N. van Oort
Please Note
84 records found
1
Not Just a Stop: Understanding Safety at Small Train Stations
A Qualitative study on perceived safety and coping behavior at small Dutch railway stations
The findings reveal how perceived safety is shaped by multidimensional and overlapping dynamics between temporal, spatial, social, and personal factors. Temporal conditions, particularly evening and nighttime travel, as well as gender identity, act as an amplifying lens through which the other factors are interpreted and experienced. Moreover, travelers identifying as women frequently adopted coping behaviors, such as arranging pick-ups or calling someone, thereby relying on their social networks. Travelers identifying as men often described their gender as a protective factor. Furthermore, the interviews with experts indicated that institutional dynamics, including fragmented responsibilities and difficulties regarding data sharing, further complicate the specific contexts of small railway stations that influence perceived safety.
At the same time, current monitoring tools do not include the complex nature of experienced safety and the related dynamics between the different factors influencing it. This study suggests that small railway stations deserve more attention in governance and monitoring frameworks. Creating station environments where travelers feel comfortable contributes to positive travel experiences and highlights perceived safety as a crucial condition for realizing equal mobility and accessibility. ...
The findings reveal how perceived safety is shaped by multidimensional and overlapping dynamics between temporal, spatial, social, and personal factors. Temporal conditions, particularly evening and nighttime travel, as well as gender identity, act as an amplifying lens through which the other factors are interpreted and experienced. Moreover, travelers identifying as women frequently adopted coping behaviors, such as arranging pick-ups or calling someone, thereby relying on their social networks. Travelers identifying as men often described their gender as a protective factor. Furthermore, the interviews with experts indicated that institutional dynamics, including fragmented responsibilities and difficulties regarding data sharing, further complicate the specific contexts of small railway stations that influence perceived safety.
At the same time, current monitoring tools do not include the complex nature of experienced safety and the related dynamics between the different factors influencing it. This study suggests that small railway stations deserve more attention in governance and monitoring frameworks. Creating station environments where travelers feel comfortable contributes to positive travel experiences and highlights perceived safety as a crucial condition for realizing equal mobility and accessibility.
Improving Transport Demand Forecasting: Ex-post Evaluation Using Smart Card Data
A Case Study of the Hoekse Lijn
The effects of Buses with High Level of Service on ridership and operations
A Dutch case study
The objective of this work is to develop knowledge on the importance of BHLS aspects with respect to ridership and advise bus operators and transit authorities about the implementation of BHLS aspects. To investigate this, first the aspects of BHLS are determined based on a literature review. Then, the performance of bus lines on these BHLS aspects is assessed in a Dutch case study to investigate how BHLS in practice relates to the findings in literature. This is done for BHLS services as well as conventional services. The performances of the different services are compared to each other to explore what BHLS means in practice and how that relates to literature. Once the performance of each line is assessed, the relation of the BHLS aspects with ridership is investigated. This is done using a multiple linear regression model. Lastly, the impact of the deployment of dedicated BHLS vehicles on the operational efficiency is researched. Two sets of vehicle schedules are developed for a scenario where all lines are carried out by one single vehicle type and for a scenario where BHLS services have their own dedicated fleet. A framework is developed which can be used to estimate how much ridership should increase to cover the costs of a dedicated fleet.
...
The objective of this work is to develop knowledge on the importance of BHLS aspects with respect to ridership and advise bus operators and transit authorities about the implementation of BHLS aspects. To investigate this, first the aspects of BHLS are determined based on a literature review. Then, the performance of bus lines on these BHLS aspects is assessed in a Dutch case study to investigate how BHLS in practice relates to the findings in literature. This is done for BHLS services as well as conventional services. The performances of the different services are compared to each other to explore what BHLS means in practice and how that relates to literature. Once the performance of each line is assessed, the relation of the BHLS aspects with ridership is investigated. This is done using a multiple linear regression model. Lastly, the impact of the deployment of dedicated BHLS vehicles on the operational efficiency is researched. Two sets of vehicle schedules are developed for a scenario where all lines are carried out by one single vehicle type and for a scenario where BHLS services have their own dedicated fleet. A framework is developed which can be used to estimate how much ridership should increase to cover the costs of a dedicated fleet.
The Impact of Articulated Buses
The Impact of Articulated Buses: A mixed-method Approach
Determining the optimal stop and line spacing of an urban bus network for different area types
A weighted total travel time model for optimising the bus stop and line spacing for different urban area types based on sociodemographic characteristics
The focus of this thesis is the stop and line spacing of the bus, the distance between sequential bus stop and parallel bus lines respectively. Changing the stop and line spacing have an effect on the running time of bus users, with a trade-off between the walking and in-vehicle time. A self-designed total travel time model optimises the weighted total travel time with weights on the walking time and frequency. The frequency here is based on a combination of stop and line spacing. The stop and line spacing affect the running time and thus how much a vehicle serves a route per hour. This in turn determines the frequency. The result of the total travel time model is an optimal stop spacing of around 540 meter and a line spacing of 700 meter. Analytical models by other researchers found an optimum stop spacing in between 600 and 650 meter and a line spacing of 750 meter. In practice guidelines are used where the stop spacing is around 400 meter and the line spacing is around 550 meter. The result of the self-designed model is different because the walking weight is exponential and the frequency weight is tied to the running time of the bus. The conclusion is that the optimal stop spacing is higher than is mostly applied in bus networks. A higher stop spacing means a higher average speed and a lower travel time in the bus. The downside of a higher stop spacing is that the walking distance increases which effects the ridership. This is the reason the self-designed total travel time model has a larger focus on the walking distance, and has resulted in a lower optimal stop and line spacing than in analytical models of other researchers.
The effect the walking distance has on the use of the bus is different in different area types in an urban agglomeration. A regression analysis on the relation between the stop spacing and sociodemographic characteristics has been performed for the analysis of this effect. The data used for the regression analysis is gathered for the city of Rotterdam and the surrounding towns. For an area with a high population density, income, and car ownership in combination with a large distance, around 10 km, from the city center a stop spacing of 600 to 700 meter is recommended. The lower the distance to the city center, the lower the stop spacing, and thus for a similar area type around 5 km from the city center a stop spacing of 500 to 550 meter is recommended. For an area with an average population density, income and car ownership in combination with a high distance (10 km) to the city center a stop spacing of 475 to 525 meter and in combination with a lower distance (5 km) a stop spacing of 450 to 475 meter is recommended. The reason why these values are lower than for the first area type is because there are more activity facilities. This means that there are more potential destinations in this area type and for users a stop close to a destination is important for the choice to use public transport. The larger the area of these facilities, and the higher the number of facilities, the lower the stop spacing. In the city center the recommended stop spacing is therefore 425 to 450 meter. The exception to this is the area close to the central station of Rotterdam, here the stop spacing is higher (550 to 600 meter) because close to a station people are not going to use the bus, but the train which has a higher operation speed and thus is a higher quality mode. For an area with a low population density, income and car ownership a stop spacing of 550 to 650 meter is recommended. In this area type there are a lot of captive riders, who are dependent on public transport and are willing to walk further than other types of public transport users. For this group it is important that bus stops are close to activity centers. If the stop spacing in this area becomes too high the number of trips made by captives decreases, even if the number of users stays the same. Having bus stops close to destinations compensate for the higher stop spacing. These destinations could also be a train, metro and/or tram stop. This complies with the higher willingness to walk, and makes the bus network more efficient.
For the line spacing it is more complicated to recommend values for certain area types. However recommendations are given to the network type and design, which is closely related to the line spacing. Because different area types have different characteristics a hybrid network is the most effective solution. In the city center a grid network is used to distribute users equally. Radial lines are used to connect areas outside the city center with the city center and ring lines are used to create connections between these areas if the demand for this is there. The further away from the city center the lower the bus stop density.
...
The focus of this thesis is the stop and line spacing of the bus, the distance between sequential bus stop and parallel bus lines respectively. Changing the stop and line spacing have an effect on the running time of bus users, with a trade-off between the walking and in-vehicle time. A self-designed total travel time model optimises the weighted total travel time with weights on the walking time and frequency. The frequency here is based on a combination of stop and line spacing. The stop and line spacing affect the running time and thus how much a vehicle serves a route per hour. This in turn determines the frequency. The result of the total travel time model is an optimal stop spacing of around 540 meter and a line spacing of 700 meter. Analytical models by other researchers found an optimum stop spacing in between 600 and 650 meter and a line spacing of 750 meter. In practice guidelines are used where the stop spacing is around 400 meter and the line spacing is around 550 meter. The result of the self-designed model is different because the walking weight is exponential and the frequency weight is tied to the running time of the bus. The conclusion is that the optimal stop spacing is higher than is mostly applied in bus networks. A higher stop spacing means a higher average speed and a lower travel time in the bus. The downside of a higher stop spacing is that the walking distance increases which effects the ridership. This is the reason the self-designed total travel time model has a larger focus on the walking distance, and has resulted in a lower optimal stop and line spacing than in analytical models of other researchers.
The effect the walking distance has on the use of the bus is different in different area types in an urban agglomeration. A regression analysis on the relation between the stop spacing and sociodemographic characteristics has been performed for the analysis of this effect. The data used for the regression analysis is gathered for the city of Rotterdam and the surrounding towns. For an area with a high population density, income, and car ownership in combination with a large distance, around 10 km, from the city center a stop spacing of 600 to 700 meter is recommended. The lower the distance to the city center, the lower the stop spacing, and thus for a similar area type around 5 km from the city center a stop spacing of 500 to 550 meter is recommended. For an area with an average population density, income and car ownership in combination with a high distance (10 km) to the city center a stop spacing of 475 to 525 meter and in combination with a lower distance (5 km) a stop spacing of 450 to 475 meter is recommended. The reason why these values are lower than for the first area type is because there are more activity facilities. This means that there are more potential destinations in this area type and for users a stop close to a destination is important for the choice to use public transport. The larger the area of these facilities, and the higher the number of facilities, the lower the stop spacing. In the city center the recommended stop spacing is therefore 425 to 450 meter. The exception to this is the area close to the central station of Rotterdam, here the stop spacing is higher (550 to 600 meter) because close to a station people are not going to use the bus, but the train which has a higher operation speed and thus is a higher quality mode. For an area with a low population density, income and car ownership a stop spacing of 550 to 650 meter is recommended. In this area type there are a lot of captive riders, who are dependent on public transport and are willing to walk further than other types of public transport users. For this group it is important that bus stops are close to activity centers. If the stop spacing in this area becomes too high the number of trips made by captives decreases, even if the number of users stays the same. Having bus stops close to destinations compensate for the higher stop spacing. These destinations could also be a train, metro and/or tram stop. This complies with the higher willingness to walk, and makes the bus network more efficient.
For the line spacing it is more complicated to recommend values for certain area types. However recommendations are given to the network type and design, which is closely related to the line spacing. Because different area types have different characteristics a hybrid network is the most effective solution. In the city center a grid network is used to distribute users equally. Radial lines are used to connect areas outside the city center with the city center and ring lines are used to create connections between these areas if the demand for this is there. The further away from the city center the lower the bus stop density.
The impact of large-scale parking capacity reductions on bicycle and public transportation demand
A Case Study for Rotterdam
This thesis investigates how large-scale parking space removal affects travel behaviour in Rotterdam, a city with relatively high car use but suitable conditions for car-lite policies. Three intervention scenarios (20%, 40%, and 60% parking reductions) are analysed using the V-MRDH macroscopic, multimodal transport model. To address the model's limitations, a supplementary estimation method was developed to more realistically approximate shifts in transport demand.
Results show that a 20% reduction could lead to a peak-hour modal shift of up to 2,566 motorists, corresponding to increases of 2.8% in public transport and 2.2% in cycling. Although average loads remain within capacity, some tram and bus lines face overcrowding during the busiest 15 minutes. The findings highlight the need for strategic public transport planning and model enhancements to accurately assess behavioural responses to parking policy changes. ...
This thesis investigates how large-scale parking space removal affects travel behaviour in Rotterdam, a city with relatively high car use but suitable conditions for car-lite policies. Three intervention scenarios (20%, 40%, and 60% parking reductions) are analysed using the V-MRDH macroscopic, multimodal transport model. To address the model's limitations, a supplementary estimation method was developed to more realistically approximate shifts in transport demand.
Results show that a 20% reduction could lead to a peak-hour modal shift of up to 2,566 motorists, corresponding to increases of 2.8% in public transport and 2.2% in cycling. Although average loads remain within capacity, some tram and bus lines face overcrowding during the busiest 15 minutes. The findings highlight the need for strategic public transport planning and model enhancements to accurately assess behavioural responses to parking policy changes.
The impact of low-car residential neighbourhoods on mobility behaviour
A case study of Cartesius in Utrecht from residents’ perspectives
Mobility Design Strategies For Sustainable Development In Peripheral Urban Areas
A case study in travel choice behaviour for Rijnenburg, Utrecht
Face Validity in Participatory Value Evaluation (PVE)
Exploring Segment-specific Perceptions and their Influence on Transport Decision-Making
Degrees of urbanisation in transport modelling
Analysing the impact of the spatial environment on travel behaviour using cluster analysis and propensity score matching
The work in this thesis shows that the LMS is currently unable to accurately capture the effect of the differences in spatial environment effectively. The LMS uses the Degree of Urbanisation (DU) to model the spatial environment, which proves insufficient to correctly model travel behaviour.
This conclusion was reached by using a cluster analysis and propensity score matching and by comparing the ‘real’ modal split (OViN) with the ‘predicted’ modal split (LMS). All zones in the Netherlands were clustered based on characteristics of the spatial environment. This was done using the so-called D-variables.
The propensity score matching showed that the differences in modal split between the different DUs and the different clusters are caused by both demographic characteristics and differences in the spatial environment. However, the effect of the spatial environment is larger.
Policy makers and other users of the LMS should be aware that testing policies or future scenarios in the LMS that change aspects of the spatial environment might introduce additional uncertainties in the forecasts of some regions.
...
The work in this thesis shows that the LMS is currently unable to accurately capture the effect of the differences in spatial environment effectively. The LMS uses the Degree of Urbanisation (DU) to model the spatial environment, which proves insufficient to correctly model travel behaviour.
This conclusion was reached by using a cluster analysis and propensity score matching and by comparing the ‘real’ modal split (OViN) with the ‘predicted’ modal split (LMS). All zones in the Netherlands were clustered based on characteristics of the spatial environment. This was done using the so-called D-variables.
The propensity score matching showed that the differences in modal split between the different DUs and the different clusters are caused by both demographic characteristics and differences in the spatial environment. However, the effect of the spatial environment is larger.
Policy makers and other users of the LMS should be aware that testing policies or future scenarios in the LMS that change aspects of the spatial environment might introduce additional uncertainties in the forecasts of some regions.
Job accessibility in industrial areas
A case study in the IJmond region of spatiotemporal accessibility using a gravity model
In this thesis a model is constructed that evaluates the impact of a set of disruption scenarios on different switch configurations. Four key performance indicators are found in literature that can measure resilience quantitatively: costs (number of switches), rate of cancelled services, punctuality and time to recover. In interviews with rail experts, weightings for the four KPIs are derived which are used to calculate a score for all disruption scenarios and infrastructure layouts. A trade-off between costs and resilience is made in order to find the optimal switch configuration for a double track and four track layout which provides the highest capacity during disruptions. The set of disruptions including cause, location and duration come from an extensive analysis of disruption events of the past 6.5 years in the Netherlands.
Currently, NS is cancelling many trains since NS is examined on punctuality and not on the number of trains. This research aims to cancel as few trains as possible which is also examined in a case study where the model is validated. The Dutch railway line Utrecht Centraal – Arnhem Centraal appears to have a weak spot and by proposing new switch location configurations on a part of this line the score increased, despite the fact that more switches have been included in the proposed solution. Since the big renovation, 108 switches in Utrecht Centraal were removed by ProRail which improved punctuality, capacity and speed but decreased the flexibility: a disruption between Utrecht and Arnhem (or Eindhoven) currently has a lot of impact on the train service between Amsterdam and Utrecht, because short turning options in Utrecht are rare. This means that trains are now being cancelled completely or short turn already in Amsterdam. By using smart options with new rerouting strategies, capacity between Amsterdam and Utrecht can be kept high with the proposed solution during a disruption between Utrecht and Arnhem or Eindhoven. ...
In this thesis a model is constructed that evaluates the impact of a set of disruption scenarios on different switch configurations. Four key performance indicators are found in literature that can measure resilience quantitatively: costs (number of switches), rate of cancelled services, punctuality and time to recover. In interviews with rail experts, weightings for the four KPIs are derived which are used to calculate a score for all disruption scenarios and infrastructure layouts. A trade-off between costs and resilience is made in order to find the optimal switch configuration for a double track and four track layout which provides the highest capacity during disruptions. The set of disruptions including cause, location and duration come from an extensive analysis of disruption events of the past 6.5 years in the Netherlands.
Currently, NS is cancelling many trains since NS is examined on punctuality and not on the number of trains. This research aims to cancel as few trains as possible which is also examined in a case study where the model is validated. The Dutch railway line Utrecht Centraal – Arnhem Centraal appears to have a weak spot and by proposing new switch location configurations on a part of this line the score increased, despite the fact that more switches have been included in the proposed solution. Since the big renovation, 108 switches in Utrecht Centraal were removed by ProRail which improved punctuality, capacity and speed but decreased the flexibility: a disruption between Utrecht and Arnhem (or Eindhoven) currently has a lot of impact on the train service between Amsterdam and Utrecht, because short turning options in Utrecht are rare. This means that trains are now being cancelled completely or short turn already in Amsterdam. By using smart options with new rerouting strategies, capacity between Amsterdam and Utrecht can be kept high with the proposed solution during a disruption between Utrecht and Arnhem or Eindhoven.
On Track or On the Road?
Understanding the impact of detailed attitudes over time and the experienced value of train travel time on the train versus car decision
Planning the route to higher bus quality
Creating a roadmap for effective implementation of high-quality bus systems in the Netherlands
In this thesis, through a literature review and interviews with Dutch stakeholders, a definition of the high-quality bus system is found, including its main characteristics. The most important part of this thesis is to map the existing problems. In order to solve these problems, a roadmap is designed to help with the implementation by breaking the process down into smaller, more manageable steps.
A high-quality bus system should above all be fast, frequent, and reliable, but comfort is also important for such a system. The most commonly used terms are Bus Rapid Transit (BRT) and Bus with a High Level of Service (BHLS). The literature and the interviews confirm that there are still many problems that should be addressed in the roadmap, from cooperation problems to short-term thinking by politicians, from blind commitment to rail to underestimating the social value of public transport.
The proposed roadmap consists of eight blocks. It begins with the creation of a city-wide vision for mobility and the formulation of a programme of requirements for the entire public transport system (i). These are translated into objectives and a programme of requirements for the line to be designed (ii). In addition, cooperation with all parties should be well regulated (iii). The next important step is to analyse the target group of (potential) passengers, the stops and the bottlenecks and intersections (iv). Only after that is the system choice made (v). If a high-quality bus system is chosen, it needs to be designed (vi). Among other things, the route, the operation, the first/last mile transport and the branding have to be designed. When the design is ready, the line can be built, and operation can begin (vii). This can be done in stages, addressing the main bottlenecks first and the rest later. This is followed by the often overlooked evaluation, which is important for learning lessons for future projects.
Stakeholder feedback and a case study show the value of the roadmap for implementation in the Netherlands. By choosing the modality later in the process, compromises in quality are avoided. In addition, the in-depth analyses improve the quality of the line and enable faster implementation by identifying the main bottlenecks. The main value of the roadmap lies in its integral approach, rather than an approach from one perspective. This means that the problems of all stakeholders can be addressed to design a high-quality public transport system that works for all. ...
In this thesis, through a literature review and interviews with Dutch stakeholders, a definition of the high-quality bus system is found, including its main characteristics. The most important part of this thesis is to map the existing problems. In order to solve these problems, a roadmap is designed to help with the implementation by breaking the process down into smaller, more manageable steps.
A high-quality bus system should above all be fast, frequent, and reliable, but comfort is also important for such a system. The most commonly used terms are Bus Rapid Transit (BRT) and Bus with a High Level of Service (BHLS). The literature and the interviews confirm that there are still many problems that should be addressed in the roadmap, from cooperation problems to short-term thinking by politicians, from blind commitment to rail to underestimating the social value of public transport.
The proposed roadmap consists of eight blocks. It begins with the creation of a city-wide vision for mobility and the formulation of a programme of requirements for the entire public transport system (i). These are translated into objectives and a programme of requirements for the line to be designed (ii). In addition, cooperation with all parties should be well regulated (iii). The next important step is to analyse the target group of (potential) passengers, the stops and the bottlenecks and intersections (iv). Only after that is the system choice made (v). If a high-quality bus system is chosen, it needs to be designed (vi). Among other things, the route, the operation, the first/last mile transport and the branding have to be designed. When the design is ready, the line can be built, and operation can begin (vii). This can be done in stages, addressing the main bottlenecks first and the rest later. This is followed by the often overlooked evaluation, which is important for learning lessons for future projects.
Stakeholder feedback and a case study show the value of the roadmap for implementation in the Netherlands. By choosing the modality later in the process, compromises in quality are avoided. In addition, the in-depth analyses improve the quality of the line and enable faster implementation by identifying the main bottlenecks. The main value of the roadmap lies in its integral approach, rather than an approach from one perspective. This means that the problems of all stakeholders can be addressed to design a high-quality public transport system that works for all.
Unravelling night train travel behaviour
A stated preference survey into the influence of operational and personal factors
Moving towards a carless place of work
A qualitative research on hospital employees