Representing mental maps and cognitive learning in micro-simulation models of activity-travel choice dynamics

This paper develops a model, based on Bayesian beliefs networks, for representing mental maps and cognitive learning into micro-simulation models of activity-travel behavior. Mental maps can be used to address the problem that choice sets in models of travel demand are often ad hoc specified. The theory underlying the model is discussed, a specification is derived and numerical simulation is used to illustrate the properties of the model.     

A conceptual and methdological framework for the generation of activity-travel patterns

This paper develops a conceptual framework for the generation of activity and travel patterns in the context of more general structures and presents an integrated model system as a step toward development of an improved travel demand forecasting model system. We propose a two-stage structure to model activity and travel behavior. The first stage, the stop generation and stop/auto allocation models, consists of the choices for the number of household maintenance stops and the allocation of stops and autos to household members. The second stage, the tour formation model, includes the choices for the number of tours and the assignment of stops to tours for each individual, conditional on the choices in the first stage. Empirical results demonstrate that individual and household socio-demographics are important factors affecting the first stage choices, the generation of maintenance stops and the allocation of stops and autos among household members, and the second stage choices, the number of tours and the assignment of stops to tours. This revised version was published online in June 2006 with corrections to the Cover Date.     

Temporal disaggregation of travel demand for high resolution emissions inventories

The commonly used photochemical air quality model, the Urban Airshed Model (UAM), requires emission estimates with grid-based, hourly resolution. In contrast, travel demand models, used to simulate the travel activity model inputs for the transportation-related emissions estimation, typically only provide traffic volumes for a specific travel period (e.g. the a.m. and p.m. peak periods). A few transportation agencies have developed procedures to allocate period-based travel demand data into hourly emission inventories for regional grid cells. Because there was no theoretical framework for disaggregating period-based volumes to hourly volumes, application of these procedures frequently relied upon a single hypothetical hourly distribution of travel volumes. This study presents a new theoretical modeling framework that integrates traffic count data and travel demand model link volume estimates to derive intra-period hourly volume estimates by trip purpose. We propose a new interpretation of the model coefficients and define hourly allocation factors by trip purpose. These allocation factors can be used to disaggregate the travel demand model ‘period-based’ simulation volumes into hourly resolution, thereby improving grid-based, hourly emission estimates in the UAM.     

The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

Estimating induced travel for evaluation of metropolitan highway expansion

This paper demonstrates how induced travel can be estimated for incorporation into the evaluation process for highway expansion projects, at a sketch planning level of analysis. The approach is useful especially in cases where four-step urban travel models are either unavailable or are unable to forecast the full induced demand effects. The methodology is applied to a hypothetical freeway expansion analysis. Our analysis suggests that the magnitude of travel induced by highway expansion increases significantly as a function of initial congestion levels prior to expansion. However, under even extreme scenarios of initial congestion and consequent forecasted induced travel, there is a positive impact with respect to congestion relief.     

A real time forecasting tool for dynamic travel time from clustered time series

This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.     

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.

Short-term traffic flow rate forecasting based on identifying similar traffic patterns

The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management.     

A review of the impact of parking policy measures on travel demand

This paper reviews the empirical evidence relating to the impact of parking policy measures on the demand for parking and for travel. Disaggregate modal choice models, disaggregate parking location models and site‐specific studies of parking behaviour are examined. With regard to modal choice models, it is concluded that few studies deal adequately with parking factors, but that there is some support for the view that parking policy measures are a relatively important influence on modal choice. When parking location models are examined parking policy variables are shown to have a substantial impact on choice of parking location. With regard to site‐specific studies, the paper concludes that there is a great variation in the parking price elasticities quoted, which reflects partly the methodological problems associated with such studies. Suggestions to improve model specification are made.     

Short‐term travel time forecasts for transport information system in hong kong

In this paper, a case study is carried out in Hong Kong for demonstration of the Transport Information System (TIS) prototype. A traffic flow simulator (TFS) is presented to forecast the short‐term travel times that can be served as a predicted travel time database for the TIS in Hong Kong. In the TFS, a stochastic deviation coefficient is incorporated to simulate the minute‐by‐minute fluctuation of traffic flows within the peak hour period. The purposes of the case study are: 1) to show the applicability of the TFS for larger‐scale road network; and 2) to illustrate the short‐term forecasting of path travel times in practice. The results of the case study show that the TFS can be applied to real network effectively. The predicted travel times are compared with the observed travel times on the selected paths for an OD pair. The results show that the observed path travel times fall in the 90% confidence interval of the predicted path travel times.     

The influence of individuals’ environmental attitudes and urban design features on their travel patterns in sustainable neighborhoods in the UK

This paper explores the influence of individuals’ environmental attitudes and urban design features on travel behavior, including mode choice. It uses data from residents of 13 new neighborhood UK developments designed to support sustainable travel. It is found that almost all respondents were concerned about environmental issues, but their views did not necessarily ‘match’ their travel behavior. Individuals’ environmental concerns only had a strong relationship with walking within and near their neighborhood, but not with cycling or public transport use. Residents’ car availability reduced public transport trips, walking and cycling. The influence of urban design features on travel behaviors was mixed, higher incidences of walking in denser, mixed and more permeable developments were not found and nor did residents own fewer cars than the population as a whole. Residents did, however, make more sustainable commuting trips than the population in general. Sustainable modes of travel were related to urban design features including secured bike storage, high connectivity of the neighborhoods to the nearby area, natural surveillance, high quality public realm and traffic calming. Likewise the provision of facilities within and nearby the development encouraged high levels of walking.     

The influence of personal security fears on women's travel patterns

This paper investigates the influence that women's fears and apprehension about attack and harassment have on use of transport facilities. After initial consideration of the factors that most affect perceived risk, the personal security aspects of different modes are reviewed by reference to a survey conducted in Southampton in 1986. High levels of perceived insecurity were found, particularly for walking at night, in parks and subways and when waiting for public transport services in isolated areas. The conclusions argue that personal security is an important but neglected issue, deserving of greater attention by transport planners.     

Telecommuting and travel: state of the practice,state of the art

This paper provides an overview of the status of telecommuting in the United States, especially as it relates to changes in travel behavior. Regarding the state of the practice, the paper discusses some refinements to the definition of telecommuting that have developed through increased operational experience. It reports several policy statements involving telecommuting, and explores the appeal of telecommuting as a public policy instrument. It highlights some trends in the implementation of home-based and work center-based telecommuting, and suggests that visible public-sector involvement has been crucial to the increased activity in this area.In sketching the state of the art, the paper outlines some frequently-stated hypotheses on telecommuting and travel behavior, and summarizes current empirical findings relating to those hypotheses. Finally, it suggests a variety of topics suitable for further research. These include studying factors influencing the ultimate adoption levels of telecommuting; impacts on energy/air quality, mode choice, and location/urban form; interactions with other transportation demand management strategies; relationships to the traditional urban travel demand forecasting process; cost/benefit tradeoffs; and telecommuting centers.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Temporal transferability: trade-off between data newness and the number of observations for forecasting travel demand

Recent and large amounts of data are crucial for forecasting travel demand. However, in some cases, an older time point may have more data than a more recent time point. A trade-off between older data with a large number of observations and recent data with a smaller number of observations has not been investigated in the context of temporal transferability. In this paper, this trade-off is examined in the context of journey-to-work mode choice behaviours by utilising repeated cross-sectional data collected in Nagoya, Japan. Models estimated utilising different numbers of observations (ranging from 50 to 10,000) obtained at different time points (1971, 1981, and 1991) are applied to the forecasting of behaviours for 2001. Bootstrapping provides insights with statistical meaning. One finding is that the minimum number of observations from a recent time point that is required to produce a forecast statistically significantly better than that produced by older data with a larger number of observations is surprisingly stable, even when the number of observations from the older time point varies considerably. For example, 300–500 stable observations from 1981 produced forecasts that were statistically significantly better than that produced by 500–10,000 wide-ranging observations from 1971. Analysing the trade-off can help determine an efficient survey interval and sample size in an era of declining budgets for travel surveys.     

Discrete mode-choice analysis of urban travel demand by the Analytic Hierarchy Process

This paper develops a new procedure for the problem of multimodal urban corridor travel demand estimation by using the Analytic Hierarchy Process (AHP). Certain conceptual and operational features of the AHP are common to the discrete choice theory-based modeling approach. Whereas the computational and data requirements of standard discrete choice models are immense, the proposed AHP approach deals efficiently with multidimensionality, nested demand structure and discrete travel decision making behavior. The paper concludes by summarizing the AHP-aided, step-by-step procedure for metropolitan travel demand (modal split) estimation.     

A dynamic analysis of person and household activity and travel patterns using data from the first two waves in the Puget Sound Transportation Panel

Daily activity participation and travel patterns are examined using data from the Puget Sound Transportation Panel (PSTP), which contains two-pairs of daily travel diary information (wave 1 in 1989 and wave 2 in 1990). Summary data of the travel diaries at the person and household levels are obtained using cluster analysis. At the person level, four clusters are found reasonable for both activity and travel. The four-cluster solutions indicate substantial day-to-day variation in activity participation and similarity in travel behavior over time. Temporal changes are analyzed using contingency table methods and log-linear models. The analysis reveals that activity participation and mobility present many regularities over time. Transitions within each wave show strong dependence between two days for both activity and travel, with higher dependency for the travel patterns than for the activity patterns. These are consistent when the analysis focuses on a longer period such as a year. Temporal dependencies appear to be stronger in the household-based than the person-based analysis. A hierarchical structure is also found in the relationship between activity and travel clusters. The link between activity and travel is much stronger within a day, weaker from one day to another, and the least strong from one year to the next. Important irregularities, however, are found and may be due to scheduling time-frames adopted by the respondents that are only partially captured in the data used.     

A structural equations analysis of commuters' activity and travel patterns

An exploratory analysis of commuters' activity and travel patterns was carried out using activity-based travel survey data collected in the Washington, DC metropolitan area to investigate and estimate relationships among socio-demographics, activity participation, and travel behavior. Structural equations modeling methodology was adopted to determine the structural relationships among commuters' demographics, activity patterns, trip generation, and trip chaining information. Three types of structural equations model systems were estimated: one that models relationships between travel and activity participation, another that captures trade-offs between in-home and out-of-home activity durations, and a third that models the generation of complex work trip chains. The model estimation results show that strong relationships do exist among commuters' socio-demographic characteristics, activity engagement information, and travel behavior. The finding that significant trade-offs exist between in-home and out-of-home activity participation is noteworthy in the context of in-home vs. out-of-home substitution effects. Virtually all of the results obtained in this paper corroborate earlier findings reported in the literature regarding relationships among time use, activity participation, and travel. This revised version was published online in June 2006 with corrections to the Cover Date.