On fitting mode specific constants in the presence of new options in RP/SP models

We treat the problem of fitting alternative specific constants (ASC) in models estimated with a mixture of revealed preference (RP) and stated preference (SP) data to forecast the market shares of new alternatives. This important problem can have non-trivial solutions, particularly when some of the SP alternatives are completely revamped versions of existing ones (i.e., an advanced passenger train replacing a normal railway service). As there is no explicit treatment of this problem in the literature we examined it in depth and illustrated it empirically using data especially collected to analyse mode choice in a corridor to the West of Cagliari. We propose a hopefully useful guide to this art (as no practical recipes seem to serve all purposes). Careful specification of the systematic component of utility functions in RP and SP, including the ASC, serves to illuminate the true nature of the underlying error structure in the different data sets, yielding superior forecasting models.     

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.     

Correcting state dependence and serial correlation in the RP/SP combined estimation method

Revealed preference (RP) data and stated preference (SP) data have complementary characteristics for model estimation. To enhance the advantages of both data types, a combined estimation method is proposed. This paper discusses the method and practical considerations in applying it, and introduces a new method of considering serial correlation of RP and SP data. An empirical analysis is also presented.     

Gender differences in activity participation,time-of-day and duration choices: new evidence from Calgary

To date only limited research has quantified differences between female and male activity patterns, and analyses at an individual activity level are scarce. Past research has focused on investigating gender differences in mobility levels based on observed travel patterns, especially those related to commuting. This article reports new evidence based on analyses of a household activity survey data-set collected from a Canadian city – Calgary – in 2001. Results show that contemporary females and males have a very similar activity participation pattern. On the other hand, analyses applied to activity starting times support the view that there are minor gender differences in time-of-day choices. In addition, duration and survival analyses through log-rank and Wilcoxon tests show that women and men tend to spend more or less time on some of the 10 weekend/weekday activities, and thus indicate that they share different domestic and societal responsibilities: males tend to spend longer time for out-of-home activities, such as work, school, social, and out-of-town; whereas females contribute more to domestic work, including shopping, eating, and religious activity. In general, this article contributes new evidence to gender differences in activity participation, time-of-day, and duration choices at the individual activity level. Such differences may influence travelers’ time, mode, and location choices and thus have important implications for the complexity of an activity-based modeling framework. These implications are discussed along with recommendations for incorporating gender differences in an activity-based modeling framework.     

Interactive travel choices and traffic forecast in a doubly dynamical system with user inertia and information provision

This study models the joint evolution (over calendar time) of travelers’ departure time and mode choices, and the resulting traffic dynamics in a bi-modal transportation system. Specifically, we consider that, when adjusting their departure time and mode choices, travelers can learn from their past travel experiences as well as the traffic forecasts offered by the smart transport information provider/agency. At the same time, the transport agency can learn from historical data in updating traffic forecast from day to day. In other words, this study explicitly models and analyzes the dynamic interactions between transport users and traffic information provider. Besides, the impact of user inertia is taken into account in modeling the traffic dynamics. When exploring the convergence of the proposed model to the dynamic bi-modal commuting equilibrium, we find that appropriate traffic forecast can help the system converge to the user equilibrium. It is also found that user inertia might slow down the convergence speed of the day-to-day evolution model. Extensive sensitivity analysis is conducted to account for the impacts of inaccurate parameters adopted by the transport agency.     

Impact of traveler advisory systems on driving speed: some new evidence

This paper explores the effects of driving behavior using in-vehicle and out-of-vehicle traffic advisory information relating to adverse weather and incident conditions. A full-size, fixed-based driving simulator is used to collect data on drivers’ speed behavior under four different advisory-information conditions: in-vehicle messages, out-of-vehicle messages, both types of messages, and no messages. The findings of this study suggest an interesting phenomenon in that, while messages are significant in reducing speeds in the area of adverse conditions, drivers tend to compensate for this speed reduction by increasing speeds downstream when such adverse conditions do not exist. As a result, the net safety effects of such message systems are ambiguous.     

Demand for taxi services: new elasticity evidence

This paper investigates the factors that influence the choice of, and hence demand for taxis services, a relatively neglected mode in the urban travel task. Given the importance of positioning preferences for taxi services within the broader set of modal options, we develop a modal choice model for all available modes of transport for trips undertaken by individuals or groups of individuals in a number of market segments. A sample of recent trips in Melbourne in 2012 was used to develop segment-specific mode choice models to obtain direct (and cross) elasticities of interest for cost and service level attributes. Given the nonlinear functional form of the way attributes of interest are included in the modal choice models, a simple set of mean elasticity estimates are not behaviourally meaningful; hence a decision support system is developed to enable the calculation of mean elasticity estimates under specific future service and pricing levels. Some specific direct elasticity estimates are provided as the basis of illustrating the magnitudes of elasticity estimates under likely policy settings.     

Understanding variability,habit and the effect of long period activity plan in modal choices: a day to day,week to week analysis on panel data

Understanding variability in individual behaviour is crucial for the comprehension of travel patterns and for the development and evaluation of planning policies. But, with only one notable exception, there are no studies on the intrinsic variability in the individual preferences for mode choices in absence of external changes in the transport infrastructures. This requires using continuous panel data. Few papers have studied mode choice with continuous panel data but mainly focused on the panel correlation. In this work we use a six-week travel diary survey to study the intrinsic variability in the individual preferences for mode choices, the effect of long period plans and habitual behaviour in the daily mode choices. Mixed logit models are estimated that account for the above effects as well as for systematic and random heterogeneity over individual preferences and responses. We also account for correlation over several time periods. Our results suggest that individual tastes for time and cost are fairly stable but there is a significant systematic and random heterogeneity around these mean values and in the preferences for the different alternatives. We found that there is a strong inertia effect in mode choice that increases with (or is reinforced by) the number of time the same tour is repeated. The sequence of mode choice made is influenced by the duration of the activity and the weekly structure of the activities     

Examining the relationship between active travel,weather, and the built environment: a multilevel approach using a GPS-enhanced dataset

This study examines how the built environment and weather conditions influence the use of walking as a mode of transport. The Halifax Regional Municipality in Nova Scotia, Canada is the study area for this work. Data are derived from three sources: a socio-demographic questionnaire and a GPS-enhanced prompted recall time-use diary collected between April 2007 and May 2008 as part of the Halifax Space-Time Activity Research project, a daily meteorological summary from Environment Canada, and a comprehensive GIS dataset from the regional municipality. Two binary logit multilevel models are estimated to examine how the propensity to use walking is influenced by the built environment and weather while controlling for socio-demographic characteristics. The built environment is measured via five attributes in one model and a walkability index (derived from the five attributes) in the other. Weather conditions are shown to affect walking use in both models. Although the walkability index is significant, the results demonstrate that this significance is driven by specific attributes of the built environment—in the case of this study, population density and to a lesser extent, pedestrian infrastructure.     

Reassessing the commuting penalty for immigrants: new evidence from Spain

Transportation - This article examines the differences in commuting length between native and immigrant employees in Spain, a relevant issue since immigrants' longer commuting times may, among...     

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.     

Modelling energy consumption of electric freight vehicles in urban pickup/delivery operations: analysis and estimation on a real-world dataset

Electric Freight Vehicles (EFVs) are a promising and increasingly popular alternative to conventional trucks in urban pickup/delivery operations. A key concerned research topic is to develop trip-based Tank-to-Wheel (TTW) analyses/models for EFVs energy consumption: notably, there are just a few studies in this area. Leveraging an earlier research on passenger electric vehicles, this paper aims at filling this gap by proposing a microscopic backward highly-resolved power-based EFVs energy consumption model (EFVs-ECM). The model is estimated and validated against real-world data, collected on a fleet of five EFVs in the city centre of Rome, for a total of 144 observed trips between subsequent pickup/delivery stops. Different model specifications are tested and contrasted, with promising results, in line with previous findings on electric passenger vehicles.     

Modelling media choices in business organizations: Implications for analyzing telecommunications-transportation interactions

A framework to analyze the impact of the new electronic communication technologies on transportation systems in the business setting has been proposed. A component of the framework that focuses on the media choice processes of individuals was empirically tested using scenarios in which the physical mail media, courier services, the telephone, voice mail, and electronic mail were alternatives to complete information communication tasks. The media choice model developed in this research emphasizes the influence of the context of the communication activity on media choice. Individual and organizational characteristics are explicitly considered, along with variables describing the communication activity itself, and the relationship between the initiator and the recipient of the communication. These characteristics determine the relative importance of a set of communication attributes and personal needs which are then matched against the individual's perceptions and feelings about the media to form preferences and choices in specific contexts. Empirical results are presented using preference logit models for a series of information communication situations. Significant variations in preferences and the levels of significance of the independent variables provided evidence of the strong influence the context of the communication activity has on media choice. Implications of these results are discussed in terms of other types of business meetings which may involve choices between telecommunications media and transportation modes. The primary contribution of this research is a methodology that can be extended to rigorously analyze telecommunications-transportation interactions. The paper discusses some of the issues that need to be addressed in modelling telecommunications-transportation interactions.     

Evaluating road safety audit procedures: some questions and a new method of study

This article discusses the current views on the application of road safety audits (RSAs) as a tool for the improvement of road safety and proposes a new method of study for detailed evaluation of some features that can influence their effectiveness. This is demonstrated in the practical setting of RSAs applied to existing road reviews or inspections. Starting from a review of published sources on RSAs in different countries, a set of questions has been identified and a case study undertaken on the impact of alternative RSA procedures on its overall effectiveness. RSA effectiveness is measured both as agreement with a safety expert assessment and to an accident-based study. The main features analyzed in the case study include the type of observer used for data collection and the type of checklist used as a guide to field work. Based on an exploratory study, the performance of RSA application is evaluated using weighted indices of concordance and disagreement, and the rating of detection or omission for the observations gathered in the accident diagnosis of the safety problems at the intersection studied, supplemented by a statistical analysis of the influence of selected covariates on these scores. The main results of the case study can be summarized as clear support to team work for field observation and also to employing less-experienced personnel for field observation. The study also stresses the need for procedures/criteria for priority setting. These conclusions are useful for the selection of alternative RSA procedures in agencies responsible for promoting or enforcing RSA and in professional teams carrying out RSA tasks. Several features deserving further study are also identified.     

Evolution of latent modal captivity and mode choice patterns for commuting trips: A longitudinal analysis using repeated cross-sectional datasets

This paper presents an investigation of the temporal evolution of commuting mode choice preference structures. It contributes to two specific modelling issues: latent modal captivity and working with multiple repeated crossectional datasets. In this paper latent modal captivity refers to captive reliance on a specific mode rather than all feasible modes. Three household travel survey datasets collected in the Greater Toronto and Hamilton Area (GTHA) over a ten-year time period are used for empirical modelling. Datasets collected in different years are pooled and separate year-specific scale parameters and coefficients of key variables are estimated for different years. The empirical model clearly explains that there have been significant changes in latent modal captivity and the mode choice preference structures for commuting in the GTHA. Changes have occurred in the unexplained component of latent captivities, in transportation cost perceptions, and in the scales of commuting mode choice preferences. The empirical model also demonstrates that pooling multiple repeated cross-sectional datasets is an efficient way of capturing behavioural changes over time. Application of the proposed mode choice model for practical policy analysis and forecasting will ensure accurate forecasting and an enhanced understanding of policy impacts.     

Understanding repetitive travel mode choices in a stable context: A panel study approach

It is argued that most travel mode choices are repetitive and made in a stable context. As an example, the everyday use of public transport is analyzed based on a panel survey with a random sample of about 1300 Danish residents interviewed up to three times in the period 1998–2000. The use of public transport is traced back to attitudes towards doing so, beliefs about whether or not public transportation can cover one’s transport needs, and car ownership. The influence of these variables is greatly attenuated when past behavior is accounted for, however. For subjects without a car, behavior changes are in the direction of greater consistency with current attitudes and perceptions. For car owners, current attitudes are inconsequential. The temporal stability of transport behavior is also higher for car-owners than for non-owners.     

Sustainable commute in a car-dominant city: Factors affecting alternative mode choices among university students

This paper studies university students’ commute and housing behaviors using samples from Los Angeles, a place notorious for car dependence and dominance. It finds that being embedded in this place does not make university students drive alone more than their peers in other places. Being multimodal and having a discounted transit pass increase the odds of alternative modes while holding a parking permit reduces the odds of these modes. Commute distance is positively related to carpool and telecommuting. Gender, status (undergraduate vs. gradate) and age are significantly correlated to biking, walking or public transit. Students living alone are more likely to commute by driving alone than other students. Having friends and classmates living nearby increases the odds of taking public transit. Due to data constraints, this study cannot prove whether there is any correlation between information contagion and the effects of living alone and having friends and classmates living nearby on alternative mode choice. But it proposes that the issue be worthwhile of further investigations. Base on the above, the paper recommends a comprehensive travel demand management program, utilization of information contagion effects of students and promotion of multimodal commute to better promote alternative mode of commute among university students.     

Transport/telecommunication interactions: Empirical evidence from a videoconferencing field trial in the United Kingdom

Interactions between transport and telecommunications have been an issue for much speculative assessment but little empirical research. The recent development of many new telecommunication services is now providing the opportunity, however, to monitor the use of telecommunication facilities from the moment of introduction, and to evaluate their impact on communications behaviour. This paper describes a study of user reactions in a videoconferencing field trial in the United Kingdom which considered these questions. In companies where regular use became established, the role of videoconferencing was primarily as a complement to traditional face-to-face meetings, as well as to simpler forms of communication, substituting for both to an extent, and also being used in its own right; 87% of survey respondents felt their trips had decreased, and 75% found it a satisfactory alternative to travel. A decline in spontaneity within meetings was compensated by shorter duration, a higher degree of task orientation, increased effectiveness, and speedier decision making. In firms where regular use did not occur, various factors relating to the planning and management of the facility were mainly responsible. rather than any failings in the basic concept of videoconferencing.     

Accounting for dynamic speed limit control in a stochastic traffic environment: A reinforcement learning approach

This paper proposes a novel dynamic speed limit control model accounting for uncertain traffic demand and supply in a stochastic traffic network. First, a link based dynamic network loading model is developed to simulate the traffic flow propagation allowing the change of speed limits. Shockwave propagation is well defined and captured by checking the difference between the queue forming end and the dissipation end. Second, the dynamic speed limit problem is formulated as a Markov Decision Process (MDP) problem and solved by a real time control mechanism. The speed limit controller is modeled as an intelligent agent interacting with the stochastic network environment stochastic network environment to assign time dependent link based speed limits. Based on different metrics, e.g. total network throughput, delay time, vehicular emissions are optimized in the modeling framework, the optimal speed limit scheme is obtained by applying the R-Markov Average Reward Technique (R-MART) based reinforcement learning algorithm. A case study of the Sioux Falls network is constructed to test the performance of the model. Results show that the total travel time and emissions (in terms of CO) are reduced by around 18% and 20% compared with the base case of non-speed limit control.     

Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas

Understanding people flow at a citywide level is critical for urban planning and commercial development. Thanks to the ubiquity of human location tracking devices, many studies on people mass movement with mobility logs have been conducted. However, high cost and severe privacy policy constraints still complicate utilization of these data in practice. There is no dataset that anyone can freely access, use, modify, and share for any purpose. To tackle this problem, we propose a novel dataset creation approach (called Open PFLOW) that continuously reports the spatiotemporal positions of all individual’s in urban areas based on open data. With fully consideration of the privacy protection, each entity in our dataset does not match the actual movement of any real person, so that the dataset can be totally open to public as part of data infrastructure. Because the result is shown at a disaggregate level, users can freely modify, process, and visualize the dataset for any purpose. We evaluate the accuracy of the dataset by comparing it with commercial datasets and traffic census indicates that it has a high correlation with mesh population and link-based traffic volume.