A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.     

浅谈快捷建库与数据处理系统的功能设计

介绍了快捷建库与数据处理系统涉及的内容和研究开发的技术路线,并对该系统的各项模块功能进行了详细的阐述,提出了基于ArcGIS平台实现系统各功能模块的理论可行性综合功能的操作方法。     

Designing efficient and balanced police patrol districts on an urban street network

In police planning, a territory is often divided into several patrol districts with balanced workloads, in order to repress crime and provide better police service. Conventionally, in this districting problem, there is insufficient consideration of the impacts of street networks. In this study, we propose a street-network police districting problem (SNPDP) that explicitly uses streets as basic underlying units. This model defines the workload as a combination of different attributes and seeks an efficient and balanced design of districts. We also develop an efficient heuristic to generate high-quality districting plans in an acceptable time. The capability of the algorithm is demonstrated in comparison to an exact linear programming solver on simulated datasets. The SNPDP model is successfully implemented and tested in a case study in London, and the generated police districts have different characteristics that are consistent with the crime risk and land use distribution. Besides, we demonstrate that SNPDP is superior to an aggregation grid-based model regarding the solution quality. This model has the potential to generate street-based districts with balanced workloads for other districting problems, such as school districting and health care districting.     

Open-source approaches for location cover models: capabilities and efficiency

Journal of Geographical Systems - Location cover models are aimed at siting facilities so as to provide service to demand efficiently. These models are crucial in the management, planning and...     

A Balanced Route Design for Min-Max Multiple-Depot Rural Postman Problem (MMMDRPP): a police patrolling case

Providing distributed services on road networks is an essential concern for many applications, such as mail delivery, logistics and police patrolling. Designing effective and balanced routes for these applications is challenging, especially when involving multiple postmen from distinct depots. In this research, we formulate this routing problem as a Min-Max Multiple-Depot Rural Postman Problem (MMMDRPP). To solve this routing problem, we develop an efficient tabu-search-based algorithm and propose three novel lower bounds to evaluate the routes. To demonstrate its practical usefulness, we show how to formulate the route design for police patrolling in London as an MMMDRPP and generate balanced routes using the proposed algorithm. Furthermore, the algorithm is tested on multiple adapted benchmark problems. The results demonstrate the efficiency of the algorithm in generating balanced routes.