Posts in category English

Understanding the Modal Shift in Response to Bike-sharing Systems in the City of Delft

The introduction of bike-sharing systems has revitalized cycling in many cities around the world. In general, the bike-sharing systems operated worldwide can be divided into two categories: docked bike-sharing and dockless bike-sharing. In the docked bike-sharing system, users have to rent bicycles from designated docking stations and then return them to the available lockers in the docking stations. The dockless bike-sharing system is designed to provide more freedom and flexibility to travellers in terms of bicycle accessibility. In contrast to docked bike-sharing, riders are free to leave bicycles in both physical and geo-fencing designated parking areas provided in public space with or without bicycle racks.

As a greener travel mode, bike-sharing is competitive in short distance travel and people who have long commuting distance are more likely to choose public transit integration with it. Previous research has shown that bike-sharing reduces car and taxi useage and increases cycling in almost every city. Bike-sharing system has been shown to reduce trip demand of public transportation including train, metro and bus.

In Delft as a student city in the Netherlands, cycling is seen as the most important mode of transport within the city. There exist three different bike-sharing schemes in operations, including OV-fiets, Mobike and Swapfiets. OV-fiets was introduced in the Netherlands in 2003 [4]. The bicycles should always be brought back to the location where the rental started. At this moment, there are almost 300 rental locations consisting of 20500 bicycles. Mobike is a dockless bike-sharing service and is more flexible than the existing docked bike-sharing alternative. Mobike extended the operations to Delft in March 2018 with a focus on the university campus. Swapfiets, launched in 2014, is a bicycle-rental system on a subscription basis, can be used for regular private trips. Now it has over 50,000 customers in 38 cities in Europe. The coexistence of different bike-sharing schemes in Delft enables this city to be a test bed for bike-sharing research.
This paper aims to understand the modal shift dynamics and the factors influence travellers’ choices in response to different bike-sharing systems by conducting a survey targeting OV-fiets users, Mobike users and Swapfiets users and private-bike users.

Find the CRB presentation and abstract of Xinwei Ma: Presentation and ABSTRACT

Ridership impacts of the introduction of a dockless bike-sharing scheme, a data-driven case study

In recent years, growing concerns over climate change, pollution, congestion and unhealthy lifestyles have contributed to increasing attention to sustainable transport modes such as cycling in general and more particularly the bicycle-transit combination. As part of the policy to promote cycling, bike-sharing programs were introduced in the past decades. The development of smart bicycle locks in combination with the possibilities of smartphones, made a new type of bike-sharing possible, in literature known as dockless, free-floating or fourth generation bike-sharing. In the new dockless, model, users are able to start and end their trip at their origin and destination without having to find a nearby docking station. Compared with traditional bike-sharing programs, dockless bike-sharing systems integrate mobile payment and global positioning system (GPS) tracking into the system; these features greatly increase the ease of use and management of the system.

This paper is set up around a pilot implementation of the dockless bike-sharing system of Mobike in Delft, the Netherlands. Our research deals with what can be learned from this pilot and analyzing the critical success factors for a sustainable bike-sharing system based on the data of the Delft Mobike pilot. The focus of this paper is on the combined bicycle and transit mode. This research is based on an experimental method for collecting operational data from the bikesharing system, being the first research based on trip data of a dockless bike-sharing system in Western Europe.

Find the Cycling Research Board abstract and presentation of Sven Boor: Presentation and ABSTRACT

Impact assessment of new North/South metro line in Amsterdam

Large infrastructural projects are usually evaluated ex-ante before the decision to build the project is taken. However, after construction and opening of such project a thorough ex-post analysis is rare. In this paper we present an overview of such an evaluation study conducted in Amsterdam, capital of The Netherlands, including some first results. Research themes in the study are public transport, mobility and accessibility, public space and liveability and spatial economics. In this paper we focus on effects on public transport.

The new north-south metro line in Amsterdam became operational in summer 2018. This was accompanied by changes to the existing bus and tram network to provide feeder services to the new line, as well as to remove duplicate routes. Apart from adding significant capacity to the public transport network, the new line and the accompanying changes to the network are expected to improve travel times, reliability, accessibility and comfort levels (at least on average; not for all individual travellers).
The changes in such service quality attributes is expected to lead to a change in travel behaviour in terms of public transport route choice, mode choice (between public transport and private modes or within public transport), destination choice, departure time choice or addition of new trips (induced demand).
The objective of this study is to identify the main effects of the new metro line on existing and new passengers. We pay attention to the following aspects:
– Passenger volumes.
– Travel times, where the following distinction can be made:
o in-vehicle time;
o waiting time at the first stop;
o transfer walking time;
o transfer waiting time.
– Number of transfers.
– Network flows / crowding in vehicles.
– Reliability: travel time variance on the journey level.
– Accessibility: number of inhabitants and jobs reachable within a travel time budget.

Data sources for the study are GTFS timetable data (open source), Smart card data (both from within the city of Amsterdam as for the regional feeder bus services) and Automated Vehicle Location data. To measure perceived quality of the PT network, a survey is conducted among inhabitants of Amsterdam. In this survey approximately 3.800 respondents were asked about the travel time perceptions of their last PT trip, both before and after opening of the metro line. Finally, for a sample of travellers the entire trip is followed by a GPS tracking app.

Bicycle and Transit: a Powerful Combination

Cities are facing mobility related problems such as traffic congestion and air pollution. The combination of bicycle and transit offers a sustainable alternative to individual motorized transport. It combines the benefits of both modes, namely speed, flexibility and accessibility. This paper merges several results of our recent studies in this combined mode. The bicycle and transit mode is at first reviewed from a governance point of view. After this top-down approach a shift to the actual bicycle and transit users is made. The objective of this paper is to understand the characteristics of the bicycle-transit combination. Understanding the bicycle-transit chain makes it possible to improve the design of the chain by adapting policies which enhances (further) growth of this sustainable transport mode.

Regarding the governance point of view: two metropolitan areas in the world where both bicycle and transit systems are highly developed are compared. The metropolitan region of Copenhagen and the Dutch Randstad conurbation. In the Netherlands the governance structure of spatial planning and transit planning has gradually been shifted from local and national level to provincial level. Furthermore, many provinces are a key stakeholder when developing so called bicycle highways. The combination of responsibilities for (i) spatial planning, (ii) transit, and (iii) bicycle planning has proven to be extremely successful when making the most out of the bicycle-transit combination. It is seen that the results of the integration of transit and spatial planning highly encourages citizens to use the bicycle and transit mode.
In addition to our policy-related analysis, the actual bicycle and transit user has been examined. It is seen that the current users of the combined mode are mainly middle-aged, male, full-time employees. Catchment areas of transit stops depends on multiple factors. One of these factors is quality of the transit supply. In comparison to low level services, high level services attract users from twice as far. While over 40% of the Dutch train traveller uses the bike to get from home to the station, modal shift might be possible regarding egress trips and from and to high level bus, tram and metro services. Dockless bikes are helpful regarding egress transport. In the city of Delft, approximately 15% of the MoBike dockless bike trips are related to the train stations.
Finally, it is concluded that the combination of bicycle and transit is a successful and sustainable transport combination. Both from a governance and user perspective, there are major opportunities regarding the egress side of the bicycle transit chain. Furthermore, the transition of low level transit to high level transit makes the bicycle-transit combination more attractive, transit authorities are therefore highly encouraged to facilitate bicycle parking and shared bicycle facilities at their transit stops.

Check the ETC presentation with Raymond Huisman HERE

Autonomous vehicles meet Public Transport: the future of automated vehicles in public transport

The technology of automated vehicles is developing rapidly and the vehicles offer a lot of benefits. They claim to be safer, more environmentally friendly and they can provide transport for everyone, including people who currently don’t have access to transportation. The focus seemed to be on the development of automated private vehicles, but the focus seems to shift from private transportation to automated public transportation.

The Netherlands has been pro-active in testing automated vehicles on public roads. This paper gives an overview of the projects and pilots with automated vehicles as public transport in the Netherlands as well as the remaining research questions. Also, preliminary results of passenger related studies regarding expected ridership and perception are discussed in this paper. Information was gathered by performing desk research and conducting interviews with twelve public transport authorities. During these interviews we spoke about threats and opportunities as well as feasibility, visions and knowledge gaps. Subsequently we spoke about what the future of public transport would look like and how we can anticipate on these upcoming technologies. Lastly we asked about (future) pilot locations with automated vehicles. These locations are included on a map of the Netherlands.

In many places in the Netherlands there is or has already been experiments with automated vehicles (3 – 4). These pilots, experiments or demonstrations are often focused on the technical aspects. However, the challenges regarding the deployment of an automated vehicle extends beyond the technical level. The interviewed parties indicate that it is important to focus, with the upcoming pilots, more on the traveler and the position of the vehicle within the existing public transport network. The interviewed parties stress that it is important to think about the long-term implementation.

The current public transport contracts as we know them, will likely change with the arrival of automated vehicles. Concessions are already becoming more flexible and space is created to experiment with new concepts such as automated vehicles. During a concession, it is possible to experiment alongside the established service and a transition path can be mapped out. Tendering an automated shuttle has not (yet) taken place in the Netherlands (5). The public transport authorities are clear about the future: automated vehicles in public transport do not come with a ‘big bang’ but will gradually find their way.

Check the ETC presentation of Reanne Boersma, Arthur Scheltes and Niels van Oort HERE

Impacts of replacing a fixed transit line by a Demand Responsive Transit system

The diffusion of the smartphone and the urban sprawl is pushing both private and public actors to revisit the concept of the demand-responsive transit (DRT). Mokumflex is a DRT pilot program that replaced the regular bus service in low-density areas of Amsterdam, the Netherlands, for 12 months. The close collaboration with the private enterprise that conducted the system but also with the local bus operator allowed the authors to have access to precise databases, giving this article empirical information for both the situation before and after the implementation. These insights help to understand DRT systems and support (future) design of DRT and transit systems. A few indicators were chosen for the comparison: distances, ridership, costs, Greenhouse Gases (GHG), emissions and population’s perception. The ridership dropped, however, for being “demand-tailored”, the mileage per passenger reduced, improving the costs and GHG emissions. In regards to population’s perception, the system enjoyed a good evaluation.

Find the Thredbo presentation of Felipe Coutinho HERE and the paper HERE

The advantages of multi-modal concessions, two analyses in the Netherlands

Public transport authorities are aiming for more integrated concessions, including bus, train services, to provide a better experience for travellers. This paper describes the analysis of the effect of multimodal concessions.First, the Dutch Province of Limburg moved from uni-modal to a multimodal concession. The paper analyses effects of that choice had for network design, travel times (using weighted generalized travel time), travel costs, patronage (using smart card data analysis), and coordinative interactions between operator and authority (based on interviews). Second, the paper analyses three different forms of coordination between bus and train services, using the STO model. It compares three regional concession in the Netherlands in Limburg, Fryslân, and Groningen. They represent one region with a multi-modal concession under net-cost, one region with multiple unimodal concessions under net-cost and one region with multiple unimodal concessions under mixed forms of contract. The paper concludes that multi-modal concessions provide some real-world advantages to travellers and authorities. However, to what extent these advantages materialize is dependent on a number of key factors, including the type of contracts used, the number of transport authorities active in the concession area and the role that the transport authority wants to take up.

Find the Thredbo presentation of Gerald Hoekstra HERE and the paper HERE

Willingness to share rides in on-demand services for different market segments

The impact of on-demand urban transport services on traffic reduction will depend on the willingness to share (WTS) of individuals. However, the extent to which individuals are willing to share remains largely unknown. By means of a stated preference experiment, this study analyses the WTS of respondents by comparing their preferences towards individual and pooled rides. Urban Dutch individuals are the target population of this study. In our research, we: 1) quantify the WTS in on-demand services with different number of passengers to disentangle the sharing aspect from related time-cost considerations (e.g. detours); 2) investigate which distinct (latent) market segments exist in regards to the WTS and value of time (VOT) for these on-demand services, and 3) analyse which socioeconomic characteristics and travel patterns can help explain taste variations. Despite the large majority of current on-demand rides being individual, we found that less than one third of respondents have strong preferences for not sharing their rides. Also, we found
heterogeneity not only in the values of the WTS of individuals, but also in the way this disutility is perceived (per-ride or proportional to the in-vehicle time).

Find the Thredbo presentation of Maria Alonso-Gonzalez HERE

Impact analysis of a new metro line in Amsterdam using automated data sources

A new metro line (the north-south line) was opened in Amsterdam in July 2018, adding significant capacity to the existing urban public transport network consisting of bus, tram and metro modes. The opening of the metro line was accompanied by changes to the existing bus and tram network, such as removal of duplicate routes and addition of feeder routes.

Traditionally, the impact of such a network change was measured either ex-ante or post-op based on surveys or model forecasts (Vuk 2005; Knowles 1996; Engebretsen, Christiansen, and Strand 2017). However, with the availability of automated data sources such as the smart card data, the exact impact on transit demand and service quality can now be measured. However, so far this has been limited to analysing the changes in travel times and reliability at a trip level (Fu and Gu 2018), excluding transfers.
This research utilises smart card and AVL data to study the impact of the new line on travel patterns (passenger flows), travel times and reliability from a passenger perspective by considering journeys including transfers. The metrics are calculated at a stop-cluster level, enabling also a distributional analysis of the impacts. Such a post-op analysis of any policy intervention or network change could be used to refine the demand predictive ex-ante tools.

Check the Transit Data workshop contributions of Malvika Dixit: Presentation and Extended abstract

Forecasting bus ridership with trip planner usage data: a machine learning application

Currently, public transport gives much attention to environmental impact, costs and traveler satisfaction. Good short-term demand forecasting models can help improve these performance indicators. It can help prevent denied boarding and overcrowding in busses by detecting insufficient capacity beforehand. It could be used to operate more economically by decreasing the frequency or the size of the bus if there is overcapacity. Moreover, it could help operators plan their busses during incidental occasions like big public events where little information is known. Finally, it could be used to reliably inform the travelers on the current crowdedness.
This study investigates the usefulness of a new data source; the usage data of a trip planner. In the Netherlands there are multiple trip planners available for users to help find the most optimal (multimodal) journeys. These trip planners require a date, a time and an origin and destination, which they use to construct multiple alternative journeys from which the user can choose. For this study the data of 9292 was used, being the major trip planner in the Netherlands including all public transport modes.
We developed a model for forecasting the number of people boarding and a model for forecasting the number of people alighting at a certain stop. These forecasts are defined at the vehicle-stop level. By summing the number of people boarding and subtracting the number of people alighting along the trip the forecasted number of passengers after a stop is calculated.

We compare five different machine learning models: multiple linear regression, decision tree, random forests, neural networks and support vector regression with a radial basis kernel. We compare these models with two simple rules: 1 predict the same number as last week, and 2 predict the historic average as number. The models are implemented in the Scikit-Learn library of Python. The data is stored in a PostgresSQL database.
The trip planner datasets and smart card dataset are merged and preprocessed. The resulted dataset is rather sparse; a lot of stops have zero passengers boarding or alighting or requests suggesting to do so. Therefore we investigated if subsampling is needed. From the datasets useful data is selected and features are constructed. The features are standardized. Different number of features are tested, these features are selected based on recursive elimination using a simple random forests model. Finally, the hyperparameters of the models are tuned and the optimal configurations are stored. The scores are validated by using cross validation.

Find more details in the following contributions by Jop van Roosmalen: Transit Data workshop presentation and MSc thesis

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