Public transport in rural areas is under pressure because demand is low and dispersed. To reduce costs, flexible and on-demand services are often proposed as alternatives for conventional bus services. Conventional services are generally not suitable for rural areas, because the demand is low and dispersed. In this paper, a stated preference survey is designed to identify the preferences of rural bus users for alternative services. Other than the traditional bus, two other modes are included in this study: a demand responsive transport (DRT) service and an express bus service with bike-sharing services for last mile transport. Given the on-demand nature of these alternatives, flexibility-and reliability-related attributes are included in the stated preference survey. The results from the choice model indicate that the reliability and flexibility aspects do not have a large effect on the preference for the on-demand alternatives. Instead, cost, access and egress times, and in-vehicle time play a bigger role in individuals’ preferences toward the different alternatives. A sensitivity analysis shows that changes in the operational characteristics can make the on-demand alternatives more attractive. However, many bus users still prefer the conventional bus service over the on-demand alternatives.@en

Public transport in rural areas is under pressure because demand is low and dispersed. To reduce costs, flexible and on-demand services are often proposed as alternatives for conventional bus services. Conventional services are generally not suitable for rural areas, because the demand is low and dispersed. In this paper, a stated-preference survey is designed in order to identify the preferences of rural bus users for alternative services. Other than the traditional bus, two other modes are included in our study: a demand responsive transport (DRT) service and an express bus service with bike-sharing services for last mile transport. Given the on-demand nature of these alternatives, we include flexibility and reliability related attributes in the stated preference survey. The results from the choice model indicate that the reliability and flexibility aspects do not have a large effect on the preference of the on-demand alternatives. Instead, cost, access and egress times, and in-vehicle time play a bigger role in individuals' preferences towards the different alternatives. A sensitivity analysis shows that changes in the operational characteristics can make the on-demand alternatives more attractive. However, still a large number of bus users prefers the conventional bus service over the on-demand alternatives.@en

Mobility as a Service (MaaS) is expected to significantly change mobility patterns, yet it is still not clear who will embrace this new mobility paradigm and how MaaS will impact passengers’ transportation. In the paper, we identify factors relevant for MaaS adoption based on a survey comprised of over thousand respondents in the Netherlands. We find five clusters in relation to individuals’ inclinations to adopt MaaS in the context of urban mobility. We characterize each of the clusters, allowing for the examining of different customer segments regarding MaaS. The cluster with the highest inclination for future MaaS adoption is also the largest cluster (representing one third of respondents). Individuals in this cluster have multimodal weekly mobility patterns. On the contrary, current unimodal car users are the least likely to adopt MaaS. We identify high (mobility) ownership need and low technology adoption (present in three of the five clusters) as the main barriers that can hinder MaaS adoption. Policies that directly address these two barriers can stimulate MaaS adoption.

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The diffusion of the smartphone and the urban sprawl is pushing both private and public actors to revisit the concept of demand-responsive transport (DRT). This paper provides a historical overview of DRT experiences, understanding their pros and cons. In addition, it presents the case study of Mokumflex, a 12-month DRT pilot program that replaced the regular bus service in low-density areas of Amsterdam, the Netherlands. Based on a close collaboration with the private enterprise that operated the service and also with the local bus operator, we performed an empirical before and after comparison. These insights help to understand the impacts of DRT systems and support (future) design of DRT and public transport. A set of indicators was chosen for the intermodal comparison: travel distances, ridership, costs, Greenhouse Gases (GHG), emissions and population's perception. Ridership dropped from 78.1 passengers/day to 15.9 passengers/day, however, for being “demand-tailored”, passenger-km reduced even more, going from 1252.8 km/day to 136.6 km/day, hence reducing the costs and GHG emissions per passenger. In regards to population's perception, the system enjoyed a good evaluation.

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Simulation studies suggest that pooled on-demand services (also referred to as Demand Responsive Transport, ridesharing, shared ride-hailing or shared ridesourcing services) have the potential to bring large benefits to urban areas while inducing limited time losses for their users. However, in reality, the large majority of users request individual rides (and not pooled rides) in existing on-demand services, leading to increases in motorised vehicle miles travelled. In this study, we investigate to what extent fare discounts, additional travel time, and the (un)willingness to share the ride with (different numbers of) other passengers play a role in the decision of individuals to share rides. To this end, we design a stated preference study targeting Dutch urban individuals. In our research, we (1) disentangle the sharing aspect from related time–cost trade-offs (e.g. detours), (2) investigate preference heterogeneity regarding the studied attributes and identify distinct market segments, and (3) simulate scenarios to understand the impact of the obtained parameters in the breakdown between individual and pooled services. We find that less than one third of respondents have strong preferences against sharing their rides. Also, we find that different market segments vary not only in their values of the willingness to share, but also in how they perceive this willingness to share (per-ride or proportional to the in-vehicle time). Further, the scenario analysis demonstrates that the share of individuals who are willing to share rides depends primarily on the time–cost trade-offs, rather than on the disutility stemming from pooling rides per se.

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Simulation studies suggest that pooled on-demand services (also referred to as ridesplitting or shared ride-hailing/ridesourcing services) have the potential to bring large benefits to urban areas with little time loss for their users. However, the large majority of on-demand requests in existing services are for individual rides. In this study, we investigate to what extent fare discounts, time losses, and the (un)willingness to share the ride with (different numbers of) other passengers play a role in the decision of individuals to share rides. To this end, we designed a stated preference study, and simulated different scenarios based on an estimated discrete choice Mixed Logit model. We found that the share of individuals who are willing to share their rides depends primarily on the time-cost trade-off they encounter, rather than on the disutility stemming from pooling rides per se. We also found that the disutility of having one or two extra passengers is constant regardless of the trip length, whereas it further increases with trip distance in the event that one shares the ride with four additional passengers. The depicted scenarios show the impacts that larger/smaller time-cost trade-offs and different number of individuals in a ride would have on the shares of individual versus pooled on-demand services. These insights are relevant to on-demand service providers who wish to offer pooled services and to transportation authorities who may be interested in internalising the externalities stemming from the increase in vehicle miles travelled related with individual rides.@en

A new range of tailored, on-demand mobility alternatives are emerging worldwide; amongst these are pooled on-demand services, i.e., shared ride-hailing services such as UberPOOL or ViaVan. Simulation studies have shown the potential benefits of these services in urban areas, yet their ridership is still very limited. This thesis examines the behavioural reasons underlying the adoption of such services. To this end, it includes a series of quantitative studies and suggests a series of policy implications based on the performed analyses. In this thesis, pooled on-demand services are also analysed in the broader context of Mobility as a Service (MaaS).@en

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.@en

The uptake of on-demand services is increasing rapidly all over the world. However, the market share of their pooled version (ridesharing, e.g., UberPOOL or LyftLine) is still low, despite its potential in addressing the mobility challenges that dense urban cities are facing. In this research, we analyse user preferences towards pooled on-demand services regarding their time-reliability-cost trade-offs. We study, via stated preference experiments, the value of time (VOT) and value of reliability (VOR) of the different trip stages (waiting stage, in-vehicle stage, and transfer stage when combined with line-based public transport). We target urban Dutch individuals (N = 1006), and address commuting and leisure trips. Results show in-vehicle VOT for pooled on-demand services to amount to 7.88–10.80 €/h. These values are somewhat higher than known values of traditional public transport. We also find waiting VOT (both before the trip and during the transfer stage) to be lower than values previously reported in literature. In general, we find VOR to be lower than VOT: the reliability ratio (VOR/VOT ratio) for both the waiting stage and the in-vehicle stage being around 0.5. In order to understand different preferences, we also estimate latent class choice models. The analysis shows that the main difference between classes pertains to the overall time-cost and reliability-cost trade-offs (VOT and VOR values) rather than in different valuations of the reliability ratio. In addition to serving as input for demand forecasting models such as macroscopic static assignment and agent-based simulation models, our findings can support service providers in developing their strategy when designing pooled on-demand services.@en

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).@en

Demand-responsive transport (DRT) services (collective on-demand services, such as shared ridesourcing and microtransit) offer a collective flexible travel alternative that can potentially complement fixed transit (FT). The combination of an on-demand and line-based service holds the promise of improved mobility and increased service coverage. However, to date, it remains unknown whether DRT services deliver these 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 can help identify whether DRT is used as a complement or a substitute for 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.

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On-demand modes are playing an ever-increasing role in urban transportation. In
particular, Demand Responsive Transit (DRT) (shared on-demand modes such as
shared ride-hailing and microtransit) can complement scheduled public transport
thanks to their collective nature. These services offer flexible and convenient
mobility: they are not bound to fixed routes or schedules as public transport is.
However, this flexibility also encompasses an extra risk related to service reliability and availability: “(When) will I get the requested ride?”. This work analyses and quantifies the Value of Reliability (VoR) of these new services via a stated preference survey. The study covers both the reliability components intrinsic to DRT as well as the transfer penalty when in combination with scheduled public transport. Unobserved components of the choice behaviour are captured via latent variables in the choice model. This study contributes to the inclusion of DRT services in transportation demand models, and to better understand the role of collective transport in future integrated mobility solutions.@en

The urban transportation sector is undergoing significant changes. Firstly, the shared-economy paradigm has led to both car sharing schemes and Transportation Network Companies (TNCs, such as Uber or Lyft). And secondly, on-going technology developments are underway to make autonomous driving a reality. However, individual, even if not privately owned, modes of transport are unlikely to be able to cope with all the transport demand in urban areas. The public transportation sector can potentially combine collective transport with the latest societal and technological innovations leading to collective demand responsive transport (DRT). Demand responsive transport can be described as a collective on-demand mode of transport which provides flexible mobility in time and possibly also in space. In the last decades, DRT was primarily conceived as a mode of transport for rural areas or to assist the elderly and the disabled. Lately, however, research studies have started considering the feasibility of this mode of transport for urban settings. Recent investigations of DRT have located their case studies in Lisbon (Portugal) (Martinez et al., 2015) and Hino (Japan) (Atasoy et al., 2015). These studies focus mainly on the algorithms behind such a system. Helsinki (Finland) even implemented a real pilot between 2012-2015. Nevertheless, there is still lack of knowledge on how the shift away from fixed schedules and routes is perceived by the users. Are those aspects perceived positively as injecting flexibility to the system (“I am not fixed to a timetable”) or are they perceived negatively as inducing uncertainty to the trip ( “Will I get a suitable match next time?”)?. Our study aims at providing a broader understanding of the fuzzy variable time inherent to DRT in the mode choice decision. Different studies have addressed mode shift including DRT services and reliability aspects. Ryley et al. (2014) includes the deviation from the expected starting time of the trip as one of the attributes when comparing bus or car to DRT. Khattak et al. (2004) asks respondents to rate different reliability and flexibility statements concerning DRT. In Diana (2010), respondents rate their mode shift propensity and different cognitive and affective modal attitudes. However, none of the mentioned approaches quantifies the different flexibility-uncertainty characteristics that surround DRT and includes these perceptions in the overall SP experiment. Our approach is expected to shed light into how the time attributes that appear in DRT influence mode choice. Next to the flexible-uncertain characteristics surrounding DRT, our study aims at studying the perception of DRT versus conventional public transport as well as examining mode choice preference against the car and individual on-demand services (Taxi and Uber). To deal with these questions, a stated preference (SP) experiment is performed. To avoid overloading respondents, the new attributes related to flexibility-uncertainty that appear in the DRT mode will be understood as part of a time construct in an HII (Hierarchical Information Integration) experiment. HII is a method for handling multi-attribute judgement problems with a large number of attributes; in it, a logical decomposition of the decision problem takes place (Louviere, 1984). The HII can be seen as a separated SP experiment in which different levels of the variables that define the construct are rated. This methodology makes it easier for abstract concepts (such as the flexibility-uncertainty construct in our research) to be quantified. The HII experiment will be quantified in a 9 point scale. The outcome of our HII experiment will then be incorporated in the higher-level mode choice experiment as a flexibility-uncertainty attribute of DRT. Richter et al. (2012) also included HII in their mode choice SP experiment to analyse quality of connection, comfort and information. To answer our research questions, a web-based survey among the Dutch population is designed, conducted and analysed. In the Netherlands, 91% of the households have access to the internet and 81% of the Dutch population uses internet daily or almost daily (CBS, 2016). Hence, a web-based survey is considered sufficient to get a representative sample of the population. A stated preference survey, as opposed to revealed preference survey is performed since the envisioned DRT system is not yet established. Jain et al. (2017) reviews existing studies on DRT services and identifies shopping and social trips as the most recurrent trip purposes for the use of DRT systems. Based on this finding, our SP is explained in a context of a free-time trip (as opposed to a business or a commuting trip). We also fix the context to an urban/suburban context (as opposed to rural context), since we want to study the impact that DRT services can have in the city. The addressed variables are walking time, waiting time, in-vehicle time, travel cost and the constructs resulting from the HII experiment. In addition to the SP choice experiment, the survey includes socio-economic variables, trip routines and questions concerning the inclination to use DRT. These will be compared with the values of the Netherlands Mobility Panel (MPN). The MPN is a yearly survey performed nationwide that includes a household questionnaire, an individual questionnaire and RP data of a 3-day travel diary (Hoogendoorn-Lanser et al., 2015). Biogeme is used for values estimation and SPSS to analyse the surveys’ responses. A NL model within the RUM paradigm is taken as the appropriate model to be estimated since the NL model overcomes the restrictive MNL assumption of the independence of irrelevant alternatives (IIA). This study expects to increase the understanding of the time flexibility-uncertainty that surrounds DRT. The results of this study will enable the analysis of potential modal shift and the extent of travel generation and substitution in the event that a DRT service is offered. By testing various nested structures, the correlations of DRT with conventional public transport and individual travel modes can be identified@en

Mobility as a Service (MaaS) is a recent innovative transport concept, anticipated to induce significant changes in the current transport practices. However, there is ambiguity surrounding the concept; it is uncertain what are the core characteristics of MaaS and in which way they can be addressed. Further, there is a lack of an assessment framework to classify their unique characteristics in a systematic manner, even though several MaaS schemes have been implemented around the world. In this study, we define this set of attributes through a literature review, which is then used to describe selected MaaS schemes and existing applications. We also examine the potential implications of the identified core characteristics of the service on the following three areas of transport practices: travel demand modelling, a supply-side analysis, and designing business model. Finally, we propose the necessary enhancements needed to deliver such an innovative service like MaaS, by establishing the state of art in those fields.@en

Mobility as a Service (MaaS) is entering the transportation market. MaaS aims at the full integration of the existing transportation services and it offers tailored mobility packages to the user. In MaaS ecosystems, on-demand services play an important role as complement to public transport due to their flexibility. However, to date, most attention has been placed on individual on-demand services. This study focuses on Demand Responsive Transport (DRT): collective on-demand services. Using an on-line survey, we analysed the characteristics of the respondents who chose different modes of transport among their selected modes. Results find a distinctive pattern in the willingness of users to use different modes, with different levels in what could be considered as a multimodality ladder. The different rungs of it would be: 1st car (if available), 2nd public transport, 3rd DRT and 4th taxi-like services. This way, a person standing on the third rung would include car, public transport and DRT in their consideration set, but not taxi. This finding suggests that, if implemented in the right way, DRT services can attract a larger number of users than taxi-like services, especially in a MaaS ecosystem where initial barriers to try this service can be lessened. @en

De impliciete belofte van Mobility as a Service is dat we hiermee vanzelf het autobezit zullen terugdringen. Maar is dat wel zo? Onderzoekers van de TU Delft deden onderzoek naar de mogelijkheden van een typische en voor de stad ideale MaaS-component: vraaggestuurd openbaar vervoer. Zal deze nieuwe modaliteit op korte termijn tot de gewenste modal shifts leiden?@en