This thesis constitutes a first step towards measuring the passenger capacity of station platforms. First, it defines platform capacity on the basis of the locations of queues at exit escalators from platforms, the presence of passengers in the platform-edge danger zone and the duration of stops. It then renders capacity measurable using real-life data covering train stops and passenger behaviour on platforms.@en

Amsterdam Central Station is the second busiest station in The Netherlands. Several bottlenecks in the 125 years old monumental station exist due to present train and pedestrian volumes. Further traffic growth is expected. To understand and quantify the options for redesign, a thorough understanding of the interaction between the core traffic functionalities is required. The paper will demonstrate high-level evaluations of different train positions, train schedules and platform exits, using a tailor-made macroscopic passenger transfer chain model. This approach, the model and the outcomes helped the stakeholders involved to reach consensus regarding the choices to be made for the future.@en

The train station beneath Schiphol Airport in Amsterdam, Netherlands, is one of the fastest-growing ones in the country. Passenger growth has resulted in severe congestion at platforms, a situation that hinders passenger processes at the platform, particularly the boarding and alighting processes. This situation has a negative impact on station dwell times. Inefficient movement of pedestrian traffic at Schiphol Airport train station, a primary hub in the national railway network, has negative consequences for many trains and passengers in that network. In the short term, efficiency improvements are the only viable option to cope with passenger growth. An unfavorable stopping position for trains has been identified as a potential source of the inefficiency. The station layout causes concentrations of boarding passengers at doors in the front sections of trains. Because of these concentrations, the exchange of passengers and the station dwell times take longer at this station than necessary. Because existing research did not provide generalized insights, Netherlands Railways and Delft University of Technology in the Netherlands have conducted a series of field experiments. The main objective was to test whether adjustments of the train stopping position along the platform would result in lower station dwell times because of a more efficient process of passenger exchange. A slight adjustment of train stopping position resulted in a 20% decrease of station dwell times during peak demand and contributed to railway capacity. Moreover, dwell time variation decreased by approximately 50% and thereby promoted more robust train operations (punctuality).@en

The macroscopic fundamental diagram (MFD) relates the flow, density and speed of an entire network. So far, the MFD has been mostly applied to cases where pedestrians and vehicles were aiming to reach their destinations as fast as possible. However, pedestrian facilities involve different behaviours. Especially in train stations, travellers spend more time waiting than walking. Moreover, complex passenger flows (i.e. flows in different directions moving to stairs and escalators distributed over the platform) may occur on the platform, due to passengers. In this paper we show that passenger flows on platforms can be described by an MFD.@en

Slow modes have an increasing share in urban mobility. The lack of accurate revealed data has so far hampered scientific research aimed at unravelling slow mode mobility. Multiple types of data can be collected to gain better insight into slow mode transport and traffic operations, such as counts on specific locations (cross-sections), distributions of flows over the network and dynamics thereof. Typical data collection techniques for vehicular traffic, such as induction loops, cannot be applied, among other things due to the fact that slow modes are not restricted to lanes. Therefore, other, non-intrusive, ways to collect these data need to be investigated.In our paper we look at the applicability of Bluetooth (BT) and WiFi sensors to collect data on pedestrian and cycle flows, using two case studies. The first case study covers the data filtering process, to come from the raw sensor data to the information necessary for behavioural research. It describes the application of 9 sensors in the inner city of Amsterdam. The second case study deals with a BT/WiFi sensor network, installed in the station of Utrecht, the Netherlands. Using these data, we have successfully estimated choice models for the route choice and activity choice behaviour of departing train travellers, showing the potential use of BT/WiFi as a (revealed) data source for modelling travel behaviour in a station.@en

As of 2014, three Dutch train stations have been equipped with automated pedestrian traffic sensors, as part of the SMART Station initiative of NS Stations. These stations are Utrecht Central station, Amsterdam Airport Schiphol train station and Leiden Central station. SMART Station consists of hybrid Bluetooth/WiFi sensors for tracking and infrared sensors for counting. Combining data from both sensor types results in useful insights into the pedestrian dynamics. In this paper, four SMART Station cases are presented. Firstly, an estimation of escalator capacity will be presented. Secondly, we will show the temporal and spatial flow characteristics of a very large bike parking facility. Thirdly, the use of train stations by non-train passengers will be explored. Fourthly and finally, the occupancy of a station hall will be explored. @en

Due to rapid rail passenger growth in the last years, crowding challenges have risen at several stations in Switzerland and The Netherlands. Particularly at platforms, safety risks can increase when a station is operated near or at pedestrian capacity during peak times. Passengers are observed to cross the safety line at busy platforms when a train is due to arrive. From our daily practice, we expect high densities to be one of the main factors driving use of the danger zone at platforms of train stations. Some previous academic research has also addressed pedestrian densities at the platforms to be one of the factors driving the involuntary use of the danger zone. By using stereo sensors to track persons on a platform in Bern (Switzerland) as well as in Utrecht Centraal (The Netherlands) we were able to show that the use of the danger zone is related to the pedestrian density but also to other factors like obstacles on the platform. An additional survey conducted in Switzerland showed that the persons interviewed were not always aware of their use of the danger zone. Risks are not always clear.@en

Due to rapid rail passenger growth in the last years, crowding challenges have risen at several stations in Switzerland and The Netherlands. Particularly at platforms, safety risks can increase when a station is operated near or at pedestrian capacity during peak times. Passengers are observed to cross the safety line at busy platforms when a train is due to arrive. From our daily practice, we expect high densities to be one of the main factors driving use of the danger zone at platforms of train stations. Some previous academic research has also addressed pedestrian densities at the platforms to be one of the factors driving the involuntary use of the danger zone. By using stereo sensors to track persons on a platform in Bern (Switzerland) as well as in Utrecht Centraal (The Netherlands) we were able to show that the use of the danger zone is related to the pedestrian density but also to other factors like obstacles on the platform. An additional survey conducted in Switzerland showed that the persons interviewed were not always aware of their use of the danger zone. Risks are not always clear.@en

Train stations play a vital role in the door to door travel experience of train passengers. From the passengers’ value of time perspective, the station is the weakest link in total time value of the journey. Within the station the transfer function – moving between the various transport modes and waiting at the next service - is the core function, because it connects all transport modes. Therefore, an optimal transfer process is a key requirement for a well performing station. The basic requirement for optimization of the transfer process is to understand how many passengers use the transfer facilities, and at what time. This information is delivered by the Station Transfer Model, which taps into data already available at the train operating and station management divisions of Netherlands Railways (NS). With the model, the volumes of pedestrian flows at platforms, escalators and stairs can be estimated for various time frames. The Station Transfer Model generates output that is useful for evaluation of the commercial performance of the station’s retail. It can also be used to estimate the required capacity of stairs and escalators between the platforms and the station hall, tunnels or bridges. When renewed data is available, NS intents to further develop the model to increase the quality of the output.@en

Train stations play a vital role in the door to door travel experience of train passengers. From the passengers’ value of time perspective, the station is the weakest link in total time value of the journey. Within the station the transfer function – moving between the various transport modes and waiting at the next service - is the core function, because it connects all transport modes. Therefore, an optimal transfer process is a key requirement for a well performing station. The basic requirement for optimization of the transfer process is to understand how many passengers use the transfer facilities, and at what time. This information is delivered by the Station Transfer Model, which taps into data already available at the train operating and station management divisions of Netherlands Railways (NS). With the model, the volumes of pedestrian flows at platforms, escalators and stairs can be estimated for various time frames. The Station Transfer Model generates output that is useful for evaluation of the commercial performance of the station’s retail. It can also be used to estimate the required capacity of stairs and escalators between the platforms and the station hall, tunnels or bridges. When renewed data is available, NS intents to further develop the model to increase the quality of the output.@en

Train stations play a vital role in the door to door travel experience of train passengers. From the passengers’ value of time perspective, the station is the weakest link in total time value of the journey. Within the station the transfer function – moving between the various transport modes and waiting at the next service - is the core function, because it connects all transport modes. Therefore, an optimal transfer process is a key requirement for a well performing station. The basic requirement for optimization of the transfer process is to understand how many passengers use the transfer facilities, and at what time. This information is delivered by the Station Transfer Model, which taps into data already available at the train operating and station management divisions of Netherlands Railways (NS). With the model, the volumes of pedestrian flows at platforms, escalators and stairs can be estimated for various time frames. The Station Transfer Model generates output that is useful for evaluation of the commercial performance of the station’s retail. It can also be used to estimate the required capacity of stairs and escalators between the platforms and the station hall, tunnels or bridges. When renewed data is available, NS intents to further develop the model to increase the quality of the output.@en

In the Netherlands, rail passenger transport has grown considerably during the last decades. Although growth has been seen nationwide, it has concentrated at a few of the largest stations in the country (e.g. Amsterdam Central and Utrecht Central station). As a result, these stations are being redeveloped. Furthermore, this growth trend is expected to continue into the future. This also means that (temporary) overcrowding can arise at these stations. These trends have triggered a need for more insights into the behaviour of passengers inside train stations. With these insights, existing stations can be better managed, and redevelopments can be better designed and planned. This paper presents quantitative research results regarding the factors that influence route and activity location choice behaviour of departing passengers at train stations. For this study, SMART Station data has been used. This automated data collection system is based on Bluetooth, Wi-Fi and infrared sensors. It generates revealed preference data on passenger routes and activities in train stations. Estimated discrete choice models based on these data show which factors influence the choice of departing passengers for vertical infrastructure facilities (i.e. escalators and stairways; route choice) and the choice of retail outlets in the station building (activity location choice). The factors significantly influencing route choice behaviour of departing passengers are: travel time, walking distance, train stop location alongside the platform and right side orientation of the vertical infrastructure facility which provides access to the platforms. Travel time and walking distance appeared to be highly correlated. To get a better model fit, only the first has been included in the final model, together with train stop location and orientation. Travel time is the most important factor. In the activity location choice model, also four factors turned out to be of significant influence: travel time from station entrance to the retail outlet, total distance (from entrance via outlet to the platform), the requirement to make a detour for a shop visit, and right side orientation. Distance is the most important factor in this model. To the best of our knowledge, before this study there has been no empirical, quantitative evidence of the factors which contribute to route and activity location choices inside a train station. Due to the large amount of revealed preference data which has been collected by Bluetooth/Wi-Fi-sensors, this study can be considered as first of its kind in the field of train passenger route and activity choice behaviour in train stations.@en

In the Netherlands, rail passenger transport has grown considerably during the last decades. Although growth has been seen nationwide, it has concentrated at a few of the largest stations in the country (e.g. Amsterdam Central and Utrecht Central station). As a result, these stations are being redeveloped. Furthermore, this growth trend is expected to continue into the future. This also means that (temporary) overcrowding can arise at these stations. These trends have triggered a need for more insights into the behaviour of passengers inside train stations. With these insights, existing stations can be better managed, and redevelopments can be better designed and planned. This paper presents quantitative research results regarding the factors that influence route and activity location choice behaviour of departing passengers at train stations. For this study, SMART Station data has been used. This automated data collection system is based on Bluetooth, Wi-Fi and infrared sensors. It generates revealed preference data on passenger routes and activities in train stations. Estimated discrete choice models based on these data show which factors influence the choice of departing passengers for vertical infrastructure facilities (i.e. escalators and stairways; route choice) and the choice of retail outlets in the station building (activity location choice). The factors significantly influencing route choice behaviour of departing passengers are: travel time, walking distance, train stop location alongside the platform and right side orientation of the vertical infrastructure facility which provides access to the platforms. Travel time and walking distance appeared to be highly correlated. To get a better model fit, only the first has been included in the final model, together with train stop location and orientation. Travel time is the most important factor. In the activity location choice model, also four factors turned out to be of significant influence: travel time from station entrance to the retail outlet, total distance (from entrance via outlet to the platform), the requirement to make a detour for a shop visit, and right side orientation. Distance is the most important factor in this model. To the best of our knowledge, before this study there has been no empirical, quantitative evidence of the factors which contribute to route and activity location choices inside a train station. Due to the large amount of revealed preference data which has been collected by Bluetooth/Wi-Fi-sensors, this study can be considered as first of its kind in the field of train passenger route and activity choice behaviour in train stations.@en

Due to rapid rail passenger growth in the last years, crowding challenges have risen at several stations in Switzerland and The Netherlands. Particularly at platforms, safety risks can increase when a station is operated near or at pedestrian capacity. Therefore, Swiss and Dutch station managers started several initiatives to measure crowding-related safety risks. Recently, pedestrian measurement technology has improved substantially. New technology is capable of anonymously tracking individual pedestrians within a predefined area under high intensity conditions. This technology has not been implemented at train stations before. Therefore the Swiss and Dutch station managers have developed and applied a methodology to determine the validity of the data which are generated by the newest generation of pedestrian measurement systems at the stations of Bern (CH), Amsterdam Zuid, and Utrecht Centraal (NL). This paper presents the results of the tests in both countries and their (first) implications for science and practice.@en

Due to rapid rail passenger growth in the last years, crowding challenges have risen at several stations in Switzerland and The Netherlands. Particularly at platforms, safety risks can increase when a station is operated near or at pedestrian capacity. Therefore, Swiss and Dutch station managers started several initiatives to measure crowding-related safety risks. Recently, pedestrian measurement technology has improved substantially. New technology is capable of anonymously tracking individual pedestrians within a predefined area under high intensity conditions. This technology has not been implemented at train stations before. Therefore the Swiss and Dutch station managers have developed and applied a methodology to determine the validity of the data which are generated by the newest generation of pedestrian measurement systems at the stations of Bern (CH), Amsterdam Zuid, and Utrecht Centraal (NL). This paper presents the results of the tests in both countries and their (first) implications for science and practice.@en