Route choice of cyclists in Zurich

This paper presents the first route choice model for bicyclists estimated from a large sample of GPS observations and overcomes the limitations inherent in the generally employed stated preference approach. It employs an improved mode detection algorithm for GPS post-processing to determine trips made by bicycle, which are map matched to an enriched street network. The alternatives are generated as a random sample from an exhaustive, but constrained search. Accounting for the similarity between the alternatives with the path-size factor the MNL estimates show that the elasticity with regards to trip length is nearly four times larger than that with respect to the share of bike paths. The elasticity with respect to the product of length and maximum gradient of the route is small. No other variable describing the routes had an impact. The heterogeneity of the cyclists is captured through interaction terms formulated on their average behaviour.     

Influence of vehicular traffic on urban air quality – A case study of Hyderabad,India

In the present study, impact of vehicular traffic emissions on black carbon aerosol mass concentration, trace gases and ground reaching solar radiation were analyzed during nationwide truck strike of 5–12 January, 2009 over urban environment of Hyderabad, India. A significant reduction of about 57%, 60%, 40% and 50% was observed in black carbon, particulate matter, carbon monoxide and ozone respectively during nationwide truck strike period. Results of the study are important for source apportionment of pollutants as the strike created natural laboratory for studying the impact of diesel operated trucks on urban air quality.     

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.     

Transit assignment under crowded conditions

This paper presents a transit assignment algorithm for crowded networks. Both congestion in vehicles and queuing at stations are explicitly taken into account in predicting passenger flows for a fixed pattern of origin-destination trip demands. The overflow effects due to insufficient capacity of transit lines are considered to be concentrated at transit stations, while the in-vehicle congestion effects (or discomforts) are considered to be dependent on in-vehicle passenger volume. Overflow delay at a transit station is dependent on the number of excess passengers required to wait for the next transit car. We use a logit model to determine the split between passengers that chose to wait for the next transit car and passengers that chose to board on the alternative transit lines. The proposed algorithm predicts how passenger will choose their optimal routes under both queuing and crowded conditions.     

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.

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.

Measuring community reaction to urban transportation impacts using Von Neumann‐Morgenstern utility theory

This paper attempts to measure the impacts of urban transportation system improvements or changes on the community. The community's perceptions of the impacts are represented by its utilities (or disutilities) over various ranges of values of the multiple attributes representing these impacts. The utility technique used in the evaluation is based upon von Neumann‐Morgenstern (vN‐M, 1947) Utility Theory, and is applied using Raiffa's (1970) Fractile Method.

The paper specifically applies the technique to model the perceptions of five subgroups within a community to the impact of a new light rail transit system that is being incorporated in the transportation system of the City of Calgary.

Results of the modeling indicate explicitly how the community changes its perception over ranges of values of the attributes evaluated. Biases of various subgroups within the community over these attributes are also shown. Statistical tests indicate that aggregated utility perceptions can represent the utility perceptions of the individual subgroups quite reasonably.     

Motorway signals and the detection of incidents

A mathematical model of automobile trip tours is presented. Within a framework of eight common restrictions on automobile trip making, all travel behavior is assumed random and all of the ways in which tours can be arranged are assumed equally likely. Three probability distributions are derived from the model: (1) the probability that a household makes a given number of tours in a day; (2) the probability that a household makes a given number of trips in a day; and (3) the probability that a tour reaches a given number of destinations. It is shown that the model agrees with similar probability distributions generated from home‐interview data for Milwaukee.     

Dynamics of LRT growth: Karlsruhe since 1975

The public transport system of Karlsruhe, in particular its innovative services on jointly used heavy rail lines, has received substantial attention throughout the past 20 years. The discussion of the system and of its development has been rather limited in the past, mostly highlighting technical aspects. This paper provides an overview of the development, including the urban development, of the region and of the funding system. The ridership and financial development of the operator is documented in detail. Three case studies focus on the integration of the system development in the general political process of the region and demonstrate the limitations of the operator under its current ownership structures.