Modelling demand in restricted parking zones

Ibeas, Ángel; Cordera, Rubén; dell’Olio, Luigi; Moura, José Luis

doi:10.1016/j.tra.2011.03.004

Cited by 30 publications

(23 citation statements)

References 29 publications

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“…Parking modeling has been a topic of interest since the 1970s and has been studied from various angles, including parking patterns, impacts on traffic, off-street parking technologies, parking policy, choice and location models, and economic models describing parking conditions (Arnott & Rowse, 1991;Bonsall & Palmer, 2004;Hensher & King, 2001;Ibeas, Cordera, dell'Olio, & Moura, 2011;Van Der Goot, 1982;Wong, Tong, Lam, & Fung, 2000). An early review by Young, Thompson, and Taylor (1991) reported three categories of parking related models: driver behavior with respect to parking (parking choice), optimal positioning of parking lots (parking allocation), and interaction of parking operations with other transportation system elements and infrastructures (parking interaction).…”

Section: Parking Modeling: Objectives and Methodsmentioning

confidence: 99%

A Real-Time Parking Prediction System for Smart Cities

Vlahogianni

Κεπαπτσόγλου

Tsetsos

et al. 2015

Journal of Intelligent Transportation Systems

204

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A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the "smart" city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.

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Section: Parking Modeling: Objectives and Methodsmentioning

confidence: 99%

A Real-Time Parking Prediction System for Smart Cities

Vlahogianni

Κεπαπτσόγλου

Tsetsos

et al. 2015

Journal of Intelligent Transportation Systems

204

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“…This comparison was made to establish the best specification of a hedonic regression to be introduced into a land use-transport interaction model (UrbanSim). The authors chose the SEM model as the most appropriate because the SDM model showed a large number of variables not significant and because the GWR showed a strong correlation between the estimated parameters, a phenomenon also found in other research (Ibeas et al, 2011;Wheeler and Tiefelsdorf, 2005).…”

Section: Bibliographic Reviewmentioning

confidence: 98%

Modelling transport and real-estate values interactions in urban systems

Ibeas

Cordera

dell’Olio

et al. 2012

Journal of Transport Geography

Self Cite

118

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a b s t r a c tThis article presents hedonic Multiple Linear Regression models (MLR), spatial autoregressive hedonic models (SAR), Spatial autoregressive hedonic in the Error term Models (SEMs) and spatial Durbin hedonic models (SDMs) to estimate house price variations in metropolitan areas as a result of changing environmental and accessibility conditions. The goodness of fit of the different models has been compared along with a series of hypotheses about the performance of the specifications considering spatial relationships between observations. The case study for such analysis was the urban area of Santander (Spain). The models which considered spatial dependence between observations offered a greater degree of fit in a scenario showing strong spatial correlation in MLR residuals. The SEM model combined with a QueenContiguity matrix provided a good fit to the data and at the same time presented significant parameters with theoretically coherent signs. This model estimated increases of 1.8% for each additional transit line present in the areas of housing, as well as a reduction of 1.1% in their prices for each additional minute in travelling time to the Central Business District. Closeness to the train stations, however, implied reductions in house prices.

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“…Additionally, Kelly and Clinch (2006) found a 50% increase in the cost of on-street parking in central Dublin reduced demand and duration of stay by 15% and 16.5% respectively. However, the effects of parking regulations need to be carefully considered to avoid adverse congestion-related effects due to a lack of supply (Ibeas et al, 2011). Furthermore, increasing parking charges to encourage modal shift would likely be unpopular; in a 2011 study by the DfT (UK) only 20% of respondents considered increasing parking charges as an acceptable way for the government to encourage greater PT usage.…”

Section: Parking Regulationsmentioning

confidence: 99%

Challenges and opportunities in developing urban modal shift

Batty

Palacín

González-Gil

2015

Travel Behaviour and Society

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Agriculture and Engineering, Newcastle upon Tyne NE1 7RU, UK * Contact: paul.batty2@ncl.ac.uk; +44 (0) 191 208 8657 Abstract Continued urbanisation, and the resultant increase in urban trips, presents one of the greatest challenges to the environmental, economic and social sustainability of society. Given that the modal split between transport modes has remained relatively unaltered in recent decades, this suggests that the levels of private car usage will lead to even greater levels of congestion and air pollution in urban areas. Therefore, a modal shift from private to public transport needs to be effected with urgency. However, whilst in theory this could be achieved with relative ease, numerous societal, political and economic barriers have thus far prevented such a shift from occurring. These have been analysed in detail, using a holistic approach which simultaneously considers all stakeholder needs. Recognising that traveller opinions and requirements are fundamental in effecting modal shift measures, the effects of public transport quality attributes on encouraging modal shift are discussed, accompanied by an updated version of the UK Department for Transport's hierarchy of public transport needs. This investigation then proceeds to analyse the effectiveness of methods to control urban car usage, before discussing solutions to address the barriers to a notable, successful modal shift, including guidance on how to design modal shift programmes. This paper provides useful and insightful guidance for all those involved in attempting to evoke sustainable mobility through a modal shift to public transport systems.2

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Modelling demand in restricted parking zones

Cited by 30 publications

References 29 publications

A Real-Time Parking Prediction System for Smart Cities

A Real-Time Parking Prediction System for Smart Cities

Modelling transport and real-estate values interactions in urban systems

Challenges and opportunities in developing urban modal shift

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