parking apps

Civil Engineering BarcelonaTech

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TRANSPORTATION PLANNING AND MANAGEMENT IN THE TERRITORY

M.Eng. in Civil Engineering

Course REPORTS 2014-2015 – Group 20 (Q2)

A28. PARKING APPS

IRENE MARTÍNEZ VILLALONGA

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Barcelona, May 27, 2015


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1. INTRODUCTION AND OBJECTIVES Cities are locations with a high level concentration of economic activities and are complex spatial structures that are supported by transport systems. In larger cities, the complexity and the potential for disruptions are greater. The transport problems are related to urban areas and happen when the diverse requirements of urban mobility are not satisfied. Congestion is one of the most important transport problems in large cities; it is interrelated with parking because looking for a parking space creates delays and affects local circulation. It is estimated that cruising for spaces represents roughly 30% of the traffic. This issue has affected the parking industry, where pricing is now seen as a mechanism for managing congestion. That is why in recent years there has been a considerable growth in the demand for technology for on-street parking: smart parking has become an obvious way of decreasing operations costs and gaining profit through increased occupancy. Planning smart parking strategies can result in huge advances and profit for cities; for instance congestion can be dramatically reduced by incorporating technologies such as street sensors, real time way-finding systems and apps that get drivers to their final destination without searching. As it was said before, many smartphone applications have been launched with the aim of reduce traffic congestion but also reduce driver frustration, fuel consumption and exhaust emissions. A huge variety of them can be found depending on what kind of information or help the driver needs: for example you can find the application “Parker”; it uses sensor-based smart parking technology to help drivers find available spots in real time. Another type of app can be “Orlando parkIN” that gives users a view of parking locations sorted by price and location. It also integrates with Google Maps to provide directions based on current location. The smart city (we can generalize for smart park) ecosystem is composed of 3 main stakeholders: citizens, cities and utilities. Citizens enjoy smart services (these include smart park) that improve their life in terms of health (less CO2 emissions) and economically. Then we have the second stakeholder, the cities; where using smart services the city officials will be able to monitor the environment in real time, to react immediately and in case of necessity to establish automated control processes. The last one stakeholder are the utilities; they offer smart city services using ICT infrastructure for prevention of resource disruptions or for fast maintenance actions among others. There are more stakeholders like business owners, employees, property owners and any other parties or groups with a direct interest in parking that should be taken into account. To find an optimal parking spot we have to define the user’s objective function; it combines proximity to destination and parking cost and also ensures that the overall parking capacity is efficiently used. At the k-th decision point, we define the objective function as:

J k = min!

x!"!∈!!(!)!∈!(!)∪!(!)

· J!"(k)

With the decision variables:

x!" =0 if user i is not assigned to resource j1 if user i is assigned to resource j

And with the user cost function:

J!" = λ! ·M!" kM!

+ 1 − λ! ·D!"D!


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2. DECISION VARIABLES, TRADE-OFFS AND KPIs A smart park application uses contextual modeling in order to predict the behavior in specific conditions and combinations of them. The next variables can be combined in a model: Total travel time Walking distance to building entrances Day of the week Current events in the city Parking conditions (safety, shade, etc.) Network disruptions Traffic searching for parking Parking price Variable pricing Comparing to the conventional parking without the technology help, the advantage of the use of park applications can be shown clearly: the main purpose of using park apps is to reduce traffic congestion and pollution by getting the shortest vehicle kilometers of travel (time) in urban area to get to the first choice parking space. Usually, the total travel time is easy to measure and it can represent the meaning of vehicle kilometers of travel. Hence, it can represent a relevant decision variable. On the other hand if we assume that drivers are homogeneous and rational, we can suppose that they want to minimize their walking distances form where they park to their destination building. So that, the walking distance to building entrances can be considered as an important variable as well. Regarding the trade-offs, parking apps put an interesting “dilemma” in front of us: we have to decide if we want a very accurate system, very fast or making it widely usable. If we want a very accurate system it will also be slower because of the computation of the models. In the same line, if simpler methods are implemented this might lead to system being unreliable at times. The three variables have to be combined, for instance if the extensive analysis about the prediction of occupancy levels based on historic data might be run periodically on the back end so as to determine certain coefficients denoting the expected occupancy levels based on time of day events. This will lead to an easier computation and it will not putt additional load on user’s smart phone. Respecting the parking applications KPIs, there are many issues that play an important role in the indicators. Some of them are: Number of downloads: more downloads! more possibilities to have more active users. Number of registered users: it represents how many users that downloaded the app are using it. Positive reviews of the app: it can give an idea of the number of users that used the app and that maybe will continue using it because they find it useful. CO2 emissions: a decrease of the CO2 emissions can be related to the use of parking apps since they can help to reduce traffic congestion. Traffic congestion: less traffic congestion can mean that parking apps are being used, because, as it was said before, they can reduce traffic congestion avoiding users to search for a space.


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3. METHODOLOGY / MODELING AND EXAMPLE(s) As it was shown in the first chapter, the user’s objective function combines the variables of proximity to destination and parking cost to find an optimal parking spot. In order to establish a relationship between the variables, we first will define some parameters. At the k-th decision point, the state of the allocation system (X(k)) is defined as:

𝑋 𝑘 = {𝑊 𝑘 ,𝑅 𝑘 ,𝑃 𝑘 } Where W(k) shows that user “i” is in the Wait queue, R(k) shows that user “i” is in the Reserve queue and P(k)={p1(k),…,pN(k)} is a set describing the state of the j-th resource with pj(k) denoting the number of free parking spaces at resource j, j=1,…,N. Another parameter to define is the state of the i-th user (Si(k)):

𝑆𝑖 𝑘 = {𝑧𝑖 𝑘 , 𝑟𝑖 𝑘 , 𝑞𝑖 𝑘 ,Ω𝑖(𝑘)} Where zi(k) is the location of the user “i”, ri(k) is the total time that user “i” has spent in the Reserve queue, Ωi(k) is a feasible resource set for user “i” and qi(k) is the reservation status of user “i”:

𝑞! = 0 𝑖𝑓 𝑖 ∈𝑊 𝑘 𝑗 𝑖𝑓 𝑢𝑠𝑒𝑟 𝑖 𝑖𝑠 𝑟𝑒𝑠𝑒𝑟𝑣𝑖𝑛𝑔 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒 𝑗

To contribute to the determination of Ωi(k) we impose a constraint that limits the set of feasible resources. If the user is assigned a resource j located at yj, the mentioned constraint will be:

𝐷!" ≤ 𝐷! Where Dij = ||di – yi|| The next parameter for user “i” is denoted by Mi. It is an upper bound on the cost this user is willing to accept for having reserved and used a resource. The approach taken into account does not depend on the specific pricing scheme used, but it will be assumed that each user cost is a function of the total reservation time ri(k) and the travelling time from the user location at the kth decision time (zi(k)) to a resource location yj. So that, we define the total expected cost for using resource j, evaluated at the k-th decision time as:

𝑀!"( 𝑟! 𝐾 , 𝑡!" 𝑘 ) Where 𝑡!" 𝑘 is the travelling time and depends on the distance to a resource location and also on random traffic conditions. Now, we can establish another constraint:

𝑀!"( 𝑟! 𝐾 , 𝑡!" 𝑘 ) ≤ 𝑀! This contributes to limit the set of feasible resources. With all the needed parameters defined, the objective function that has to be minimized at each decision point. The cost function is formed by the next weighted sum:


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𝐽!" = 𝜆! ·𝑀!" 𝑘𝑀!

+ 1− 𝜆! ·𝐷!"𝐷!

Where 𝜆! ∈ [0,1] the weight that reflects the relative importance assigned by the user between cost and walking distance between the parking spot and his destination. It is necessary to remember that the main aim of smart parking is to make allocations for the greater possible number of users while achieving the minimum user cost defined by 𝐽!"(𝑘). Regarding to average driving distance, it can be clearly seen how smart parking reduces travelling time compared to “blind searching” (without any kind of parking information) and other similar strategies.

Figure 1: Traffic searching for parking. Comparison under different parking guidance strategies

We can also see how variable pricing affects to average driving distance as well:

Figure 2: Traffic searching for parking under different pricing schemes


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It can be clearly seen how smart parking leads to a reduction of the travelling time and it will lead to less traffic congestion and CO2 emissions. The commented graphs are from a study of the behavior of cars searching for parking in the city of San Francisco. On the following bar chart it is shown how the implantation of smart parking systems generates revenues all around the world:

Figure 3: Annual Smart parking systems revenue by region, world markets: 2015-2024

Regarding to the utility of parking apps, they are being used as smart parking tools in many cities around the world. Some good examples are: San Francisco, Los Angeles, New York, Stockholm, Beijing, Shanghai, São Paulo, and the Netherlands. For instance, in Los Angeles, users can access occupancy data to determine the availability of parking spaces and then pay for them with their smart phones. Apart from producing environmental benefits, parking apps improve the utilization of existing parking and leads to greater revenues for the administration or parking owners (it depens on the case). On the other hand, the Barcelona city council has developed an application (Apparkb) that helps the user to pay for his/her parking in the green/blue zone without using the parking meter, but it does not help the driver to find any spot. Hence, it does not give environmental benefits apart from using less paper for the tickets and it does not induce a parking rotation system.


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4. CONCLUSIONS AND RECOMMENDATIONS Parking reservation and guidance is an extension of traditional navigation services, ones that drive users to their destination block, and also guides them to available parking spots closest to their final destination. All of these services are likely to be fully integrated into a number of online search, social networking and e-commerce platforms. As cities evolve, the need to better manage parking will be growing. Researchers are beginning to estimate the true costs of free parking, higher traffic congestion, higher emissions of greenhouse gases in urban areas that contribute to global warming. Reducing parking’s massive geographical and environmental footprint is an important long-term objective, and cost-effectively matching demand for parking with its infrastructure supply is one of the key contributions of smart parking. Finally, to help cities strive to solve the problem of mobility in the most congested urban areas, large-scale implementation of smart parking will be an important key point for making cities more sustainable. In the long run smart parking will be able to transform our urban landscapes, making them more amenable to people rather tan cars. With the advent of smart parking and more mobility alternatives, better use of existing parking will drive decreased demand for the country parking space surplus, opening avenues for surface parking and changing the urban distribution of cities re-greening the land to create or expand living space, businesses and recreational areas.

REFERENCES Daniel B. Work and Alexandre M. Bayen. Impacts of the mobile internet on transportation cyberphysical systems: Traffic monitoring using smartphones. In National Workshop for Research on High-Confidence Transportation Cyber-Physical Systems: Automotive, Aviation and Rail, 2008. Geng, Y., and Cassandras, C.G., “A New “Smart Parking” System Based on Optimal Resource Allocation and Reservations”, Proc. of 14th IEEE Intelligent Transportation Systems Conf., pp. 979-984, Nov. 2011. Leephakpreeda T. ‘Car-parking guidance with fuzzy knowledge-based decision making’. Building and Environment 42 (2007) 803–809, 2007 Mischa Dohler, Ignasi Vilajosana, Xavi Vilajosana, Jordi LLosa. Smart Cities: An Action Plan. Worldsensing, Barcelona, Spain; CTTC, Barcelona, Spain; UOC, Barcelona, Spain. Jean-Paul Rodrigue, Claude Comtois, Brian Slack. The Geography of Transport Systems. New York: Routledge (2013) International Parking Institute: http://parking.org/ Navigant research – smart parking systems: https://www.navigantresearch.com/ New Energy News: http://newenergynews.blogspot.com.es