Public perceptions of the use of dynamic message signs

Intelligent transportation systems have been promoted as a means to improve both the efficiency and safety of the road network. The effectiveness of advanced technologies in improving road safety has been an area of research which has thus far yielded mixed results. In order to ensure that advanced technologies deliver on their intended outcomes, more research has to be devoted to understanding road users' perceptions and reactions to these systems. This study examines drivers' perceptions of the use of dynamic message signs and their self‐reported reactions to the messages displayed. In general, drivers support the use of highway electronic boards for traffic incident reports and weather information which have an impact on traffic delays and level of service. They also think that it is a good idea to display road safety messages and to remind drivers to drive safely and be courteous on the roads. Moreover, most drivers reported that they do read and think about the messages displayed and react positively to some of the road safety messages.     

Deconstructing development density: Quality, quantity and price effects on household non-work travel

Smart growth and transit-oriented development proponents advocate increasing the density of new land development and infill redevelopment. This is partly in order to reduce auto use, by reducing distances between trip origins and destinations, creating a more enjoyable walking environment, slowing down road travel, and increasing the market for transit. But research investigating how development density influences household travel has typically been inadequate to account for this complex set of hypotheses: it has used theoretically unjustified measures, has not accounted for spatial scale very well, and has not investigated potentially important combinations of measures. Using data from a survey of metropolitan households in California, measures of development density corresponding to the main hypotheses about how density affects travel—activity density affecting distance traveled, network load density affecting the speed of auto travel, and built form density affecting the quality of walking—are tested as independent variables in models of auto trip speed and individual non-work travel. Residential network load density is highly negatively correlated with the speed of driving, and is also highly correlated with non-work travel, both singly and in combination with other measures. Activity density and built form density are not as significantly related, on their own. These results suggest that denser development will not influence travel very much unless road level-of-service standards and parking requirements are reduced or eliminated.     

Passengers’ willingness to pay for airport ground access time savings

There are cases when passengers are willing to pay a premium to reduce the travel time, in particular when the trip has to be made. This paper aims to provide insight into factors that determine passengers’ willingness to pay to reduce travel time for their ground access to an airport. A methodology is developed that comprises two steps: the identification of the passengers with zero willingness to pay and from the rest the estimation of the additional price they are willing to pay to reduce their travel time. For the first step a Probit model was formulated and for the second a linear regression model. To this purpose, data has been collected employing stated preference from passengers at the Athens International Airport. It has been found that a high percentage of passengers have zero willingness to pay, and of the remaining ones those using public transport have a significant willingness to pay to reduce access travel time. The methodology and the models are structured in such a way that their transferability to any airport environment is possible, thus providing a useful tool for decisions relating to airport ground access measures.     

Modeling children’s school travel mode and parental escort decisions

Understanding of the activity-travel patterns of children is becoming increasingly important to various policy makers. Further, there is also a growing recognition that intra-household interactions need to be explicitly accommodated in travel models for realistic forecasts and policy evaluation. In the light of these issues, this paper contributes towards an overall understanding of the school-travel behavior of children and the related interdependencies among the travel patterns of parents and children. An econometric model is formulated to simultaneously determine the choice of mode and the escorting person for children’s travel to and from school. The 2000 San Francisco Bay Area Travel Survey (BATS) data are used in the model estimation process. Empirical results indicate that the characteristics of child like age, gender, and ethnicity, and employment and work flexibility characteristics of the parents have strong impacts on the mode choice decisions. In addition, the impacts of some of these attributes on the choice of mode to school are different from the corresponding impacts on the choice of mode from school. The distance between home and school is found to strongly and negatively impact the choice of walking to and from school, with the impact being stronger for walking to school. Several land-use and built-environment variables were explored, but were found not to be statistically significant predictors.

A link-node complementarity model and solution algorithm for dynamic user equilibria with exact flow propagations

In this paper, we propose a link-node complementarity model for the basic deterministic dynamic user equilibrium (DUE) problem with single-user-class and fixed demands. The model complements link-path formulations that have been widely studied for dynamic user equilibria. Under various dynamic network constraints, especially the exact flow propagation constraints, we show that the continuous-time dynamic user equilibrium problem can be formulated as an infinite dimensional mixed complementarity model. The continuous-time model can be further discretized as a finite dimensional non-linear complementarity problem (NCP). The proposed discrete-time model captures the exact flow propagation constraints that were usually approximated in previous studies. By associating link inflow at the beginning of a time interval to travel times at the end of the interval, the resulting discrete-time model is predictive rather than reactive. The solution existence and compactness condition for the proposed model is established under mild assumptions. The model is solved by an iterative algorithm with a relaxed NCP solved at each iteration. Numerical examples are provided to illustrate the proposed model and solution approach. We particularly show why predictive DUE is preferable to reactive DUE from an algorithmic perspective.     

A week in the life of baby boomers: how do they see the spatial–temporal organization of their activities and travel?

In this paper, we take an initial look at the spatial and temporal flexibility in the activity patterns of the so-called “baby-boomer” cohort (born 1947–1966) in comparison with younger and older adults. Using a unique longitudinal survey carried in Quebec City from 2002 to 2005, we explore activity patterns and trip rates over a seven-day observation period during the first wave, and take a first look at some aspects of their evolution over two subsequent waves at about one-year intervals. We model the propensity to undertake activities within selected conventional non-work classifications such as “shopping” and “leisure”, and also according to respondents’ own perceptions of the spatial and temporal flexibility of each out-of-home activity that they had executed. While we cannot strictly separate cohort effects from age-related effects, after controlling for gender and household structure, we infer that age and related lifestyle effects dominate in explaining these propensities. However, the boomers were the only age stratum to increase their total out-of-home activity participation over the course of the panel, an intriguing starting point for the future study of this cohort.

Examining the influence of multidestination service orientation on transit service productivity: a multivariate analysis

Between 1990 and 2000, U.S. transit agencies added service and increased ridership, but the ridership increase failed to keep pace with the service increase. The result was a decline in service effectiveness (or productivity). This marks the continuation of a long-running and often-studied trend. The scholarly literature attributes this phenomenon, at least in part, to transit agency decisions to decentralize their service rather than focus on serving the traditional CBD market. Many scholars argue that a decentralized service orientation is both ineffective and inefficient because it attracts few riders and requires large per-rider subsidies. This research tests whether a non-traditional, decentralized service orientation, called multidestination service, results in reduced service productivity. Contrary to what the literature suggests, we find that MSAs whose transit agencies pursued a multidestination service orientation did not experience lower productivity. These results indicate that policies that have encouraged the growth of decentralized transit services have not necessarily been detrimental to the industry.

Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California

Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography, with different indicators affecting each group differentially. This analysis complements past studies that used regression models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method here is richer than many other methods used to study the ethnically diverse population of California and shows the addition of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined accordingly.

Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)

Studies of urban travel behaviour typically focus on weekday activities and commuting. This is surprising given the rising contribution of discretionary activities to daily travel that has occurred during the last few decades. Moreover, current understanding of the relationship between travel behaviour and land use remains incomplete, with little research carried out to explore spatial properties of activity-travel behaviour during the off-peak and weekend time periods. Weekend behaviours, for example, influenced by the availability of time and the spatiotemporal distribution of “weekend” destinations, likely produce spatially and temporally distinct activity-travel patterns. Using data from the first wave of the Toronto Travel-Activity Panel Survey (TTAPS), this paper examines an area of research that has received little attention; namely, the presence of spatial variety in activity-travel behaviour. The paper begins by looking at the extent to which individuals engage in spatially repetitive location choices during the course of a single week. Area-based measures of geographical extent and activity dispersion are then used to expose differences in weekday-to-weekend and day-to-day activity-travel patterns. Examination of unclassified activities carried out over a 1 week period reveals a level of spatial repetition that does not materialise across activities classified by type, travel mode, and planning strategy. Despite the inherent spatial flexibility offered by the personal automobile, spatial repetition is also found to be surprisingly similar across travel modes. The results also indicate weekday-to-weekend, and day-to-day fluctuations in spatial properties of individual activity-travel behaviour. These findings challenge the utility of the short-run survey as an instrument for capturing archetypal patterns of spatial behaviour. In addition, the presence of a weekday-to-weekend differential in spatial behaviour suggests that policies targeting weekday travel reduction could have little impact on travel associated with weekend activities.

Driver behavior formulation in intersection dilemma zones with phone use distraction via a logit-Bayesian network hybrid approach

Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near signalized intersections where drivers hesitate when making decisions related to their driving behaviors. Therefore, the dilemma zone has been identified as an area with high crash potential. This article utilizes a logit-based Bayesian network (BN) hybrid approach to investigate drivers' decision patterns in a dilemma zone with phone use, based on experimental data from driving simulations from the National Advanced Driving Simulator (NADS). Using a logit regression model, five variables were found to be significant in predicting drivers' decisions in a dilemma zone with distractive phone tasks: older drivers (50–60 years old), yellow signal length, time to stop line, handheld phone tasks, and driver gender. The identified significant variables were then used to train a BN model to predict drivers' decisions at a dilemma zone and examine probabilistic impacts of these variables on drivers' decisions. The analysis results indicate that the trained BN model was effective in driver decision prediction and variable influence extraction. It was found that older drivers, a short yellow signal, a short time to stop line, nonhandheld phone tasks, and female drivers are factors that tend to result in drivers proceeding through intersections in a dilemma zone with phone use distraction. These research findings provide insight in understanding driver behavior patterns in a dilemma zone with distractive phone tasks.