23 Transportation Research Record: Journal of the Transportation Research Board, No. 2537, Transportation Research Board, Washington, D.C., 2015, pp. 23–32. DOI: 10.3141/2537-03 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 rider- ship modeling was used to explore simultaneously the inﬂuence of spa- tial, 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 R 2 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. Transit systems support a broad range of goals that include provision of mobility to the disadvantaged, access to employment or attraction centers, air pollution reduction, congestion reduction, and the promo- tion of economic development. Understanding the factors that inﬂu- ence transit ridership is very important to achieve these goals and increase transit market potential. Previous research shows the effects on transit usage of demo- graphic, socioeconomic, and spatial variables as well as the transit level of service and the relative performance of transit compared with other transportation modes. The density of population and employ- ment, the diversity in land use, and the accessibility of the transit system are seen as important aspects. However, the literature also reveals some shortcomings. Not many studies take spatial, population, and network characteristics into account simultaneously. Furthermore, most research is con- ducted on an aggregate, usually system- or regionwide, level. To capture the microscale effects analyses at a disaggregated scale are necessary. For instance, variation in land use characteristics between different parts of routes can best be captured and examined at the station level. In addition, changes in networks and stops might have speciﬁc effects on ridership that cannot be captured by conventional cross-sectional (CS) analysis since time may be necessary to adjust to new networks. Direct transit ridership modeling is a method capable of exploring the inﬂuence of the characteristics of the transit stop and its surround- ings as well as transit supply at the stop level on the number of pas- sengers boarding and alighting from transit vehicles at a particular transit stop (i.e., transit stop–level ridership). Most direct ridership models use a CS multiple regression method with station-based rider- ship as the dependent variable and different characteristics of the sta- tion environment and transit level of service as independent variables. This method is mainly developed and used to investigate the effects of spatial development in station areas on future transit use, espe- cially in the context of smart growth and transit-oriented devel- opment (1). In contrast to the traditional four-step travel demand forecasting models, direct ridership models are able to capture the effects of the built environment and transit services on transit rider- ship (2). Although transit-oriented development is not particularly relevant for local bus transit services, direct ridership modeling can be used to gain an understanding of the factors explaining transit ridership. Most of the direct ridership modeling research, and especially studies addressing bus-based systems, has been conducted for transit systems in the United States (3–6). Given the substantial differences between U.S. and European cities in terms of the quality of the transit systems, the general attitude toward transit, the role of bicycles in the transport system, and spatial patterns, it is relevant to examine the inﬂuence of land use characteristics and transit supply on transit ridership in a European setting. In the current study, the factors that inﬂuence transit stop–level ridership are analyzed for the bus-based transit system in the region of Arnhem–Nijmegen in the Netherlands. CS multiple regression models were used with bus stop ridership explained by land use, sociodemographic, and transit supply characteristics. The outcomes are compared with those for panel data models for bus ridership, and changes in ridership are compared with changes in network character- istics. This comparison reveals the forecasting quality of CS models: if coefﬁcients are fairly equal, the CS model will predict changes relatively well; if coefﬁcients deviate, it is necessary to investigate the reasons for this deviation and consider methods for improving the models. Factors Inﬂuencing Stop-Level Transit Ridership in Arnhem–Nijmegen City Region, Netherlands Kasper Kerkman, Karel Martens, and Henk Meurs Institute for Management Research, Radboud University Nijmegen, P.O. Box 9108, Nijmegen, 6500 HK, Netherlands. Corresponding author: K. Kerkman, k.kerkman@ fm.ru.nl.