A Hierarchical Approach to Optimizing Bus Stop Distribution in Large and Fast Developing Cities

Abstract

Public transit plays a key role in shaping the transportation structure of large and fast growing cities. To cope with high population and employment density, such cities usually resort to multi-modal transit services, such as rail, BRT and bus. These modes are strategically connected to form an effective transit network. Among the transit modes, bus stops need to be properly deployed to maintain an acceptable walking accessibility. This paper presents a hierarchical process for optimizing bus stop locations in the context of fast growing multi-modal transit services. Three types of bus stops are identified hierarchically, which includes connection stops, key stops and ordinary stops. Connection stops are generated manually to connect with other transit facilities. Key stops and ordinary stops are optimized with coverage models that are respectively weighted by network centrality measure and potential demand. A case study in a Chinese city suggests the hierarchical approach may generate more effective stop distribution.

Prioritization by consensus of enhancements for sustainable mobility in urban areas

“…Bus transport plays a key role in the mobility of the cities (Huang and Liu, 2014). This solution consists in enlarging the bus fleet, with the aim of opening new lines, reducing the time lapse between buses, and providing service during the night time, especially during the weekends.…”

A Novel Spatial-Temporal Voronoi Diagram-Based Heuristic Approach for Large-Scale Vehicle Routing Optimization with Time Constraints

“…With the advancement of geographic information science (GIS), space-related problem decision-making has been embedded in spatial decision support systems (SDSSs) to provide flexible transportation service in both public and private sectors [3], such as the design of school bus routes [4], collection of solid waste [5,6], distribution of goods in chain business [7], design of police patrol routes [8], and planning express deliveries [9]. Spatial function tools are used for intelligent transportation operation in many services including management of geo-referenced transport network data, geocoding massive human demands, positioning moving vehicles, and navigating routes for drivers [5][6][7][8][9].…”

“…There are two types of heuristic algorithms for the VRPTW, which include local search and evolutionary optimization. Local search iteratively modifies local route structures to improve solution quality and follows a single trajectory to explore the solution space [3,6,7]. Evolutionary optimization simultaneously searches multiple parts of the whole solution space to converge on the best solution [5,19,20].…”

“…Spatial principle has exhibited good performance in space-related problem decision-making [21][22][23] in problems such as bus stop location [3], hazmat route planning [20], and path covering [24]. To efficiently solve VRO, from a spatial perspective, a few speed-up strategies have been developed to handle spatial issues and extend solvable VRO instances up to thousands of customers, which include the granular neighborhood [25], the kth nearest neighbors [26], and the k-ring-shaped Voronoi neighbors [27].…”

GIS for Sustainable Urban Transport

“…We see application of typical GIS strengths, such as digital map production (articles 1 and 2) [1,2], dynamic modelling (article 3) [3], spatial inventory of facilities/events (articles 1, 2, 4, 5 and 6) [1,2,[4][5][6], spatial data integration (articles 1, 2 and 5) [1,2,5] and spatial analysis (articles 1, 2, 4 and 6) [1,2,4,6], where it can be noted that several authors are using these GIS capabilities in combination and in integration with various statistical techniques (articles 1, 3 and 5) [1,3,5].…”