1,721,151 research outputs found
New High-Speed Rail Lines and Market Competition
No full textThe new 1,000 km of high-speed rail (HSR) line between Turin and
Salerno, Italy, were completed in 2009, and new service by the Italian
railways state-owned company Trenitalia started in December of that
year. Furthermore, in anticipation of the European open-access regulatory
framework, starting from April 2012, the new HSR private operator
Nuovo Trasporto Viaggiatori entered the market and is competing
with the incumbent Trenitalia. This is the first case of large-scale competition
between nonsubsidized HSR operators on the same line (i.e.,
infrastructure managed by the state-owned company, Rete Ferroviaria
Italiana). This study provides an overview of the short-term dual
effects of the opening of the new HSR line operating with a single service
provider (between 2010 and 2012) and of the competition between the
two operators starting from 2012. Before-and-after effects on services,
prices, and attracted and generated demand based on several data
sources, including ad hoc surveys and a system of mathematical models,
are presented. Results suggest that the dual effect of new HSR lines
and the opening of competition in the market has been producing a significant
increase in HSR supply (194% of trains per day) and demand
(198% of passenger-kilometers per year). The market share in the HSR
core area increased about 20% at the expense of air and automobile
modes. About 13.8 million extra trips per year were estimated in 2013
with respect to rail demand in 2009 (8.3 million diverted from other
modes and 5.5 million induced trips) despite the severe economic crisis
faced by the country in the same period
The Impacts of High-Speed Rail Development on Territorial Cohesion: A Method with Two Case Studies in Italy
No full textIn the last decade, the concept of ‘cohesion’ has become increasingly important to study the distributional effects of infrastructure investment between different population groups, i.e., social cohesion, or between different zones inside a study area, i.e., territorial cohesion. Nevertheless, despite a growing interest, in the literature there is still a lack of consolidated methodologies and guidelines to assess the impacts on cohesion of transport infrastructure. In the attempt to bridge up this gap, this paper proposes a method based on the estimation of zonal accessibility variations as proxy for the effectiveness of the investment, and on statistical indices estimating accessibility dispersion as a proxy for the territorial cohesion. Two case studies are presented: the completion of the High-Speed Rail (HSR) line between Turin–Venice (Northern-Italy) and the construction of the new HSR line between Naples and Bari (Southern-Italy). The application shows that the investment in the more industrialized and densely populated areas of North Italy turns out to be more effective in terms of accessibility improvement than in less developed Southern ones. However, while the former increases inequalities in terms of accessibility and risks to amplify the gap between North and South of the Country, the latter (Naples–Bari) tends to reduce such disparities
Travel demand matrix estimation methods integrating the full richness of observed traffic flow data from congested networks
No full textIn strategic transport models, road travel demand matrices are usually estimated using estimation methods that fuse prior or synthetic travel demand matrices with flow data observed on individual roads (‘links’) in the network. On the one hand, ever more data on flows, speeds and/or densities on link level is available, driven by technological advances (e.g. PnD’s, smartphones, IoT), trends in transport policy towards smarter usage instead of expansion of the network and the smart mobility concepts arising from them. On the other hand, the urgency of robust and sound estimation procedures is triggered by rising congestion levels on these networks that are at an all-time high.In this paper we address the known difficulties when estimating travel demand using link flows observed on a network with high levels of congestion. Such a network incorporates at least several active bottlenecks, which influence flow values both upstream (queues will form) and downstream (flow is metered). This implies that, on such a network, observed link flow values may represent either 1) the unconstrained travel demand for that link, 2) a proportion of the capacity of a set of upstream links, 3) the capacity of the normative (in terms of capacity deficit) downstream link or 4) a combination of these quantities. Which quantity each observed link flow represents depends on the specific traffic conditions in the network. Note that in practice a very large portion of observed flows is affected by flow metering (2) whereas only a small portion is unaffected (1) or affected by queues (3 or 4).Demand matrix estimation methods use a traffic assignment model to assess the relationship between travel demand and link flow in intercept information. If the assignment model that provides the intercept information does not strictly adhere to link capacity constraints, as with static traffic assignment models, flow metering effects of bottlenecks (2) are not taken into account and all traffic is considered unaffected (1), thereby forcing incorrect assumptions upon the estimation. Therefore, matrix estimation methods using these models should only be applied on observed flows values that are unaffected (1), rendering them mostly useless on networks with high congestion levels. Note that by nature these assignment models should actually not be applied on study areas with congestion altogether.Current practice to use observed flows affected by congestion (2, 3 or 4) is to derive unconstrained link demand values from the observed flow values, for example using the ‘Tonenmethodiek’ (used in the Dutch LMS/NRM models), or similar techniques that shift observed flows to upstream unconstrained links. Then, instead of the actual observed flows, these post-processed link demand values are used during matrix estimation. As such, these methods exhibit poor tractability and robustness and do not integrate any information from the assignment model about the composition of routes on the observed links.This paper describes and compares three novel demand matrix estimation methods for large scale strategic congested transport models that use assignment models that strictly adhere to link capacity constraints, allowing them to explicitly consider the conditions under which link flows are observed. It compares these methods to the current practice and gives practical insights from applications, thereby demonstrating that these methods allow for usage of (big) data sources such as floating car data, congestion patterns and (route) travel time observations. Using these novel approaches, the need to post-process synthetic link demands is taken away, thereby increasing tractability and robustness of the matrix estimation methods and allowing for use of observed congestion patterns as additional input. Furthermore, these methods more efficiently reveal inconsistencies between model link capacities and observed congestion patterns and inconsistencies between count values, allowing the modeler to correct the model network and other matrix estimation input.Authors continue research on the topic, the next goal being to extent the methods to support estimation of OD demand covering multiple time period(s), which should eventually lead to a method that supports 24 hour estimation.Transport and Plannin
Mobility as a Service (MaaS) for university communities: Modeling preferences for integrated public transport bundles
Full text linkIn order to investigate the role that Mobility as a Service (MaaS) could play in university communities to reduce car dependency and moderate car-oriented travel behavior, this paper examines individuals’ stated interest in adopting MaaS bundles in academic environments, where its potential is still largely underexplored. The study involves a large-scale survey campaign carried out within a university community in Milan (Italy), comprising 1873 answers from faculty members, technical-administrative staff, and students. The paper discusses the factors affecting behavioral intentions towards a potential MaaS adoption on the basis of aggregate statistics and discrete choice models estimates. This research highlights that there is a real opportunity to market MaaS in university communities, but an accurate user-centered design of the MaaS solutions is needed, based on individuals’ preferences and actual mobility needs. Results suggest that MaaS has a broader potential user base among individuals under 35 years old and Public Transport subscribers, and that MaaS bundles involving shared mobility services are attractive by residents in the city center, while reserved parking at interchange facilities is more attractive to commuters coming from suburban areas
Heterogeneity in users’ intention-to-use Urban Air Mobility services
No full textUrban Air Mobility (UAM) is receiving increasing attention by the industry and the scientific community. The first commercial services are expected to be launched in a short period. However, users’ acceptance and willingness-to-use UAM should not be taken for granted and need to be carefully assessed. This study presents the results of a large-scale RP-SI (Revealed Preference-Stated Intention) survey carried out in the metropolitan area of Milan (Italy): it was designed to collect data aiming at both profiling potential UAM passengers and developing models to assess the impact of different factors on users’ intention to-use UAM services. The results obtained through hybrid ordered choice modeling analysis outlined the statistical importance of three identified latent constructs (i.e., flying concerns, propension towards technology and UAM safety concerns) in explaining the intention of using UAM services. Particularly, concerns regarding the flight or the UAM safety have been found to have a minor negative influence in the intention to use UAM compared to the user's propension towards technology which has instead a major positive influence. Moreover, a lower propensity towards embracing these new aerial services has been found in females and in those currently not traveling alone, while those traveling for business purposes or using taxis for daily travels exhibit a higher propensity towards their adoption
Urban Air Mobility (UAM): Airport shuttles or city-taxis?
Full text linkIn the last years, Urban Air Mobility (UAM) has been receiving increasing attention and even if the first services are expected to be launched shortly, there is still uncertainty about which type of commercial services (e.g., airport shuttles or city-taxis) will be implemented at an early stage, as well as which price point will be perceived as affordable by travelers. Based on data collected through a large-scale survey campaign in the Milan metropolitan area (Italy), in this paper passengers' value of travel time savings for different UAM services are estimated using advanced discrete choice modeling. Estimated mixed logit models allowed to comparatively analyze the differences between the two potential use cases, i.e., airport shuttle and city-taxi services. Results show a willingness to pay for UAM services from/to airports that is greater (in a range of 44%–57%) than for travelling within the metropolitan area, and greater (in a range of 31%–44%) for business travels than for other purposes, indicating that the most financially sustainable UAM services will potentially be available for airport-shuttle connections from/to central business districts
Urban Air Mobility Passengers’ Profiling: Evidence from Milan Airports, Italy
No full textUrban air mobility (UAM) is expected to offer new travel options for passengers to and from airports in the near future, despite uncertainties associated with regulatory issues, environmental concerns, and societal impacts. This paper analyzes the socioeconomic and behavioral factors that could influence users’ modal choices (including UAM services) for accessing and egressing airports. Using revealed and stated preference data collected at Milan airports (Italy), mixed logit and hybrid choice modeling specifications are estimated and compared with profile potential UAM passengers. Our findings suggest that the level of service, socioeconomic factors, and trip-related variables explain passengers’ choices better than latent traits such as fear of flying, propensity for technological advances, and expectations about the safety of UAM services. In other words, the additional complexity of hybrid choice modeling is not justified by the slight gain in likelihood compared with the estimated mixed logit model. The results also indicate that high-income individuals traveling for business purposes are the most likely demand segment to use UAM services, at least initially. Moreover, highly educated individuals and employees who have their travel expenses reimbursed for work trips are less likely to choose UAM services for airport access or egress, preferring traditional ground taxis
Consumers’ expectations and attitudes towards owning, sharing, and riding autonomous vehicles
Full text linkIt is still unclear whether autonomous vehicles will mainly bring benefits or not to the sustainable development of people's mobility. Opinion among various stakeholders diverge since autonomous driving may have different use cases, and potential impacts will depend on how consumers will deal with it: following an ownership-based or a consumption-based approach, using autonomous vehicles as individual (as a private car), shared (as a taxi service), or collective (as a public transport service) means of transport. This paper aims at shedding light on future mobility scenarios by investigating travelers’ expectations, attitudes, and intentions towards adopting autonomous vehicles. The research method involves the estimation of hybrid choice models based on data collected through a Stated Intention survey. Results of an exploratory study conducted in Italy show that the willingness-to-adopt autonomous vehicles can be explained by both observable and latent traits of individuals, giving evidence of different policy implications. Moreover, the desire to experiment autonomous driving is on average very high, but consumers are more willing to share or ride autonomous vehicles, rather than purchasing them for personal use
Evidence from the Italian High-speed Rail Market: Competition between Modes and Between HSR Operators
No full textThis chapter presents an overview of the dual effects of the opening of the new HSR (300 Km/h) line in Italy, with a single operator between 2010 and 2012 and with the entry of a new operator since 2012. Before-and-after effects on supply, demand and prices are presented, allowing the analysis of the evolution of new HSR services on the multimodal intercity travel market as well as a first evaluation of the competition within a typically monopolistic market. Analyses are based on source data (laws and regulations, business plans, timetables, prices) as well as ad hoc extensive surveys, such as on-board counts on the high-speed and intercity trains, retrospective surveys, and revealed preference/stated preference (RP/SP) interviews., Results from an integrated modeling system developed to assess the effects of timetables/services/prices in terms of HSR and competing modes (air/ auto/railways) were used in the analyses
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