The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

In a district heating engineering project, the design of the heating route is an indispensable but laborious process. This paper proposes a planning indicator to measure the suitability of a candidate heating route, and provides an intelligent method and a convenient tool for the preliminary design of the district heating route. The Fengrun heating engineering project was chosen as a case study. The remote sensing imagery and OpenStreetMap were used as the data sources. First, the remote sensing imagery was classified into five classes and converted into binary images. Second, the district heating route planning indicator was defined based on the cost function. The cost function and the updating strategy of the ant colony system algorithm were modified according to the heating route selection requirement. Additionally, the parallel computing technology was adopted to improve the efficiency. With the help of the open source Cesium engine and the three-dimensional (3D) WebGIS technology, an interactive route design platform that combined our algorithm was finally provided. The optimum routes by the platform were compared to the corresponding sequential algorithm, the route selected manually, as well as the commercial ArcGIS platform. The proposed algorithm can get 28 candidate routes with better indicator values than the manually selected route. Compared to the corresponding sequential algorithm, our algorithm improved the efficiency by 4.789 times. The proposed 3D WebGIS tool is more applicable and user-friendly for the heating route design.

Map-matching is a fundamental issue for location-based applications and traffic pattern analysis. Widely utilized approach considers map-matching process as a Hidden Markov Model (HMM). However, it usually produces roundabout paths and takes redundant computation since Markov assumption restricts that the algorithm cannot make a full use of context information and it is easy to be misled. Therefore, in this paper, to achieve a higher running efficiency as well as matching precision, we propose a novel Seed Growing Matching (SGM). The main idea is that SGM appoints several road intersections as seeds to be starting points and each seed will grow interactively along with the fitted polyline of GPS trajectory. SGM ensures the continuity of matched path inherently. It also makes growing decisions based on the contextual information so that it gets rid of time-consuming shortest path computation which is necessary for HMM-based approaches. We conduct the real dataset-based experiments to demonstrate the performances of SGM. The results show that SGM outperforms HMM-based approach and achieves about twice the matching precision of baseline and runs about five times faster on our dataset.