Analysis of mobility data – A focus on Mobile Mapping Systems

In light of growing urban traffic, car parking becomes increasingly critical for cities to manage. As a result, the prediction of parking occupancy has sparked significant research interest in recent years. While many external data sources have been considered in the prediction models, the underlying geographic context has mostly been ignored. Thus, in order to study the contribution of geospatial information to parking occupancy prediction models, road network centrality, land use, and Point of Interest (POI) data were incorporated in Random Forest (RF) and Artificial Neural Network (ANN, specifically Feedforward Neural Network FFNN) prediction models in this work. Model performances were compared to a baseline, which only considers historical and temporal input data. Moreover, the influence of the amount of training data, the prediction horizon, and the spatial variation of the prediction were explored. The results show that the inclusion of geospatial information led to a performance improvement of up to 25% compared to the baseline. Besides, as the prediction horizon expanded, predictions became less reliable, while the relevance of geospatial data increased. In general, land use and POI data proved to be more beneficial than road network centrality. The amount of training data did not have a significant influence on the performance of the RF model. The ANN model, conversely, achieved optimal results on a training input of 5 days. Likely attributable to varying occupancy patterns, prediction performance disparities could be identified for different parking districts and street segments. Generally, the RF model outperformed the ANN model on all predictions.

Section: Introductionmentioning

confidence: 99%

Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study

Balmer

Weibel

Huang

2021

“…Mobile mapping system (Puente et al 2013;Li 2006;Novak 1995;Marinelli et al 2017;Sester 2020), born in the early 1990s, is one of the cutting-edge techniques of modern surveying and mapping. A mobile mapping system is the composition of global satellite positioning, inertial navigation, image processing, photogrammetry, laser scanning, geographic information, and integrated control technology.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

Shao

Cheng

et al. 2021

“…Fusing panoramic image data collected by MMS and LiDAR data, a true color point cloud threedimensional model can be obtained, which can be applied to road target detection, high-precision map making and urban three-dimensional scene display, et al (Shao and Cai 2018;Shao, Zhang, and Wang 2017;Shao et al 2016;Shao, Wu, and Li 2021). It becomes increasingly important since the emergence of some novel techniques, such as autonomous driving, urban brain, digital twin city, et al (Shao et al 2020;Li et al 2013;Shao and Li 2011;Shams et al 2018;Li et al 2016;Craciun et al 2014;Yoshimura et al 2016;Yi and Li 2007;Sester 2020;Yang and Wang 2016;Hollick, Helmholz, and Belton 2016;Ghouaiel and Lefèvre 2016;Sun et al 2016). In this study, we have designed an MMS as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Urban Geospatial Information Acquisition Mobile Mapping System based on close-range photogrammetry and IGS site calibration

Guo

Zhou

Zhao

et al. 2021

The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measurement accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relationship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation parameters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision requirements of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as autonomous driving, digital twin city, urban brain et al.