Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach

GPS is taken as the most prevalent positioning system in practice. However, in urban areas, as the GPS satellite signal could be blocked by buildings, the GPS positioning is not accurate due to multi-path errors. Estimating the negative impact of urban environments on GPS accuracy, that is the GPS environment friendliness (GEF) in this paper, will help to predict the GPS errors in different road segments. It enhances user experiences of location-based services and helps to determine where to deploy auxiliary assistant positioning devices. In this paper, we propose a method of processing and analysing massive historical bus GPS trajectory data to estimate the urban road GEF integrated with the contextual information of roads. First, our approach takes full advantage of the particular feature that bus routes are fixed to improve the performance of map matching. In order to estimate the GEF of all roads fairly and reasonably, the method estimates the GPS positioning error of each bus on the roads that are not covered by its route, by taking POIinformation, tag information of roads, and building layout information into account. Finally, we utilize a weighted estimation strategy to calculate the GEF of each road based on the GPS positioning performance of all buses. Based on one month of GPS trajectory data of 4835 buses within the second ring road in Chengdu, China, we estimate the GEF of 8831 different road segments and verify the rationality of the results by satellite maps, street views, and field tests.

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Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9030181 ◽

2020 ◽

Vol 9 (3) ◽

pp. 181

Author(s):

Banqiao Chen ◽

Chibiao Ding ◽

Wenjuan Ren ◽

Guangluan Xu

Keyword(s):

Clustering Algorithm ◽

Recall Rate ◽

Location Based Services ◽

Trajectory Data ◽

Intersection Detection ◽

Map Generation ◽

Gps Trajectory Data ◽

Road Intersections ◽

Road Maps ◽

Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

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Taxi fuel consumption and emissions estimation model based on the reconstruction of driving trajectory

Advances in Mechanical Engineering ◽

10.1177/1687814017708708 ◽

2017 ◽

Vol 9 (7) ◽

pp. 168781401770870 ◽

Cited By ~ 2

Author(s):

Jiancheng Weng ◽

Quan Liang ◽

Guoliang Qiao ◽

Zhihong Chen ◽

Jian Rong

Keyword(s):

Fuel Consumption ◽

Urban Areas ◽

Operating Conditions ◽

Bench Test ◽

Trajectory Data ◽

Estimation Model ◽

Driving Cycles ◽

Gps Trajectory Data ◽

Gps Trajectory ◽

Fuel Consumption And Emissions

Monitoring operating vehicles’ fuel consumption and emissions is necessity for evaluating fuel saving and emissions reduction. Taxis are one of the key objects needed energy consumption monitoring in passenger transport system. However, the traditional data collection methods for vehicle fuel consumption and emissions had high cost and inconvenient maintenance. This study aims at proposing an approach to estimate taxi fuel consumption and emissions based on the global position system (GPS) trajectory data. The bench test experiment was first conducted with three different driving cycles: cruising, acceleration and deceleration, and the composite driving cycle including these two. Then, models to calculate fuel consumption and emission based on the driving trajectory reconstruction were proposed. Therefore, the taxis’ fuel consumption and emissions could be got through GPS trajectory data corresponding to these three driving cycles. The model accuracy were verified that fuel consumption (92%) and CO2 emission (95%) fit the measurements much better than CO, NOx, and HC emission models (60%–70%). Furthermore, taking fuel consumption per 100 km as dependent variable, the relative errors between the model’s outputs and field measurements were 1.9% in urban areas and 11.2% in comprehensive operating conditions (i.e. both urban and suburb areas).

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Generating urban road intersection models from low-frequency GPS trajectory data

International Journal of Geographical Information Science ◽

10.1080/13658816.2018.1510124 ◽

2018 ◽

Vol 32 (12) ◽

pp. 2337-2361 ◽

Cited By ~ 10

Author(s):

Min Deng ◽

Jincai Huang ◽

Yunfei Zhang ◽

Huimin Liu ◽

Luliang Tang ◽

...

Keyword(s):

Low Frequency ◽

Trajectory Data ◽

Urban Road ◽

Gps Trajectory Data ◽

Gps Trajectory

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Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

Sensors ◽

10.3390/s21217341 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7341

Author(s):

Xueying Song ◽

Zheng Yang ◽

Tao Wang ◽

Chaoyang Li ◽

Yi Zhang ◽

...

Keyword(s):

Traffic Flow ◽

Road Network ◽

Traffic Assignment ◽

Trajectory Data ◽

Dynamic Traffic ◽

Urban Road ◽

Assignment Model ◽

Flow Assignment ◽

Gps Trajectory Data ◽

Gps Trajectory

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

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Automatically Tracking Road Centerlines from Low-Frequency GPS Trajectory Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030122 ◽

2021 ◽

Vol 10 (3) ◽

pp. 122

Author(s):

Banqiao Chen ◽

Chibiao Ding ◽

Wenjuan Ren ◽

Guangluan Xu

Keyword(s):

Active Contour ◽

Low Frequency ◽

Tracking Algorithm ◽

Location Based Services ◽

Trajectory Data ◽

The Road ◽

Gps Trajectory Data ◽

Road Maps ◽

Gps Trajectory ◽

Refinement Algorithm

High-quality digital road maps are essential prerequisites of location-based services and smart city applications. The massive and accessible GPS trajectory data generated by mobile GPS devices provide a new means through which to generate maps. However, due to the low sampling rate and multi-level disparity problems, automatically generating road maps is challenging and the generated maps cannot yet meet commercial requirements. In this paper, we present a GPS trajectory data-based road tracking algorithm, including an active contour-based road centerline refinement algorithm as the necessary post-processing. First, the low-frequency trajectory data were transferred into a density estimation map representing the roads through a kernel density estimator, for a seeding algorithm to automatically generate the initial points of the road-tracking algorithm. Then, we present a template-matching-based road-direction extraction algorithm for the road trackers to conduct simple correction, based on local density information. Last, we present an active contour-based road centerline refinement algorithm, considering both the geometric information of roads and density information. The generated road map was quantitatively evaluated using maps offered by the OpenStreetMap. Compared to other methods, our approach could produce a higher quality map with fewer zig-zag roads, and therefore more accurately represents reality.

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A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽

10.15388/informatica.2018.196 ◽

2019 ◽

Vol 30 (1) ◽

pp. 33-52 ◽

Cited By ~ 1

Author(s):

Pengfei HAO ◽

Chunlong YAO ◽

Qingbin MENG ◽

Xiaoqiang YU ◽

Xu LI

Keyword(s):

Compression Algorithm ◽

Trajectory Data ◽

Gps Trajectory Data ◽

Gps Trajectory

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Enabling Smart Urban Services with GPS Trajectory Data

10.1007/978-981-16-0178-1 ◽

2021 ◽

Author(s):

Chao Chen ◽

Daqing Zhang ◽

Yasha Wang ◽

Hongyu Huang

Keyword(s):

Trajectory Data ◽

Urban Services ◽

Gps Trajectory Data ◽

Gps Trajectory

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An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090411 ◽

2019 ◽

Vol 8 (9) ◽

pp. 411 ◽

Cited By ~ 3

Author(s):

Tang ◽

Deng ◽

Huang ◽

Liu ◽

Chen

Keyword(s):

Road Network ◽

Sampling Rate ◽

Quality Data ◽

Automatic Method ◽

Trajectory Data ◽

The Road ◽

High Sampling Rate ◽

Map Generation ◽

Gps Trajectory Data ◽

Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

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