Henk Meurs

Spatial structure and mobility

Abstract The aim of this paper is to contribute to a clearer understanding of the extent to which the spatial structure and planning of the residential environment can explain mobility, in general, and the choice of mode of transport, in particular, and what spatial planning and traffic management aspects play a significant role in this. The research showed that certain aspects of the planned environment do indeed have a clear impact on mobility. These effects are particularly apparent in trips made for shopping and social or recreational purposes. It is mainly personal characteristics that largely or almost entirely determine commuter traffic. An integral approach to the planning of residential areas is required to achieve any great changes in mobility. Only then may we expect spatial planning to have any significant impact on car use.

Factors Influencing Stop-Level Transit Ridership in Arnhem–Nijmegen City Region, Netherlands

The factors that influence transit ridership are explored at the level of individual transit stops for the local and regional bus transit system in the region of Arnhem–Nijmegen in the Netherlands. Direct transit ridership modeling was used to explore simultaneously the influence of spatial, population, and network characteristics on bus stop–level ridership (number of passengers boarding and alighting from transit vehicles at each particular transit stop). Cross-sectional multiple regression models were built for two periods: March 2012 and March 2013. Between these periods, the regional transit supply changed considerably because of the start of a new tender period. The outcomes of the cross-sectional multiple regression models were compared with fixed-effects panel data models, which related the changes in ridership between both periods to the changes in transit supply characteristics. The adjusted R2 of the two cross-sectional models are .772 and .762, respectively; this finding shows that the models perform well in explaining the variance in ridership. Most selected independent variables are highly significant, and their influence on ridership is largely in line with expectations. The cross-sectional and the panel data models show large similarities, but the values of most coefficients in the panel data model are only about half of the corresponding variables in the cross-sectional models. This finding is likely due to the potential adjustment time that travelers need to get used to the changes in transit services and to an overestimation of the importance of transit supply because of the endogeneity between supply and potential demand in the cross-sectional models.

Quick-Scan Appraisal Method to Determine Cost-Effectiveness of Traffic and Demand Management Measures

This paper presents a quick-scan approach to assess the cost-effectiveness of smaller and poorly demarcated transportation measures; the approach can be used as an initial scan while packages are established to solve specific transportation problems. This paper adds to the available evaluation literature and relies on a combination of expert opinions and simple models rather than on data-intensive, four-stage transportation models. The approach consists of five steps and yields an assessment of the cost-effectiveness of the measure that is being evaluated. As an illustration of this approach, the cost-effectiveness of a pricing measure within a large Dutch travel demand management program was determined to illustrate the approach itself and the plausibility of its results. It was concluded that the proposed method was suitable for an initial quick-scan assessment. This assessment would be valuable in the first selection of packages of measures and could support policy makers who must decide in which measures to invest, even when those measures have not yet been described or designed at a highly detailed level.