Micromobility Station Placement Optimization for a Rural Setting

Abstract

Micromobility is an evolving form of transportation modality that uses small human- or electric-powered vehicles to move people short distances. Planners expected that bike sharing, the first form of micromobility, would reduce traffic congestion, cut travel cost, reduce pollution, enable connectivity with other modes of transport, and promote public health. However, micromobility options also brought new challenges such as the difficulty of placement decisions to encourage adoption and to minimize conflict with other transport modes. Sound deployment decisions depend on the unique environmental characteristics and demographics of a location. Most studies analyzed deployments in high-density urban areas. This research determines the best locations for 5 new bike-sharing stations in Fargo, North Dakota, a small urban area in the rural United States. The workflow combines a geographic information system (GIS), level of traffic stress (LTS) ratings, and location-allocation optimization models. The spatial analysis considered 18 candidate station locations and eliminated those that fell within the 700-meter isochrone walking distance of the 11 existing stations. This case study demonstrates a scalable workflow that planners can repeat to achieve sustainable micromobility deployments by considering the land use, population density, activity points, and characteristics of the available pathways in their unique setting.

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

“…In terms of infrastructure planning, Murray and Feng [109] was the only study that considered the mode of transport of walking and the authors were looking to determine an optimal street lighting scheme. For bike-sharing systems, problems related to infrastructure considered the location of stations [78,148,156,174]. Focusing on specific aspects of the studies, Hu et al [174] defined as decision variables the location of new stations, the removal of old ones without affecting the performance of the system, and the reallocation of docks between stations; Yang et al [78] considered dynamic demand in their problem; Askarzadeh and Bridgelall [156] considered covered activity points, such as the presence of transit stops, parks, restaurants, commercial areas, industrial centres, and universities in areas of high population density to determine the station areas; and Qian et al [148] included disadvantaged communities and park areas in their formulation.…”

“…For bike-sharing systems, problems related to infrastructure considered the location of stations [78,148,156,174]. Focusing on specific aspects of the studies, Hu et al [174] defined as decision variables the location of new stations, the removal of old ones without affecting the performance of the system, and the reallocation of docks between stations; Yang et al [78] considered dynamic demand in their problem; Askarzadeh and Bridgelall [156] considered covered activity points, such as the presence of transit stops, parks, restaurants, commercial areas, industrial centres, and universities in areas of high population density to determine the station areas; and Qian et al [148] included disadvantaged communities and park areas in their formulation. Related to rail systems, Kang et al [152] aimed at selecting the points of intersections of the new railway alignment, while Chen et al [154] sought to determine the locations and capacity of railbased park-and-ride sites.…”

“…At last, for a multimodal network to integrate buses to the rail system, Almasi et al [196] focused on network design (routes), frequencies setting, and operational aspects (dwell time). 168, 170, 171, 173-177, 180, 181, 183-186, 188, 190, 192-199] Considering the perspective of passengers alone, studies sought to minimise uncovered demand [174], distances in the network [156], and passengers' travel time [68,93,127,190], and maximise accessibility [147] and direct transfers without waiting [127]. For the operators perspective alone, studies aimed at minimising costs or maximising revenue [84,98,100,101,152,154,160,182,185,192], minimising travelled distance or vehicle mileage [72-74, 92, 175, 187, 191], minimising workload [73] and rebalancing amount [192], maximising covered demand [73,176], rebalancing utility [191], the total social net benefit [47], and the average passenger flow [173].…”

Integrating Urban Air Mobility into a Public Transit System: A GIS-Based Approach to Identify Candidate Locations for Vertiports

“…Loidl et al (2019) identified preferred locations for bike stations using a spatial framework and overlay analysis with demographic characteristics [28]. Askarzadeh and Bridgelall (2021) solved a location-allocation problem using a spatial optimization model within a geographic information system (GIS) framework and identified the best locations for five new bikesharing stations in Fargo, North Dakota [29].…”

Shifting the focus

“…Yet, station-based versions of these services can still increase access to essential opportunities in lower-density urban areas. To this end, station location needs to be optimised by considering local characteristics, such as the location of places of interest like shopping centres or education institutions (Askarzadeh & Bridgelall, 2021). Car and other long-ranged vehiclesharing services can be particularly suited to respond to the needs of less dense areas, especially those with potentially higher car dependency.…”