Posts tagged revealed preference
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
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
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
Wat gaat MaaS ons brengen?
MaaS congres 2018: Niels van Oort is assistant professor public transport aan de TU Delft en doet onderzoek naar de effecten van nieuwe vervoerssytemen. Hij gaat de mogelijke impact van MaaS op reizigers en maatschappij toelichten, met voorbeelden van verschillende pilots en onderzoeken.
Zie HIER zijn bijdrage aan het MaaS congres 2018
The Potential of Demand Responsive Transport as a Complement to Public Transport
Demand Responsive Transport (DRT) offers a collective flexible travel alternative that can potentially complement Fixed Transit (FT). The combination of an on-demand and line-based services holds the promise of improved mobility and increased service coverage. However, insofar it remains unknown whether DRT services deliver such much anticipated improvements.
This study presents an assessment framework to evaluate the performance of DRT and related changes in accessibility and performs an empirical analysis for a recently introduced DRT service in the Netherlands. The framework includes a performance benchmark between DRT and FT based on the computation of generalized journey times of the DRT rides and the FT alternatives, and it can help identify whether DRT is used as complement or substitute of FT.
The framework covers the spatial and temporal dimensions, and the explicit consideration of rejected trips is an integral part of the evaluation. Results suggest large accessibility improvements for DRT users, especially for some underserved origin-destination pairs.
Read more of this work of Maria J. Alonso Gonzalez: TRB Paper and TRB Presentation
Improving predictions of the impact of disturbances on public transport usage based on smart card data
The availability of smart card data from public transport travelling the last decades allows analyzing current and predicting future public transport usage. Public transport models are commonly applied to predict ridership due to structural network changes, using a calibrated parameter set. Predicting the impact of planned disturbances, like temporary track closures, on public transport ridership is however an unexplored area. In the Netherlands, this area becomes increasingly important, given the many track closures operators are confronted with the last and upcoming years. We investigated the passenger impact of four planned disturbances on the public transport network of Den Haag, the Netherlands, by comparing predicted and realized public transport ridership using smart card data. A two-step search procedure is applied to find a parameter set resulting in higher prediction accuracy. We found that in-vehicle time in rail-replacing bus services is perceived ≈1.1 times more negatively compared to in-vehicle time perception in the initial tram line. Besides, passengers do not seem to perceive the theoretical benefit of the usually higher frequency of rail-replacement bus services compared to the frequency of the replaced tram line. At last, no higher waiting time perception for temporary rail-replacement services could be found, compared to regular tram and bus services. The new parameter set leads to substantially higher prediction accuracy compared to the default parameter set. It supports public transport operators to better predict the required supply of rail-replacement services and to predict the impact on their revenues.
Read our TRB paper HERE
Find the poster HERE