Prioritizing bicycle paths in Belo Horizonte City,Brazil: Analysis based on user preferences and willingness considering individual heterogeneity

Using bicycles as a commuting mode has proven to be beneficial to both urban traffic conditions and travelers’ health. In order to efficiently design facilities and policies that will stimulate bicycle use, it is necessary to first understand people’s attitudes towards bicycle use, and the factors that may influence their preferences. Such an understanding will enable reliable predictions of bicycle use willingness level, based on which cycling facility construction can be reasonably prioritized.As people often have different perceptions on exercising, green transportation, and traffic conditions, effects of potentially influencing factors on people’s willingness of using bicycles tend to be highly heterogeneous. This paper uses a random parameter ordered probit model to analyze how travelers’ willingness of using bicycles is influenced by various socio-economic factors in Belo Horizonte City, Brazil, with the consideration of individual heterogeneity. The data was collected through the 2010 bicycle use survey in Belo Horizonte City. Results show that, first, the willingness of using bicycle is favored by middle income class household, and negatively related with commuting time. Second, people who rent apartments tend to be more willing to use bicycles. Third, if a person is currently walking a long time to work, he/she would be most willing to commute with a bicycle in the future. Those currently commuting a relatively short distance by motorcycle and bus follow this group in terms of willingness to commute by bicycle in the future. Car users seem to be difficult to convert to bicycle users. Moreover, the estimation shows clear evidence that significant individual heterogeneity indeed exists, especially for education level, necessitating the consideration of such an effect. With the calibrated model, residents’ willingness of using bicycle commuting is then estimated for the entire Belo Horizonte City using the 2010 Census and the 2012 O/D survey data. The results are cross validated using the bicycle path preference information, also obtained from the 2010 bicycle use survey.     

Tradable credits scheme and transit investment optimization for a two‐mode traffic network

This paper proposes an optimization model to minimize the “system costs” and guide travelers' behavior by exploring the optimal bus investment and tradable credits scheme design in a bimodal transportation system. Travelers' transport mode choice behavior (car or bus) and the modal equilibrium conditions between these two forms of transport are studied in the tradable credits scheme. Public transport priority is highlighted by charging car travelers credits only. The economies of scale presented by the transit system under the tradable credit scheme are analyzed by comparing the marginal cost and average cost. Numerical examples are presented to demonstrate the model. Furthermore, the effects of tradable credits schemes on bus investment and travelers' modal choice behavior are explored based on scenario discussions. Copyright © 2016 John Wiley & Sons, Ltd.     

Enhanced traffic information dissemination to facilitate toll road utilization: a nested logit model of a stated preference survey in Texas

This paper investigates the effects of the provision of traffic information on toll road usage based on a stated preference survey conducted in central Texas. Although many researchers have studied congestion pricing and traffic information dissemination extensively, most of them focused on the effects that these instruments individually produce on transportation system performance. Few studies have been conducted to elaborate on the impacts of traffic information dissemination on toll road utilization. In this study, 716 individuals completed a survey to measure representative public opinions and preferences for toll road usage in support of various traffic information dissemination classified by different modes, contents, and timeliness categories. A nested logit model was developed and estimated to identify the significant attributes of traffic information dissemination, traveler commuting patterns, routing behavior, and demographic characteristics, and analyze their impacts on toll road utilization. The results revealed that the travelers using dynamic message sign systems as their primary mode of receiving traffic information are more likely to choose toll roads. The potential toll road users also indicated their desire to obtain traffic information via internet. Information regarding accident locations, road hazard warnings, and congested roads is frequently sought by travelers. Furthermore, high-quality congested road information dissemination can significantly enhance travelers’ preferences of toll road usage. Specifically the study found that travelers anticipated an average travel time saving of about 11.3 min from better information; this is about 30 % of travelers’ average one-way commuting time. The mean value of the time savings was found to be about $11.82 per hour, close to ½ of the average Austin wage rate. The model specifications and result analyses provide in-depth insights in interpreting travelers’ behavioral tendencies of toll road utilization in support of traffic information. The results are also helpful to shape and develop future transportation toll system and transportation policy.     

Determinants of ground transport modal choice in long-distance trips in Spain

In transport economics, modeling modal choice is a fundamental key for policy makers trying to improve the sustainability of transportation systems. However, existing empirical literature has focused on short-distance travel within urban systems. This paper contributes to the limited number of investigations on mode choice in medium- and long-distance travel. The main objective of this research is to study the impacts of socio-demographic and economic variables, land-use features and trip attributes on long-distance travel mode choice. Using data from 2007 Spanish National Mobility Survey we apply a multilevel multinomial logit model that accounts for the potential problem of spatial heterogeneity in order to explain long-distance travel mode choice. This approach permits us to compute how the probability of choosing among private car, bus and train varies depending on the traveler spatial location at regional level. Results indicate that travelers characteristics, trip features, cost of usage of transport modes and geographical variables have significant impacts on long-distance mode choice.     

Modeling traveler mode choice behavior of a new high-speed rail corridor in China

This study examines mode choice behavior for intercity business and personal/recreational trips. It uses multinomial logit and nested logit methods to analyze revealed preference data provided by travelers along the Yong-Tai-Wen multimodal corridor in Zhejiang, China. Income levels are found to be positively correlated with mode share increases for high-speed rail (HSR), expressway-based bus, and auto modes, while travel time and trip costs are negatively correlated with modal shift. Longer distance trips trigger modal shifts to HSR services but prevent modal shift to expressway-based auto use due to escalation of fuel cost and toll charges. Travelers are less elastic in their travel time and cost for trips by nonexpressway-based auto use modes. The magnitude of elasticity for travel time is higher than trip costs for business trips and lower for personal/recreational trips. The study provides some policy suggestions for transportation planners and decision-makers.     

Modeling heterogeneous parking choice behavior on university campuses

Studies on campus parking indicate more severe problems and a wider range of characteristics than commercial parking because of limited parking places, special conditions, specific policies and enclosed space on university campuses. Heterogeneous characteristics are usually ignored in analyses of campus parking behavior. In this paper, a mixed logit model is applied to analyze parking choice behavior on a campus using data collected from a stated-preference survey of Tongji University, Shanghai, China. The heterogeneity of individuals with various sociodemographic characteristics is evaluated by interaction terms and random parameters. Comparison between the proposed approach and the conditional logit model shows that the results of the mixed logit model are more interpretable because they are not limited by the independence from irrelevant alternatives assumption. Key factors that have considerable effects on campus parking choices are identified and analyzed. Important regularities are also concluded from elasticity analyses. Finally, the campus is divided into two areas according to the walking distance to a new parking lot, and the modeling results show that area-specific policies should be established because the two areas have quite distinct parking choice features.     

Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice

Concerns over transportation energy consumption and emissions have prompted more studies into the impacts of built environment on driving-related behavior, especially on car ownership and travel mode choice. This study contributes to examine the impacts of the built environment on commuter’s driving behavior at both spatial zone and individual levels. The aim of this study is threefold. First, a multilevel integrated multinomial logit (MNL) and structural equation model (SEM) approach was employed to jointly explore the impacts of the built environment on car ownership and travel mode choice. Second, the spatial context in which individuals make the travel decisions was accommodated, and spatial heterogeneities of car ownership and travel mode choice across traffic analysis zones (TAZs) were recognized. Third, the indirect effects of the built environment on travel mode choice through the mediating variable car ownership were calculated, in other words, the intermediary nature of car ownership was considered. Using the Washington metropolitan area as the study case, the built environment measures were calculated for each TAZ, and the commuting trips were drawn from the household travel survey in this area. To estimate the model parameters, the robust maximum likelihood (MLR) method was used. Meanwhile, a comparison among different model structures was conducted. The model results suggest that application of the multilevel integrated MNL and SEM approach obtains significant improvements over other models. This study give transportation planners a better understanding on how the built environment influences car ownership and commuting mode choice, and consequently develop effective and targeted countermeasures.     

A parameterized consideration set model for airport choice: an application to the San Francisco Bay Area

Airport choice is an important air travel-related decision in multiple airport regions. This paper proposes the use of a probabilistic choice set multinomial logit (PCMNL) model for airport choice that generalizes the multinomial logit model used in all earlier airport choice studies. The paper discusses the properties of the PCMNL model, and applies it to examine airport choice of business travelers residing in the San Francisco Bay Area. Substantive policy implications of the results are discussed. Overall, the results indicate that it is important to analyze the choice (consideration) set formation of travelers. Failure to recognize consideration effects of air travelers can lead to biased model parameters, misleading evaluation of the effects of policy action, and a diminished data fit.     

Route switching behavior on freeways with the provision of different types of real-time traffic information

This study investigates route switching behavior on freeways in reaction to the provision of different types of real-time traffic information. The experimental design of the stated preference survey is based on four types of real-time information provided to travelers who were randomly selected at rest areas. The four types of real-time information defined in this paper are qualitative, quantitative, qualitative guidance, and quantitative guidance. The bounded rationality framework, also known as indifference band approach, is applied to model the freeway route switching behavior. Two important variables, travel time and travel cost, are included in the indifference band. In this study, the best route switching rule, travelers’ current routes as compared to the best route, is investigated to further provide valuable insights into freeways travelers’ route switching behavior with the provision of different types of real-time traffic information.     

Optimal transit fare in a bimodal network under demand uncertainty and bounded rationality

This paper investigates the optimal transit fare in a simple bimodal transportation system that comprises public transport and private car. We consider two new factors: demand uncertainty and bounded rationality. With demand uncertainty, travelers are assumed to consider both the mean travel cost and travel cost variability in their mode choice decision. Under bounded rationality, travelers do not necessarily choose the travel mode of which perceived travel cost is absolutely lower than the one of the other mode. To determine the optimal transit fare, a bi‐level programming is proposed. The upper‐level objective function is to minimize the mean of total travel cost, whereas the lower‐level programming adopts the logit‐based model to describe users' mode choice behaviors. Then a heuristic algorithm based on a sensitivity analysis approach is designed to solve the bi‐level programming. Numerical examples are presented to illustrate the effect of demand uncertainty and bounded rationality on the modal share, optimal transit fare and system performance. Copyright © 2013 John Wiley & Sons, Ltd.     

Commuters’ behavior towards upgraded bus services in Greater Beirut: Implications for greenhouse gas emissions,social welfare and transport policy

Climate change is one of the most critical environmental challenges faced in the world today. The transportation sector alone contributes to 22% of carbon emissions, of which 80% are contributed by road transportation. In this paper we investigate the potential private car greenhouse gas (GHG) emissions reduction and social welfare gains resulting from upgrading the bus service in the Greater Beirut Area. To this end, a stated preference (SP) survey on mode switching from private car to bus was conducted in this area and analyzed by means of a mixed logit model. We then used the model outputs to simulate aggregate switching behavior in the study area and the attendant welfare and environmental gains and private car GHG emissions reductions under various alternative scenarios of bus service upgrade. We recommend a bundle of realistic bus service improvements in the short term that will result in a reasonable shift to buses and measurable reduction in private car emissions. We argue that such improvements will need to be comprehensive in scope and include both improvements in bus level of service attributes (access/egress time, headway, in-vehicle travel time, and number of transfers) and the provision of amenities, including air-conditioning and Wi-Fi. Moreover, such a service needs to be cheaply priced to achieve reasonably high levels of switching behavior. With a comprehensively overhauled bus service, one would expect that bus ridership would increase for commuting purposes at first, and once the habit for it is formed, for travel purposes other than commuting, hence dramatically broadening the scope of private car GHG emissions reduction. This said, this study demonstrates the limits of focused sectorial policies in targeting and reducing private car GHG emissions, and highlights the need for combining behavioral interventions with other measures, most notably technological innovations, in order for the contribution of this sector to GHG emissions mitigation to be sizable.     

Analysis of hurricane evacuee mode choice behavior

The purpose of this study is to explain the evacuee mode choice behavior of Miami Beach residents using survey data from a hypothetical category four hurricane to reveal different evacuees’ plans. Evacuation logistics should incorporate the needs of transit users and car-less populations with special attention and proper treatment. A nested logit model has been developed to explain the mode choice decisions for evacuees’ from Miami Beach who use non-household transportation modes, such as special evacuation bus, taxi, regular bus, riding with someone from another household and another type of mode denoted and aggregated as other. Specifically, the model explains that the mode choice decisions of evacuees’, who are likely to use different non-household transportation modes, are influenced by several determining factors related to evacuees’ socio-demographics, household characteristics, evacuation destination and previous experience. The findings of this study will help emergency planners and policy-makers to develop better evacuation plans and strategies for evacuees depending on others for their evacuation transportation.     

Fuzzy preference based traffic assignment problem

This paper introduces a fuzzy preference based model of route choice. The core of the model is FiPV (Fuzzy individuelle Präferenzen von Verkehrsteilnehmern or fuzzy traveler preferences), that is a choice function based on fuzzy preference relations for travel decisions. The proposed model may be the first application of fuzzy individual choice in traffic assignment and probably also the first in this class to consider the spatial knowledge of individual travelers. It is argued that travelers do not or cannot always follow the maximization principle. Therefore we formulate a model that also takes into account the travelers with non-maximizing behavior. The model is based on fuzzy preference relations, of which elements are fuzzy pairwise comparisons between the available alternatives.     

Accommodating preference heterogeneity in commuting mode choice: an empirical investigation in Shaoxing,China

A latent class model is developed to accommodate preference heterogeneity across commuters with respect to their mode choice between electric bike, private car, and public bus within the context of China. A three-segment solution – ‘electric bike individuals’, ‘private car addicts’, and ‘public bus enthusiasts’ – is identified, each characterized by heterogeneous preferences regarding specific mode attributes and unique socio-demographic profile. The choice model confirms the determinative effects of perceived alternative attributes on commuting mode choice, while the traditionally used objective attributes – travel time and cost – are found to have relatively small influences. The membership model provides solid explanations for these segment-specific preferences. This study provides a better understanding of the nature of mode choice behavior, which can be useful for strategies tailored to a specific segment in order to promote the use of sustainable transport modes.     

Modeling traffic's flow-density relation: Accommodation of multiple flow regimes and traveler types

This research investigates freeway-flow impacts of different traveler types by specifying and applying a latent-segmentation model of congested and uncongested driving behaviors. Drivers in uncongested conditions are assumed to drive at self-chosen speeds, while drivers in congested conditions are assumed to take speed as given and choose a spacing (between their vehicle and the previous vehicle). Several classes of driver-vehicle combinations are distinguished in a data set based on double-loop-detector pulses and a household travel survey. These classifications are made on the basis of vehicle type and gender, leading to class estimates of speeds and spacings. The segmentation model is specified as a logit function of density, weather, and vehicle type, leading to estimates of congested-condition probabilities. Unobserved heterogeneity is incorporated in all models via common error assumptions.Results indicate that segmentation models are promising tools for traffic data analysis and that information on travelers, their vehicles, and weather conditions explains significant variation in flow data. By clarifying a greater understanding of traffic conditions and traveler behavior explains much scatter in the fundamental relation between flow, speed, and density, can assist regions in their traffic-management efforts and engineers in their design of roadway facilities. Ultimately, such improvements to travel networks should enhance quality of life.     

Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment

To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers’ behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers’ parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers’ parking behaviour can be incorporated into traffic assignment models and used to support parking systems.     

Accounting for systematic heterogeneity across car commuters in response to multiple TDM policies: case study of Tehran

Modeling commuters’ choice behavior in response to transportation demand management (TDM) helps in predicting the consequences of TDM policies. Although research looking at choice behavior has evolved to investigate preference heterogeneity in response to factors influencing mode choice, as far as we know, no study has considered taste variation across commuters in response to multiple TDM policies. This paper investigates the presence of systematic preference heterogeneity across commuters, in response to the TDM policies that can be explained by their socio-economic or commuting-related characteristics. Analysis is based on results of a stated preference survey developed using a Design of Experiments approach. Five policies were assessed in order to study the impact they had on how commuters chose their mode of transportation. These include increasing parking cost, increasing fuel cost, implementing cordon pricing, reducing transit time and improving access to transit facilities. For the sake of assessing both systematic and random preference heterogeneity across car commuters, a form of the Mixed Multinomial Logit (MMNL) model that identifies sources of heterogeneity and consequently makes the choice models less restrictive in considering both systematic and random preference variation across individuals was developed. The sample includes 366 individuals who regularly commute to their workplace in the city center of Tehran, Iran. The likelihood function value of this model shows a significant improvement compared to the base MNL model, using the same variables. The MMNL model shows that taste variation across the studied commuters results in differences in influences estimated for three policies: increasing parking cost, reducing transit time and improving access to transit. The analysis examines several distributions for random parameters to test the impacts of restricting distributions to allow for only normality. The results confirm the potential to improve model fit with alternative distributions.     

Access to and competition between airports: a case study for the San Francisco Bay area

In this paper (nested) logit models that describe the combined access mode-airport-choice are estimated. A three level nested logit model is rejected. A two level nested logit model with the airport choice at the top level and the access mode choice at the lower level is preferred. From the estimation results, it is concluded that business travelers have a higher value of time than leisure travelers. In the (conditional) access mode choice, leisure travelers have a higher access cost elasticity (in absolute value), while business travelers have a higher access time elasticity (in absolute value). In general, access time is of large importance in the competition between airports in a region.     

Identifying variations and co-variations in discrete choice models

This study introduces an extended version of a standard multilevel cross-classified logit model which takes co-variations into account, i.e., variations jointly caused by two or more unobserved factors. Whilst focusing on mode choice behavior, this study deals with four different types of variation: spatial variations, inter-individual variations, intra-individual variations and co-variations between inter-individual and spatial variations. Such co-variations represent individual-specific spatial effects, reflecting different responses to the same space among individuals, which may for example be due to differences in their spatial perceptions. In our empirical analysis, we use data from Mobidrive (a continuous six-week travel survey) to clarify the existence of co-variation effects by comparing two models with and without co-variation terms. The results of this analysis indicate that co-variations certainly exist, especially for utility differences in bicycle and public transport use in comparison with car use. We then sequentially introduce four further sets of explanatory variables, examine the sources of behavioral variations and determine what types of influential factors are dominant in mode choice behavior.     

A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand

In this paper we analyze demand for cycling using a discrete choice model with latent variables and a discrete heterogeneity distribution for the taste parameters. More specifically, we use a hybrid choice model where latent variables not only enter into utility but also inform assignment to latent classes. Using a discrete choice experiment we analyze the effects of weather (temperature, rain, and snow), cycling time, slope, cycling facilities (bike lanes), and traffic on cycling decisions by members of Cornell University (in an area with cold and snowy winters and hilly topography). We show that cyclists can be separated into two segments based on a latent factor that summarizes cycling skills and experience. Specifically, cyclists with more skills and experience are less affected by adverse weather conditions. By deriving the median of the ratio of the marginal rate of substitution for the two classes, we show that rain deters cyclists with lower skills from bicycling 2.5 times more strongly than those with better cycling skills. The median effects also show that snow is almost 4 times more deterrent to the class of less experienced cyclists. We also model the effect of external restrictions (accidents, crime, mechanical problems) and physical condition as latent factors affecting cycling choices.