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Using conjoint analysis to incorporate heterogeneous preferences into multimodal transit trip simulations
Abstract
Urban transportation systems involve thousands of individuals making choices between routes with multiple modes and transfers. For transportation system simulations to produce realistic results, modelers need to incorporate these users and their choices. Choice-based conjoint surveys provide an attractive solution for obtaining flexible utility models that can be used to predict choices for a wide variety of trips. In this study, we demonstrate an example using conjoint survey data of commuter mode choice in the Washington, D.C. metro area (N = 1651). We sample commuters who primarily drive and those that take transit. We examine preferences for different types of multimodal trips, including those with intramodal and intermodel transfers. We find that trips involving a bus transfer are the least preferred while both drivers and transit users both value metro similarly to driving. We also find that walking during transit trips is an important barrier, with the travel time penalty for walking being 60% higher than that of time in a vehicle. Our findings highlight the significance of accounting for differences in modal transfer types in transportation system simulations. Reducing arrival time uncertainty was not a significant factor in commuter mode choice, and commuters' value of time was similar across all vehicle types, suggesting that increasing the relative speed of transit modes may only have a marginal effect on commuter substitution away from personal vehicles.
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This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according to a chosen distribution. The package is faster than other similar packages such as mlogit, gmnl, mixl, and apollo, and it supports utility models specified with “preference space” or “willingness-to-pay (WTP) space” parameterizations, allowing for the direct estimation of marginal WTP. The typical procedure of computing WTP post-estimation using a preference space model can lead to unreasonable distributions of WTP across the population in mixed logit models. The paper provides a discussion of some of the implications of each utility parameterization for WTP estimates. It also highlights some of the design features that enable logitr's performant estimation speed and includes a benchmarking exercise with similar packages. Finally, the paper highlights additional features that are designed specifically for WTP space models, including a consistent user interface for specifying models in either space and a parallelized multi-start optimization loop, which is particularly useful for searching the solution space for different local minima when estimating models with non-convex log-likelihood functions.
From early studies of time allocation onward, it has been acknowledged that the "productive" nature of travel could affect its utility. Currently, at the margin an individual may choose transit over a shorter automobile trip, if thereby she is able to use the travel time more productively. On the other hand, recent advancements toward partly/fully automated vehicles are poised to revolutionize the perception and utilization of travel time in cars, and are further blurring the role of travel as a crisp transition between location-based activities. To quantify these effects, we created and administered a survey to measure travel multitasking attitudes and behaviors, together with general attitudes, mode-specific perceptions, and standard socioeconomic traits (N = 2229 Northern California commuters). In this paper, we present a revealed preference mode choice model that accounts for the impact of multitasking attitudes and behavior on the utility of various alternatives. We find that the propensity to engage in productive activities on the commute, operationalized as using a laptop/tablet, significantly influences utility and accounts for a small but non-trivial portion of the current mode shares. For example, the model estimates that commuter rail, transit, and car/vanpool shares would respectively be 0.11, 0.23, and 1.18 percentage points lower, and the drive-alone share 1.49 percentage points higher, if the option to use a laptop or tablet while commuting were not available. Conversely, in a hypothetical autonomous vehicles scenario, where the car would allow a high level of engagement in productive activities, the drive-alone share would increase by 1.48 percentage points. The results empirically demonstrate the potential of a multitasking propensity to reduce the disutility of travel time. Further, the methodology can be generalized to account for other properties of autonomous vehicles, among other applications.
Open-source software improves the reproducibility of scientific research. Because existing open-source tools often do not offer dedicated support for longitudinal data collection on phones and computers, we built formr, a study framework that enables researchers to conduct both simple surveys and more intricate studies. With automated email and text message reminders that can be sent according to any schedule, longitudinal and experience-sampling studies become easy to implement. By integrating a web-based application programming interface for the statistical programming language R via OpenCPU, formr allows researchers to use a familiar programming language to enable complex features. These can range from adaptive testing, to graphical and interactive feedback, to integration with non-survey data sources such as self-trackers or online social network data. Here we showcase three studies created in formr: a study of couples with dyadic feedback; a longitudinal study over months, which included social networks and peer and partner ratings; and a diary study with daily invitations sent out by text message and email and extensive feedback on intraindividual patterns.
The aim of this article is to identify the factors affecting user perception of transfers in order to reduce the penalty associated with transfers. Policy makers can thus concentrate on the most relevant factors to achieve a more effective investment of public funds. We carried out an extensive review of the literature and identified a total of 45 factors. A qualitative study was then conducted in two Spanish cities, Vitoria and Madrid, consisting of various focus groups stratified by age. The results indicate that a combination of factors influence the perception of transfers. Time spent during transfers, both walking and waiting, is the main penalty associated with transfers. However, the provision of real-time passenger information helps reduce the perceived waiting time. Differences were found between cities and age groups. Two new factors were identified, mainly in Madrid: mental effort and activity disruption. We, therefore, advise policy makers to concentrate their efforts on providing real-time information to passengers, improving the frequency and reliability of services, and designing transfer areas to ensure continuity between transport modes and between information and communication systems.
This article is concerned with how to conceptualize and theorize the nature of the ‘car system’ that is a particularly key, if surprisingly neglected, element in ‘globalization’. The article deploys the notion of systems as self-reproducing or autopoietic. This notion is used to understand the origins of the 20th-century car system and especially how its awesome pattern of path dependency was established and exerted a particularly powerful and self-expanding pattern of domination across the globe. The article further considers whether and how the 20th-century car system may be transcended. It elaborates a number of small changes that are now occurring in various test sites, factories, ITC sites, cities and societies. The article briefly considers whether these small changes may in their contingent ordering end this current car system. The article assesses whether such a new system could emerge well before the end of this century, whether in other words some small changes now may produce the very large effect of a new post-car system that would have great implications for urban life, for mobility and for limiting projected climate change.
This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.
In models with unobserved taste heterogeneity, distributional assumptions can be placed in two ways: (1) by specifying the
distribution of coefficients in the utility function and deriving the distribution of willingness to pay (WTP), or (2) by specifying the distribution of WTP and deriving the distribution of coefficients. In general the two approaches are equivalent, in that any mutually compatible
distributions for coefficients and WTP can be represented in either way. However, in practice, convenient distributions, such as normal or log-normal, are usually
specified, and these convenient distributions have different implications when placed on WTP’s than on coefficients. We compare models that use normal and log-normal distributions for coefficients (called models in
preference space) with models using these distributions for WTP (called models in WTP space). We find that the models in preference space fit the data better but provide less reasonable distributions of WTP than the models in WTP space. Our findings suggests that further work is needed to identify distributions that either fit better when applied in
WTP space or imply more reasonable distributions of WTP when applied in preference space.
The primary purpose of this study was to investigate how relative associations between travel time, costs, and land use patterns
where people live and work impact modal choice and trip chaining patterns in the Central Puget Sound (Seattle) region. By
using a tour-based modeling framework and highly detailed land use and travel data, this study attempts to add detail on the
specific land use changes necessary to address different types of travel, and to develop a comparative framework by which
the relative impact of travel time and urban form changes can be assessed. A discrete choice modeling framework adjusted for
demographic factors and assessed the relative effect of travel time, costs, and urban form on mode choice and trip chaining
characteristics for the three tour types. The tour based modeling approach increased the ability to understand the relative
contribution of urban form, time, and costs in explaining mode choice and tour complexity for home and work related travel.
Urban form at residential and employment locations, and travel time and cost were significant predictors of travel choice.
Travel time was the strongest predictor of mode choice while urban form the strongest predictor of the number of stops within
a tour. Results show that reductions in highway travel time are associated with less transit use and walking. Land use patterns
where respondents work predicted mode choice for mid day and journey to work travel.
This paper presents the Evolvability Analysis Framework (EAF), a new perspective on evaluating complex infrastructure systems. EAF enables decision makers to explore alternative transition paths, thus providing several multi-step options to achieve a desired end state. These multi-step transition paths can be particularly valuable when they mitigate the impact of system degradation during the deployment of new capabilities. Additionally, EAF is formulated in a manner that empowers decision makers to apply decision variables, such as a cost cap or an equity metric, which are increasingly relevant to modern decision-making. To demonstrate the method and its value, we apply the EAF to a case study inspired by Los Angeles’ Vision 2028. We identify 26 transition paths across different performance dimensions. We model the cost and performance of each transition pathway and compare them using multiple measures, including traditional benefit-cost metrics and differential impacts on different stakeholder groups. Our results show that multi-step paths outperform single-step transitions to an end state (“big bang” approaches) in most scenarios. Multi-step paths also provide valuable alternatives when particular stakeholders value non-cost metrics.
This study examines transit mode choice preferences of New Jersey commuters traveling to New York City (NYC) with the objective of assisting trans-Hudson ferry service planning on the basis of variables significantly affecting mode choice. A stated preference survey was conducted to collect data from a total of 2134 respondents who commute from northern New Jersey to NYC. A mixed multinomial logit model (MMLM) with random coefficients was used to analyze the data. The study considered the choice between four transit modes—ferry, PATH train, bus, and commuter rail—all of which can be used to cross the Hudson River to travel from New Jersey to NYC. Essential model results are presented for all four modes, but detailed results are discussed for only the ferry, PATH, and bus modes. Model results indicate that commuters' choices are affected not only by travel time and cost for the segment of the journey that crosses the Hudson River, but also reliability and comfort during that segment, number of access modes required by the journey, and travel time and cost of trips from home to stations/terminals before crossing the river. The results also show that commuters' attachment to the modes they currently use serves as a barrier to switching to other modes. The most important takeaway from this research for transit service planning is that there is a need to consider the attributes of both individual trip segments as well as entire journeys between commuters' home and work when exploring changes to transit service characteristics.
It is well known that faster and less-expensive transit systems with shorter walking distances, as well as less transfers, are associated with transit use. In this regard, recent studies have focused on estimating transit’s competitiveness and further suggested possible policy interventions. However, there were not much attention on measuring travel time and cost differences between transit and auto by considering the temporal variability. In addition, only a limited study have focused on the impact of either walking during transfers on mode choice or the stage of walking that most affects mode choice. This paper provides additional insights into the impact of travel time, cost, and transit burdens on commute mode choice based on the analysis in a transit-oriented mega city, Seoul, Korea. Together with the 2016 household travel survey data, we utilize the web-based route guide information to measure time, cost, and transit burdens for both chosen and non-chosen modes. The results reveal that commuters are significantly affected by the travel time and cost difference between modes. Additionally, the results indicate that the odds of driving increases by 4.7% and 24.3% when commuters have to transfer once or more than once, respectively. Also, commuters were mostly disinclined to walk longer distances during transfer and egress. The effect of time, cost, and transit burdens on mode choice also varied by income and age groups, further implying the group of people that should be focused when devising policies to encourage transit use. To conclude, this paper suggests potential levers for transportation policies to trigger modal shifts toward sustainable modes by using advanced data sources that better estimate the travel characteristics by departure time based on a point-to-point travel survey data.
Problem, research strategy, and findings: Few studies have examined ride-hail users’ individual preferences between ride-hail and transit. Based on a survey of ride-hail users in the Philadelphia (PA) region, I examine who uses ride-hail and investigate ride-hail users’ willingness to use ride-hail versus transit. My results suggest that more than one-quarter of respondents replaced transit with ride-hail in their last ride-hail trips. Mixed logit regression analysis based on stated preference choice experiments indicate that higher-income respondents and respondents over 30 years old are increasingly willing to choose ride-hail over transit, even though their actual ride-hail usage is lower than that among lower-income and younger respondents. Results also show that female respondents are more willing to choose ride-hail over transit than male respondents and less frequent transit users are more likely to choose ride-hail than frequent transit users. Higher cost and longer trip duration are significant deterrents for travel by either mode. Respondents consider the time spent on walking to and from transit more burdensome than in-vehicle travel time and wait time for transit and ride-hail. They consider waiting for ride-hail less burdensome than waiting for transit. Survey sampling and design limitations provide lessons for future ride-hail studies.
Takeaway for practice: Practitioners should ensure convenient, affordable travel options for lower-income residents, who are more frequent but less willing ride-hail users than higher-income residents. Female respondents’ safety concerns about transit should urge transit agencies to recognize female transit riders’ travel needs. The relationship between age and willingness to use ride-hail reminds planners to anticipate greater substitution of ride-hail for transit as the more tech-savvy generation starts entering their 30s. Last, fare reduction alone may not be enough to prompt ride-hail users to switch to transit. Service improvements that shorten the overall trip duration are imperative to make transit more attractive.
Estimating travelers’ mode choice is a major step in travel demand forecasting in the transportation planning processes. Traveler’s mode choice takes into consideration the main factors affecting their decision making. The aim of this research study is to investigate the factors affecting the mode choice of Notre Dame University-Louaize (NDU) students. A Multinomial Logit Discrete choice approach is used to model the student’s choice to commute to NDU between the available transport modes, private cars, shared taxis, taxis and public buses. The model results show that NDU students are mostly affected by the income, travel cost, and travel time. After the mode choice model is developed for NDU students, a comparison analysis is done between on the model developed for NDU students and another model developed for American University of Beirut (AUB) students, including a value of time (VOT) analysis. Results show that AUB students have resulted in a different mode choice model and higher value of time than NDU students.
Developing sustainable transportation systems in a city can be substantially assisted by promoting environmentally friendly transportation modes such as walking, cycling, and public transit rather than private cars. Strategies for promoting these desirable transportation modes can be effectively identified based on a sound understanding of how citizens choose a travel mode. In this study, we sought to enhance this understanding by exploring factors associated with commute mode choice utilizing data from a general social survey in Seoul, South Korea. Based on the data, parametric and non-parametric statistical models based on classification tree and multilevel logistic regression approaches, respectively, were developed as a way to capture influential factors associated with the mode choice decision. The models illustrate that commuters’ socio-demographic characteristics such as income, occupation, gender, and residence duration tend to significantly influence mode choice. In addition, they showed that respondents’ attitudes and behaviors, including the amount of time spent on the internet and self-assessed social status, can be strongly associated with mode choice. This study is also meaningful in that it demonstrates the potential applicability of general social survey data for investigating travel behavior considering various factors that are rarely included in general transportation surveys.
Effective low carbon transport policy-making needs to first understand what are the factors influencing residents’ modal choice and how it can be intervened. This study uses a discrete choice model to analyse the factors influencing residents’ mode choice in Beijing. A questionnaire survey was conducted in 2015, with sample data containing 865 respondents and 1704 trips collected. The results suggest that residents’ mode choice is closely related to their characteristics. Moreover, our study has linked residents’ mode choice with travel carbon emissions and estimated the emissions reduction potential of those policy measures aiming to improve public transport. For commuting and education trips, public transport improvements can reduce carbon emissions by 12.3~16.6% on average, but for other trip purposes, the reduction is only 2.9~6.8%. As commuting and education trips account for the largest proportion of urban residents’ daily travels, it suggests that policy should primarily focus on the improvement of public transport and its particular support for major commuting routes.
Pooled discrete choice models combine revealed preference (RP) data and stated preference (SP) data to exploit advantages of each. SP data is often treated with suspicion because consumers may respond differently in a hypothetical survey context than they do in the marketplace. However, models built on RP data can suffer from endogeneity bias when attributes that drive consumer choices are unobserved by the modeler and correlated with observed variables. Using a synthetic data experiment, we test the performance of pooled RP–SP models in recovering the preference parameters that generated the market data under conditions that choice modelers are likely to face, including (1) when there is potential for endogeneity problems in the RP data, such as omitted variable bias, and (2) when consumer willingness to pay for attributes may differ from the survey context to the market context. We identify situations where pooling RP and SP data does and does not mitigate each data source’s respective weaknesses. We also show that the likelihood ratio test, which has been widely used to determine whether pooling is statistically justifiable, (1) can fail to identify the case where SP context preference differences and RP endogeneity bias shift the parameter estimates of both models in the same direction and magnitude and (2) is unreliable when the product attributes are fixed within a small number of choice sets, which is typical of automotive RP data. Our findings offer new insights into when pooling data sources may or may not be advisable for accurately estimating market preference parameters, including consideration of the conditions and context under which the data were generated as well as the relative balance of information between data sources.
The disutility of transfers in multimodal public transport goes beyond the additional walking and waiting times. Although the magnitude of this pure transfer penalty has been proven to be an essential element in the structural design of public transport lines, the scarce available research reveals a wide range of values. The aim of this paper is to develop and apply a framework to estimate the value perceived and assigned by commuters to this penalty. This framework includes all the other elements considered by users in the case of a trip involving (potential) transfers, in order to obtain the impact of each one. The framework is based on the discrete choices paradigm and applied to data collected in Madrid, Spain. The results show that the pure transfer penalty is comparable to a 15.2-17.7 equivalent increase in in-vehicle minutes; i.e. longer trips may be preferred to faster alternatives with transfers, even if the additional walking and waiting times are zero. As well as the pure transfer penalty, the model also captures the effects of habit, crowding, walking, waiting and in-vehicle times, information, and the additional effect of intermodality on transfers.
Hadas, Y., and Ceder, A. “Public Transit Network Connectivity: Spatial-Based Performance Indicators”. Journal of the Transportation Research Board, No. 2143, pp. 1-8, 2010.
In developed countries such as Canada and United States, a significant number of individuals depend on automobile as their main mode of transport. There has been a stronger push towards analyzing travel behavior at the individual level so that transportation agencies can formulate appropriate strategies to reduce the auto dependency. Towards this pursuit of enhancing our understanding on travel behavior, we examine individual home to work/school commute patterns in Montreal, Canada with an emphasis on the transit mode of travel. The overarching theme of this paper is to examine the effect of the performance of the public transportation system on commuter travel mode and transit route choice (for transit riders) in Montreal. We investigate two specific aspects of commute mode choice: (1) the factors that dissuade individuals from commuting by public transit and (2) the attributes that influence transit route choice decisions (for those individuals who commute by public transit). This study employs a unique survey conducted by researchers as part of the McGill University Sustainability project. The survey collected information on commute patterns of students, faculty and staff from McGill University. In addition, detailed socio-demographic and residential location information was also collected. The analysis was undertaken using multinomial logit model for the travel mode choice component and a mixed multinomial logit model for the transit route choice component. The model estimation results were employed to conduct policy sensitivity analysis that allows us to provide recommendations to public transportation and metropolitan agencies.
Out-of-vehicle times were shown to be perceived as being more onerous than in-vehicle time by transit users when making transfers. The present study has two main objectives. The first objective is to determine the effects of uncertainty, in out-of-vehicle times during transfers, on transit users’ willingness to use transfer routes. The second objective is to determine the influence of out-of-vehicle facilities, offered by public-transport (PT) operators, on transit users’ perception of trip attributes related to transfers. A user preference survey was conducted at two major PT terminals in Auckland, New Zealand. The survey data was modelled using cumulative prospect theory and fuzzy logic. The results showed that for all trip attributes, except for comfort, transit users’ exhibited risk averse behaviour; users’ revealed greater preference for the transfer route with less uncertainty in the out-of-vehicle times. For comfort, transit users’ displayed risk-taking characteristics when the waiting time for a seat was less than 5 min. Such findings suggest that increasing the consistency in out-of-vehicle times will increase attractiveness of transfer routes thus enabling a more efficient and integrated network of PT routes to result in enlargement of ridership. Policy makers and PT planners must focus on methods of reducing uncertainty in out-of-vehicle times during transfers. Analysis of transit users’ perception of trip attributes, given their current station, revealed statistical evidence of differences for two trip attributes, transfer waiting time and vehicle delay. Such findings indicate that transit users who are accustomed to better out-of-vehicle facilities have a lower tolerance for uncertainty in transfer waiting times and delay times. To the authors’ knowledge, this study provides for the first time in literature a comparison between the two cognitive models. The comparison revealed that CPT and fuzzy logic models are both capable of representing transit users’ decision making process. However, while CPT provides an indication of transit users’ preference for a transfer route, fuzzy logic is capable of providing a closer approximation of the proportion of transit users preferring a transfer route.
The service reliability of public transit operations is gaining increased attention as agencies face immediate problems in providing credible service and attempting to reduce operational cost. Unreliable service has been cited as the major deterrent element for existing and potential passengers. Because most public transit attributes are of a stochastic nature—travel time, dwell time, demand, and so forth—the passenger is likely to experience unplanned waiting and riding times. Missed transfers are major contributors to the reliability issues of any transit system; therefore, this work focuses on the connectivity of the transit network with the reliability and comfort of transfers in multilegged trips. Because of the physical and spatial characteristics of the transit routes, it is possible to categorize the transfers into three types: nonadjacent transfer points, adjacent transfer points, and shared transfer points. Each type of transfer contributes differently to the degree of connectivity of the transit system. On the basis of transit network of routes, spatial-based performance indicators are introduced and defined. That is followed by results of a case study of the transit networks of two Israeli midsize cities.
1. Choosing as a way of life Appendix A1. Choosing a residential telecommunications bundle 2. Introduction to stated preference models and methods 3. Choosing a choice model Appendix A3. Maximum likelihood estimation technique Appendix B3. Linear probability and generalised least squares models 4. Experimental design 5. Design of choice experiments Appendix A5. 6. Relaxing the IID assumption-introducing variants of the MNL model Appendix A6. Detailed characterisation of the nested logit model Appendix B6. Advanced discrete choice methods 7. Complex, non-IID multiple choice designs 8. Combining sources of preference data 9. Implementing SP choice behaviour projects 10. Marketing case studies 11. Transportation case studies 12. Environmental valuation case studies 13. Cross and external validity of SP models.
This paper investigates the relationship between mode choice and the complexity of trip chaining patterns. An understanding of the causality between these two choice behaviors may aid in the development of tour-based travel demand modeling systems that attempt to incorporate models of trip chaining and mode choice. The relationship between these two aspects of travel behavior is represented in this paper by considering three different causal structures: one structure in which the trip chaining pattern is determined first and influences mode choice, another structure in which mode choice is determined first and influences the complexity of the trip chaining pattern, and a third structure in which neither is predetermined but both are determined simultaneously. The first two structures are estimated within a recursive bivariate probit modeling framework that accommodates error covariance. The simultaneous logit model is estimated for the third structure that allows a bidirectional simultaneous causality. The analysis and model estimation are performed separately for work tour and non-work tour samples drawn from the 2000 Swiss Microcensus travel survey. Model estimation results show that the causal structure in which trip chain complexity precedes mode choice performs best for both work and non-work tour samples. The structure in which mode choice precedes trip chaining pattern choice was found to give significantly inferior goodness-of-fit measures. These findings have important implications for the development of activity-based and tour-based modeling systems and for the design and planning of public transport systems.
In recent decades, trends in travel behaviour have been characterised by increasing trip distances and a modal shift towards the private car. This paper reports findings from longitudinal analyses of the German nation-wide travel survey KONTIV for the period 1976–2002. It focuses on travel mode choice, subdivided by distance categories, and also takes car availability and city size into account. In addition, trends in car availability itself are examined by city size categories. The results indicate that even within the same distance categories car use has considerably increased. In some cases bicycle use has increased as well. Gains in the use of the private car are mainly at the expense of trips on foot and by public transport. Accordingly, the shift in modal split towards the car is not (only) caused by increasing trip distances but took place even within distance classes. Once car availability is taken into account, the modal shifts appear to be considerably weaker. This suggests that once car availability is held constant the decision rationales of mode choice for a certain trip distance have remained relatively stable. The increase in motorisation over the study period was considerably weaker in large cities than in small towns, although the cities started from a lower level in the 1970s. Thus, the motorisation divide between cities on the one hand, and suburban and rural areas on the other hand has become ever wider. For travel mode choice, the picture is similar. What is more, the results suggest that even car owners are more inclined to walk a given distance in the cities than in small towns, even more so if they live in a central urban area. The built environment, thus, appears to have a strong impact on whether an available car is used or not.
This paper uses revealed preference and stated preference data collected from a web-based commuter survey in Austin, Texas, to estimate a commute mode choice model. This model accommodates weekly and daily commute and midday stop-making behavior, as well as travel time reliability. A mixed logit framework is used in estimation.The results emphasize the effects of commute and midday stop-making on commute mode choice. The results also indicate that travel time reliability is an important variable in commute mode choice decisions. The paper applies the estimated model to predict the potential mode usage of a proposed commuter rail option as well as to examine the impact of highway tolls. More generally, the mode choice model can be used to examine a whole range of travel mode-related policy actions for the Austin metropolitan region.
This paper presents an investigation into the impacts of the introduction of congestion charges on shopping trips in Edinburgh. Other transport policies such as parking costs, public transport costs and petrol fees have also been investigated, using logistic regression analysis. The results indicate a potential reduction in car shopping trips to the City Centre as a result of introducing congestion charging and a possible increase in shopping trips by other modes. Traffic congestion does not seem to be the main barrier for people to shop at the City Centre; rather access to and availability of parking spaces most affect the accessibility of the City Centre. Moreover, the introduction of congestion charges may result in the improvement of accessibility of public transport as well as reductions in traffic congestion allowing better access into the City Centre.
This paper uses an endogenous segmentation approach to model mode choice. This approach jointly determines the number of market segments in the travel population, assigns individuals probabilistically to each segment, and develops a distinct mode choice model for each segment group. The author proposes a stable and effective hybrid estimation approach for the endogenous segmentation model that combines an Expectation-Maximization (EM) algorithm with standard likelihood maximization routines. If access to general maximum-likelihood software is not available, the multinomial-logit based EM algorithm can be used in isolation. The endogenous segmentation model and other commonly used models in the travel demand field to capture systematic heterogeneity are estimated using a Canadian intercity mode choice dataset. The results show that the endogenous segmentation model fits the data best and provides intuitively more reasonable results compared to the other approaches.
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