20,539 research outputs found
Parking availability forecasting model
© 2019 IEEE. Parking is increasingly an issue in the world today especially in large and growing cities with contemporary urban mobility. The effort spent in searching for available parking spots results in significant loss of resources such as time, and fuel, as well as environmental pollution. Parking Availability can be influenced by many factors such as time of day, day of week, location, nearby events, weather and traffic conditions. Driven by the idea of predicting parking availability to help drivers plan ahead of time, we contribute a Parking Availability Forecasting Model, which uses a time-series analysis and machine-learning algorithms to predict the number of available parking spots at a certain location on a desired date and time. The forecasting model is trained on historical parking data from the cities of Kansas City, US and Melbourne, Australia. This paper also compares the accuracy of different time-series forecasting models, and how each of them fits our use-case scenario. Multivariate data analysis together with temperature and weather summary are used to cross-validate our forecasting model
Parking Availability Forecasting Model
Title from PDF of title page viewed September 30, 2019Thesis advisor: Mohammad Amin KuhailVitaIncludes bibliographical references (pages 22-25)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019Parking is increasingly an issue in the world today especially in large and growing cities with
contemporary urban mobility. The effort spent in searching for available parking spots results in
significant loss of resources such as time, and fuel, as well as environmental pollution. Parking
Availability can be influenced by many factors such as time of day, day of week, location, nearby
events, weather and traffic conditions. Driven by the idea of predicting parking availability to help
drivers plan ahead of time, we contribute a Parking Availability Forecasting Model, which uses a
time series analysis and machine-learning algorithms to predict the number of available parking
spots at a certain location on a desired date and time. The forecasting model is trained on
historical parking data from the cities of Kansas City, US and Melbourne, Australia. This paper
also compares the accuracy of different time-series forecasting models, and how each of them
fits our use-case scenario. Multivariate data analysis together with temperature and weather
summary are used to cross-validate our forecasting model.Introduction -- Background and related work -- Dataset -- Parking availability forecasting model -- Implementation and results -- Conclusion and future wor
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Forecasting Truck Parking Using Fourier Transformations
Truck-based transportation is the predominant mode used to transport goods and raw materials within the United States. While trucks play a major role in local commerce, a significant portion of truck activity is also long haul in nature. Long-haul truck drivers are continuously faced with the problem of not being able to secure a safe parking spot since many rest areas become fully occupied, and information about parking and availability is limited. Truck drivers faced with full parking lots/facilities either continue driving until a safe parking spot is located or park illegally. Both scenarios pose a hazard to the truck driver, as well as the surrounding road users. Disseminating forecasts of parking availability to truck drivers may help mitigate this hazard, since many truck drivers plan their parking in advance of arrival. Building on 1 year of nearly continuous truck parking data collection, this paper proposes and demonstrates a method for developing a dynamic forecasting model that can predict truck parking occupancy for any specified time within the present day, using only truck parking occupancy data from a trucking logistics facility in the northern San Joaquin Valley during 2016. Different versions of the dynamic model were studied and verified against successive weekdays with performance measured using the root-mean-square error (RMSE). Results indicated that for a particular day, the maximum error can range between 13 and 40 trucks, about 5% of the absolute maximum capacity of the facility
The prediction of parking space availability
Intelligent Parking Systems (IPS) allow customers to select a car park according to their preferences, rapidly park their vehicle without searching for the available parking space (place) or even book their place in advance avoiding queues. IPS provides the possibility to reduce the wastage of fuel (energy) while finding a parking place and consequently reduce harmful emissions. Some systems interact with in-vehicle navigation systems and provide users with information in real-time such as free places available at a given parking lot (car park), the location and parking fees. Few of these systems, however, provide information on the forecasted utilisation at specific time. This paper describes results of a traffic survey carried out at the parking lot of supermarket and the proposal of the model predicting real-time parking space availability based on these surveyed data. The proposed model is formulated as the non-homogenous Markov chains that are used as a tool for the forecasting of parking space availability. The transition matrices are calculated for different time periods, which allow for and include different drivers’ behaviour and expectations. The proposed forecasting model is adequate for potential use by IPS with the support of different communication means such as the internet, navigation systems (GPS, Galileo etc.) and personal communication services (mobile-phones)
Factors influencing the propensity to cycle to work
This paper describes the development of a mode choice model for the journey to work with special emphasis on the propensity to cycle. The model combines revealed preference (RP) and stated preference (SP) data to form a very large and comprehensive model. RP data from the National Travel Survey was combined with a specially commissioned RP survey. A number of SP surveys were also undertaken to examine the effects of different types of en-route and trip end cycle facilities and financial measures to encourage cycling.
The development of the model is described in detail. The model was used to forecast trends in urban commuting shares over time and to predict the impacts of different measures to encourage cycling. Of the en-route cycle facilities, a completely segregated cycleway was forecast to have the greatest impact, but even the unfeasible scenario of universal provision of such facilities would only result in a 55% increase in cycling and a slight reduction in car commuting. Payments for cycling to work were found to be highly effective with a £2 daily payment almost doubling the level of cycling. The most effective policy would combine improvements in en-route facilities, a daily payment to cycle to work and comprehensive trip end facilities and this would also have a significant impact on car commuting
Costs of Interchange: A Review of the Literature.
Interchange within mode influences the demand for that mode through the effect it has on time spent waiting, time spent transferring between vehicles and the inconvenience and risks involved, whilst interchange between modes has additional implications in terms of information provision, through ticketing and co-ordination. The valuation and behavioural impact of each of these factors will vary with an individual’s socio-economic and trip characteristics as well as with the precise features of the interchange.
A reduction in the costs of interchange brought about by an improvement to any of the above factors will lead to increasingly ‘seamless journeys’ and such benefits which must be quantified. Indeed, this issue has been identified as an area of key importance in the Government’s Transport White Paper (DETR, 1998a) which states:
Quick and easy interchange is essential to compete with the convenience of car use.
This message was reiterated by the draft guidance for Local Transport Plans (DETR, 1998b), which called for:
more through-ticketing, better connections and co-ordination of services, wider availability of information and improved waiting facilities.
Rather than being perceived simply as a barrier to travel, quality interchange is now also being regarded as an opportunity to create new journey opportunities. A recent report on the subject of interchange (Colin Buchanan and Partners, 1998) claimed that :
It will become more sensible and economic to base public transport networks around the concept of interchange rather than the alternative of trying to avoid it.
whilst in response to the diffuse travel patterns made possible by increased car availability, CIT (1998) commented:
people should readily be able to complete a myriad of journeys by changing services (and modes) if a through facility is not available. Ease of interchange should be something we take for granted.
Regardless of the precise direction in which transport policy and public transport provision develop, practical constraints and the fact that the most heavily trafficked routes tend to have through services places limitations on the extent to which the need to interchange can be reduced whilst no matter how fully integrated different modes of transport are the need to transfer between them cannot be removed. In contrast, the need to change would inevitably increase with the adoption of a practice of building networks around interchange to create new journey opportunities. However, there is considerable scope to improve existing interchange situations or to design new ones which impose minimum costs. Although previous empirical research has focused on the need to interchange or not, and this remains important, it is essential that research is also directed at improvements which facilitate interchange.The aims of this study, as set out in the terms of reference, are centred around the demand side response to interchange rather than the technical supply side issues relating to improving interchange and integration which have been covered in other studies (Colin Buchanan and Partners, 1998; CIT, 1998). The objectives are:
to explore the extent to which the reality and perception of interchange deters public transport use, absolutely and in relation to other deterrents
to investigate how public transport users perceive interchange; how they make choices and trade-offs in travel cost and time and the influence of interchange attributes (e.g. information, through ticketing) on those choices
to assess which components of interchange act as the greatest deterrent to travel
to investigate the extent to which interchange penalties vary according to journey purpose, distance and time of travel (or other factors)
Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids
Electric vehicle fleets and smart grids are two growing technologies. These technologies
provided new possibilities to reduce pollution and increase energy efficiency.
In this sense, electric vehicles are used as mobile loads in the power grid. A distributed
charging prioritization methodology is proposed in this paper. The solution is based
on the concept of virtual power plants and the usage of evolutionary computation
algorithms. Additionally, the comparison of several evolutionary algorithms, genetic
algorithm, genetic algorithm with evolution control, particle swarm optimization, and
hybrid solution are shown in order to evaluate the proposed architecture. The proposed
solution is presented to prevent the overload of the power grid
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