Coverage location modeling of optimal parking locations for dockless e-scooters to reduce illegal parking

The illegal parking problem is the biggest problem around the world. Seoul and many metorpolitan cities are making great efforts to solve the problem by introducing policies to expand parking supply and curb parking demand. However, in urban areas of metropolitan cities, land prices are high and no site is available for parking lots, a vast budget is spent to make additional parking lots. Therefore, the creation of extra parking lots is extremely limited. Also, it is not easy to obtain parking lots because urban areas have a large floating population, an there is a variation in the floating population over time. In this paper, we present a method for selecting the optimal location for public parking lots through the Maximum Coverage Location Problem (MCLP). It allows policymakers to distribute parking demand in a metropolitan city and reduce parking concentration. Moreover, congestion on nearby roads can be alleviated by reducing queues for parking and wandering time to find a parking lot and reducing social costs to prevent illegal parking.

GIScience 2016 Short Paper Proceedings Location Optimization of Fire Stations: Trade-off between Accessibility and Service Coverage J. Yao 1 , X. Zhang 2 Urban Big Data Centre, University of Glasgow, 7 Lilybank Gardens, Glasgow, G12 8RZ, UK Email: Jing.Yao@glasgow.ac.uk Department of Geographical Information Science, Hohai University, 1 Xikang Road, Nanjing, 210098, China Email: Xiaoxiang@hhu.edu.cn Abstract Fire and rescue service is one of the fundamental public services provided by government in order to protect people, properties and environment from fires and other disasters, and thus promote a safe living environment. Efficient deployment of fire stations is necessary and essential if timely response to the emergencies is to be achieved. Spatial optimization approaches have been long employed in public facility location studies. In particular, coverage-based models, such as the location set covering problem (LSCP) and the maximum coverage location problem (MCLP), have been widely adopted to achieve complete or maximum coverage of service demand. This paper extends the LSCP by accounting for both partial coverage and access to the demand areas. The proposed model is applied to the optimization of fire station locations in Nanjing, China. The results can be used to assist future fire station location planning and rescue resource deployment. 1. Introduction Fire caused by humans or nature can pose hazard to people, properties and environment, and lead to psychological damage, physical injuries (even death) and significant economic losses. Fire prevention and protection is necessary and essential for a safe living environment. The associated fire and rescue service therefore needs to be properly deployed to ensure efficient fire safety management. A fundamental concern in this regard is the spatial configuration of fire stations as it is critical to timely response to emergency calls. Given the inherent spatial nature, fire station location problems have been well studied using geographical information system (GIS)-coupled location modelling (Chevalier et al. 2012; Aktas et al. 2013; Murray 2013). In particular, LSCP (Toregas et al. 1971), MCLP (Church and ReVelle 1974) and their extensions have long been employed to evaluate the locational efficiency of existing fire stations as well as seek sites for new fire stations (Chevalier et al. 2012; Murray 2013). Common goals of locating fire stations include maximizing the access to provided services, covering as much demand as possible and minimizing total costs of service provision, usually subject to available resources. In practice, two or more objectives are often considered to capture different aspects in relation to fire service delivery. The aim of this paper is to seek best locations of fire stations with spatial optimization approaches, particularly considering accessibility and service coverage. The proposed model is applied in an empirical study in Nanjing, China, to assist future fire station location planning and rescue resource deployment.

Covering problem is one of the classical facility location problems; set covering location model and maximal covering location model are two kinds of basic covering location models. After introducing those models, analyzing and expounding the relationship in application between the two kinds of model, the paper draws a conclusion that maximal covering location model needs to have qualification within the application, which is that its limited number of facilities is not greater than the minimum number of facilities obtained by set covering location model. Finally, the paper makes data experiment of traffic police allocation to verify the existence of relationship between the two models.

There are a variety of discrete facility location models that have practical relevance for operations management and management science courses. Integer linear programming (ILP) is the standard technique for solving such problems. An alternative approach that is often conceptually appealing to students is to pose the problem as one of finding the best possible subset of p facilities out of n possible candidates. I developed an Excel workbook that allows students to interactively evaluate the quality of different subsets, to run a VBA macro that finds the optimal subset, or to solve an ILP formulation that finds the optimal subset. Spreadsheets are available for five classic discrete location models: (1) the location set-covering problem, (2) the maximal covering location problem, (3) the p-median problem, (4) the p-centers problem, and (5) the simple plant location problem. The results from an assignment in a master’s-level business analytics course indicate that the workbook facilitates a better conceptual understanding of the precise nature of the discrete facility location problems by showing that they can be solved via enumeration of all possible combinations of p subsets that can be drawn from n candidate locations. More important, students directly observe the superiority of ILP as a solution approach as n increases and as p approaches n/2.

The location set‐covering problem (LSCP) and the maximal covering location problem (MCLP) have been the subject of considerable interest. As originally defined, both problems allowed facility placement only at nodes. This paper deals with both problems for the case when facility placement is allowed anywhere on the network. Two theorems are presented that show that when facility placement is unrestricted, for either the LSCP or MCLP at least one optimal solution exists that is composed entirely of points belonging to a finite set of points called the network intersect point set (NIPS). Optimal solution approaches to the unrestricted site LSCP and MCLP problems that utilize the NIPS and previously developed solution methodologies are presented. Example solutions show that considerable improvement in the amount of coverage or the number of facilities needed to insure total coverage can be achieved by allowing facility placement along arcs of the network. In addition, extensions to the arc‐covering model and the ambulance‐hospital model of ReVelle, Toregas, and Falkson are developed and solved.

Because of their widespread applicability, the set covering location problem and the maximal covering location problem have received considerable attention in the facility-location literature. There have been many extensions and modifications to these problems as they have been applied to various planning scenarios. A basic underlying assumption of the location-covering models formulated to date is that the facilities being sited are uncapacitated. Although this assumption is valid in many location-planning settings, there certainly exist situations in which this assumption severely limits the application of covering models. Capacitated versions of the set covering location problem and the maximal covering location problem have thus been formulated. In addition, the theoretical links between these models and the capacitated plant location problem, the capacitated p -median problem and, the generalized assignment problem are shown. By exploiting these links, planners can solve small and moderately sized real-world problems with existing solution methods. It is expected that these theoretical links will also give insight into developing new heuristics for large-sized capacitated covering problems.

Decision Support for Urban Shelter Locations Based on Covering Model

Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered.

Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered.

A multicriteria GIS-based decision-making approach for locating electric vehicle charging stations

The fundamental objectives of locating facilities can be summarized into three categories. The first category refers to those designed to cover demand within a specified time or distance. This objective gives rise to location problems which are known as the Location Set Covering Problem (LSCP) and the Maximal Covering Location Problem (MCLP). The LSCP seeks to locate the minimum number of facilities required to 'cover' all demand or population in an area. The MCLP is to locate a predetermined number of facilities to maximize the demand or population that is covered. The second category refers to those designed to minimize maximum distance. This results in a location problem known as the p-center problem which addresses the difficulty of minimizing the maximum distance that a demand or population is from its closet facility given that p facilities are to be located. The third category refers to those designed to minimize the average weighted distance or time. This objective leads to a location problem known as the p-median problem. The p-median problem finds the location of p facilities to minimize the demand weighted average or total distance between demand or population and their closest facility. The p-median problem is a typical combinatorial optimization problem with many practical applications such as location of warehouses, schools, health centers, shops etc. Greedy algorithms are the simplest algorithms to design however it is not easy to understand its capability and limitations. A greedy algorithm solves a global optimization problem by making a sequence of locally optimal decisions. That is a greedy algorithm always chooses the next step of an algorithm that is locally optimal. For example for Facility Location Problem we will consider the facilities for which decisions regarding locally optimal locations will be made. The decisions that are made regarding where to locate successive facilities by a greedy method are permanent. That is the greedy algorithms make permanent decisions about the construction of a solution, based on the restricted consideration such as choosing a location that gives a minimum cost. Greedy algorithms for facility location problems are constructive in principle. They are designed to give solutions of fairly good quality without using much time that is needed to compute better quality solutions by other algorithms. The most natural and simple heuristic for the p- median problem is the greedy algorithm. For the p-median problem to locate facilities, this algorithm picks a most 'cost-effective' facility until every required number of facilities p is located. We propose a modified form of the myopic (greedy) algorithm for the p-median problem. The new algorithm is simple and it gives relatively quality solutions. We demonstrated the importance of the removal of extreme values from a distance matrix before locating the first facility. The modification of the algorithm involves the removal of the extreme or large values from each column of the distance matrix. We then determine the first facility (1-median) after the removal of the extreme values. We revert to the original distance matrix after the first facility (1-median) is located. We then determine the additional facilities using the original distance matrix. We compare the results obtained by the original Myopic algorithm with the modified version using the 400 random problems. The results demonstrate the efficiency and superiority of our new method.

Where should new service facilities be located is a key question in ensuring healthcare accessibility. In previous healthcare literature, most researchers applied either maximal covering location problem (MCLP) or location set covering problem (LSCP) to address the location selection problems. The MCLP tries to maximize population with access with a limited number of facilities; while the LSCP ensures full coverage with a minimum number of facilities. However, researchers rarely applied both models and compare their results. Moreover, most literature applied Euclidean distance to generate service coverages and failed to demonstrate the geo-processing steps in the application of location problems with the integration of geographic information system (GIS). To complement existing literature, this paper proposes a network-based covering location problem (Net-CLP) building on traditional location problems. The Net-CLP incorporates two sub-models: a network-based maximal covering location problem (Net-MCLP) and a network-based location set covering problem (Net-LSCP). The goal of Net-CLP in this paper is threefold: (1) the network-based coverage is based on real world transportation networks depending on different travel thresholds; (2) addressing the location problem applying both Net-MCLP and Net-LSCP to fully evaluate candidate facility sites, considering service capabilities; (3) demonstrating the integration of GIS in location problems, with a case study of Hillsborough County, Florida.

IoT based smart parking model using Arduino UNO with FCFS priority scheduling

Good citizens will not do anything that can harm the State even though it is related to personal needs. This study examines the citizenship review of illegal parking in the city of Jayapura, which is detrimental to the state because parking fees are not included in regional income but for personal interests, and this shows the low awareness of being a good citizen. With a qualitative method, this study aims to reveal the causes of rampant illegal parking, the problems that arise, and how local governments respond to them. The sample selected was ten people consisting of five illegal parking officers, three official parking officers, and two officers from the Jayapura City Regional Revenue Service. From the results of the study obtained information that illegal parking in the city of Jayapura is caused by factors: urgent needs, lack of job opportunities, and lack of human resource skills. The impact is a decrease in regional income and inconvenience in the parking area. There are no less illegal parking attendants who act arrogantly, asking for money by force in excess of the parking fee that should have been even though he did not carry out his duties to regulate vehicles that were parked properly. The Regional Government has taken several actions, including controlling parking locations, providing counseling to illegal parking officers, placing official parking officers, and inviting illegal parking officers to register themselves as official parking officers.

Wandering behavior is a serious problem among the elderly in nursing homes, yet it has received relatively little study As multi-family houses built before the '80s in South Korea were designed, it did not take account into issues related to parking so that at present parking became a big issue. The Seoul Metropolitan Government tried various ways to sort out the parking problem in multi-family house areas, but inadequate parking and illegal parking are still an ongoing problem. Thus, the purpose of this study was to examine current situation on resident parking only and illegal parking centered on a congested area of multi-family houses in Seoul and present the improvement plan of parking problems through case investigation home and abroad. As the result of the survey, it has been identified that the subject area consists of 506 households in total and 186 parking spaces and also, 100 cars in parking space or parking lot in housing space and 143 in illegal parking. Green parking business and open access parking system were suggested as solution plan for parking problem. Thus, it is expected that through green parking business for 6 houses and the parking lot of elementary schools around, the illegal parking problem would be sorted out.