Identification of e-Scooter Shared (ESS) Stations by using a GIS-based MCDM Approach

DOI: https://doi.org/10.52939/ijg.v19i5.2663

The use of micromobility devices, or MMDs, has improved recently and is now being observed to have been utilised as a means of transportation, as well as a first-mile-last-mile mode for mobility. Since they are a relatively new trend in urban transportation, electric scooters or e-scooter should be taken into account by city planners and policymakers while assembling a variety of transportation alternatives for their region [1]. Many cities throughout the world are embracing the idea of shared mobility due to its environmental advantages, such as a reduction in driving and automobile ownership as well as enhanced accessibility and connectivity [2]. Recently, there is notably an increase in the usage of shared micro-mobility tools including bicycles, e-bikes, and e-scooters [3]. National Priority Area 9 (BK9), or Safer Micro-mobility, is the focus of the Malaysian Road Safety Plan (PKJRM) 2022–2030. Establishing a management system and raising awareness of the use of micromobility, as well as developing the infrastructure for micromobility in suitable locations, are the two sub-areas under this BK9 [4].

Any vehicle intended to travel at or below 50 km/h and driven by an internal combustion engine, a combination of an internal combustion engine and human power, or by a combination of electrically powered mechanisms is referred to as a micro-mobility device (MMD) in Malaysia. The internal combustion engine and human power is regarded as a micro-mobility vehicle [5]. Most city inhabitants have already noticed the massive increase in shared bikes and scooters that have appeared throughout their area.

This is partially due to the frequent hurried, chaotic rollouts of shared micro-mobility services. Effective legislation and collaboration between organizations and users are needed.

Local governments utilising the e-scooter sharing (ESS) services network frequently choose the sites for service locations. Certain stations where the devices are locked are necessary for every ESS system. It is crucial to make choices on the initial locations and dimensions of stations as well as how to develop an existing system by adding stations or rearranging existing stations [6]. Yet, creating an effective ESS system with appropriate station placements is a difficult challenge. The relative station positions within the deployment network and their connections to tourist destinations, public transportation, and users must be considered [7] in order to build a successful shared service network. Consequently, a quantitative site appropriateness analysis for ESS services is essential. This study’s objectives were to evaluate the ESS deployment services now available in Shah Alam, Selangor, and to find suitable station sites by comparing them to active stations. A geographic information system was combined with multiple multi-criteria decision-making strategies to resolve conflicting criteria (GIS). MCDA techniques are being employed frequently in similar previous research thus, this study implemented MCDA in GIS analysis to find the suitable locations as there are several criteria needed in the decision-making.

2. GIS-MCDM Approach in Micro-mobility

First-last-mile connection is still a persistent problem for Malaysians. The bicycle sharing system was introduced in Malaysia as an alternative mode in the year 2017. The service intends to help in reducing the number of private vehicles on the road as well as first-mile and last-mile solution. With the evolution of shared micro-mobility services, several companies started to enter Malaysia in 2018 [8]. The 12th Malaysia Plan already mentions micro mobility, and the government has put in place measures to promote its use as a first-last-mile connector [9]. According to traffic psychology specialist and travel behaviour analyst, micro-mobility should be a supporting mode and a viable option for sustainable transportation [10]. Beam has started operating in Shah Alam from April 2021 until August 2022, starting with 500 devices. This trial and pilot operation is a collaboration project between the city council of Shah Alam and several bicycle sharing companies. However, there are several issues and challenges identified during this period, among them operators’ capability, infrastructure, and user behaviours, as well as regulation on implementation from the government [11].

However, according to the Ministry of Transportation, some micro-mobility vehicles, including e-scooter, cannot be driven on public road. The Road Traffic (Prohibition of Use of Certain Micro-mobility Vehicles) Rules 2021 had published this ban, which had been in force since December 17, 2021 [5]. This restriction is due to a number of issues including safety and management controls over its use. Nevertheless, several stakeholders are drafting regulations and laws to enable the operation of this micro-mobility vehicle to be implemented. MCDA is being employed frequently in research, both for traditional micro-mobility and e-micro-mobility. Their responsibility is to offer resources that will be utilised to rank and analyse possibilities for making decisions in the face of numerous, frequently incompatible criteria [12] and [13]. Based on the desired result, the best option among several possibilities is evaluated and chosen using computational approaches that incorporate a number of factors and order of preference. Based on the chosen criteria, their matching values, and specified weights, the application returns a ranking result [14]. Numerous multi-criteria decision-making techniques, including the Analytical Hierarchical Process (AHP), Analytical Network Process (ANP), Case-Based Reasoning (CBR), Data Envelopment Analysis (DEA), Fuzzy-AHP, Genetic Algorithm (GA), Mathematical Programming, Simple Multi-Attribute Rating Technique (SMART), and their hybrids, have been proposed for provider selection [13] and [15]. Table 1 shows the comparison of each method with its purpose, advantages, and disadvantages.

3. Methodology

The methodology of this study is shown in Figure 1. It started with identifying criteria that influence the potential locations for ESS stations. Next, the data representing the criteria were collected in two (2) parts. First, the rating of the criteria was collected from experts by interviewing them. Second, the spatial data representing the criteria on ground was selected. Once the data had been collected, they were processed by calculating the weightage of the criteria based on the rating obtained from experts’ choice by using AHP decision rules and the criterion map for each criterion was prepared by rasterization and reclassification. Once both data were ready, the suitability analysis for ESS stations were modelled by using Weighted Overlay Analysis. The site verification was then conducted to review the potential location for ESS stations. Lastly, the map visualizing the potential locations of ESS stationswas create.

Table 1: MCDM methods with its advantages and disadvantages

Figure 1: Flowchart of Methodology

This suggests that the city centre of Shah Alam is a good location for ESS operations. There are no rules for determining the ideal separation between ESS services. According to [16], 250m is the estimated distance that people will walk to reach the ESS station (2018). As a result, the appropriate spots that were more than 250 metres from the present station and not in an area with EXTREMELY SUITABLE conditions were excluded. This characteristic is also taken into account because public transportation has a higher weight.

Out 50 of the 53 CANDIDATE LOCATION points were found in extremely acceptable locations, and of these, 19 were deemed UNSUITABLE because they were situated within 250 meters of an ESS station. A total of 31 alternative ESS services sites were found using this approach. Due to big numbers, study eventually picked the location based on the 3 primary criteria: adjacent to public transport and residential area; provided with bike lane/pathway; 250 meters or less from each station. In certain cases, the same area has many locations. To make decisions, this study employs RANDOM SAMPLING analysis in Excel. The results suggest 19 potential sites for an ESS station. The verification process must next be carried out to confirm that the chosen site is appropriate for additional ESS stations. A total of 19 location choices have been verified in the field. As a result, a total of 10 locations were identified as unsuitable for the proposed new ESS site. Among the rejection factors for other locations are as follows: Gated and guarded residential area. Therefore, riders cannot access the station; Unsuitable bike lane/pathway conditions. The surfaces condition that are hazard to ride; Slope terrain. This can pose a danger to the user. Users can crash if they fail to control the device and speed; No connectivity. No public transport, any place of interest such as shop or mall and disconnect route.

The local authority should take into consideration nine new location proposals that are acceptable for ESS station placements as S1, S2, and S3 (Figure 3). Most of the new ESS installations are found in suburban areas. In addition to having bike lanes or pathways, all locations also have area that can be used as station sites. In addition to site suitability, user safety is taken into account during the survey to make sure that the location choice is not only cozy, convenient, but also secure.

According to the findings of the observation, S2, S3, and S5 are situated close to the bus stop. The position is very close to the Shah Alam hospital, according to the results of the survey in S1. For S2, however, it is discovered to be close to homes, businesses, and schools. S3 is close to both apartments and terraced homes. Also, the position is not far from university’s main entrance. The intersection that connects housing with the main road is not far from S4. The position of S5 is one of the comparatively decent stations since it connects bus stops, residential, and business sector. Students, who are also the intended user group, will be drawn to S6's location at a university’s entrance. The S7, S8, and S9 stations are situated in densely populated neighborhoods and quarters. Also, the location's suitability enables for the creation of a dockless station, which will reduce the operator's and the local authority's preparatory expenses.

5. Conclusion

This study used AHP analysis integrated with the GIS to achieve the objectives. The main steps of study approach are displayed in the flow-chart shown in Section 2. This study highlighted eight (8) criteria that can be utilised in finding the ESS locations. The criteria were weighed, and their weightage used in weighted overlay analysis to determine the suitability of sites for the ESS. Based on the suitability index found, nineteen (19) locations were selected as potential locations for ESS stations. Due to the system's advantages, ESS services have become more widely used and implemented in recent years. Cities have been able to easily incorporate the technology due to its accessibility and environmental friendliness. The ESS are a useful technique for reducing the traffic congestion and pollution. This programme could serve as attractions for commuting. Utilizing ESS services, people can travel and explore new areas. Additionally, cities that make use of these systems discover that the networks for ESS improve their current intercity transit systems. As was previously indicated, the environmental advantages of the ESS are its most significant contributions.

Acknowledgement

The authors would like to thank all who have been involved in this study especially UiTM and MIROS for supporting this study as well as the experts and data providers.

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