Abstract
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).
Highlights
The procedure to determine area parking demand in the Czech Republic is generally carried out in the following three basic approaches
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
Summary
The procedure to determine area parking demand in the Czech Republic is generally carried out in the following three basic approaches. Different square quantities (square units) are taken as the generator of the number of customers, of students, of clerks, of transit frequency, of passenger cars per time (trip generation rate), parking generation rate and other information are derived from experience based on pre-carried out traffic surveys. These parking space projects are often further modified and particularized by different influences (with the usage of several coefficients) as for urban, suburban, and rural areas or for a type of shopping, density of population, transit accessibility, offer of special goods, discount actions, the growth of motor vehicles number per capita, etc. Results of psychology studies imply that reducing car use must be promoted by emphasizing the positive consequences of reducing car use (De Groot et al 2008). Yan et al (2019) simulated policy results and they found out some synergistic effects between policy measures; when pricing and policy measures reducing search and egress time are combined, they shape parking demand more than the sum of their individual effects if isolated implementation
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