Semi-Automatic Tool for Bus Route Map Matching

Medina, Ordoñez; Sergio, A

doi:10.5334/baw.18

Cited by 4 publications

(4 citation statements)

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“…The issues related to the correct road side selection and overlapping bus trips are not mentioned. Ordóñez et al [6] developed a semi-automatic procedure to combine public bus routes information with a high resolution network as an extension to MATSim. First, a simple map-matching algorithm is applied on a per route basis.…”

Section: Related Workmentioning

confidence: 99%

GTFS bus stop mapping to the OSM network

Vuurstaek

Cich

Knapen

et al. 2020

Future Generation Computer Systems

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Section: Related Workmentioning

confidence: 99%

GTFS bus stop mapping to the OSM network

Vuurstaek

Cich

Knapen

et al. 2020

Future Generation Computer Systems

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“…Second, if no additional information is available, the map matching process assumes that the shortest path between two stops is followed. In most cases this assumption is valid, but it may not hold for all paths, especially when stops are spaced farther apart (Li 2012; Ordóñez Medina 2016). These methods also assume a road network that is stable over time, which may not be true, especially in rapidly developing regions.…”

Section: Discussionmentioning

confidence: 99%

“…Prior studies have established methods for matching a transit schedule represented in the General Transit Feed Specification (GTFS) format to a representation of the road network, although not for longitudinal comparison (Li 2012; Dimond et al, 2016; Ordóñez Medina 2016; Wessel et al, 2017; Bhagat-Conway 2018). The concept of splitting routes at road intersections and stops was inspired by a similar project intended for aggregating transit performance within shared segments (Brown 2021).…”

Section: Introductionmentioning

confidence: 99%

An open-source program for spatial decomposition of bus transit networks

Caros

Stewart

Attanucci

2023

Environment and Planning B: Urban Analytics and City Science

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Comparing transit operations and performance over time is often challenging due to changes in route identifiers and service patterns. It is particularly difficult if the analysis period spans a major network design change where bus routes are realigned and renamed. This paper presents a new open-source software package that simplifies longitudinal transit performance analysis by decomposing a bus network into block-length segments that remain stable over time. The package then matches transit networks across time periods using segment geometry rather than potentially unstable route or stop identifiers. This package provides an efficient method for transit planners, advocacy groups, policy makers and urban scientists to analyze changes in transit performance over time without any prior knowledge of network design changes.

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“…Note that there exist several research efforts to assign GTFS (General Transit Feed Specification) bus stops to road networks. 22 They either rely on the shortest path model 23 or maintain a set of candidate paths connecting successive geographic points. 24 The difference between the above works and our work is that we focus on developing efficient method to discover the sequence of road segments covered by a bus line, instead of only the road segments corresponding to the bus stops of the bus line.…”

Section: Map Matching For Sparse Trajectoriesmentioning

confidence: 99%

Discover the road sequences of bus lines using bus stop information and historical bus locations

Zhou

Jiang

2019

International Journal of Distributed Sensor Networks

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Discovering the road sequences traveled by the bus lines is of great importance for public transport management, such as bus journey time prediction and multimodal travel route recommendation, as the road sequence provides important information on roadway characteristics such as the number of intersections and traffic signals, road type, and number of lanes. This article develops methods to discover the covered road sequence for a given bus line, using bus stop information as well as historical bus locations (i.e. locations where buses had appeared in history). To solve the problem, we first construct a high-quality global positioning system (GPS) trajectory and then employ a novel map-matching algorithm to the resultant dense trajectory to obtain the road sequence of the bus line. We focus on constructing high-quality trajectory with dense GPS points by (1) forming an initial bus stop trajectory using ordered bus stop coordinates, (2) identifying sufficient suitable historical bus locations, and (3) inserting the identified bus locations into proper positions of the bus stop trajectory. Our proposed method is evaluated on real-world bus line data involving more than 400 bus services in Singapore.

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Semi-Automatic Tool for Bus Route Map Matching

Cited by 4 publications

References 0 publications

GTFS bus stop mapping to the OSM network

GTFS bus stop mapping to the OSM network

An open-source program for spatial decomposition of bus transit networks

Discover the road sequences of bus lines using bus stop information and historical bus locations

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