Posts in category Papers
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
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
Combining Speed Adjustment and Holding Control for Regularity based Transit Operations
Vehicle bunching often occurs in high-frequency transit systems leading to deterioration of service reliability. It is thus necessary to control vehicles during operations. Holding control is a common solution for this situation, but it may result in longer vehicle running times. Speed adjustments can contribute to more regular operations while preventing prolonged trip times. This paper proposes a control strategy, which combines these two strategies to maintain the regularity of transit operations. The findings based on simulation study for trunk bus services in the Netherlands suggest that combining the two strategies implies both the positive and negative attributes of each control.
Find the MT-ITS presentation and paper of Aishah Imram HERE and HERE
Walking and bicycle catchment areas of tram stops: factors and insights
Pollution and congestion are important issues in urban mobility. These can potentially be solved by multimodal transport, such as the bicycle-transit combination, which
benefits from the flexible aspect of the bicycle and the wider spatial range of public transport. In addition, the bicycle can increase the catchment areas of public transport stops. Most transit operators consider a fixed 400m buffer catchment area. Currently, not much is known about what influences the size of catchment areas, especially for the bicycle as a feeder mode.
Bicycles allow for reaching a further stop in order to avoid a transfer, but it is not clear whether travelers actually do this.This paper aims to fill this knowledge gap by assessing which factors affect feeder distance and feeder mode choice. Data are collected by an on-board transit revealed preference survey among tram travelers in The Hague, The Netherlands. Both regression models and a qualitative analysis are performed to identify the factors that influence feeder distance and feeder mode choice. Results show that the median walking feeder distance is 380m, and the median cycling feeder distance is 1025m. The tram stop density and chosen feeder mode are most important in feeder distance. For feeder mode choice, the following factors are found to be influential: tram stop density, availability of a bicycle, and frequency of cycling of the tram passenger. Furthermore, the motives of respondents for choosing a stop further away are mostly related to the quality of the transit service and comfort matters, of which avoiding a transfer is named most often. In contrast, the motives for cycling relate mostly to travel time reduction and the built environment. Three important barriers for the bicycle-tram combination have been discovered: unavailability of a bicycle, insufficient and unsafe bicycle parking places. Infrequent users of the bicycle-tram combination are more inclined to travel further to a stop that suits them better.
Find the MT-ITS paper and presentation of Lotte Rijsman HERE and HERE
Impacts of charging methods and mechanisms of zero emission buses on costs and level of service
To limit global warming and strive for more liveable and sustainable cities, innovative zero-emission (ZE) buses are on the rise all around the world. Different alternative vehicle propulsion methods have been introduced during the last decades. However, for now, only trolley, battery and fuel-cell electric vehicles can be classified as (on the pipe) ZE-buses.
This research focuses on battery electric buses, since they are most cost-efficient and – therefore – the most promising option for the (near) future. An important limitation of battery electric buses is however the limited range of operations due to capacity restrictions of batteries. Batteries should be (re)charged before, during and/or after daily operations.
Different charging methods, including different charging power systems are available to charge battery electric vehicles. As far as known to the authors, scientific literature focusing on the operations and charging scheduling of electric buses is scarce. In this study, a comparison of different applied charging methods for electric buses is obtained.
A ZE-bus station simulation method is developed to assess charging methods and charging regulations with regards to their impacts on a variety of costs and level of service indicators. This simulation-based method is multi applicable, since it is particularly based on general automated vehicle location (AVL) data. To demonstrate our model, a case study at Schiphol (Amsterdam Airport) is performed.
This research concludes that the shift to ZE-bus transit is involved with higher costs and passenger disturbances. Investment costs of ZE-buses increase substantially: Most electric vehicles are around 60 to 80 percent more expensive than conventional diesel engine vehicles and additional charging infrastructure investments are required. Benefits of electric operations, including vehicle propulsion cost savings up to 70 percent, are not able to compensate these high investments.
The charging method choice contain trade-offs between level of service and (vehicle and charging infrastructure) investment costs. (Slow) depot charging offers opportunities for operations on short distance lines. However, additional vehicles are required in order to replace a vehicle which should be recharged. In this respect, conventional timetables could be complied and the level of service remains unchanged.
To prevent fleet overcapacity, vehicles should be recharged fast (with high charging power) along the line. Slight charging related delays could occur, especially when the number of charging systems is not sufficient, and/or the charging times are relatively long. Bus end stations and terminals are suitable as fast charging locations, since charging time could be combined with buffer time there.
Finally, dynamic/in-motion charging offers opportunities to prevent charging related delays completely due to combined charging and operation time. This charging method is still in its infancy stage yet, so focus is more on (innovative) static charging methods now.
Find the MT-ITS presentation wih Max Wiercx and Raymond Huisman: HERE
Does ride-sourcing absorb the demand for car and public transport in Amsterdam?
The emergence of innovative mobility services, is changing the way people travel in urban areas. Such systems offer on-demand service (door-to-door or stop-to-stop, individual or shared) to passengers. In addition to providing flexible services to passengers, past studies suggested that such services could effectively absorb the demand for private cars thereby reducing network congestion and demand for parking. This study investigates the potential of a ride-sourcing service to absorb the demand for public transport and private cars for the city of Amsterdam. Results indicate that a ride-sourcing vehicle could potentially serve the demand currently served by nine privately owned vehicles and that a fleet size equivalent to 1.3% and 2.6% of the total public transport trips, are required to provide door-to-door and stop-to-stop times comparable to those yielded by the current public transport system. Results from the modal shift indicate that most PT trips are substituted by active modes and most car trips are substituted by ride-sourcing service.
Find the MT-ITS paper and presentation of Jishnu Narayan HERE and HERE
Calibrating Route Choice Sets for an Urban Public Transport Network using Smart Card Data
Identifying the set of alternatives from which travellers choose their routes is a crucial step in estimation and application of route choice models. These models are necessary for the prediction of network flows that are vital for the planning of public transport networks. However, choice set identification is typically difficult because while selected routes are observed, those considered are not. Approaches proposed in literature are not completely satisfactory, either lacking transferability across networks (observation-driven methods) or requiring strong assumptions regarding traveller behaviour (uncalibrated choice set generation methodologies (CSGM)). Therefore, this study proposes a constrained enumeration CSGM that applies the non-compensatory decision model, elimination-by-aspects, for choice set formation. Subjective assumptions of traveller preferences are avoided by calibrating the decision model using observed route choice behaviour from smart card data, which is becoming increasingly available in public transport systems around the world. The calibration procedure also returns two key insights regarding choice set formation behaviour: (i) the ranking of different attributes by their importance, and (ii) the acceptable detours for each attribute. To demonstrate the methodology and investigate choice set formation behaviour, the tram and bus networks of The Hague, Netherlands are used as a case study.
Find the MT-ITS paper and presentation of Sanmay Shelat HERE and HERE
Passenger Travel Time Reliability for Multi-Modal Public Transport Journeys
Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Hence, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on uni-modal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing ‘Reliability Buffer Time’ metric to journeys with multi-modal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro and tram services. By using a consistent method for all journeys in the network, reliability can be compared between different modes or between multiple routes for the same origin-destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple modes. It can also be used as an input to behavioral models such as mode, route or departure time choice models.
Find the TRB paper and presentation of Malvika Dixit HERE and HERE
Operations of zero-emission buses: impacts of charging methods and mechanisms on costs and the level of service
To limit global warming and strive for more liveable and sustainable cities, innovative zero-emission buses are on the rise all around the world. For now, only trolley, battery and fuel-cell electric vehicles can be classified as (on the pipe) zero-emission vehicles. Different charging methods, including different charging systems and power, are available to charge battery electric vehicles. However, scientific literature focused on the operation and charging scheduling of electric vehicles is scarce.
In this study, a comparison of different applied charging methods for electric buses is obtained. A new ZE-bus station simulation method is developed to assess charging methods and charging regulations with regard to their impacts on costs and level of service.
The shift to zero emission bus transport is meant for achieving more sustainable and liveable cities. However, this research concludes that this is involved with higher costs and passenger disturbances. The investment costs increase substantially. Benefits of electric operations, including vehicle propulsion cost savings up to 70 percent, are not able to compensate these high investments. (Slow) depot charging offers opportunities for operations on short distance lines. The depot location should be close to a bus station and additional fleet is required. In order to prevent fleet overcapacity, vehicles should be recharged with high charging power along the line, preferably at combined bus stations and terminals in order to prevent charging related delays. Dynamic/In-motion charging – still in its infancy stage yet – offers opportunities to prevent these delays due to combined charging and operation time.