A multinomial logit model was set up based only on route-specific variables, such as travel times and the proportion of express-train-involved travel time. Furthermore, the influence of trip-related properties, individual-specific characteristics, and latent factors on the preference for express trains was identified by adopting an interaction variable model. As a result, passenger transferrals from, or to, other lines, had a positive influence on the choice of express trains, but those who regularly used line 9 were less dependent on express trains. Passengers with pre-trip plans tended to prefer the use of express trains. Regarding the latent variables, passengers who were well aware of the line 9 system and sensitive to sanitation tended to choose express trains, while those who were sensitive to comfort tended to avoid express trains.     

Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function

Advanced Traveler Information Systems (ATIS) provide travelers with real time traffic information to optimize their travel choices. The objective of this paper is to model drivers' diversion from their normal routes in the provision of ATIS. Five different scenarios of traffic information are used. Generalized Estimating Equations (GEE) framework with repeated observations and binomial probit link function is introduced and implemented. GEE with four different correlation structures including the independent case are developed and compared with each other and with regular Maximum Likelihood Estimation (MLE). A travel simulator was used. Sixty-five subjects have traveled 10 simulated trial days each on a 40-link realistic network with real historical congestion levels. The results showed that providing traffic information increases the probability of drivers' diversion from their normal routes. Adding advice to the pre-trip and/or en-route information encourages drivers to divert. Providing en-route in addition to the pre-trip information with or without advice increases the diversion probability. High travel time on the normal route and less travel time on the diverted route increase the probability of diversion. High-educated drivers are less likely to divert. Expressway users are more likely to divert from their normal routes under ATIS. Drivers' familiarity with the device that provides the information and high number of traffic signals on the normal route increase the diversion probability.     

Influences on bicycle use

A stated preference experiment was performed in Edmonton in Canada to both examine the nature of various influences on bicycle use and obtain ratios among parameter values to be used in the development of a larger simulation of household travel behaviour. A total of 1128 questionnaires were completed and returned by current cyclists. Each questionnaire presented a pair of possible bicycle use alternatives and asked which was preferred for travel to a hypothetical all-day meeting or gathering (business or social). Alternatives were described by specifying the amounts of time spent on three different types of cycling facility and whether or not showers and/or secure bicycle parking were available at the destination. Indications of socio-economic character and levels of experience and comfort regarding cycling were also collected. The observations thus obtained were used to estimate the parameter values for a range of different utility functions in logit models representing this choice behaviour. The results indicate, among other things, that time spent cycling in mixed traffic is more onerous than time spent cycling on bike lanes or bike paths; that secure parking is more important than showers at the destination; and that cycling times on roadways tend to become less onerous as level of experience increases. Some of these results are novel and others are consistent with findings regarding bicycle use in work done by others, which is seen to add credence to this work. A review of previous findings concerning influences on cycling behaviour is also included.     

Stated adaptation surveys and choice process: Some methodological issues

The "Stated Adaptation" survey is an interactive technique which allows us to obtain a clearer picture of the attitudes and behaviours of individuals when confronted with hypothetical situations, in particular inexperienced travel conditions. This method makes use of a simulation game whose purpose is to explore on small samples individuals' choice processes when selecting between the different transport alternatives which are available to them. This paper describes how gaming-simulation is designed, with reference to the issues tackled by two surveys which have recently been carried out in France (reactions to urban road pricing and perception of electric vehicles). It describes the benefits of this experimental approach which allows stated behaviours to be checked to a considerable degree. The limits and potential developments of this survey technique are also discussed.     

Solving network equilibrium problems on multimodal urban transportation networks with multiple user classes

The capacity of the high‐speed train to compete against travel demand in private vehicles is analysed. A hypothetical context analysed as the high‐speed alternative is not yet available for the route studied. In order to model travel demand, experimental designs were applied to obtain stated preference information. Discrete choice logit models were estimated in order to derive the effect of service variables on journey utility. From these empirical demand models, it was possible to predict for different travel contexts and individuals the capacity of the high‐speed train to compete with the car, so determining the impact of the new alternative on modal distribution. Furthermore, individual willingness to pay for travel time saving is derived for different contexts. The results allow us to confirm that the high‐speed train will have a significant impact on the analysed market, with an important shift of passengers to the new rail service being expected. Different transport policy scenarios are derived. The cost of travel appears to a great extent to be a conditioning variable in the modal choice. These results provide additional evidence for the understanding of private vehicle travel demand.     

An activity pattern – destination land use choice model of low income households of informal settlements – Case study of Delhi

Various transportation studies carried out in India, while estimating the travel demand, do not take into consideration the travel characteristics of different income groups. The conventional transportation travel demand model lacks the ability to address the travel needs of the urban poor. This paper explores the factors influencing the travel destinations of urban poor living in informal settlements and finds that travel times have a significant negative impact on the choice to travel and influences the choice of the destinations. The study also finds that the inhabitants of informal settlements are adversely affected by urban policies that displace them and rehabilitate them far from their employment opportunities and that the travel characteristics of low income households living in informal settlements are significantly different from higher income households.     

Value of leisure time based on individuals' mode choice behavior

Values of time have been defined in various forms such as value of leisure time (shadow price of time), value of travel time, and value of saving time, and are mostly measured based on individuals' travel choice behavior. The main purpose of this study is to estimate the value of leisure time by general mode choice models. The estimated level can be used to evaluate the benefits from the increasing leisure time gained by people in Taiwan after the government has practiced a series of policies to shorten employee's working hours in the last few years. To justify the application, this study reviews and reinterprets the theoretical results of some major works on value of time derivations. Then to practically estimate the value of leisure time, it suggests a method of combining revealed preference and stated preference data for application. Finally, it conducts an empirical study on travelers' mode choices behavior in Taiwan to carry out the method suggested. The value of leisure time is estimated at 56NT$ per hour (around 1.65US$/hr), which is even lower than the minimum wage rate regulated by Taiwan government.