From GPS traces to a routable road map

Cao, Lili; Krumm, John

doi:10.1145/1653771.1653776

Cited by 273 publications

(241 citation statements)

References 4 publications

Supporting

Mentioning

240

Contrasting

Unclassified

Order By: Relevance

“…For the qualitative evaluation, the results were overlaid onto an OpenStreetMap tile map. For the quantitative evaluation, the F-score [30] was used and compared with those of Cao and Krumm's method [23] and Davies et al's method [20], both of which are classical methods. The experimental data and results are described in the following.…”

Section: Results Of the Case Study Of Beijing Chinamentioning

confidence: 99%

“…2017, 6, 400 3 of 15 for generating road networks have been proposed in recent years [16,17]. In general, these methods can be organized into three categories [18]: (1) point clustering [19][20][21], which assumes that the input raw data consist of a set of points that are then clustered in various ways (such as by the k-means algorithm) to obtain street segments that are finally connected to form a road network; (2) incremental track insertion [10,[22][23][24][25][26], which constructs a road network by incrementally inserting trajectory data into an initially empty graph; and (3) intersection linking [27][28][29], in which the intersection vertices of the road network are first detected and then linked together by recognizing suitable road segments. Some of the representative algorithms of each category are listed in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Zhang

Liu

Qian

et al. 2017

IJGI

View full text Add to dashboard Cite

Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map.

show abstract

Section: Results Of the Case Study Of Beijing Chinamentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Zhang

Liu

Qian

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…Cao and Krumm [2] proposed a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. Their method begins by smoothing the raw traces into a coherent set of paths using a novel aggregation technique that pulls together traces that belong to the same road in response to simulated potential energy wells created around each trace.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Localization of Agricultural Machinery Operations

Vidaković¹,

Malbaša²,

Konjović³

et al. 2017

Proceedings of the International Scientific Conference - Sinteza 2017

View full text Add to dashboard Cite

Abstract:The utilization of agricultural mechanization is crucial to its efficient exploitation that, in turn, significantly influences the entire agricultural economy as well as soil quality. Contemporary agricultural machines are GPS enabled, allowing localization and recording of their motion. In this paper we present an approach to spatio-temporal localization of agricultural mechanization operations based on a linear classifier in fuzzy linear space. We trained a classifier on data labeled using a fuzzy linear classifier in order to create a training set for a data mining predictor that may be helpful in automated management of records describing agricultural machinery's dynamics. The training set was comprised of spatial coordinates of the machine recorded from a GPS device and then manually classified into two classes, denoting whether the machine is utilized for its primary purpose (productive operations like sowing, cultivating, spraying, etc.) or for non-productive operations such as moving the machine in transport from/to fields.

show abstract

“…The first category is about making use of taxi GPS traces for urban planning and traffic management. The existing work includes automatic map construction [3], detecting hot spots and frequent travel patterns [8], predicting road traffic conditions [4], informing land use and function distribution [11], [13], uncovering inefficient road network connectivity [18], planning optimal driving route [14] and various applications such as next passenger finding [15], anomalous trajectory discovery [17]. Among the taxi GPS trace related papers, the work addressing "hotspots" and frequent travel OD patterns are relevant to our work for identifying candidate bus stops and providing passenger flow data among potential bus stops, but there is no paper except one [2] using those data for bus route planning.…”

Section: Related Workmentioning

confidence: 99%

B-Planner: Night bus route planning using large-scale taxi GPS traces

Chen

Zhang

Zhou

et al. 2013

2013 IEEE International Conference on Pervasive Computing and Communications (PerCom)

View full text Add to dashboard Cite

Abstract-Taxi GPS traces provide us with rich information about the human mobility pattern in modern cities. Instead of designing the bus route based on inaccurate human survey regarding people's mobility pattern, we intend to address the night-bus route planning issue by leveraging taxi GPS traces. In this paper, we propose a two-phase approach based on the crowd-sourced GPS data for night-bus route planning. In the first phase, we develop a process to cluster "hot" areas with dense passenger pick-up/drop-off, and then propose effective methods to split big "hot" areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops as well as bus operation time constraints, we derive several effective rules to build bus routing graph and prune the invalid stops and edges iteratively. We further develop two heuristic algorithms to automatically generate candidate bus routes, and finally we select the best route which expects the maximum number of passengers under the given conditions. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set which contains more than 1.57 million passenger delivery trips, generated by 7,600 taxis for a month in Hangzhou, China.

show abstract

From GPS traces to a routable road map

Cited by 273 publications

References 4 publications

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Spatio-Temporal Localization of Agricultural Machinery Operations

B-Planner: Night bus route planning using large-scale taxi GPS traces

Contact Info

Product

Resources

About